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Sample records for danish qsar database

  1. Danish Gynecological Cancer Database

    PubMed Central

    Sørensen, Sarah Mejer; Bjørn, Signe Frahm; Jochumsen, Kirsten Marie; Jensen, Pernille Tine; Thranov, Ingrid Regitze; Hare-Bruun, Helle; Seibæk, Lene; Høgdall, Claus

    2016-01-01

    Aim of database The Danish Gynecological Cancer Database (DGCD) is a nationwide clinical cancer database and its aim is to monitor the treatment quality of Danish gynecological cancer patients, and to generate data for scientific purposes. DGCD also records detailed data on the diagnostic measures for gynecological cancer. Study population DGCD was initiated January 1, 2005, and includes all patients treated at Danish hospitals for cancer of the ovaries, peritoneum, fallopian tubes, cervix, vulva, vagina, and uterus, including rare histological types. Main variables DGCD data are organized within separate data forms as follows: clinical data, surgery, pathology, pre- and postoperative care, complications, follow-up visits, and final quality check. DGCD is linked with additional data from the Danish “Pathology Registry”, the “National Patient Registry”, and the “Cause of Death Registry” using the unique Danish personal identification number (CPR number). Descriptive data Data from DGCD and registers are available online in the Statistical Analysis Software portal. The DGCD forms cover almost all possible clinical variables used to describe gynecological cancer courses. The only limitation is the registration of oncological treatment data, which is incomplete for a large number of patients. Conclusion The very complete collection of available data from more registries form one of the unique strengths of DGCD compared to many other clinical databases, and provides unique possibilities for validation and completeness of data. The success of the DGCD is illustrated through annual reports, high coverage, and several peer-reviewed DGCD-based publications. PMID:27822089

  2. The Danish Melanoma Database

    PubMed Central

    Hölmich, Lisbet Rosenkrantz; Klausen, Siri; Spaun, Eva; Schmidt, Grethe; Gad, Dorte; Svane, Inge Marie; Schmidt, Henrik; Lorentzen, Henrik Frank; Ibfelt, Else Helene

    2016-01-01

    Aim of database The aim of the database is to monitor and improve the treatment and survival of melanoma patients. Study population All Danish patients with cutaneous melanoma and in situ melanomas must be registered in the Danish Melanoma Database (DMD). In 2014, 2,525 patients with invasive melanoma and 780 with in situ tumors were registered. The coverage is currently 93% compared with the Danish Pathology Register. Main variables The main variables include demographic, clinical, and pathological characteristics, including Breslow’s tumor thickness, ± ulceration, mitoses, and tumor–node–metastasis stage. Information about the date of diagnosis, treatment, type of surgery, including safety margins, results of lymphoscintigraphy in patients for whom this was indicated (tumors > T1a), results of sentinel node biopsy, pathological evaluation hereof, and follow-up information, including recurrence, nature, and treatment hereof is registered. In case of death, the cause and date are included. Currently, all data are entered manually; however, data catchment from the existing registries is planned to be included shortly. Descriptive data The DMD is an old research database, but new as a clinical quality register. The coverage is high, and the performance in the five Danish regions is quite similar due to strong adherence to guidelines provided by the Danish Melanoma Group. The list of monitored indicators is constantly expanding, and annual quality reports are issued. Several important scientific studies are based on DMD data. Conclusion DMD holds unique detailed information about tumor characteristics, the surgical treatment, and follow-up of Danish melanoma patients. Registration and monitoring is currently expanding to encompass even more clinical parameters to benefit both patient treatment and research. PMID:27822097

  3. Danish Urogynaecological Database

    PubMed Central

    Hansen, Ulla Darling; Gradel, Kim Oren; Larsen, Michael Due

    2016-01-01

    The Danish Urogynaecological Database is established in order to ensure high quality of treatment for patients undergoing urogynecological surgery. The database contains details of all women in Denmark undergoing incontinence surgery or pelvic organ prolapse surgery amounting to ~5,200 procedures per year. The variables are collected along the course of treatment of the patient from the referral to a postoperative control. Main variables are prior obstetrical and gynecological history, symptoms, symptom-related quality of life, objective urogynecological findings, type of operation, complications if relevant, implants used if relevant, 3–6-month postoperative recording of symptoms, if any. A set of clinical quality indicators is being maintained by the steering committee for the database and is published in an annual report which also contains extensive descriptive statistics. The database has a completeness of over 90% of all urogynecological surgeries performed in Denmark. Some of the main variables have been validated using medical records as gold standard. The positive predictive value was above 90%. The data are used as a quality monitoring tool by the hospitals and in a number of scientific studies of specific urogynecological topics, broader epidemiological topics, and the use of patient reported outcome measures. PMID:27826217

  4. Danish Palliative Care Database

    PubMed Central

    Groenvold, Mogens; Adsersen, Mathilde; Hansen, Maiken Bang

    2016-01-01

    Aims The aim of the Danish Palliative Care Database (DPD) is to monitor, evaluate, and improve the clinical quality of specialized palliative care (SPC) (ie, the activity of hospital-based palliative care teams/departments and hospices) in Denmark. Study population The study population is all patients in Denmark referred to and/or in contact with SPC after January 1, 2010. Main variables The main variables in DPD are data about referral for patients admitted and not admitted to SPC, type of the first SPC contact, clinical and sociodemographic factors, multidisciplinary conference, and the patient-reported European Organisation for Research and Treatment of Cancer Quality of Life Questionaire-Core-15-Palliative Care questionnaire, assessing health-related quality of life. The data support the estimation of currently five quality of care indicators, ie, the proportions of 1) referred and eligible patients who were actually admitted to SPC, 2) patients who waited <10 days before admission to SPC, 3) patients who died from cancer and who obtained contact with SPC, 4) patients who were screened with European Organisation for Research and Treatment of Cancer Quality of Life Questionaire-Core-15-Palliative Care at admission to SPC, and 5) patients who were discussed at a multidisciplinary conference. Descriptive data In 2014, all 43 SPC units in Denmark reported their data to DPD, and all 9,434 cancer patients (100%) referred to SPC were registered in DPD. In total, 41,104 unique cancer patients were registered in DPD during the 5 years 2010–2014. Of those registered, 96% had cancer. Conclusion DPD is a national clinical quality database for SPC having clinically relevant variables and high data and patient completeness. PMID:27822111

  5. The Danish Testicular Cancer database.

    PubMed

    Daugaard, Gedske; Kier, Maria Gry Gundgaard; Bandak, Mikkel; Mortensen, Mette Saksø; Larsson, Heidi; Søgaard, Mette; Toft, Birgitte Groenkaer; Engvad, Birte; Agerbæk, Mads; Holm, Niels Vilstrup; Lauritsen, Jakob

    2016-01-01

    The nationwide Danish Testicular Cancer database consists of a retrospective research database (DaTeCa database) and a prospective clinical database (Danish Multidisciplinary Cancer Group [DMCG] DaTeCa database). The aim is to improve the quality of care for patients with testicular cancer (TC) in Denmark, that is, by identifying risk factors for relapse, toxicity related to treatment, and focusing on late effects. All Danish male patients with a histologically verified germ cell cancer diagnosis in the Danish Pathology Registry are included in the DaTeCa databases. Data collection has been performed from 1984 to 2007 and from 2013 onward, respectively. The retrospective DaTeCa database contains detailed information with more than 300 variables related to histology, stage, treatment, relapses, pathology, tumor markers, kidney function, lung function, etc. A questionnaire related to late effects has been conducted, which includes questions regarding social relationships, life situation, general health status, family background, diseases, symptoms, use of medication, marital status, psychosocial issues, fertility, and sexuality. TC survivors alive on October 2014 were invited to fill in this questionnaire including 160 validated questions. Collection of questionnaires is still ongoing. A biobank including blood/sputum samples for future genetic analyses has been established. Both samples related to DaTeCa and DMCG DaTeCa database are included. The prospective DMCG DaTeCa database includes variables regarding histology, stage, prognostic group, and treatment. The DMCG DaTeCa database has existed since 2013 and is a young clinical database. It is necessary to extend the data collection in the prospective database in order to answer quality-related questions. Data from the retrospective database will be added to the prospective data. This will result in a large and very comprehensive database for future studies on TC patients.

  6. Danish Colorectal Cancer Group Database.

    PubMed

    Ingeholm, Peter; Gögenur, Ismail; Iversen, Lene H

    2016-01-01

    The aim of the database, which has existed for registration of all patients with colorectal cancer in Denmark since 2001, is to improve the prognosis for this patient group. All Danish patients with newly diagnosed colorectal cancer who are either diagnosed or treated in a surgical department of a public Danish hospital. The database comprises an array of surgical, radiological, oncological, and pathological variables. The surgeons record data such as diagnostics performed, including type and results of radiological examinations, lifestyle factors, comorbidity and performance, treatment including the surgical procedure, urgency of surgery, and intra- and postoperative complications within 30 days after surgery. The pathologists record data such as tumor type, number of lymph nodes and metastatic lymph nodes, surgical margin status, and other pathological risk factors. The database has had >95% completeness in including patients with colorectal adenocarcinoma with >54,000 patients registered so far with approximately one-third rectal cancers and two-third colon cancers and an overrepresentation of men among rectal cancer patients. The stage distribution has been more or less constant until 2014 with a tendency toward a lower rate of stage IV and higher rate of stage I after introduction of the national screening program in 2014. The 30-day mortality rate after elective surgery has been reduced from >7% in 2001-2003 to <2% since 2013. The database is a national population-based clinical database with high patient and data completeness for the perioperative period. The resolution of data is high for description of the patient at the time of diagnosis, including comorbidities, and for characterizing diagnosis, surgical interventions, and short-term outcomes. The database does not have high-resolution oncological data and does not register recurrences after primary surgery. The Danish Colorectal Cancer Group provides high-quality data and has been documenting an

  7. Danish Colorectal Cancer Group Database

    PubMed Central

    Ingeholm, Peter; Gögenur, Ismail; Iversen, Lene H

    2016-01-01

    Aim of database The aim of the database, which has existed for registration of all patients with colorectal cancer in Denmark since 2001, is to improve the prognosis for this patient group. Study population All Danish patients with newly diagnosed colorectal cancer who are either diagnosed or treated in a surgical department of a public Danish hospital. Main variables The database comprises an array of surgical, radiological, oncological, and pathological variables. The surgeons record data such as diagnostics performed, including type and results of radiological examinations, lifestyle factors, comorbidity and performance, treatment including the surgical procedure, urgency of surgery, and intra- and postoperative complications within 30 days after surgery. The pathologists record data such as tumor type, number of lymph nodes and metastatic lymph nodes, surgical margin status, and other pathological risk factors. Descriptive data The database has had >95% completeness in including patients with colorectal adenocarcinoma with >54,000 patients registered so far with approximately one-third rectal cancers and two-third colon cancers and an overrepresentation of men among rectal cancer patients. The stage distribution has been more or less constant until 2014 with a tendency toward a lower rate of stage IV and higher rate of stage I after introduction of the national screening program in 2014. The 30-day mortality rate after elective surgery has been reduced from >7% in 2001–2003 to <2% since 2013. Conclusion The database is a national population-based clinical database with high patient and data completeness for the perioperative period. The resolution of data is high for description of the patient at the time of diagnosis, including comorbidities, and for characterizing diagnosis, surgical interventions, and short-term outcomes. The database does not have high-resolution oncological data and does not register recurrences after primary surgery. The Danish

  8. The Danish Bladder Cancer Database

    PubMed Central

    Hansen, Erik; Larsson, Heidi; Nørgaard, Mette; Thind, Peter; Jensen, Jørgen Bjerggaard

    2016-01-01

    Aim of database The aim of the Danish Bladder Cancer Database (DaBlaCa-data) is to monitor the treatment of all patients diagnosed with invasive bladder cancer (BC) in Denmark. Study population All patients diagnosed with BC in Denmark from 2012 onward were included in the study. Results presented in this paper are predominantly from the 2013 population. Main variables In 2013, 970 patients were diagnosed with BC in Denmark and were included in a preliminary report from the database. A total of 458 (47%) patients were diagnosed with non-muscle-invasive BC (non-MIBC) and 512 (53%) were diagnosed with muscle-invasive BC (MIBC). A total of 300 (31%) patients underwent cystectomy. Among the 135 patients diagnosed with MIBC, who were 75 years of age or younger, 67 (50%) received neoadjuvent chemotherapy prior to cystectomy. In 2013, a total of 147 patients were treated with curative-intended radiation therapy. Descriptive data One-year mortality was 28% (95% confidence interval [CI]: 15–21). One-year cancer-specific mortality was 25% (95% CI: 22–27%). One-year mortality after cystectomy was 14% (95% CI: 10–18). Ninety-day mortality after cystectomy was 3% (95% CI: 1–5) in 2013. One-year mortality following curative-intended radiation therapy was 32% (95% CI: 24–39) and 1-year cancer-specific mortality was 23% (95% CI: 16–31) in 2013. Conclusion This preliminary DaBlaCa-data report showed that the treatment of MIBC in Denmark overall meet high international academic standards. The database is able to identify Danish BC patients and monitor treatment and mortality. In the future, DaBlaCa-data will be a valuable data source and expansive observational studies on BC will be available. PMID:27822081

  9. The Danish Prostate Cancer Database

    PubMed Central

    Nguyen-Nielsen, Mary; Høyer, Søren; Friis, Søren; Hansen, Steinbjørn; Brasso, Klaus; Jakobsen, Erik Breth; Moe, Mette; Larsson, Heidi; Søgaard, Mette; Nakano, Anne; Borre, Michael

    2016-01-01

    Aim of database The Danish Prostate Cancer Database (DAPROCAdata) is a nationwide clinical cancer database that has prospectively collected data on patients with incident prostate cancer in Denmark since February 2010. The overall aim of the DAPROCAdata is to improve the quality of prostate cancer care in Denmark by systematically collecting key clinical variables for the purposes of health care monitoring, quality improvement, and research. Study population All Danish patients with histologically verified prostate cancer are included in the DAPROCAdata. Main variables The DAPROCAdata registers clinical data and selected characteristics for patients with prostate cancer at diagnosis. Data are collected from the linkage of nationwide health registries and supplemented with online registration of key clinical variables by treating physicians at urological and oncological departments. Main variables include Gleason scores, cancer staging, prostate-specific antigen values, and therapeutic measures (active surveillance, surgery, radiotherapy, endocrine therapy, and chemotherapy). Descriptive data In total, 22,332 patients with prostate cancer were registered in DAPROCAdata as of April 2015. A key feature of DAPROCAdata is the routine collection of patient-reported outcome measures (PROM), including data on quality-of-life (pain levels, physical activity, sexual function, depression, urine and fecal incontinence) and lifestyle factors (smoking, alcohol consumption, and body mass index). PROM data are derived from questionnaires distributed at diagnosis and at 1-year and 3-year follow-up. Hitherto, the PROM data have been limited by low completeness (26% among newly diagnosed patients in 2014). Conclusion DAPROCAdata is a comprehensive, yet still young clinical database. Efforts to improve data collection, data validity, and completeness are ongoing and of high priority. PMID:27843346

  10. The Danish Cardiac Rehabilitation Database

    PubMed Central

    Zwisler, Ann-Dorthe; Rossau, Henriette Knold; Nakano, Anne; Foghmar, Sussie; Eichhorst, Regina; Prescott, Eva; Cerqueira, Charlotte; Soja, Anne Merete Boas; Gislason, Gunnar H; Larsen, Mogens Lytken; Andersen, Ulla Overgaard; Gustafsson, Ida; Thomsen, Kristian K; Boye Hansen, Lene; Hammer, Signe; Viggers, Lone; Christensen, Bo; Kvist, Birgitte; Lindström Egholm, Cecilie; May, Ole

    2016-01-01

    Aim of database The Danish Cardiac Rehabilitation Database (DHRD) aims to improve the quality of cardiac rehabilitation (CR) to the benefit of patients with coronary heart disease (CHD). Study population Hospitalized patients with CHD with stenosis on coronary angiography treated with percutaneous coronary intervention, coronary artery bypass grafting, or medication alone. Reporting is mandatory for all hospitals in Denmark delivering CR. The database was initially implemented in 2013 and was fully running from August 14, 2015, thus comprising data at a patient level from the latter date onward. Main variables Patient-level data are registered by clinicians at the time of entry to CR directly into an online system with simultaneous linkage to other central patient registers. Follow-up data are entered after 6 months. The main variables collected are related to key outcome and performance indicators of CR: referral and adherence, lifestyle, patient-related outcome measures, risk factor control, and medication. Program-level online data are collected every third year. Descriptive data Based on administrative data, approximately 14,000 patients with CHD are hospitalized at 35 hospitals annually, with 75% receiving one or more outpatient rehabilitation services by 2015. The database has not yet been running for a full year, which explains the use of approximations. Conclusion The DHRD is an online, national quality improvement database on CR, aimed at patients with CHD. Mandatory registration of data at both patient level as well as program level is done on the database. DHRD aims to systematically monitor the quality of CR over time, in order to improve the quality of CR throughout Denmark to benefit patients. PMID:27822083

  11. The Danish Cardiac Rehabilitation Database.

    PubMed

    Zwisler, Ann-Dorthe; Rossau, Henriette Knold; Nakano, Anne; Foghmar, Sussie; Eichhorst, Regina; Prescott, Eva; Cerqueira, Charlotte; Soja, Anne Merete Boas; Gislason, Gunnar H; Larsen, Mogens Lytken; Andersen, Ulla Overgaard; Gustafsson, Ida; Thomsen, Kristian K; Boye Hansen, Lene; Hammer, Signe; Viggers, Lone; Christensen, Bo; Kvist, Birgitte; Lindström Egholm, Cecilie; May, Ole

    2016-01-01

    The Danish Cardiac Rehabilitation Database (DHRD) aims to improve the quality of cardiac rehabilitation (CR) to the benefit of patients with coronary heart disease (CHD). Hospitalized patients with CHD with stenosis on coronary angiography treated with percutaneous coronary intervention, coronary artery bypass grafting, or medication alone. Reporting is mandatory for all hospitals in Denmark delivering CR. The database was initially implemented in 2013 and was fully running from August 14, 2015, thus comprising data at a patient level from the latter date onward. Patient-level data are registered by clinicians at the time of entry to CR directly into an online system with simultaneous linkage to other central patient registers. Follow-up data are entered after 6 months. The main variables collected are related to key outcome and performance indicators of CR: referral and adherence, lifestyle, patient-related outcome measures, risk factor control, and medication. Program-level online data are collected every third year. Based on administrative data, approximately 14,000 patients with CHD are hospitalized at 35 hospitals annually, with 75% receiving one or more outpatient rehabilitation services by 2015. The database has not yet been running for a full year, which explains the use of approximations. The DHRD is an online, national quality improvement database on CR, aimed at patients with CHD. Mandatory registration of data at both patient level as well as program level is done on the database. DHRD aims to systematically monitor the quality of CR over time, in order to improve the quality of CR throughout Denmark to benefit patients.

  12. The Danish Collaborative Bacteraemia Network (DACOBAN) database.

    PubMed

    Gradel, Kim Oren; Schønheyder, Henrik Carl; Arpi, Magnus; Knudsen, Jenny Dahl; Ostergaard, Christian; Søgaard, Mette

    2014-01-01

    The Danish Collaborative Bacteraemia Network (DACOBAN) research database includes microbiological data obtained from positive blood cultures from a geographically and demographically well-defined population serviced by three clinical microbiology departments (1.7 million residents, 32% of the Danish population). The database also includes data on comorbidity from the Danish National Patient Registry, vital status from the Danish Civil Registration System, and clinical data on 31% of nonselected records in the database. Use of the unique civil registration number given to all Danish residents enables linkage to additional registries for specific research projects. The DACOBAN database is continuously updated, and it currently comprises 39,292 patients with 49,951 bacteremic episodes from 2000 through 2011. The database is part of an international network of population-based bacteremia registries from five developed countries on three continents. The main purpose of the DACOBAN database is to study surveillance, risk, and prognosis. Sex- and age-specific data on background populations enables the computation of incidence rates. In addition, the high number of patients facilitates studies of rare microorganisms. Thus far, studies on Staphylococcus aureus, enterococci, computer algorithms for the classification of bacteremic episodes, and prognosis and risk in relation to socioeconomic factors have been published.

  13. The Danish Nonmelanoma Skin Cancer Dermatology Database.

    PubMed

    Lamberg, Anna Lei; Sølvsten, Henrik; Lei, Ulrikke; Vinding, Gabrielle Randskov; Stender, Ida Marie; Jemec, Gregor Borut Ernst; Vestergaard, Tine; Thormann, Henrik; Hædersdal, Merete; Dam, Tomas Norman; Olesen, Anne Braae

    2016-01-01

    The Danish Nonmelanoma Skin Cancer Dermatology Database was established in 2008. The aim of this database was to collect data on nonmelanoma skin cancer (NMSC) treatment and improve its treatment in Denmark. NMSC is the most common malignancy in the western countries and represents a significant challenge in terms of public health management and health care costs. However, high-quality epidemiological and treatment data on NMSC are sparse. The NMSC database includes patients with the following skin tumors: basal cell carcinoma (BCC), squamous cell carcinoma, Bowen's disease, and keratoacanthoma diagnosed by the participating office-based dermatologists in Denmark. Clinical and histological diagnoses, BCC subtype, localization, size, skin cancer history, skin phototype, and evidence of metastases and treatment modality are the main variables in the NMSC database. Information on recurrence, cosmetic results, and complications are registered at two follow-up visits at 3 months (between 0 and 6 months) and 12 months (between 6 and 15 months) after treatment. In 2014, 11,522 patients with 17,575 tumors were registered in the database. Of tumors with a histological diagnosis, 13,571 were BCCs, 840 squamous cell carcinomas, 504 Bowen's disease, and 173 keratoakanthomas. The NMSC database encompasses detailed information on the type of tumor, a variety of prognostic factors, treatment modalities, and outcomes after treatment. The database has revealed that overall, the quality of care of NMSC in Danish dermatological clinics is high, and the database provides the necessary data for continuous quality assurance.

  14. A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models

    PubMed Central

    Zhang, Shuxing; Golbraikh, Alexander; Oloff, Scott; Kohn, Harold; Tropsha, Alexander

    2008-01-01

    A novel Automated Lazy Learning Quantitative Structure-Activity Relationship (ALL-QSAR) modeling approach has been developed based on the lazy learning theory. The activity of a test compound is predicted from locally weighted linear regression model using chemical descriptors and biological activity of the training set compounds most chemically similar to this test compound. The weights with which training set compounds are included in the regression depend on the similarity of those compounds to a test compound. We have applied the ALL-QSAR method to several experimental chemical datasets including 48 anticonvulsant agents with known ED50 values, 48 dopamine D1-receptor antagonists with known competitive binding affinities (Ki), and a Tetrahymena pyriformis dataset containing 250 phenolic compounds with toxicity IGC50 values. When applied to database screening, models developed for anticonvulsant agents identified several known anticonvulsant compounds that were not only absent in the training set but highly chemically dissimilar to the training set compounds. This initial success indicates that ALL-QSAR can be further exploited as a general tool for accurate bioactivity prediction and database screening in drug design and discovery. Due to its local nature, the ALL-QSAR approach appears to be especially well suited for the development of highly predictive models for the sparse or unevenly distributed datasets. PMID:16995729

  15. A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models.

    PubMed

    Zhang, Shuxing; Golbraikh, Alexander; Oloff, Scott; Kohn, Harold; Tropsha, Alexander

    2006-01-01

    A novel automated lazy learning quantitative structure-activity relationship (ALL-QSAR) modeling approach has been developed on the basis of the lazy learning theory. The activity of a test compound is predicted from a locally weighted linear regression model using chemical descriptors and the biological activity of the training set compounds most chemically similar to this test compound. The weights with which training set compounds are included in the regression depend on the similarity of those compounds to a test compound. We have applied the ALL-QSAR method to several experimental chemical data sets including 48 anticonvulsant agents with known ED50 values, 48 dopamine D1-receptor antagonists with known competitive binding affinities (Ki), and a Tetrahymena pyriformis data set containing 250 phenolic compounds with toxicity IGC50 values. When applied to database screening, models developed for anticonvulsant agents identified several known anticonvulsant compounds that were not only absent in the training set but highly chemically dissimilar to the training set compounds. This initial success indicates that ALL-QSAR can be further exploited as a general tool for accurate bioactivity prediction and database screening in drug design and discovery. Because of its local nature, the ALL-QSAR approach appears to be especially well-suited for the development of highly predictive models for the sparse or unevenly distributed data sets.

  16. The Danish National Quality Database for Births

    PubMed Central

    Andersson, Charlotte Brix; Flems, Christina; Kesmodel, Ulrik Schiøler

    2016-01-01

    Aim of the database The aim of the Danish National Quality Database for Births (DNQDB) is to measure the quality of the care provided during birth through specific indicators. Study population The database includes all hospital births in Denmark. Main variables Anesthesia/pain relief, continuous support for women in the delivery room, lacerations (third and fourth degree), cesarean section, postpartum hemorrhage, establishment of skin-to-skin contact between the mother and the newborn infant, severe fetal hypoxia (proportion of live-born children with neonatal hypoxia), delivery of a healthy child after an uncomplicated birth, and anesthesia in case of cesarean section. Descriptive data Data have been collected since 2010. As of August 2015, data on women and children representing 269,597 births and 274,153 children have been collected. All data for the DNQDB is collected from the Danish Medical Birth Registry. Registration to the Danish Medical Birth Registry is mandatory for all maternity units in Denmark. During the 5 years, performance has improved in the areas covered by the process indicators and for some of the outcome indicators. Conclusion Measuring quality of care during childbirth has inspired and enabled staff to attend to the quality of the care they provide and has led to improvements in most of the areas covered. PMID:27822105

  17. The Danish Nonmelanoma Skin Cancer Dermatology Database

    PubMed Central

    Lamberg, Anna Lei; Sølvsten, Henrik; Lei, Ulrikke; Vinding, Gabrielle Randskov; Stender, Ida Marie; Jemec, Gregor Borut Ernst; Vestergaard, Tine; Thormann, Henrik; Hædersdal, Merete; Dam, Tomas Norman; Olesen, Anne Braae

    2016-01-01

    Aim of database The Danish Nonmelanoma Skin Cancer Dermatology Database was established in 2008. The aim of this database was to collect data on nonmelanoma skin cancer (NMSC) treatment and improve its treatment in Denmark. NMSC is the most common malignancy in the western countries and represents a significant challenge in terms of public health management and health care costs. However, high-quality epidemiological and treatment data on NMSC are sparse. Study population The NMSC database includes patients with the following skin tumors: basal cell carcinoma (BCC), squamous cell carcinoma, Bowen’s disease, and keratoacanthoma diagnosed by the participating office-based dermatologists in Denmark. Main variables Clinical and histological diagnoses, BCC subtype, localization, size, skin cancer history, skin phototype, and evidence of metastases and treatment modality are the main variables in the NMSC database. Information on recurrence, cosmetic results, and complications are registered at two follow-up visits at 3 months (between 0 and 6 months) and 12 months (between 6 and 15 months) after treatment. Descriptive data In 2014, 11,522 patients with 17,575 tumors were registered in the database. Of tumors with a histological diagnosis, 13,571 were BCCs, 840 squamous cell carcinomas, 504 Bowen’s disease, and 173 keratoakanthomas. Conclusion The NMSC database encompasses detailed information on the type of tumor, a variety of prognostic factors, treatment modalities, and outcomes after treatment. The database has revealed that overall, the quality of care of NMSC in Danish dermatological clinics is high, and the database provides the necessary data for continuous quality assurance. PMID:27822110

  18. The Danish Head and Neck Cancer database

    PubMed Central

    Overgaard, Jens; Jovanovic, Aleksandar; Godballe, Christian; Grau Eriksen, Jesper

    2016-01-01

    Aim of the database The Danish Head and Neck Cancer database is a nationwide clinical quality database that contains prospective data collected since the early 1960s. The overall aim of this study was to describe the outcome of the national strategy for multidisciplinary treatment of head and neck cancer in Denmark and to create a basis for clinical trials. Study population The study population consisted of all Danish patients referred for treatment of squamous cell carcinoma of the larynx, pharynx, oral cavity, or neck nodes from unknown primary or any histopathological type (except lymphoma) of cancer in the nasal sinuses, salivary glands, or thyroid gland (corresponding to the International Classification of Diseases, tenth revision, classifications C.01–C.11, C.30–C.32, C.73, and C.80). Main variables The main variables used in the study were symptoms and the duration of the symptoms; etiological factors; pretreatment and diagnostic evaluation, including tumor–node–metastasis classification, imaging, histopathology, and laboratory tests; primary treatment with semidetailed information of radiotherapy, surgery, and medical treatment; follow-up registration of tumor status and side effects; registration of relapse and treatment thereof; and registration of death and cause of death. Main results Data from >33,000 patients have been recorded during a period of >45 years. In this period, the outcome of treatment improved substantially, partly due to better treatment as a result of a series of continuous clinical trials and subsequent implementation in national guidelines. The database has furthermore been used to describe the effect of reduced waiting time, changed epidemiology, and influence of comorbidity and socioeconomic parameters. Conclusion Half a century of registration of head and neck cancer treatment and outcome has created the basis for understanding and has substantially contributed to improve the treatment of head and neck cancer at both

  19. The Danish National Penile Cancer Quality database

    PubMed Central

    Jakobsen, Jakob Kristian; Öztürk, Buket; Søgaard, Mette

    2016-01-01

    Aim of database The Danish National Penile Cancer Quality database (DaPeCa-data) aims to improve the quality of cancer care and monitor the diagnosis, staging, and treatment of all incident penile cancer cases in Denmark. The aim is to assure referral practice, guideline adherence, and treatment and development of the database in order to enhance research opportunities and increase knowledge and survival outcomes of penile cancer. Study population The DaPeCa-data registers all patients with newly diagnosed invasive squamous cell carcinoma of the penis in Denmark since June 2011. Main variables Data are systematically registered at the time of diagnosis by a combination of automated data-linkage to the central registries as well as online registration by treating clinicians. The main variables registered relate to disease prognosis and treatment morbidity and include the presence of risk factors (phimosis, lichen sclerosus, and human papillomavirus), date of diagnosis, date of treatment decision, date of beginning of treatment, type of treatment, treating hospital, type and time of complications, date of recurrence, date of death, and cause of death. Descriptive data Registration of these variables correlated to the unique Danish ten-digit civil registration number enables characterization of the cohort, individual patients, and patient groups with respect to age; 1-, 3-, and 5-year disease-specific and overall survival; recurrence patterns; and morbidity profile related to treatment modality. As of August 2015, more than 200 patients are registered with ∼65 new entries per year. Conclusion The DaPeCa-data has potential to provide meaningful, timely, and clinically relevant quality data for quality maintenance, development, and research purposes. PMID:27822104

  20. The Danish National Penile Cancer Quality database.

    PubMed

    Jakobsen, Jakob Kristian; Öztürk, Buket; Søgaard, Mette

    2016-01-01

    The Danish National Penile Cancer Quality database (DaPeCa-data) aims to improve the quality of cancer care and monitor the diagnosis, staging, and treatment of all incident penile cancer cases in Denmark. The aim is to assure referral practice, guideline adherence, and treatment and development of the database in order to enhance research opportunities and increase knowledge and survival outcomes of penile cancer. The DaPeCa-data registers all patients with newly diagnosed invasive squamous cell carcinoma of the penis in Denmark since June 2011. Data are systematically registered at the time of diagnosis by a combination of automated data-linkage to the central registries as well as online registration by treating clinicians. The main variables registered relate to disease prognosis and treatment morbidity and include the presence of risk factors (phimosis, lichen sclerosus, and human papillomavirus), date of diagnosis, date of treatment decision, date of beginning of treatment, type of treatment, treating hospital, type and time of complications, date of recurrence, date of death, and cause of death. Registration of these variables correlated to the unique Danish ten-digit civil registration number enables characterization of the cohort, individual patients, and patient groups with respect to age; 1-, 3-, and 5-year disease-specific and overall survival; recurrence patterns; and morbidity profile related to treatment modality. As of August 2015, more than 200 patients are registered with ∼65 new entries per year. The DaPeCa-data has potential to provide meaningful, timely, and clinically relevant quality data for quality maintenance, development, and research purposes.

  1. QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors.

    PubMed

    Tarasova, Olga A; Urusova, Aleksandra F; Filimonov, Dmitry A; Nicklaus, Marc C; Zakharov, Alexey V; Poroikov, Vladimir V

    2015-07-27

    Large-scale databases are important sources of training sets for various QSAR modeling approaches. Generally, these databases contain information extracted from different sources. This variety of sources can produce inconsistency in the data, defined as sometimes widely diverging activity results for the same compound against the same target. Because such inconsistency can reduce the accuracy of predictive models built from these data, we are addressing the question of how best to use data from publicly and commercially accessible databases to create accurate and predictive QSAR models. We investigate the suitability of commercially and publicly available databases to QSAR modeling of antiviral activity (HIV-1 reverse transcriptase (RT) inhibition). We present several methods for the creation of modeling (i.e., training and test) sets from two, either commercially or freely available, databases: Thomson Reuters Integrity and ChEMBL. We found that the typical predictivities of QSAR models obtained using these different modeling set compilation methods differ significantly from each other. The best results were obtained using training sets compiled for compounds tested using only one method and material (i.e., a specific type of biological assay). Compound sets aggregated by target only typically yielded poorly predictive models. We discuss the possibility of "mix-and-matching" assay data across aggregating databases such as ChEMBL and Integrity and their current severe limitations for this purpose. One of them is the general lack of complete and semantic/computer-parsable descriptions of assay methodology carried by these databases that would allow one to determine mix-and-matchability of result sets at the assay level.

  2. The Danish Microbiology Database (MiBa) 2010 to 2013.

    PubMed

    Voldstedlund, M; Haarh, M; Mølbak, K

    2014-01-09

    The Danish Microbiology Database (MiBa) is a national database that receives copies of reports from all Danish departments of clinical microbiology. The database was launched in order to provide healthcare personnel with nationwide access to microbiology reports and to enable real-time surveillance of communicable diseases and microorganisms. The establishment and management of MiBa has been a collaborative process among stakeholders, and the present paper summarises lessons learned from this nationwide endeavour which may be relevant to similar projects in the rapidly changing landscape of health informatics.

  3. Towards Global QSAR Model Building for Acute Toxicity: Munro Database Case Study

    PubMed Central

    Chavan, Swapnil; Nicholls, Ian A.; Karlsson, Björn C. G.; Rosengren, Annika M.; Ballabio, Davide; Consonni, Viviana; Todeschini, Roberto

    2014-01-01

    A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity. PMID:25302621

  4. Towards global QSAR model building for acute toxicity: Munro database case study.

    PubMed

    Chavan, Swapnil; Nicholls, Ian A; Karlsson, Björn C G; Rosengren, Annika M; Ballabio, Davide; Consonni, Viviana; Todeschini, Roberto

    2014-10-09

    A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity.

  5. Heme Oxygenase Database (HemeOxDB) and QSAR Analysis of Isoform 1 Inhibitors.

    PubMed

    Amata, Emanuele; Marrazzo, Agostino; Dichiara, Maria; Modica, Maria N; Salerno, Loredana; Prezzavento, Orazio; Nastasi, Giovanni; Rescifina, Antonio; Romeo, Giuseppe; Pittalà, Valeria

    2017-07-14

    Due to increasing interest in the field of heme oxygenases (HOs), we built a ligand database called HemeOxDB that includes the entire set of known HO-1 and HO-2 inhibitors, resulting in more than 400 compounds. The HemeOxDB is available online at http://www.researchdsf.unict.it/hemeoxdb/, and having a robust search engine allows end users to build complex queries, sort tabulated results, and generate color-coded two- and three-dimensional graphs. This database will grow to be a tool for the design of potent and selective HO-1 or HO-2 inhibitors. We were also interested in virtually searching for alternative inhibitors, and, for the first time in the field of HOs, a quantitative structure-activity relationship (QSAR) model was built using half-maximal inhibitory concentration (IC50 ) values of the whole set of known HO-1 inhibitors, taken from the HemeOxDB and employing the Monte Carlo technique. The statistical quality suggested that the model is robust and possesses desirable predictive potential. The screening of US Food and Drug Administration (FDA)-approved drugs, external to our dataset, suggested new predicted inhibitors, opening the way for replacing imidazole groups. The HemeOxDB and the QSAR model reported herein may help in prospectively identifying or repurposing new drugs with optimal structural attributes for HO enzyme inhibition. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. The Danish quality database for prehospital emergency medical services

    PubMed Central

    Frischknecht Christensen, Erika; Berlac, Peter Anthony; Nielsen, Henrik; Christiansen, Christian Fynbo

    2016-01-01

    Aim of database The aim of the Danish quality database for prehospital emergency medical services (QEMS) is to assess, monitor, and improve the quality of prehospital emergency medical service care in the entire prehospital patient pathway. The aim of this review is to describe the design and the implementation of QEMS. Study population The study population consists of all “112 patient contacts” defined as emergency patients, where the entrance to health care is a 112 call forwarded to one of the five regional emergency medical coordination centers in Denmark since January 1, 2014. Estimated annual number of included “112 patients” is 300,000–350,000. Main variables We defined nine quality indicators and the following variables: time stamps for emergency calls received at one of the five regional emergency medical coordination centers, dispatch of prehospital unit(s), arrival of first prehospital unit, arrival of first supplemental prehospital unit, and mission completion. Finally, professional level and type of the prehospital resource dispatched to an incident and end-of-mission status (mission completed by phone, on scene, or admission to hospital) are registered. Descriptive data Descriptive data included age, region, and Danish Index for Emergency Care including urgency level. Conclusion QEMS is a new database under establishment and is expected to provide the basis for quality improvement in the prehospital setting and in the entire patient care pathway, for example, by providing prehospital data for research and other quality databases. PMID:27843347

  7. Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.

    PubMed

    Ko, Gene M; Garg, Rajni; Bailey, Barbara A; Kumar, Sunil

    2016-01-01

    Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

  8. Validity of data in the Danish Colorectal Cancer Screening Database

    PubMed Central

    Thomsen, Mette Kielsholm; Njor, Sisse Helle; Rasmussen, Morten; Linnemann, Dorte; Andersen, Berit; Baatrup, Gunnar; Friis-Hansen, Lennart Jan; Jørgensen, Jens Christian Riis; Mikkelsen, Ellen Margrethe

    2017-01-01

    Background In Denmark, a nationwide screening program for colorectal cancer was implemented in March 2014. Along with this, a clinical database for program monitoring and research purposes was established. Objective The aim of this study was to estimate the agreement and validity of diagnosis and procedure codes in the Danish Colorectal Cancer Screening Database (DCCSD). Methods All individuals with a positive immunochemical fecal occult blood test (iFOBT) result who were invited to screening in the first 3 months since program initiation were identified. From these, a sample of 150 individuals was selected using stratified random sampling by age, gender and region of residence. Data from the DCCSD were compared with data from hospital records, which were used as the reference. Agreement, sensitivity, specificity and positive and negative predictive values were estimated for categories of codes “clean colon”, “colonoscopy performed”, “overall completeness of colonoscopy”, “incomplete colonoscopy”, “polypectomy”, “tumor tissue left behind”, “number of polyps”, “lost polyps”, “risk group of polyps” and “colorectal cancer and polyps/benign tumor”. Results Hospital records were available for 136 individuals. Agreement was highest for “colorectal cancer” (97.1%) and lowest for “lost polyps” (88.2%). Sensitivity varied between moderate and high, with 60.0% for “incomplete colonoscopy” and 98.5% for “colonoscopy performed”. Specificity was 92.7% or above, except for the categories “colonoscopy performed” and “overall completeness of colonoscopy”, where the specificity was low; however, the estimates were imprecise. Conclusion A high level of agreement between categories of codes in DCCSD and hospital records indicates that DCCSD reflects the hospital records well. Further, the validity of the categories of codes varied from moderate to high. Thus, the DCCSD may be a valuable data source for future research on

  9. The Danish National Database for Asthma: establishing clinical quality indicators

    PubMed Central

    Hansen, Susanne; Hoffmann-Petersen, Benjamin; Sverrild, Asger; Bräuner, Elvira V.; Lykkegaard, Jesper; Bodtger, Uffe; Agertoft, Lone; Korshøj, Lene; Backer, Vibeke

    2016-01-01

    Asthma is one of the most common chronic diseases worldwide affecting more than 300 million people. Symptoms are often non-specific and include coughing, wheezing, chest tightness, and shortness of breath. Asthma may be highly variable within the same individual over time. Although asthma results in death only in extreme cases, the disease is associated with significant morbidity, reduced quality of life, increased absenteeism, and large costs for society. Asthma can be diagnosed based on report of characteristic symptoms and/or the use of several different diagnostic tests. However, there is currently no gold standard for making a diagnosis, and some degree of misclassification and inter-observer variation can be expected. This may lead to local and regional differences in the treatment, monitoring, and follow-up of the patients. The Danish National Database for Asthma (DNDA) is slated to be established with the overall aim of collecting data on all patients treated for asthma in Denmark and systematically monitoring the treatment quality and disease management in both primary and secondary care facilities across the country. The DNDA links information from population-based disease registers in Denmark, including the National Patient Register, the National Prescription Registry, and the National Health Insurance Services register, and potentially includes all asthma patients in Denmark. The following quality indicators have been selected to monitor trends: first, conduction of annual asthma control visits, appropriate pharmacological treatment, measurement of lung function, and asthma challenge testing; second, tools used for diagnosis in new cases; and third, annual assessment of smoking status, height, and weight measurements, and the proportion of patients with acute hospital treatment. The DNDA will be launched in 2016 and will initially include patients treated in secondary care facilities in Denmark. In the nearby future, the database aims to include asthma

  10. Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data.

    PubMed

    Zhu, Xiao; Kruhlak, Naomi L

    2014-07-03

    Drug-induced liver injury (DILI) is one of the most common drug-induced adverse events (AEs) leading to life-threatening conditions such as acute liver failure. It has also been recognized as the single most common cause of safety-related post-market withdrawals or warnings. Efforts to develop new predictive methods to assess the likelihood of a drug being a hepatotoxicant have been challenging due to the complexity and idiosyncrasy of clinical manifestations of DILI. The FDA adverse event reporting system (AERS) contains post-market data that depict the morbidity of AEs. Here, we developed a scalable approach to construct a hepatotoxicity database using post-market data for the purpose of quantitative structure-activity relationship (QSAR) modeling. A set of 2029 unique and modelable drug entities with 13,555 drug-AE combinations was extracted from the AERS database using 37 hepatotoxicity-related query preferred terms (PTs). In order to determine the optimal classification scheme to partition positive from negative drugs, a manually-curated DILI calibration set composed of 105 negatives and 177 positives was developed based on the published literature. The final classification scheme combines hepatotoxicity-related PT data with supporting information that optimize the predictive performance across the calibration set. Data for other toxicological endpoints related to liver injury such as liver enzyme abnormalities, cholestasis, and bile duct disorders, were also extracted and classified. Collectively, these datasets can be used to generate a battery of QSAR models that assess a drug's potential to cause DILI.

  11. A comprehensive model for reproductive and developmental toxicity hazard identification: I. Development of a weight of evidence QSAR database.

    PubMed

    Matthews, Edwin J; Kruhlak, Naomi L; Daniel Benz, R; Contrera, Joseph F

    2007-03-01

    A weight of evidence (WOE) reproductive and developmental toxicology (reprotox) database was constructed that is suitable for quantitative structure-activity relationship (QSAR) modeling and human hazard identification of untested chemicals. The database was derived from multiple publicly available reprotox databases and consists of more than 10,000 individual rat, mouse, or rabbit reprotox tests linked to 2134 different organic chemical structures. The reprotox data were classified into seven general classes (male reproductive toxicity, female reproductive toxicity, fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity), and 90 specific categories as defined in the source reprotox databases. Each specific category contained over 500 chemicals, but the percentage of active chemicals was low, generally only 0.1-10%. The mathematical WOE model placed greater significance on confirmatory observations from repeat experiments, chemicals with multiple findings within a category, and the categorical relatedness of the findings. Using the weighted activity scores, statistical analyses were performed for specific data sets to identify clusters of categories that were correlated, containing similar profiles of active and inactive chemicals. The analysis revealed clusters of specific categories that contained chemicals that were active in two or more mammalian species (trans-species). Such chemicals are considered to have the highest potential risk to humans. In contrast, some specific categories exhibited only single species-specific activities. Results also showed that the rat and mouse were more susceptible to dysmorphogenesis than rabbits (6.1- and 3.6-fold, respectively).

  12. Existing data sources for clinical epidemiology: The Danish National Database of Reimbursed Prescriptions.

    PubMed

    Johannesdottir, Sigrun Alba; Horváth-Puhó, Erzsébet; Ehrenstein, Vera; Schmidt, Morten; Pedersen, Lars; Sørensen, Henrik Toft

    2012-01-01

    The Danish health care system provides partial reimbursement of most prescription medications in Denmark. The dispensation of prescription medications is registered in administrative databases. Each time a prescription is redeemed at a pharmacy, an electronic record is generated with information related to the user, prescriber, the pharmacy, and the dispensed drug. The National Health Service gathers this information for administration of the drug reimbursement plan. Recently, this information became the basis for the establishment of a new research database, the Danish National Database of Reimbursed Prescriptions (DNDRP). In this paper, we review the content, coverage, quality, linkage, access, and research possibilities of this new database. The database encompasses the reimbursement records of all reimbursed drugs sold in community pharmacies and hospital-based outpatient pharmacies in Denmark since 2004. On average, approximately 3.5 million users are recorded in the database each year. During the coverage period, the number of annual prescription redemptions increased by 15%. Most dispensed prescriptions are in the categories "alimentary tract and metabolism", "cardiovascular system", "nervous system", and "respiratory system". Individuals are identified by the unique central personal registration (CPR) number assigned to all persons born in or immigrating to Denmark. The new database fully complies with Denmark's Act on Processing of Personal Data, while avoiding additional restrictions imposed on data use at the Danish National Prescription Registry, administered by Statistics Denmark. Most importantly, CPR numbers are reversibly encrypted, which allows re-identification of drug users; furthermore, the data access is possible outside the servers of Statistics Denmark. These features open additional opportunities for international collaboration, validation studies, studies on adverse drug effects requiring review of medical records, studies involving contact

  13. Linkage between the Danish National Health Service Prescription Database, the Danish Fetal Medicine Database, and other Danish registries as a tool for the study of drug safety in pregnancy

    PubMed Central

    Pedersen, Lars H; Petersen, Olav B; Nørgaard, Mette; Ekelund, Charlotte; Pedersen, Lars; Tabor, Ann; Sørensen, Henrik T

    2016-01-01

    A linked population-based database is being created in Denmark for research on drug safety during pregnancy. It combines information from the Danish National Health Service Prescription Database (with information on all prescriptions reimbursed in Denmark since 2004), the Danish Fetal Medicine Database, the Danish National Registry of Patients, and the Medical Birth Registry. The new linked database will provide validated information on malformations diagnosed both prenatally and postnatally. The cohort from 2008 to 2014 will comprise 589,000 pregnancies with information on 424,000 pregnancies resulting in live-born children, ∼420,000 pregnancies undergoing prenatal ultrasound scans, 65,000 miscarriages, and 92,000 terminations. It will be updated yearly with information on ∼80,000 pregnancies. The cohort will enable identification of drug exposures associated with severe malformations, not only based on malformations diagnosed after birth but also including those having led to termination of pregnancy or miscarriage. Such combined data will provide a unique source of information for research on the safety of medications used during pregnancy. PMID:27274312

  14. An ensemble model of QSAR tools for regulatory risk assessment

    DOE PAGES

    Pradeep, Prachi; Povinelli, Richard J.; White, Shannon; ...

    2016-09-22

    Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflictingmore » predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0

  15. An ensemble model of QSAR tools for regulatory risk assessment

    SciTech Connect

    Pradeep, Prachi; Povinelli, Richard J.; White, Shannon; Merrill, Stephen J.

    2016-09-22

    Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0

  16. An ensemble model of QSAR tools for regulatory risk assessment.

    PubMed

    Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

    2016-01-01

    Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0

  17. Existing data sources for clinical epidemiology: the Danish Patient Compensation Association database

    PubMed Central

    Tilma, Jens; Nørgaard, Mette; Mikkelsen, Kim Lyngby; Johnsen, Søren Paaske

    2015-01-01

    Any patient in the Danish health care system who experiences a treatment injury can make a compensation claim to the Danish Patient Compensation Association (DPCA) free of charge. The aim of this paper is to describe the DPCA database as a source of data for epidemiological research. Data to DPCA are collected prospectively on all claims and include information on patient factors and health records, system factors, and administrative data. Approval of claims is based on injury due to the principle of treatment below experienced specialist standard or intolerable, unexpected extensiveness of injury. Average processing time of a compensation claim is 6–8 months. Data collection is nationwide and started in 1992. The patient’s central registration system number, a unique personal identifier, allows for data linkage to other registries such as the Danish National Patient Registry. The DPCA data are accessible for research following data usage permission and make it possible to analyze all claims or specific subgroups to identify predictors, outcomes, etc. DPCA data have until now been used only in few studies but could be a useful data source in future studies of health care-related injuries. PMID:26229505

  18. Quantitative Structure activity Relationship Analysis of Pyridinone HIV-1 Reverse Transcriptase Inhibitors using the k Nearest Neighbor Method and QSAR-based Database Mining

    NASA Astrophysics Data System (ADS)

    Medina-Franco, Jose Luis; Golbraikh, Alexander; Oloff, Scott; Castillo, Rafael; Tropsha, Alexander

    2005-04-01

    We have developed quantitative structure-activity relationship (QSAR) models for 44 non-nucleoside HIV-1 reverse transcriptase inhibitors (NNRTIs) of the pyridinone derivative type. The k nearest neighbor ( kNN) variable selection approach was used. This method utilizes multiple descriptors such as molecular connectivity indices, which are derived from two-dimensional molecular topology. The modeling process entailed extensive validation including the randomization of the target property (Y-randomization) test and the division of the dataset into multiple training and test sets to establish the external predictive power of the training set models. QSAR models with high internal and external accuracy were generated, with leave-one-out cross-validated R 2 ( q 2) values ranging between 0.5 and 0.8 for the training sets and R 2 values exceeding 0.6 for the test sets. The best models with the highest internal and external predictive power were used to search the National Cancer Institute database. Derivatives of the pyrazolo[3,4- d]pyrimidine and phenothiazine type were identified as promising novel NNRTIs leads. Several candidates were docked into the binding pocket of nevirapine with the AutoDock (version 3.0) software. Docking results suggested that these types of compounds could be binding in the NNRTI binding site in a similar mode to a known non-nucleoside inhibitor nevirapine.

  19. Real-time surveillance of laboratory confirmed influenza based on the Danish microbiology database (MiBa).

    PubMed

    Voldstedlund, Marianne; Haahr, Malene; Emborg, Hanne-Dorthe; Bang, Henrik; Krause, Tyra

    2013-01-01

    The Danish microbiology database (MiBa) is a national database that automatically accumulates patient test results from all Danish Departments of Clinical Microbiology. As an example for use of MiBa, we describe the real-time surveillance of laboratory confirmed influenza established in October 2012. It functions without any extra burdens of reporting by laboratories or clinicians. This is an important improvement of the existing surveillance for influenza like illness (ILI) which includes only limited virological testing. The MiBa based surveillance adds complete national virological data which are specific for influenza, in contrast to ILI, and serves as a tool for regional and national preparedness and planning.

  20. Danish Prostate Cancer Registry – methodology and early results from a novel national database

    PubMed Central

    Helgstrand, JT; Klemann, N; Røder, MA; Toft, BG; Brasso, K; Vainer, B; Iversen, P

    2016-01-01

    Background Systematized Nomenclature of Medicine (SNOMED) codes are computer-processable medical terms used to describe histopathological evaluations. SNOMED codes are not readily usable for analysis. We invented an algorithm that converts prostate SNOMED codes into an analyzable format. We present the methodology and early results from a new national Danish prostate database containing clinical data from all males who had evaluation of prostate tissue from 1995 to 2011. Materials and methods SNOMED codes were retrieved from the Danish Pathology Register. A total of 26,295 combinations of SNOMED codes were identified. A computer algorithm was developed to transcode SNOMED codes into an analyzable format including procedure (eg, biopsy, transurethral resection, etc), diagnosis, and date of diagnosis. For validation, ~55,000 pathological reports were manually reviewed. Prostate-specific antigen, vital status, causes of death, and tumor-node-metastasis classification were integrated from national registries. Results Of the 161,525 specimens from 113,801 males identified, 83,379 (51.6%) were sets of prostate biopsies, 56,118 (34.7%) were transurethral/transvesical resections of the prostate (TUR-Ps), and the remaining 22,028 (13.6%) specimens were derived from radical prostatectomies, bladder interventions, etc. A total of 48,078 (42.2%) males had histopathologically verified prostate cancer, and of these, 78.8% and 16.8% were diagnosed on prostate biopsies and TUR-Ps, respectively. Future perspectives A validated algorithm was successfully developed to convert complex prostate SNOMED codes into clinical useful data. A unique database, including males with both normal and cancerous histopathological data, was created to form the most comprehensive national prostate database to date. Potentially, our algorithm can be used for conversion of other SNOMED data and is available upon request. PMID:27729813

  1. Prediction of 222Rn in Danish dwellings using geology and house construction information from central databases.

    PubMed

    Andersen, Claus E; Raaschou-Nielsen, Ole; Andersen, Helle Primdal; Lind, Morten; Gravesen, Peter; Thomsen, Birthe L; Ulbak, Kaare

    2007-01-01

    A linear regression model has been developed for the prediction of indoor (222)Rn in Danish houses. The model provides proxy radon concentrations for about 21,000 houses in a Danish case-control study on the possible association between residential radon and childhood cancer (primarily leukaemia). The model was calibrated against radon measurements in 3116 houses. An independent dataset with 788 house measurements was used for model performance assessment. The model includes nine explanatory variables, of which the most important ones are house type and geology. All explanatory variables are available from central databases. The model was fitted to log-transformed radon concentrations and it has an R(2) of 40%. The uncertainty associated with individual predictions of (untransformed) radon concentrations is about a factor of 2.0 (one standard deviation). The comparison with the independent test data shows that the model makes sound predictions and that errors of radon predictions are only weakly correlated with the estimates themselves (R(2) = 10%).

  2. Reusable data in public health data-bases-problems encountered in Danish Children's Database.

    PubMed

    Høstgaard, Anna Marie; Pape-Haugaard, Louise

    2012-01-01

    Denmark have unique health informatics databases e.g. "The Children's Database", which since 2009 holds data on all Danish children from birth until 17 years of age. In the current set-up a number of potential sources of errors exist - both technical and human-which means that the data is flawed. This gives rise to erroneous statistics and makes the data unsuitable for research purposes. In order to make the data usable, it is necessary to develop new methods for validating the data generation process at the municipal/regional/national level. In the present ongoing research project, two research areas are combined: Public Health Informatics and Computer Science, and both ethnographic as well as system engineering research methods are used. The project is expected to generate new generic methods and knowledge about electronic data collection and transmission in different social contexts and by different social groups and thus to be of international importance, since this is sparsely documented in the Public Health Informatics perspective. This paper presents the preliminary results, which indicate that health information technology used ought to be subject for redesign, where a thorough insight into the work practices should be point of departure.

  3. Fusobacterium necrophorum findings in Denmark from 2010 to 2014 using data from the Danish microbiology database.

    PubMed

    Bank, Steffen; Jensen, Anders; Nielsen, Hanne Merete; Kristensen, Lena Hagelskjaer; Voldstedlund, Marianne; Prag, Jørgen

    2016-12-01

    Fusobacterium necrophorum findings in Denmark and estimation of the incidence of F. necrophorum bacteraemia was described using data from the nationwide Danish microbiology database (MiBa). All microbiological reports on any Fusobacterium species in Denmark were extracted for a period of 5 years from 2010 to 2014 from MiBa and from the local department of clinical microbiology. The overall incidence of F. necrophorum bacteraemia from 2010 to 2014 was 2.8 cases per million/year vs 9.4 in the age group 15-24 years. F. necrophorum was rare in blood cultures from children and middle-aged patients and then raised again. However, 48 of 232 cases of Fusobacterium bacteraemia were not identified to species level, so the incidences of F. necrophorum bacteraemia may be underestimated in our study. F. necrophorum was found in throat swabs in the age group between 13 and 40 years and in otitis media in children below 2 years in those departments which performed anaerobic culture. The incidence of F. necrophorum bacteraemia found was comparable to earlier reported figures for Lemierre's syndrome. Fusobacterium bacteraemia should always be identified to species level. © 2016 APMIS. Published by John Wiley & Sons Ltd.

  4. QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA's OpenFoodTox database.

    PubMed

    Toropov, Andrey A; Toropova, Alla P; Marzo, Marco; Dorne, Jean Lou; Georgiadis, Nikolaos; Benfenati, Emilio

    2017-07-01

    Optimal (flexible) descriptors were used to establish quantitative structure - activity relationships (QSAR) for toxicity of pesticides (n=116) towards rainbow trout. A heterogeneous set of hundreds of pesticides has been used, taken from the EFSA's chemical Hazards Database: OpenFoodTox. Optimal descriptors are preparing from simplified molecular input-line entry system (SMILES). So-called, correlation weights of different fragments of SMILES are calculating by the Monte Carlo optimization procedure where correlation coefficient between endpoint and optimal descriptor plays role of the target function. Having maximum of the correlation coefficient for the training set, one can suggest that the optimal descriptor calculated with these correlation weights can correlate with endpoint for external validation set. This approach was checked up with three different distributions into the training (≈85%) set and external validation (≈15%) set. The statistical characteristics of these models are (i) for training set correlation coefficient (r(2)) ranges 0.72-0.81, and root mean squared error (RMSE) ranges 0.54-1.25; (ii) for external (validation) set r(2) ranges 0.74-0.84; and RMSE ranges 0.64-0.75. Computational experiments have shown that presence of chlorine, fluorine, sulfur, and aromatic fragments is promoter of increase for the toxicity. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Incorporation of the CrossFire Beilstein Database into the Organic Chemistry Curriculum at the Royal Danish School of Pharmacy

    NASA Astrophysics Data System (ADS)

    Brøgger Christensen, S.; Franzyk, Henrik; Frølund, Bente; Jaroszewski, Jerzy W.; Stærk, Dan; Vedsø, Per

    2002-06-01

    The CrossFire Beilstein database has been incorporated into the organic chemistry curriculum at the Royal Danish School of Pharmacy as a powerful pedagogic tool. During a laboratory course in organic synthesis the database enables the students to get comprehensive overviews of known synthetic methods for a given compound. During a laboratory course in identification and as a part of an applied course in organic spectroscopy the students use the database for obtaining lists of all recorded isomeric compounds, facilitating an exhaustive identification. The main entrances for identification purposes are molecular formulas deduced either from titrations or from mass spectra combined with partial structures identified by chemical tests, or by interpretation of spectra. Thus, identifications made using the CrossFire Beilstein database will exclude some possibilities and point to correct structures from a selection of existing compounds. This appears to help the learning process considerably.

  6. The Danish Fracture Database can monitor quality of fracture-related surgery, surgeons' experience level and extent of supervision.

    PubMed

    Andersen, Morten Jon; Gromov, Kiril; Brix, Michael; Troelsen, Anders

    2014-06-01

    The importance of supervision and of surgeons' level of experience in relation to patient outcome have been demonstrated in both hip fracture and arthroplasty surgery. The aim of this study was to describe the surgeons' experience level and the extent of supervision for: 1) fracture-related surgery in general; 2) the three most frequent primary operations and reoperations; and 3) primary operations during and outside regular working hours. A total of 9,767 surgical procedures were identified from the Danish Fracture Database (DFDB). Procedures were grouped based on the surgeons' level of experience, extent of supervision, type (primary, planned secondary or reoperation), classification (AO Müller), and whether they were performed during or outside regular hours. Interns and junior residents combined performed 46% of all procedures. A total of 90% of surgeries by interns were performed under supervision, whereas 32% of operations by junior residents were unsupervised. Supervision was absent in 14-16% and 22-33% of the three most frequent primary procedures and reoperations when performed by interns and junior residents, respectively. The proportion of unsupervised procedures by junior residents grew from 30% during to 40% (p < 0.001) outside regular hours. Interns and junior residents together performed almost half of all fracture-related surgery. The extent of supervision was generally high; however, a third of the primary procedures performed by junior residents were unsupervised. The extent of unsupervised surgery performed by junior residents was significantly higher outside regular hours. not relevant. The Danish Fracture Database ("Dansk Frakturdatabase") was approved by the Danish Data Protection Agency ID: 01321.

  7. Survival of ovarian cancer patients in Denmark: Results from the Danish gynaecological cancer group (DGCG) database, 1995-2012.

    PubMed

    Edwards, Hellen McKinnon; Noer, Mette Calundann; Sperling, Cecilie Dyg; Nguyen-Nielsen, Mary; Lundvall, Lene; Christensen, Ib Jarle; Høgdall, Claus

    2016-06-01

    Ovarian cancer has a high mortality rate, especially in Denmark where mortality rates have been reported higher than in adjacent countries with similar demographics. This study therefore examined recent survival and mortality among Danish ovarian cancer patients over an 18-year study period. This nationwide registry-based observational study used data from the Danish Gynecology Cancer Database, Danish Pathology Registry, and Danish National Patient Registry. All patients with ovarian cancer diagnosed between 1995 and 2012 were included in the study. The data sources were linked via the patients' personal identification number and the analyses included data on cancer stage, age, survival, surgery status and comorbidity. The computed outcome measures were age-adjusted mortality rates and age-adjusted overall and relative survival rates for one and five years. We identified 9972 patients diagnosed with ovarian cancer in the period 1995-2012. The absolute one-year mortality rate decreased from 42.8 (CI 40.3-45.6) in 1995-1999 to 28.3 (CI 25.9-30.9) in 2010-2012, and the five-year mortality rate decreased from 28.2 (CI 27.0-29.5) in 1995-1999 to 23.9 (CI 22.9-25.0) in 2005-2009. After stratification by age, comorbidity and cancer stage, the decrease in one-year mortality was most substantial in the 65-74 year old age group 41.1 (CI 38.8-43.5) to 26.5 (CI 24.4-28.7) and for stage III 39.1 (CI 35.1-43.6) to 22.9 (CI 19.9-26.5) and stage IV 91.3 (CI 80.8-103.2) to 41.9 (CI 35.5-49.5). For overall survival, we showed an increase in one-year survival from 68% (CI 66-69%) in 1995-1999 to 76% (CI 74-78%) in 2010-2012 and an increase in five-year survival from 33% (CI 32-35%) in 1995-1999 to 36% (CI 34-38%) in 2005-2009. Relative survival showed similar increases through the period. Ovarian cancer survival in Denmark has improved substantially from 1995 to 2012, bringing Denmark closer to the standards set by adjacent countries.

  8. Validation of the 5-year tetanus, diphtheria, pertussis and polio booster vaccination in the Danish childhood vaccination database.

    PubMed

    Wójcik, Oktawia P; Simonsen, Jacob; Mølbak, Kåre; Valentiner-Branth, Palle

    2013-01-30

    In Denmark, data from the childhood vaccination database are used to calculate vaccination coverage (VC) for childhood vaccinations. However, there may be under-reporting in this database. Accurate VC estimates are necessary for adjusting vaccination strategies and providing population-level protection. The main purpose of this study was to validate the reporting of the tetanus, diphtheria, pertussis and polio (Tdap-IPV) booster in the childhood vaccination database, identify reasons a child was not vaccinated, for the unregistered vaccinations, identify where the vaccination was provided, and to adjust calculations of the VC accordingly. Children registered in the Danish Civil Registry System (residing legally in Denmark) from the 2000 to 2003 birth cohorts without a recorded Tdap-IPV booster in the childhood vaccination database were randomly selected for this cross-sectional, questionnaire-based study. The adjusted VC in the population was calculated by adding the fraction of the study population registered with the Tdap-IPV booster in the childhood vaccination database to the fraction of the study population who reported being vaccinated on the questionnaire but who were not register according to the childhood vaccination database. Of the 574 contacted parents, 386 (67%) completed a questionnaire; 272 (70%) reported that their child received the Tdap-IPV booster, with 121 (44%) providing the date of vaccination. Most commonly reported reasons for not receiving the booster included forgetting (37%) and not wanting the vaccination (16%). The majority (89%) of children who received the booster were vaccinated by their general practitioners (GPs); 6% abroad and <1% in a hospital. Using a conservative approach, considering only those who used a vaccination card to answer the questionnaire and who provided an exact data of vaccination, the adjusted Tdap-IPV booster VC was 85.6% (95% CI, 85.1-86.3%) compared to 82% from the childhood vaccination database. We

  9. How can the research potential of the clinical quality databases be maximized? The Danish experience.

    PubMed

    Nørgaard, M; Johnsen, S P

    2016-02-01

    In Denmark, the need for monitoring of clinical quality and patient safety with feedback to the clinical, administrative and political systems has resulted in the establishment of a network of more than 60 publicly financed nationwide clinical quality databases. Although primarily devoted to monitoring and improving quality of care, the potential of these databases as data sources in clinical research is increasingly being recognized. In this review, we describe these databases focusing on their use as data sources for clinical research, including their strengths and weaknesses as well as future concerns and opportunities. The research potential of the clinical quality databases is substantial but has so far only been explored to a limited extent. Efforts related to technical, legal and financial challenges are needed in order to take full advantage of this potential. © 2016 The Association for the Publication of the Journal of Internal Medicine.

  10. Uncertainty in QSAR predictions.

    PubMed

    Sahlin, Ullrika

    2013-03-01

    It is relevant to consider uncertainty in individual predictions when quantitative structure-activity (or property) relationships (QSARs) are used to support decisions of high societal concern. Successful communication of uncertainty in the integration of QSARs in chemical safety assessment under the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system can be facilitated by a common understanding of how to define, characterise, assess and evaluate uncertainty in QSAR predictions. A QSAR prediction is, compared to experimental estimates, subject to added uncertainty that comes from the use of a model instead of empirically-based estimates. A framework is provided to aid the distinction between different types of uncertainty in a QSAR prediction: quantitative, i.e. for regressions related to the error in a prediction and characterised by a predictive distribution; and qualitative, by expressing our confidence in the model for predicting a particular compound based on a quantitative measure of predictive reliability. It is possible to assess a quantitative (i.e. probabilistic) predictive distribution, given the supervised learning algorithm, the underlying QSAR data, a probability model for uncertainty and a statistical principle for inference. The integration of QSARs into risk assessment may be facilitated by the inclusion of the assessment of predictive error and predictive reliability into the "unambiguous algorithm", as outlined in the second OECD principle.

  11. Development of an algorithm to identify urgent referrals for suspected cancer from the Danish Primary Care Referral Database

    PubMed Central

    Toftegaard, Berit Skjødeberg; Guldbrandt, Louise Mahncke; Flarup, Kaare Rud; Beyer, Hanne; Bro, Flemming; Vedsted, Peter

    2016-01-01

    Background Accurate identification of specific patient populations is a crucial tool in health care. A prerequisite for exploring the actions taken by general practitioners (GPs) on symptoms of cancer is being able to identify patients urgently referred for suspected cancer. Such system is not available in Denmark; however, all referrals are electronically stored. This study aimed to develop and test an algorithm based on referral text to identify urgent cancer referrals from general practice. Methods Two urgently referred reference populations were extracted from a research database and linked with the Primary Care Referral (PCR) database through the unique Danish civil registration number to identify the corresponding electronic referrals. The PCR database included GP referrals directed to private specialists and hospital departments, and these referrals were scrutinized. The most frequently used words were integrated in the first version of the algorithm, which was further refined by an iterative process involving two population samples from the PCR database. The performance was finally evaluated for two other PCR population samples against manual assessment as the gold standard for urgent cancer referral. Results The final algorithm had a sensitivity of 0.939 (95% confidence intervals [CI]: 0.905–0.963) and a specificity of 0.937 (95% CI: 0.925–0.963) compared to the gold standard. The positive and negative predictive values were 69.8% (95% CI: 65.0–74.3) and 99.0% (95% CI: 98.4–99.4), respectively. When applying the algorithm on referrals for a population without earlier cancer diagnoses, the positive predictive value increased to 83.6% (95% CI: 78.7–87.7) and the specificity to 97.3% (95% CI: 96.4–98.0). Conclusion The final algorithm identified 94% of the patients urgently referred for suspected cancer; less than 3% of the patients were incorrectly identified. It is now possible to identify patients urgently referred on cancer suspicion from

  12. Description of OPRA: A Danish database designed for the analyses of risk factors associated with 30-day hospital readmission of people aged 65+ years.

    PubMed

    Pedersen, Mona K; Nielsen, Gunnar L; Uhrenfeldt, Lisbeth; Rasmussen, Ole S; Lundbye-Christensen, Søren

    2017-08-01

    To describe the construction of the Older Person at Risk Assessment (OPRA) database, the ability to link this database with existing data sources obtained from Danish nationwide population-based registries and to discuss its research potential for the analyses of risk factors associated with 30-day hospital readmission. We reviewed Danish nationwide registries to obtain information on demographic and social determinants as well as information on health and health care use in a population of hospitalised older people. The sample included all people aged 65+ years discharged from Danish public hospitals in the period from 1 January 2007 to 30 September 2010. We used personal identifiers to link and integrate the data from all events of interest with the outcome measures in the OPRA database. The database contained records of the patients, admissions and variables of interest. The cohort included 1,267,752 admissions for 479,854 unique people. The rate of 30-day all-cause acute readmission was 18.9% ( n=239,077) and the overall 30-day mortality was 5.0% ( n=63,116). The OPRA database provides the possibility of linking data on health and life events in a population of people moving into retirement and ageing. Construction of the database makes it possible to outline individual life and health trajectories over time, transcending organisational boundaries within health care systems. The OPRA database is multi-component and multi-disciplinary in orientation and has been prepared to be used in a wide range of subgroup analyses, including different outcome measures and statistical methods.

  13. Medication errors involving anticoagulants: Data from the Danish patient safety database.

    PubMed

    Henriksen, Jakob Nørgaard; Nielsen, Lars Peter; Hellebek, Annemarie; Poulsen, Birgitte Klindt

    2017-06-01

    Reporting of adverse incidents is mandatory in Denmark. All reported adverse incidents are made anonymously, and stored in an encrypted database. It is the purpose of this descriptive study to describe the severity of adverse medication incidents caused by oral anticoagulants in hospitals. All moderate, severe and fatal reports concerning non-vitamin K antagonist oral anticoagulants were analyzed from date of marketing until July 8 2014. The data collection for warfarin was from January 1 2014 until July 8 2014. Three independent specialists in clinical pharmacology evaluated the severity of incident outcomes. A total of 147 adverse medication incidents were analyzed, and showed that de facto or potentially fatal and serious incidents were most frequently associated with sector change (admission to or discharge from hospital, or undergoing surgery) and resulted from insufficient or excess dosing. Physicians should be aware when prescribing and changing anticoagulant therapy to avoid severe or fatal incidents.

  14. Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

    PubMed Central

    Myint, Kyaw Z.; Xie, Xiang-Qun

    2015-01-01

    This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. PMID:25502380

  15. 20170402 - OPERA: A QSAR tool for physicochemical properties and environmental fate predictions (ACS Spring meeting)

    EPA Science Inventory

    The collection of chemical structures and associated experimental data for QSAR modeling is facilitated by the increasing number and size of public databases. However, the performance of QSAR models highly depends on the quality of the data used and the modeling methodology. The ...

  16. OPERA: A QSAR tool for physicochemical properties and environmental fate predictions (ACS Spring meeting)

    EPA Science Inventory

    The collection of chemical structures and associated experimental data for QSAR modeling is facilitated by the increasing number and size of public databases. However, the performance of QSAR models highly depends on the quality of the data used and the modeling methodology. The ...

  17. Modeling robust QSAR.

    PubMed

    Polanski, Jaroslaw; Bak, Andrzej; Gieleciak, Rafal; Magdziarz, Tomasz

    2006-01-01

    Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.

  18. The Use of Qsar and Computational Methods in Drug Design

    NASA Astrophysics Data System (ADS)

    Bajot, Fania

    The application of quantitative structure-activity relationships (QSARs) has significantly impacted the paradigm of drug discovery. Following the successful utilization of linear solvation free-energy relationships (LSERs), numerous 2D- and 3D-QSAR methods have been developed, most of them based on descriptors for hydrophobicity, polarizability, ionic interactions, and hydrogen bonding. QSAR models allow for the calculation of physicochemical properties (e.g., lipophilicity), the prediction of biological activity (or toxicity), as well as the evaluation of absorption, distribution, metabolism, and excretion (ADME). In pharmaceutical research, QSAR has a particular interest in the preclinical stages of drug discovery to replace tedious and costly experimentation, to filter large chemical databases, and to select drug candidates. However, to be part of drug discovery and development strategies, QSARs need to meet different criteria (e.g., sufficient predictivity). This chapter describes the foundation of modern QSAR in drug discovery and presents some current challenges and applications for the discovery and optimization of drug candidates

  19. Discrete Derivatives for Atom-Pairs as a Novel Graph-Theoretical Invariant for Generating New Molecular Descriptors: Orthogonality, Interpretation and QSARs/QSPRs on Benchmark Databases.

    PubMed

    Martínez-Santiago, Oscar; Millán-Cabrera, Reisel; Marrero-Ponce, Yovani; Barigye, Stephen J; Martínez-López, Yoan; Torrens, Francisco; Pérez-Giménez, Facundo

    2014-05-01

    indices and other 0-3D MDs is demonstrated by using principal component analysis on a dataset of 41 heterogeneous molecules. It is concluded that the graph derivative indices are independent indices containing important structural information to be used in QSPR/QSAR and drug design studies, and permit obtaining easier, more interpretable and robust mathematical models than the majority of those reported in the literature.

  20. Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods

    PubMed Central

    Myint, Kyaw Zeyar; Xie, Xiang-Qun

    2010-01-01

    This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods. PMID:21152304

  1. TOXICO-CHEMINFORMATICS AND QSAR MODELING OF ...

    EPA Pesticide Factsheets

    This abstract concludes that QSAR approaches combined with toxico-chemoinformatics descriptors can enhance predictive toxicology models. This abstract concludes that QSAR approaches combined with toxico-chemoinformatics descriptors can enhance predictive toxicology models.

  2. Admittance to specialized palliative care (SPC) of patients with an assessed need: a study from the Danish palliative care database (DPD).

    PubMed

    Adsersen, Mathilde; Thygesen, Lau Caspar; Neergaard, Mette Asbjoern; Bonde Jensen, Anders; Sjøgren, Per; Damkier, Anette; Groenvold, Mogens

    2017-09-01

    Admittance to specialized palliative care (SPC) has been discussed in the literature, but previous studies examined exclusively those admitted, not those with an assessed need for SPC but not admitted. The aim was to investigate whether admittance to SPC for referred adult patients with cancer was related to sex, age, diagnosis, geographic region or referral unit. A register-based study with data from the Danish Palliative Care Database (DPD). From DPD we identified all adult patients with cancer, who died in 2010-2012 and who were referred to and assessed to have a need for SPC (N = 21,597).The associations were investigated using logistic regression models, which also evaluated whether time from referral to death influenced the associations. In the adjusted analysis, we found that admittance was higher for younger patients [e.g., 50-59 versus 80 + years: odds ratio (OR) = 2.03; 1.78-2.33]. There was lower odds of admittance for patients with hematological malignancies and patients from two regions: Capital Region of Denmark and Region of Southern Denmark. Lower admittance among men and patients referred from hospital departments was explained by later referral. In this first nationwide study of admittance to SPC among patients with a SPC need, we found difference in admittance according to age, diagnosis and region. This indicates that prioritization of the limited resources means that certain subgroups with a documented need have reduced likelihood of admission to SPC.

  3. Rational selection of training and test sets for the development of validated QSAR models

    NASA Astrophysics Data System (ADS)

    Golbraikh, Alexander; Shen, Min; Xiao, Zhiyan; Xiao, Yun-De; Lee, Kuo-Hsiung; Tropsha, Alexander

    2003-02-01

    Quantitative Structure-Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors ( kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R2 (q2) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q 2 for the training set and accuracy of prediction ( R 2) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.

  4. New public QSAR model for carcinogenicity

    PubMed Central

    2010-01-01

    Background One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration. Results Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B. Conclusion Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions

  5. QSARs in Netherlands water quality management policies.

    PubMed

    van der Gaag, M A

    1991-12-01

    QSARs are a useful tool for predicting the potential toxic effects of compounds for which no data are available. Within strictly defined limits, QSARs can be applied to assess the potential impact of a spill, to evaluate ecotoxicological effects and environmental fate of organics in waste water and to set priorities for water quality criteria. For a wider application, there is a need for 'worst case' SARs providing a 'safer' estimate of toxicity than QSARs with an optimum fit, which might underestimate toxicity.

  6. Internal and external validation of the long-term QSARs for neutral organics to fish from ECOSAR™.

    PubMed

    de Haas, E M; Eikelboom, T; Bouwman, T

    2011-01-01

    This study concentrates on the external validation of an existing Quantitative Structure-Activity Relationship (QSAR) model widely used for long-term aquatic toxicity to fish. In the context of the REACH legislation, QSARs are used as an alternative for experimental data to achieve a complete environmental assessment without the need for animal testing. The predictivity of the model was evaluated in order to increase the reliability of the model. We assessed whether the model met all of the OECD principles. The model was adapted to become more robust, and predictions were made with an external validation set collected from several databases. For the internal validation of the QSAR, the r², Q²(Loo) and Q²(LMO) were used as validation criteria, and for the external validation r², Q²(ext), h and the validation ratio were used. A few substances were classified as outliers and therefore the applicability domain of the QSAR had to be adjusted. The QSAR passed all validation criteria and met all the OECD principles for QSAR validation, and the long-term toxicity QSAR for fish can be applied with high certainty of a correct prediction within the limits of the inherent uncertainty of the model in cases where the substance falls within the applicability domain.

  7. The importance of data curation on QSAR Modeling ...

    EPA Pesticide Factsheets

    During the last few decades many QSAR models and tools have been developed at the US EPA, including the widely used EPISuite. During this period the arsenal of computational capabilities supporting cheminformatics has broadened dramatically with multiple software packages. These modern tools allow for more advanced techniques in terms of chemical structure representation and storage, as well as enabling automated data-mining and standardization approaches to examine and fix data quality issues.This presentation will investigate the impact of data curation on the reliability of QSAR models being developed within the EPA‘s National Center for Computational Toxicology. As part of this work we have attempted to disentangle the influence of the quality versus quantity of data based on the Syracuse PHYSPROP database partly used by EPISuite software. We will review our automated approaches to examining key datasets related to the EPISuite data to validate across chemical structure representations (e.g., mol file and SMILES) and identifiers (chemical names and registry numbers) and approaches to standardize data into QSAR-ready formats prior to modeling procedures. Our efforts to quantify and segregate data into quality categories has allowed us to evaluate the resulting models that can be developed from these data slices and to quantify to what extent efforts developing high-quality datasets have the expected pay-off in terms of predicting performance. The most accur

  8. In silico study of in vitro GPCR assays by QSAR modeling ...

    EPA Pesticide Factsheets

    The U.S. EPA is screening thousands of chemicals of environmental interest in hundreds of in vitro high-throughput screening (HTS) assays (the ToxCast program). One goal is to prioritize chemicals for more detailed analyses based on activity in molecular initiating events (MIE) of adverse outcome pathways (AOPs). However, the chemical space of interest for environmental exposure is much wider than this set of chemicals. Thus, there is a need to fill data gaps with in silico methods, and quantitative structure-activity relationships (QSARs) are a proven and cost effective approach to predict biological activity. ToxCast in turn provides relatively large datasets that are ideal for training and testing QSAR models. The overall goal of the study described here was to develop QSAR models to fill the data gaps in a larger environmental database of ~32k structures. The specific aim of the current work was to build QSAR models for 18 G-Protein Coupled Receptor (GPCR) assays, part of the aminergic category. Two QSAR modeling strategies were adopted: classification models were developed to separate chemicals into active/non-active classes, and then regression models were built to predict the potency values of the bioassays for the active chemicals. Multiple software programs were used to calculate constitutional, topological and substructural molecular descriptors from two-dimensional (2D) chemical structures. Model-fitting methods included PLSDA (partial least squares d

  9. The Danish Stroke Registry

    PubMed Central

    Johnsen, Søren Paaske; Ingeman, Annette; Hundborg, Heidi Holmager; Schaarup, Susanne Zielke; Gyllenborg, Jesper

    2016-01-01

    Aim of database The aim of the Danish Stroke Registry is to monitor and improve the quality of care among all patients with acute stroke and transient ischemic attack (TIA) treated at Danish hospitals. Study population All patients with acute stroke (from 2003) or TIA (from 2013) treated at Danish hospitals. Reporting is mandatory by law for all hospital departments treating these patients. The registry included >130,000 events by the end of 2014, including 10,822 strokes and 4,227 TIAs registered in 2014. Main variables The registry holds prospectively collected data on key processes of care, mainly covering the early phase after stroke, including data on time of delivery of the processes and the eligibility of the individual patients for each process. The data are used for assessing 18 process indicators reflecting recommendations in the national clinical guidelines for patients with acute stroke and TIA. Patient outcomes are currently monitored using 30-day mortality, unplanned readmission, and for patients receiving revascularization therapy, also functional level at 3 months poststroke. Descriptive data Sociodemographic, clinical, and lifestyle factors with potential prognostic impact are registered. Conclusion The Danish Stroke Registry is a well-established clinical registry which plays a key role for monitoring and improving stroke and TIA care in Denmark. In addition, the registry is increasingly used for research. PMID:27843349

  10. Development of a general baseline toxicity QSAR model for the fish embryo acute toxicity test.

    PubMed

    Klüver, Nils; Vogs, Carolina; Altenburger, Rolf; Escher, Beate I; Scholz, Stefan

    2016-12-01

    Fish embryos have become a popular model in ecotoxicology and toxicology. The fish embryo acute toxicity test (FET) with the zebrafish embryo was recently adopted by the OECD as technical guideline TG 236 and a large database of concentrations causing 50% lethality (LC50) is available in the literature. Quantitative Structure-Activity Relationships (QSARs) of baseline toxicity (also called narcosis) are helpful to estimate the minimum toxicity of chemicals to be tested and to identify excess toxicity in existing data sets. Here, we analyzed an existing fish embryo toxicity database and established a QSAR for fish embryo LC50 using chemicals that were independently classified to act according to the non-specific mode of action of baseline toxicity. The octanol-water partition coefficient Kow is commonly applied to discriminate between non-polar and polar narcotics. Replacing the Kow by the liposome-water partition coefficient Klipw yielded a common QSAR for polar and non-polar baseline toxicants. This developed baseline toxicity QSAR was applied to compare the final mode of action (MOA) assignment of 132 chemicals. Further, we included the analysis of internal lethal concentration (ILC50) and chemical activity (La50) as complementary approaches to evaluate the robustness of the FET baseline toxicity. The analysis of the FET dataset revealed that specifically acting and reactive chemicals converged towards the baseline toxicity QSAR with increasing hydrophobicity. The developed FET baseline toxicity QSAR can be used to identify specifically acting or reactive compounds by determination of the toxic ratio and in combination with appropriate endpoints to infer the MOA for chemicals.

  11. QSAR and 3D QSAR of inhibitors of the epidermal growth factor receptor

    NASA Astrophysics Data System (ADS)

    Pinto-Bazurco, Mariano; Tsakovska, Ivanka; Pajeva, Ilza

    This article reports quantitative structure-activity relationships (QSAR) and 3D QSAR models of 134 structurally diverse inhibitors of the epidermal growth factor receptor (EGFR) tyrosine kinase. Free-Wilson analysis was used to derive the QSAR model. It identified the substituents in aniline, the polycyclic system, and the substituents at the 6- and 7-positions of the polycyclic system as the most important structural features. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used in the 3D QSAR modeling. The steric and electrostatic interactions proved the most important for the inhibitory effect. Both QSAR and 3D QSAR models led to consistent results. On the basis of the statistically significant models, new structures were proposed and their inhibitory activities were predicted.

  12. A new computer program for QSAR-analysis: ARTE-QSAR.

    PubMed

    Van Damme, Sofie; Bultinck, Patrick

    2007-08-01

    A new computer program has been designed to build and analyze quantitative-structure activity relationship (QSAR) models through regression analysis. The user is provided with a range of regression and validation techniques. The emphasis of the program lies mainly in the validation of QSAR models in chemical applications. ARTE-QSAR produces an easy interpretable output from which the user can conclude if the obtained model is suitable for prediction and analysis.

  13. Systematic generation of chemical structures for rational drug design based on QSAR models.

    PubMed

    Funatsu, Kimito; Miyao, Tomoyuki; Arakawa, Masamoto

    2011-03-01

    The first step in the process of drug development is to determine those lead compounds that demonstrate significant biological activity with regard to a target protein. Because this process is often costly and time consuming, there is a need to develop efficient methodologies for the generation of lead compounds for practical drug design. One promising approach for determining a potent lead compound is computational virtual screening. The biological activities of candidate structures found in virtual libraries are estimated by using quantitative structure activity relationship (QSAR) models and/or computational docking simulations. In virtual screening studies, databases of existing drugs or natural products are commonly used as a source of lead candidates. However, these databases are not sufficient for the purpose of finding lead candidates having novel scaffolds. Therefore, a method must be developed to generate novel molecular structures to indicate high activity for efficient lead discovery. In this paper, we review current trends in structure generation methods for drug design and discuss future directions. First, we present an overview of lead discovery and drug design, and then, we review structure generation methods. Here, the structure generation methods are classified on the basis of whether or not they employ QSAR models for generating structures. We conclude that the use of QSAR models for structure generation is an effective method for computational lead discovery. Finally, we discuss the problems regarding the applicability domain of QSAR models and future directions in this field.

  14. Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

    PubMed Central

    Wang, Wenyi; Kim, Marlene T.; Sedykh, Alexander

    2015-01-01

    Purpose Experimental Blood–Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process. Methods We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models. Results The consensus QSAR models have R2=0.638 for fivefold cross-validation and R2=0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R2=0.646 for five-fold cross-validation and R2=0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool. Conclusions The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models. PMID:25862462

  15. QSAR models for reproductive toxicity and endocrine disruption in regulatory use – a preliminary investigation†

    PubMed Central

    Jensen, G.E.; Niemelä, J.R.; Wedebye, E.B.; Nikolov, N.G.

    2008-01-01

    A special challenge in the new European Union chemicals legislation, Registration, Evaluation and Authorisation of Chemicals, will be the toxicological evaluation of chemicals for reproductive toxicity. Use of valid quantitative structure–activity relationships (QSARs) is a possibility under the new legislation. This article focuses on a screening exercise by use of our own and commercial QSAR models for identification of possible reproductive toxicants. Three QSAR models were used for reproductive toxicity for the endpoints teratogenic risk to humans (based on animal tests, clinical data and epidemiological human studies), dominant lethal effect in rodents (in vivo) and Drosophila melanogaster sex-linked recessive lethal effect. A structure set of 57,014 European Inventory of Existing Chemical Substances (EINECS) chemicals was screened. A total of 5240 EINECS chemicals, corresponding to 9.2%, were predicted as reproductive toxicants by one or more of the models. The chemicals predicted positive for reproductive toxicity will be submitted to the Danish Environmental Protection Agency as scientific input for a future updated advisory classification list with advisory classifications for concern for humans owing to possible developmental toxic effects: Xn (Harmful) and R63 (Possible risk of harm to the unborn child). The chemicals were also screened in three models for endocrine disruption. PMID:19061080

  16. Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity.

    PubMed

    Martin, Eric; Mukherjee, Prasenjit; Sullivan, David; Jansen, Johanna

    2011-08-22

    Profile-QSAR is a novel 2D predictive model building method for kinases. This "meta-QSAR" method models the activity of each compound against a new kinase target as a linear combination of its predicted activities against a large panel of 92 previously studied kinases comprised from 115 assays. Profile-QSAR starts with a sparse incomplete kinase by compound (KxC) activity matrix, used to generate Bayesian QSAR models for the 92 "basis-set" kinases. These Bayesian QSARs generate a complete "synthetic" KxC activity matrix of predictions. These synthetic activities are used as "chemical descriptors" to train partial-least squares (PLS) models, from modest amounts of medium-throughput screening data, for predicting activity against new kinases. The Profile-QSAR predictions for the 92 kinases (115 assays) gave a median external R²(ext) = 0.59 on 25% held-out test sets. The method has proven accurate enough to predict pairwise kinase selectivities with a median correlation of R²(ext) = 0.61 for 958 kinase pairs with at least 600 common compounds. It has been further expanded by adding a "C(k)XC" cellular activity matrix to the KxC matrix to predict cellular activity for 42 kinase driven cellular assays with median R²(ext) = 0.58 for 24 target modulation assays and R²(ext) = 0.41 for 18 cell proliferation assays. The 2D Profile-QSAR, along with the 3D Surrogate AutoShim, are the foundations of an internally developed iterative medium-throughput screening (IMTS) methodology for virtual screening (VS) of compound archives as an alternative to experimental high-throughput screening (HTS). The method has been applied to 20 actual prospective kinase projects. Biological results have so far been obtained in eight of them. Q² values ranged from 0.3 to 0.7. Hit-rates at 10 uM for experimentally tested compounds varied from 25% to 80%, except in K5, which was a special case aimed specifically at finding "type II" binders, where none of the compounds were predicted to be

  17. Combinatorial QSAR Modeling of Rat Acute Toxicity by Oral Exposure

    EPA Science Inventory

    Quantitative Structure-Activity Relationship (QSAR) toxicity models have become popular tools for identifying potential toxic compounds and prioritizing candidates for animal toxicity tests. However, few QSAR studies have successfully modeled large, diverse mammalian toxicity end...

  18. Combinatorial QSAR Modeling of Rat Acute Toxicity by Oral Exposure

    EPA Science Inventory

    Quantitative Structure-Activity Relationship (QSAR) toxicity models have become popular tools for identifying potential toxic compounds and prioritizing candidates for animal toxicity tests. However, few QSAR studies have successfully modeled large, diverse mammalian toxicity end...

  19. Sensitivity Analysis of QSAR Models for Assessing Novel Military Compounds

    DTIC Science & Technology

    2009-01-01

    erties, such as log P, would aid in estimating a chemical’s environmental fate and toxicology when applied to QSAR modeling. Granted, QSAR mod- els, such...ER D C TR -0 9 -3 Strategic Environmental Research and Development Program Sensitivity Analysis of QSAR Models for Assessing Novel...Environmental Research and Development Program ERDC TR-09-3 January 2009 Sensitivity Analysis of QSAR Models for Assessing Novel Military Compound

  20. A QSAR model for predicting rejection of emerging contaminants (pharmaceuticals, endocrine disruptors) by nanofiltration membranes.

    PubMed

    Yangali-Quintanilla, Victor; Sadmani, Anwar; McConville, Megan; Kennedy, Maria; Amy, Gary

    2010-01-01

    A quantitative structure activity relationship (QSAR) model has been produced for predicting rejection of emerging contaminants (pharmaceuticals, endocrine disruptors, pesticides and other organic compounds) by polyamide nanofiltration (NF) membranes. Principal component analysis, partial least square regression and multiple linear regressions were used to find a general QSAR equation that combines interactions between membrane characteristics, filtration operating conditions and compound properties for predicting rejection. Membrane characteristics related to hydrophobicity (contact angle), salt rejection, and surface charge (zeta potential); compound properties describing hydrophobicity (log K(ow), log D), polarity (dipole moment), and size (molar volume, molecular length, molecular depth, equivalent width, molecular weight); and operating conditions namely flux, pressure, cross flow velocity, back diffusion mass transfer coefficient, hydrodynamic ratio (J(o)/k), and recovery were identified as candidate variables for rejection prediction. An experimental database produced by the authors that accounts for 106 rejection cases of emerging contaminants by NF membranes as result of eight experiments with clean and fouled membranes (NF-90, NF-200) was used to produce the QSAR model. Subsequently, using the QSAR model, rejection predictions were made for external experimental databases. Actual rejections were compared against predicted rejections and acceptable R(2) correlation coefficients were found (0.75 and 0.84) for the best models. Additionally, leave-one-out cross-validation of the models achieved a Q(2) of 0.72 for internal validation. In conclusion, a unified general QSAR equation was able to predict rejections of emerging contaminants during nanofiltration; moreover the present approach is a basis to continue investigation using multivariate analysis techniques for understanding membrane rejection of organic compounds.

  1. Modeling Liver-Related Adverse Effects of Drugs Using kNN QSAR Method

    PubMed Central

    Rodgers, Amie D.; Zhu, Hao; Fourches, Dennis; Rusyn, Ivan; Tropsha, Alexander

    2010-01-01

    Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals both in development and post-marketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the Quantitative Structure Activity Relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs. inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity and balanced (40/60) active/inactive ratio were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k nearest neighbor method and validated using external datasets. Models with high sensitivity (>73%) and specificity (>94%) for prediction of liver AEDs in external validation sets were developed. To test applicability of the models, three chemical databases (World Drug Index, Prestwick Chemical Library, and Biowisdom Liver Intelligence Module) were screened in silico and the validity of predictions was determined, where possible, by comparing model-based classification with assertions in publicly available literature. Validated QSAR models of liver AEDs based on the data from the FDA spontaneous reporting system can be employed as sensitive and specific predictors of AEDs in pre-clinical screening of drug candidates for potential hepatotoxicity in humans. PMID:20192250

  2. Identification of novel HIV-1 integrase inhibitors using shape-based screening, QSAR, and docking approach.

    PubMed

    Gupta, Pawan; Garg, Prabha; Roy, Nilanjan

    2012-05-01

    The objective of this study is to identify novel HIV-1 integrase (IN) inhibitors. Here, shape-based screening and QSAR have been successfully implemented to identify the novel inhibitors for HIV-1 IN, and in silico validation is performed by docking studies. The 2D QSAR model of benzodithiazine derivatives was built using genetic function approximation (GFA) method with good internal (cross-validated r(2)  = 0.852) and external prediction (). Best docking pose of highly active molecule of the benzodithiazine derivatives was used as a template for shape-based screening of ZINC database. Toxicity prediction was also performed using Deductive Estimation of Risk from Existing Knowledge (DEREK) program to filter non-toxic molecules. Inhibitory activities of screened non-toxic molecules were predicted using derived QSAR models. Active, non-toxic screened molecules were also docked into the active site of HIV-1 IN using AutoDock and dock program. Some molecules docked similarly as highly active molecule of the benzodithiazine derivatives. These molecules also followed the same docking interactions in both the programs. Finally, four benzodithiazine derivatives were identified as novel HIV-1 integrase inhibitors based on QSAR predictions and docking interactions. ADME properties of these molecules were also computed using Discovery Studio. © 2012 John Wiley & Sons A/S.

  3. QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells.

    PubMed

    Toropov, Andrey A; Toropova, Alla P; Puzyn, Tomasz; Benfenati, Emilio; Gini, Giuseppina; Leszczynska, Danuta; Leszczynski, Jerzy

    2013-06-01

    Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The "classic" QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of the molecular structure in the databases available on the Internet. Under such circumstances, the development of molecular descriptors calculated directly from SMILES becomes attractive alternative to "classic" descriptors. The CORAL software (http://www.insilico.eu/coral) is provider of SMILES-based optimal molecular descriptors which are aimed to correlate with various endpoints. We analyzed data set on nanoparticles uptake in PaCa2 pancreatic cancer cells. The data set includes 109 nanoparticles with the same core but different surface modifiers (small organic molecules). The concept of a QSAR as a random event is suggested in opposition to "classic" QSARs which are based on the only one distribution of available data into the training and the validation sets. In other words, five random splits into the "visible" training set and the "invisible" validation set were examined. The SMILES-based optimal descriptors (obtained by the Monte Carlo technique) for these splits are calculated with the CORAL software. The statistical quality of all these models is good. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS.

    PubMed

    Gramatica, Paola; Cassani, Stefano; Chirico, Nicola

    2014-05-15

    A database of environmentally hazardous chemicals, collected and modeled by QSAR by the Insubria group, is included in the updated version of QSARINS, software recently proposed for the development and validation of QSAR models by the genetic algorithm-ordinary least squares method. In this version, a module, named QSARINS-Chem, includes several datasets of chemical structures and their corresponding endpoints (physicochemical properties and biological activities). The chemicals are accessible in different ways (CAS, SMILES, names and so forth) and their three-dimensional structure can be visualized. Some of the QSAR models, previously published by our group, have been redeveloped using the free online software for molecular descriptor calculation, PaDEL-Descriptor. The new models can be easily applied for future predictions on chemicals without experimental data, also verifying the applicability domain to new chemicals. The QSAR model reporting format (QMRF) of these models is also here downloadable. Additional chemometric analyses can be done by principal component analysis and multicriteria decision making for screening and ranking chemicals to prioritize the most dangerous. Copyright © 2014 Wiley Periodicals, Inc.

  5. Improving confidence in (Q)SAR predictions under Canada's Chemicals Management Plan - a chemical space approach.

    PubMed

    Kulkarni, S A; Benfenati, E; Barton-Maclaren, T S

    2016-10-20

    One of the key challenges of Canada's Chemicals Management Plan (CMP) is assessing chemicals with limited/no empirical hazard data for their risk to human health. In some instances, these chemicals have not been tested broadly for their toxicological potency; as such, limited information exists on their potential to induce human health effects following exposure. Although (quantitative) structure activity relationship ((Q)SAR) models are able to generate predictions to address data gaps for certain toxicological endpoints, the confidence in predictions also needs to be addressed. One way to address this issue is to apply a chemical space approach. This approach uses international toxicological databases, for example, those available in the Organisation for Economic Co-operation and Development (OECD) QSAR Toolbox. The approach,assesses a model's ability to predict the potential hazards of chemicals that have limited hazard data that require assessment under the CMP when compared to a larger, data-rich chemical space that is structurally similar to chemicals of interest. This evaluation of a model's predictive ability makes (Q)SAR analysis more transparent and increases confidence in the application of these predictions in a risk-assessment context. Using this approach, predictions for such chemicals obtained from four (Q)SAR models were successfully classified into high, medium and low confidence levels to better inform their use in decision-making.

  6. Discovery of potent NEK2 inhibitors as potential anticancer agents using structure-based exploration of NEK2 pharmacophoric space coupled with QSAR analyses.

    PubMed

    Khanfar, Mohammad A; Banat, Fahmy; Alabed, Shada; Alqtaishat, Saja

    2017-02-01

    High expression of Nek2 has been detected in several types of cancer and it represents a novel target for human cancer. In the current study, structure-based pharmacophore modeling combined with multiple linear regression (MLR)-based QSAR analyses was applied to disclose the structural requirements for NEK2 inhibition. Generated pharmacophoric models were initially validated with receiver operating characteristic (ROC) curve, and optimum models were subsequently implemented in QSAR modeling with other physiochemical descriptors. QSAR-selected models were implied as 3D search filters to mine the National Cancer Institute (NCI) database for novel NEK2 inhibitors, whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent NEK2 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an [Formula: see text] value of 237 nM.

  7. QNA-based 'Star Track' QSAR approach.

    PubMed

    Filimonov, D A; Zakharov, A V; Lagunin, A A; Poroikov, V V

    2009-10-01

    In the existing quantitative structure-activity relationship (QSAR) methods any molecule is represented as a single point in a many-dimensional space of molecular descriptors. We propose a new QSAR approach based on Quantitative Neighbourhoods of Atoms (QNA) descriptors, which characterize each atom of a molecule and depend on the whole molecule structure. In the 'Star Track' methodology any molecule is represented as a set of points in a two-dimensional space of QNA descriptors. With our new method the estimate of the target property of a chemical compound is calculated as the average value of the function of QNA descriptors in the points of the atoms of a molecule in QNA descriptor space. Substantially, we propose the use of only two descriptors rather than more than 3000 molecular descriptors that apply in the QSAR method. On the basis of this approach we have developed the computer program GUSAR and compared it with several widely used QSAR methods including CoMFA, CoMSIA, Golpe/GRID, HQSAR and others, using ten data sets representing various chemical series and diverse types of biological activity. We show that in the majority of cases the accuracy and predictivity of GUSAR models appears to be better than those for the reference QSAR methods. High predictive ability and robustness of GUSAR are also shown in the leave-20%-out cross-validation procedure.

  8. Application of topological descriptors in QSAR and drug design: history and new trends.

    PubMed

    Gozalbes, R; Doucet, J P; Derouin, F

    2002-03-01

    Powerful methodologies for drug design and drug database screening and selection are presently available. Studies relating the structure of molecules to a property or a biological activity by means of statistical tools (QSPR and QSAR studies, respectively) are particularly relevant. An important point for this methodology is the use of good structural descriptors that are representative of the molecular features responsible for the relevant activity. Topological indices (TIs) are two-dimensional descriptors which take into account the internal atomic arrangement of compounds, and which encode in numerical form information about molecular size, shape, branching, presence of heteroatoms and multiple bonds. The usefulness of TIs in QSPR and QSAR studies has been extensively demonstrated, and they have also been used as a measure of structural similarity or diversity by their application to databases virtually generated by computer. In this article we will briefly review the history of TIs, their advantages and limitations with respect to other descriptors, and their possibilities in drug design and database selection. These applications rely on new computational techniques such as virtual combinatorial synthesis, virtual computational screening or inverse QSAR.

  9. Predictive QSAR modeling of phosphodiesterase 4 inhibitors.

    PubMed

    Kovalishyn, Vasyl; Tanchuk, Vsevolod; Charochkina, Larisa; Semenuta, Ivan; Prokopenko, Volodymyr

    2012-02-01

    A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC₅₀ of new drug-like candidates at early stages of drug development. Copyright © 2011 Elsevier Inc. All rights reserved.

  10. QSAR modeling of in vitro inhibition of cytochrome P450 3A4.

    PubMed

    Mao, Boryeu; Gozalbes, Rafael; Barbosa, Frédérique; Migeon, Jacques; Merrick, Sandra; Kamm, Kelly; Wong, Eric; Costales, Chester; Shi, Wei; Wu, Cheryl; Froloff, Nicolas

    2006-01-01

    We report the QSAR modeling of cytochrome P450 3A4 (CYP3A4) enzyme inhibition using four large data sets of in vitro data. These data sets consist of marketed drugs and drug-like compounds all tested in four assays measuring the inhibition of the metabolism of four different substrates by the CYP3A4 enzyme. The four probe substrates are benzyloxycoumarin, testosterone, benzyloxyresorufin, and midazolam. We first show that using state-of-the-art QSAR modeling approaches applied to only one of these four data sets does not lead to predictive models that would be useful for in silico filtering of chemical libraries. We then present the development and the testing of a multiple pharmacophore hypothesis (MPH) that is formulated as a conceptual extension of the traditional QSAR approach to modeling the promiscuous binding of a large variety of drugs to CYP3A4. In the simplest form, the MPH approach takes advantage of the multiple substrate data sets and identifies the binding of test compounds as either proximal or distal relative to that of a given substrate. Application of the approach to the in silico filtering of test compounds for potential inhibitors of CYP3A4 is also presented. In addition to an improvement in the QSAR modeling for the inhibition of CYP3A4, the results from this modeling approach provide structural insights into the drug-enzyme interactions. The existence of multiple inhibition data sets in the BioPrint database motivates the original development of the concept of a multiple pharmacophore hypothesis and provides a unique opportunity for formulating alternative strategies of QSAR modeling of the inhibition of the in vitro metabolism of CYP3A4.

  11. Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

    PubMed

    Papa, Ester; van der Wal, Leon; Arnot, Jon A; Gramatica, Paola

    2014-02-01

    Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated. © 2013.

  12. Inferring multi-target QSAR models with taxonomy-based multi-task learning.

    PubMed

    Rosenbaum, Lars; Dörr, Alexander; Bauer, Matthias R; Boeckler, Frank M; Zell, Andreas

    2013-07-11

    A plethora of studies indicate that the development of multi-target drugs is beneficial for complex diseases like cancer. Accurate QSAR models for each of the desired targets assist the optimization of a lead candidate by the prediction of affinity profiles. Often, the targets of a multi-target drug are sufficiently similar such that, in principle, knowledge can be transferred between the QSAR models to improve the model accuracy. In this study, we present two different multi-task algorithms from the field of transfer learning that can exploit the similarity between several targets to transfer knowledge between the target specific QSAR models. We evaluated the two methods on simulated data and a data set of 112 human kinases assembled from the public database ChEMBL. The relatedness between the kinase targets was derived from the taxonomy of the humane kinome. The experiments show that multi-task learning increases the performance compared to training separate models on both types of data given a sufficient similarity between the tasks. On the kinase data, the best multi-task approach improved the mean squared error of the QSAR models of 58 kinase targets. Multi-task learning is a valuable approach for inferring multi-target QSAR models for lead optimization. The application of multi-task learning is most beneficial if knowledge can be transferred from a similar task with a lot of in-domain knowledge to a task with little in-domain knowledge. Furthermore, the benefit increases with a decreasing overlap between the chemical space spanned by the tasks.

  13. QSAR DataBank - an approach for the digital organization and archiving of QSAR model information

    PubMed Central

    2014-01-01

    Background Research efforts in the field of descriptive and predictive Quantitative Structure-Activity Relationships or Quantitative Structure–Property Relationships produce around one thousand scientific publications annually. All the materials and results are mainly communicated using printed media. The printed media in its present form have obvious limitations when they come to effectively representing mathematical models, including complex and non-linear, and large bodies of associated numerical chemical data. It is not supportive of secondary information extraction or reuse efforts while in silico studies poses additional requirements for accessibility, transparency and reproducibility of the research. This gap can and should be bridged by introducing domain-specific digital data exchange standards and tools. The current publication presents a formal specification of the quantitative structure-activity relationship data organization and archival format called the QSAR DataBank (QsarDB for shorter, or QDB for shortest). Results The article describes QsarDB data schema, which formalizes QSAR concepts (objects and relationships between them) and QsarDB data format, which formalizes their presentation for computer systems. The utility and benefits of QsarDB have been thoroughly tested by solving everyday QSAR and predictive modeling problems, with examples in the field of predictive toxicology, and can be applied for a wide variety of other endpoints. The work is accompanied with open source reference implementation and tools. Conclusions The proposed open data, open source, and open standards design is open to public and proprietary extensions on many levels. Selected use cases exemplify the benefits of the proposed QsarDB data format. General ideas for future development are discussed. PMID:24910716

  14. Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking.

    PubMed

    Chen, Meimei; Yang, Fafu; Kang, Jie; Yang, Xuemei; Lai, Xinmei; Gao, Yuxing

    2016-11-29

    In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.

  15. QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni.

    PubMed

    Melo-Filho, Cleber C; Dantas, Rafael F; Braga, Rodolpho C; Neves, Bruno J; Senger, Mario R; Valente, Walter C G; Rezende-Neto, João M; Chaves, Willian T; Muratov, Eugene N; Paveley, Ross A; Furnham, Nicholas; Kamentsky, Lee; Carpenter, Anne E; Silva-Junior, Floriano P; Andrade, Carolina H

    2016-07-25

    Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents.

  16. Virtual Screening and Molecular Design Based on Hierarchical Qsar Technology

    NASA Astrophysics Data System (ADS)

    Kuz'min, Victor E.; Artemenko, A. G.; Muratov, Eugene N.; Polischuk, P. G.; Ognichenko, L. N.; Liahovsky, A. V.; Hromov, A. I.; Varlamova, E. V.

    This chapter is devoted to the hierarchical QSAR technology (HiT QSAR) based on simplex representation of molecular structure (SiRMS) and its application to different QSAR/QSPR tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) of the QSAR paradigm by a series of enhanced models based on molecular structure description (in a specific order from 1D to 4D). Actually, it's a system of permanently improved solutions. Different approaches for domain applicability estimation are implemented in HiT QSAR. In the SiRMS approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality, and symmetry). The level of simplex descriptors detailed increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach presented are an ability to solve QSAR/QSPR tasks for mixtures of compounds, the absence of the "molecular alignment" problem, consideration of different physical-chemical properties of atoms (e.g., charge, lipophilicity), and the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of HiT QSAR was demonstrated by its comparison with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D-4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as the predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic, biological, etc. experiments. The HiT QSAR is realized as the suite of computer programs termed the "HiT QSAR" software that so includes powerful statistical capabilities and a number of useful utilities.

  17. CURRENT PRACTICES IN QSAR DEVELOPMENT AND APPLICATIONS

    EPA Science Inventory

    Current Practices in QSAR Development and Applications

    Although it is commonly assumed that the structure and properties of a single chemical determines its activity in a particular biological system, it is only through study of how biological activity varies with changes...

  18. CURRENT PRACTICES IN QSAR DEVELOPMENT AND APPLICATIONS

    EPA Science Inventory

    Current Practices in QSAR Development and Applications

    Although it is commonly assumed that the structure and properties of a single chemical determines its activity in a particular biological system, it is only through study of how biological activity varies with changes...

  19. Quantitative Structure‐activity Relationship (QSAR) Models for Docking Score Correction

    PubMed Central

    Yamasaki, Satoshi; Yasumatsu, Isao; Takeuchi, Koh; Kurosawa, Takashi; Nakamura, Haruki

    2016-01-01

    Abstract In order to improve docking score correction, we developed several structure‐based quantitative structure activity relationship (QSAR) models by protein‐drug docking simulations and applied these models to public affinity data. The prediction models used descriptor‐based regression, and the compound descriptor was a set of docking scores against multiple (∼600) proteins including nontargets. The binding free energy that corresponded to the docking score was approximated by a weighted average of docking scores for multiple proteins, and we tried linear, weighted linear and polynomial regression models considering the compound similarities. In addition, we tried a combination of these regression models for individual data sets such as IC50, Ki, and %inhibition values. The cross‐validation results showed that the weighted linear model was more accurate than the simple linear regression model. Thus, the QSAR approaches based on the affinity data of public databases should improve docking scores. PMID:28001004

  20. Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March 2009 version

    PubMed Central

    Furuhama, A.; Toida, T.; Nishikawa, N.; Aoki, Y.; Yoshioka, Y.; Shiraishi, H.

    2010-01-01

    The KAshinhou Tool for Ecotoxicity (KATE) system, including ecotoxicity quantitative structure–activity relationship (QSAR) models, was developed by the Japanese National Institute for Environmental Studies (NIES) using the database of aquatic toxicity results gathered by the Japanese Ministry of the Environment and the US EPA fathead minnow database. In this system chemicals can be entered according to their one-dimensional structures and classified by substructure. The QSAR equations for predicting the toxicity of a chemical compound assume a linear correlation between its log P value and its aquatic toxicity. KATE uses a structural domain called C-judgement, defined by the substructures of specified functional groups in the QSAR models. Internal validation by the leave-one-out method confirms that the QSAR equations, with r2>0.7, RMSE ≤0.5, and n>5, give acceptable q2 values. Such external validation indicates that a group of chemicals with an in-domain of KATE C-judgements exhibits a lower root mean square error (RMSE). These findings demonstrate that the KATE system has the potential to enable chemicals to be categorised as potential hazards. PMID:20818579

  1. Toxico-Cheminformatics and QSPR Modeling of the Carcinogenic Potency Database

    EPA Science Inventory

    Report on the development of a tiered, confirmatory scheme for prediction of chemical carcinogenicity based on QSAR studies of compounds with available mutagenic and carcinogenic data. For 693 such compounds from the Carcinogenic Potency Database characterized molecular topologic...

  2. The Danish Nephrology Registry

    PubMed Central

    Heaf, James

    2016-01-01

    Aim of database The Danish Nephrology Registry’s (DNR) primary function is to support the Danish public health authorities’ quality control program for patients with end-stage renal disease in order to improve patient care. DNR also supplies epidemiological data to several international organizations and supports epidemiological and clinical research. Study population The study population included patients treated with dialysis or transplantation in Denmark from January 1, 1990 to January 1, 2016, with retrospective data since 1964. Main variables DNR registers patient data (eg, age, sex, renal diagnosis, and comorbidity), predialysis specialist treatment, details of eight dialysis modalities (three hemodialysis and five peritoneal dialysis), all transplantation courses, dialysis access at first dialysis, treatment complications, and biochemical variables. The database is complete (<1% missing data). Patients are followed until death or emigration. Descriptive data DNR now contains 18,120 patients, and an average of 678 is added annually. Data for each transplantation course include donor details, tissue type, time to onset of graft function, and cause of graft loss. Registered complications include peritonitis in peritoneal dialysis patients, causes of peritoneal dialysis technique failure, and transplant rejections. Fifteen biochemical variables are registered, mainly describing anemia control, mineral and bone disease, nutritional and uremia status. Date and cause of death are also included. Six quality indicators are published annually, and have been associated with improvements in patient results, eg, a reduction in dialysis patient mortality, improved graft survival, and earlier referral to specialist care. Approximately, ten articles, mainly epidemiological, are published each year. Conclusion DNR contains a complete description of end-stage renal disease patients in Denmark, their treatment, and prognosis. The stated aims are fulfilled. PMID:27843345

  3. Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research

    PubMed Central

    Fourches, Denis; Muratov, Eugene; Tropsha, Alexander

    2010-01-01

    Molecular modelers and cheminformaticians typically analyze experimental data generated by other scientists. Consequently, when it comes to data accuracy, cheminformaticians are always at the mercy of data providers who may inadvertently publish (partially) erroneous data. Thus, dataset curation is crucial for any cheminformatics analysis such as similarity searching, clustering, QSAR modeling, virtual screening, etc., especially nowadays when the availability of chemical datasets in public domain has skyrocketed in recent years. Despite the obvious importance of this preliminary step in the computational analysis of any dataset, there appears to be no commonly accepted guidance or set of procedures for chemical data curation. The main objective of this paper is to emphasize the need for a standardized chemical data curation strategy that should be followed at the onset of any molecular modeling investigation. Herein, we discuss several simple but important steps for cleaning chemical records in a database including the removal of a fraction of the data that cannot be appropriately handled by conventional cheminformatics techniques. Such steps include the removal of inorganic and organometallic compounds, counterions, salts and mixtures; structure validation; ring aromatization; normalization of specific chemotypes; curation of tautomeric forms; and the deletion of duplicates. To emphasize the importance of data curation as a mandatory step in data analysis, we discuss several case studies where chemical curation of the original “raw” database enabled the successful modeling study (specifically, QSAR analysis) or resulted in a significant improvement of model's prediction accuracy. We also demonstrate that in some cases rigorously developed QSAR models could be even used to correct erroneous biological data associated with chemical compounds. We believe that good practices for curation of chemical records outlined in this paper will be of value to all

  4. QSAR in the pharmaceutical research setting: QSAR models for broad, large problems.

    PubMed

    Sprous, D G; Palmer, R K; Swanson, J T; Lawless, M

    2010-01-01

    The field of quantitative structure activity relationships (QSAR) has evolved into an integral tool for pharmaceutical discovery. It is presently an accessible technology, as can be shown by the number papers which are easily found through PubMed literature searches. At one level, QSAR is used routinely and invisibly as an aid for the bench chemist for logP, logS, pK(a)/pK(b), metabolic stability and other such properties. Chemoinformaticians and computational chemists develop models from scratch for less routine purposes associated with lead optimization around a single target or library design around a target family such as kinase, ion channel or GPCR inhibitors. Regardless of the differences in frequency of use and the end user, any successful QSAR is successful because it rests on appropriate mathematics linking valid data and relevant descriptors. Though success is defined by the end user, the QSAR originator is well advised to validate his model and understand how it performs in different situations. The present review will cover QSAR from the ground up as it is used in pharmaceutical research environments. It will focus towards larger dataset methodologies (a minimum 100 of compounds) and by consequence will focus on 2D descriptors. It will start with the critical base of data, descriptors, equations and validation methods. It will review the broadly used and invisible QSARs for logP, pKa/pKb and metabolic stability. The review will then present progress in QSARs of broad interest which are under active development: 1) hERG liability models, 2) modeling for 2a) drug-likeness and related properties, 2b) kinase ligand likeness and 2c) GPCR ligand likeness.

  5. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

    SciTech Connect

    Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Tropsha, Alexander

    2015-04-15

    Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox. • Putative

  6. Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

    PubMed Central

    Low, Yen; Uehara, Takeki; Minowa, Yohsuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro; Sedykh, Alexander; Muratov, Eugene; Fourches, Denis; Zhu, Hao; Rusyn, Ivan; Tropsha, Alexander

    2014-01-01

    Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the

  7. QSAR Modeling and Prediction of Drug-Drug Interactions.

    PubMed

    Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A; Dmitriev, Alexander V; Muratov, Eugene N; Fourches, Denis; Kuz'min, Victor E; Poroikov, Vladimir V; Tropsha, Alexander; Nicklaus, Marc C

    2016-02-01

    Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

  8. 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

    PubMed Central

    Zhao, Manman; Zheng, Linfeng; Qiu, Chun

    2017-01-01

    Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with q2 = 0.565 (cross-validated correlation coefficient) and r2 = 0.888 (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR. PMID:28630865

  9. Residual-QSAR. Implications for genotoxic carcinogenesis

    PubMed Central

    2011-01-01

    Introduction Both main types of carcinogenesis, genotoxic and epigenetic, were examined in the context of non-congenericity and similarity, respectively, for the structure of ligand molecules, emphasizing the role of quantitative structure-activity relationship ((Q)SAR) studies in accordance with OECD (Organization for Economic and Cooperation Development) regulations. The main purpose of this report involves electrophilic theory and the need for meaningful physicochemical parameters to describe genotoxicity by a general mechanism. Residual-QSAR Method The double or looping multiple linear correlation was examined by comparing the direct and residual structural information against the observed activity. A self-consistent equation of observed-computed activity was assumed to give maximum correlation efficiency for those situations in which the direct correlations gave non-significant statistical information. Alternatively, it was also suited to describe slow and apparently non-noticeable cancer phenomenology, with special application to non-congeneric molecules involved in genotoxic carcinogenesis. Application and Discussions The QSAR principles were systematically applied to a given pool of molecules with genotoxic activity in rats to elucidate their carcinogenic mechanisms. Once defined, the endpoint associated with ligand-DNA interaction was used to select variables that retained the main Hansch physicochemical parameters of hydrophobicity, polarizability and stericity, computed by the custom PM3 semiempirical quantum method. The trial and test sets of working molecules were established by implementing the normal Gaussian principle of activities that applies when the applicability domain is not restrained to the congeneric compounds, as in the present study. The application of the residual, self-consistent QSAR method and the factor (or average) method yielded results characterized by extremely high and low correlations, respectively, with the latter resembling

  10. QSAR trout toxicity models on aromatic pesticides.

    PubMed

    Slavov, Svetoslav; Gini, Giuseppina; Benfenati, Emilio

    2008-11-01

    The pesticides originally designed to kill target organisms are dangerous for many other wild species. Since they are applied directly to the environment, they can easily reach the water basins and the topsoil. A dataset of 125 aromatic pesticides with well-expressed aquatic toxicity towards trout was subjected to quantitative structure activity relationships (QSAR) analysis aimed to establish the relationship between their molecular structure and biological activity. A literature data for LC50 concentration killing 50% of fish was used. In addition to the standard 2D-QSAR analysis, a comparative molecular field analysis (CoMFA) analysis considering the electrostatic and steric properties of the molecules was also performed. The CoMFA analysis helped the recognition of the steric interactions as playing an important role for aquatic toxicity. In addition, the transport properties and the stability of the compounds studied were also identified as important for their biological activity.

  11. Residual-QSAR. Implications for genotoxic carcinogenesis.

    PubMed

    Putz, Mihai V

    2011-06-13

    Both main types of carcinogenesis, genotoxic and epigenetic, were examined in the context of non-congenericity and similarity, respectively, for the structure of ligand molecules, emphasizing the role of quantitative structure-activity relationship ((Q)SAR) studies in accordance with OECD (Organization for Economic and Cooperation Development) regulations. The main purpose of this report involves electrophilic theory and the need for meaningful physicochemical parameters to describe genotoxicity by a general mechanism. RESIDUAL-QSAR METHOD: The double or looping multiple linear correlation was examined by comparing the direct and residual structural information against the observed activity. A self-consistent equation of observed-computed activity was assumed to give maximum correlation efficiency for those situations in which the direct correlations gave non-significant statistical information. Alternatively, it was also suited to describe slow and apparently non-noticeable cancer phenomenology, with special application to non-congeneric molecules involved in genotoxic carcinogenesis. The QSAR principles were systematically applied to a given pool of molecules with genotoxic activity in rats to elucidate their carcinogenic mechanisms. Once defined, the endpoint associated with ligand-DNA interaction was used to select variables that retained the main Hansch physicochemical parameters of hydrophobicity, polarizability and stericity, computed by the custom PM3 semiempirical quantum method. The trial and test sets of working molecules were established by implementing the normal Gaussian principle of activities that applies when the applicability domain is not restrained to the congeneric compounds, as in the present study. The application of the residual, self-consistent QSAR method and the factor (or average) method yielded results characterized by extremely high and low correlations, respectively, with the latter resembling the direct activity to parameter QSARs

  12. Topological polar surface area: a useful descriptor in 2D-QSAR.

    PubMed

    Prasanna, S; Doerksen, R J

    2009-01-01

    Topological polar surface area (TPSA), which makes use of functional group contributions based on a large database of structures, is a convenient measure of the polar surface area that avoids the need to calculate ligand 3D structure or to decide which is the relevant biological conformation or conformations. We demonstrate the utility of TPSA in 2D-QSAR for 14 sets of diverse pharmacological activity data. Even though a large pool of reports showing the importance of the classic 2D descriptors such as calculated logP (ClogP) and calculated molar refractivity (CMR) exists in the 2D-QSAR literature, this is the first report to demonstrate the value of TPSA as a relevant descriptor applicable to a large, structurally and pharmacologically diverse set of classes of compounds. We also address the limitations of applicability of this descriptor for 2D-QSAR analysis. We observed a negative correlation of TPSA with activity data for anticancer alkaloids, MT1 and MT2 agonists, MAO-B and tumor necrosis factor-alpha inhibitors and a positive correlation with inhibitory activity data for telomerase, PDE-5, GSK-3, DNA-PK, aromatase, malaria, trypanosomatids and CB2 agonists.

  13. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization.

    PubMed

    Alves, Vinicius M; Capuzzi, Stephen J; Muratov, Eugene; Braga, Rodolpho C; Thornton, Thomas; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H; Tropsha, Alexander

    2016-12-21

    Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential.

  14. Prediction oriented QSAR modelling of EGFR inhibition.

    PubMed

    Szántai-Kis, C; Kövesdi, I; Eros, D; Bánhegyi, P; Ullrich, A; Kéri, G; Orfi, L

    2006-01-01

    Epidermal Growth Factor Receptor (EGFR) is a high priority target in anticancer drug research. Thousands of very effective EGFR inhibitors have been developed in the last decade. The known inhibitors are originated from a very diverse chemical space but--without exception--all of them act at the Adenosine TriPhosphate (ATP) binding site of the enzyme. We have collected all of the diverse inhibitor structures and the relevant biological data obtained from comparable assays and built prediction oriented Quantitative Structure-Activity Relationship (QSAR) which models the ATP binding pocket's interactive surface from the ligand side. We describe a QSAR method with automatic Variable Subset Selection (VSS) by Genetic Algorithm (GA) and goodness-of-prediction driven QSAR model building, resulting an externally validated EGFR inhibitory model built from pIC50 values of a diverse structural set of 623 EGFR inhibitors. Repeated Trainings/Evaluations (RTE) were used to obtain model fitness values and the effectiveness of VSS is amplified by using predictive ability scores of descriptors. Numerous models were generated by different methods and viable models were collected. Then, intensive RTE were applied to identify ultimate models for external validations. Finally, suitable models were validated by statistical tests. Since we use calculated molecular descriptors in the modeling, these models are suitable for virtual screening for obtaining novel potential EGFR inhibitors.

  15. DYNAMIC 3D QSAR TECHNIQUES: APPLICATIONS IN TOXICOLOGY

    EPA Science Inventory

    Two dynamic techniques recently developed to account for conformational flexibility of chemicals in 3D QSARs are presented. In addition to the impact of conformational flexibility of chemicals in 3D QSAR models, the applicability of various molecular descriptors is discussed. The...

  16. DYNAMIC 3D QSAR TECHNIQUES: APPLICATIONS IN TOXICOLOGY

    EPA Science Inventory

    Two dynamic techniques recently developed to account for conformational flexibility of chemicals in 3D QSARs are presented. In addition to the impact of conformational flexibility of chemicals in 3D QSAR models, the applicability of various molecular descriptors is discussed. The...

  17. (Q)SAR: A Tool for the Toxicologist.

    PubMed

    Steinbach, Thomas; Gad-McDonald, Samantha; Kruhlak, Naomi; Powley, Mark; Greene, Nigel

    2015-01-01

    A continuing education (CE) course at the 2014 American College of Toxicology annual meeting covered the topic of (Quantitative) Structure-Activity Relationships [(Q)SAR]. The (Q)SAR methodologies use predictive computer modeling based on predefined rules to describe the relationship between chemical structure and a chemical's associated biological activity or statistical tools to find correlations between biologic activity and the molecular structure or properties of a compound. The (Q)SAR has applications in risk assessment, drug discovery, and regulatory decision making. Pressure within industry to reduce the cost of drug development and societal pressure for government regulatory agencies to produce more accurate and timely risk assessment of drugs and chemicals have necessitated the use of (Q)SAR. Producing a high-quality (Q)SAR model depends on many factors including the choice of statistical methods and descriptors, but first and foremost the quality of the data input into the model. Understanding how a (Q)SAR model is developed and applied is critical to the successful use of such a tool. The CE session covered the basic principles of (Q)SAR, practical applications of these computational models in toxicology, how regulatory agencies use and interpret (Q)SAR models, and potential pitfalls of using them. © The Author(s) 2015.

  18. Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks.

    PubMed

    Prado-Prado, Francisco J; Martinez de la Vega, Octavio; Uriarte, Eugenio; Ubeira, Florencio M; Chou, Kuo-Chen; González-Díaz, Humberto

    2009-01-15

    One limitation of almost all antiviral Quantitative Structure-Activity Relationships (QSAR) models is that they predict the biological activity of drugs against only one species of virus. Consequently, the development of multi-tasking QSAR models (mt-QSAR) to predict drugs activity against different species of virus is of the major vitally important. These mt-QSARs offer also a good opportunity to construct drug-drug Complex Networks (CNs) that can be used to explore large and complex drug-viral species databases. It is known that in very large CNs we can use the Giant Component (GC) as a representative sub-set of nodes (drugs) and but the drug-drug similarity function selected may strongly determines the final network obtained. In the three previous works of the present series we reported mt-QSAR models to predict the antimicrobial activity against different fungi [Gonzalez-Diaz, H.; Prado-Prado, F. J.; Santana, L.; Uriarte, E. Bioorg.Med.Chem.2006, 14, 5973], bacteria [Prado-Prado, F. J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte E. Bioorg.Med.Chem.2007, 15, 897] or parasite species [Prado-Prado, F.J.; González-Díaz, H.; Martinez de la Vega, O.; Ubeira, F.M.; Chou K.C. Bioorg.Med.Chem.2008, 16, 5871]. However, including these works, we do not found any report of mt-QSAR models for antivirals drug, or a comparative study of the different GC extracted from drug-drug CNs based on different similarity functions. In this work, we used Linear Discriminant Analysis (LDA) to fit a mt-QSAR model that classify 600 drugs as active or non-active against the 41 different tested species of virus. The model correctly classifies 143 of 169 active compounds (specificity=84.62%) and 119 of 139 non-active compounds (sensitivity=85.61%) and presents overall training accuracy of 85.1% (262 of 308 cases). Validation of the model was carried out by means of external predicting series, classifying the model 466 of 514, 90.7% of compounds. In order to illustrate the performance of the

  19. Improved QSARs for predictive toxicology of halogenated hydrocarbons.

    PubMed

    Trohalaki, S; Gifford, E; Pachter, R

    2000-05-01

    In our continuing efforts to provide a predictive toxicology capability, we seek to improve QSARs (quantitative structure-activity relationships) for chemicals of interest. Currently, although semi-empirical molecular orbital methods are hardly the state of the art for studying small molecules, AM1 calculations appear to be the method of choice when calculating quantum-chemical descriptors. However, with the advent of modern computational capabilities and the development of fast algorithms, ab initio molecular orbital and first principles density functional methods can be expeditiously applied in current QSAR studies. We present a study on halogenated alkanes to assess whether more accurate quantum methods result in QSARs that correlate better with experimental data. Furthermore, improved QSARs can also be obtained through development of new descriptors with explicit physical interpretations that should lead to better understanding of the mechanisms involved in the toxic response. We show that descriptors calculated from chemical intermediates may be useful in future QSARs.

  20. QSAR Modeling is not "Push a Button and Find a Correlation": A Case Study of Toxicity of (Benzo-)triazoles on Algae.

    PubMed

    Gramatica, Paola; Cassani, Stefano; Roy, Partha Pratim; Kovarich, Simona; Yap, Chun Wei; Papa, Ester

    2012-12-01

    A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapitata is used to discuss some problems and solutions in QSAR modeling, particularly in the environmental context. The relevance of data curation (not only of experimental data, but also of chemical structures and input formats for the calculation of molecular descriptors), the crucial points of QSAR model validation and the potential application for new chemicals (internal robustness, exclusion of chance correlation, external predictivity, applicability domain) are described, while developing MLR-OLS models based on molecular descriptors, calculated by various QSAR software tools (commercial DRAGON, free PaDEL-Descriptor and QSPR-THESAURUS). Additionally, the utility of consensus models is highlighted. This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases; furthermore it highlighted some general problems related to QSAR model generation and was useful in the implementation of the PaDEL-Descriptor software. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Predicting human plasma protein binding of drugs using plasma protein interaction QSAR analysis (PPI-QSAR).

    PubMed

    Li, Haiyan; Chen, Zhuxi; Xu, Xuejun; Sui, Xiaofan; Guo, Tao; Liu, Wei; Zhang, Jiwen

    2011-09-01

    A novel method, named as the plasma protein-interaction QSAR analysis (PPI-QSAR) was used to construct the QSAR models for human plasma protein binding. The intra-molecular descriptors of drugs and inter-molecular interaction descriptors resulted from the docking simulation between drug molecules and human serum albumin were included as independent variables in this method. A structure-based in silico model for a data set of 65 antibiotic drugs was constructed by the multiple linear regression method and validated by the residual analysis, the normal Probability-Probability plot and Williams plot. The R(2) and Q(2) values of the entire data set were 0.87 and 0.77, respectively, for the training set were 0.86 and 0.72, respectively. The results indicated that the fitted model is robust, stable and satisfies all the prerequisites of the regression models. Combining intra-molecular descriptors with inter-molecular interaction descriptors between drug molecules and human serum albumin, the drug plasma protein binding could be modeled and predicted by the PPI-QSAR method successfully.

  2. Fish acute toxicity syndromes and their use in the QSAR approach to hazard assessment.

    PubMed Central

    McKim, J M; Bradbury, S P; Niemi, G J

    1987-01-01

    Implementation of the Toxic Substances Control Act of 1977 creates the need to reliably establish testing priorities because laboratory resources are limited and the number of industrial chemicals requiring evaluation is overwhelming. The use of quantitative structure activity relationship (QSAR) models as rapid and predictive screening tools to select more potentially hazardous chemicals for in-depth laboratory evaluation has been proposed. Further implementation and refinement of quantitative structure-toxicity relationships in aquatic toxicology and hazard assessment requires the development of a "mode-of-action" database. With such a database, a qualitative structure-activity relationship can be formulated to assign the proper mode of action, and respective QSAR, to a given chemical structure. In this review, the development of fish acute toxicity syndromes (FATS), which are toxic-response sets based on various behavioral and physiological-biochemical measurements, and their projected use in the mode-of-action database are outlined. Using behavioral parameters monitored in the fathead minnow during acute toxicity testing, FATS associated with acetylcholinesterase (AChE) inhibitors and narcotics could be reliably predicted. However, compounds classified as oxidative phosphorylation uncouplers or stimulants could not be resolved. Refinement of this approach by using respiratory-cardiovascular responses in the rainbow trout, enabled FATS associated with AChE inhibitors, convulsants, narcotics, respiratory blockers, respiratory membrane irritants, and uncouplers to be correctly predicted. PMID:3297660

  3. Fish acute toxicity syndromes and their use in the QSAR approach to hazard assessment

    SciTech Connect

    McKim, J.M.; Bradbury, S.P.; Niemi, G.J.

    1987-04-01

    Implementation of the Toxic Substances Control Act of 1977 creates the need to reliably establish testing priorities because laboratory resources are limited and the number of industrial chemicals requiring evaluation is overwhelming. The use of quantitative structure activity relationship (QSAR) models as rapid and predictive screening tools to select more potentially hazardous chemicals for in-depth laboratory evaluation has been proposed. Further implementation and refinement of quantitative structure-toxicity relationships in aqueous toxicology and hazard assessment requires the development of a mode-of-action database. With such a database, a qualitative structure-activity relationship can be formulated to assign the proper mode of action, and respective QSAR, to a given chemical structure. In this review, the development of fish acute toxicity syndromes (FATS), which are toxic-response sets based on various behavioral and physiological-biochemical measurements, and their projected use in the mode-of-action database are outlined. Using behavioral parameters monitored in the fathead minnow during acute toxicity testing, FATS associated with acetylcholinesterase (AChE) inhibitors and narcotics could be reliably predicted. However, compounds classified as oxidative phosphorylation uncouplers or stimulants could not be resolved. Refinement of this approach by using respiratory-cardiovascular responses in the rainbow trout, enabled FATS associated with AChE inhibitors, convulsants, narcotics, respiratory blockers, respiratory membrane irritants, and uncouplers to be correctly predicted.

  4. Danish Breast Cancer Cooperative Group

    PubMed Central

    Christiansen, Peer; Ejlertsen, Bent; Jensen, Maj-Britt; Mouridsen, Henning

    2016-01-01

    Aim of database Danish Breast Cancer Cooperative Group (DBCG), with an associated database, was introduced as a nationwide multidisciplinary group in 1977 with the ultimate aim to improve the prognosis in breast cancer. Since then, the database has registered women diagnosed with primary invasive nonmetastatic breast cancer. The data reported from the departments to the database included details of the characteristics of the primary tumor, of surgery, radiotherapy, and systemic therapies, and of follow-up reported on specific forms from the departments in question. Descriptive data From 1977 through 2014, ~110,000 patients are registered in the nationwide, clinical database. The completeness has gradually improved to more than 95%. DBCG has continuously prepared evidence-based guidelines on diagnosis and treatment of breast cancer and conducted quality control studies to ascertain the degree of adherence to the guidelines in the different departments. Conclusion Utilizing data from the DBCG database, a long array of high-quality DBCG studies of various designs and scope, nationwide or in international collaboration, have contributed to the current updating of the guidelines, and have been an instrumental resource in the improvement of management and prognosis of breast cancer in Denmark. Thus, since the establishment of DBCG, the prognosis in breast cancer has continuously improved with a decrease in 5-year mortality from ~37% to 15%. PMID:27822082

  5. The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: A retrospective validation approach.

    PubMed

    Burden, Natalie; Maynard, Samuel K; Weltje, Lennart; Wheeler, James R

    2016-10-01

    The European Plant Protection Products Regulation 1107/2009 requires that registrants establish whether pesticide metabolites pose a risk to the environment. Fish acute toxicity assessments may be carried out to this end. Considering the total number of pesticide (re-) registrations, the number of metabolites can be considerable, and therefore this testing could use many vertebrates. EFSA's recent "Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters" outlines opportunities to apply non-testing methods, such as Quantitative Structure Activity Relationship (QSAR) models. However, a scientific evidence base is necessary to support the use of QSARs in predicting acute fish toxicity of pesticide metabolites. Widespread application and subsequent regulatory acceptance of such an approach would reduce the numbers of animals used. The work presented here intends to provide this evidence base, by means of retrospective data analysis. Experimental fish LC50 values for 150 metabolites were extracted from the Pesticide Properties Database (http://sitem.herts.ac.uk/aeru/ppdb/en/atoz.htm). QSAR calculations were performed to predict fish acute toxicity values for these metabolites using the US EPA's ECOSAR software. The most conservative predicted LC50 values generated by ECOSAR were compared with experimental LC50 values. There was a significant correlation between predicted and experimental fish LC50 values (Spearman rs = 0.6304, p < 0.0001). For 62% of metabolites assessed, the QSAR predicted values are equal to or lower than their respective experimental values. Refined analysis, taking into account data quality and experimental variation considerations increases the proportion of sufficiently predictive estimates to 91%. For eight of the nine outliers, there are plausible explanation(s) for the disparity between measured and predicted LC50 values. Following detailed consideration of the robustness of

  6. Receptor dependent multidimensional QSAR for modeling drug--receptor interactions.

    PubMed

    Polanski, Jaroslaw

    2009-01-01

    Quantitative Structure Activity Relationship (QSAR) is an approach of mapping chemical structure to properties. A significant development can be observed in the last two decades in this method which originated from the Hansch analysis based on the logP data and Hammett constant towards a growing importance of the molecular descriptors derived from 3D structure including conformational dynamics and solvation scenarios. However, molecular interactions in biological systems are complex phenomena generating extremely noisy data, if simulated in silico. This decides that activity modeling and predictions are a risky business. Molecular recognition uncertainty in traditional receptor independent (RI) m-QSAR cannot be eliminated but by the inclusion of the receptor data. Modeling ligand-receptor interactions is a complex computational problem. This has limited the development of the receptor dependent (RD) m-QSAR. However, a steady increase of computational power has also improved modeling ability in chemoinformatics and novel RD QSAR methods appeared. Following the RI m-QSAR terminology this is usually classified as RD 3/6D-QSAR. However, a clear systematic m-QSAR classification can be proposed, where dimension m refers to, the static ligand representation (3D), multiple ligand representation (4D), ligand-based virtual or pseudo receptor models (5D), multiple solvation scenarios (6D) and real receptor or target-based receptor model data (7D).

  7. Nanomaterials - the Next Great Challenge for Qsar Modelers

    NASA Astrophysics Data System (ADS)

    Puzyn, Tomasz; Gajewicz, Agnieszka; Leszczynska, Danuta; Leszczynski, Jerzy

    In this final chapter a new perspective for the application of QSAR in the nanosciences is discussed. The role of nanomaterials is rapidly increasing in many aspects of everyday life. This is promoting a wide range of research needs related to both the design of new materials with required properties and performing a comprehensive risk assessment of the manufactured nanoparticles. The development of nanoscience also opens new areas for QSAR modelers. We have begun this contribution with a detailed discussion on the remarkable physical-chemical properties of nanomaterials and their specific toxicities. Both these factors should be considered as potential endpoints for further nano-QSAR studies. Then, we have highlighted the status and research needs in the area of molecular descriptors applicable to nanomaterials. Finally, we have put together currently available nano-QSAR models related to the physico-chemical endpoints of nanoparticles and their activity. Although we have observed many problems (i.e., a lack of experimental data, insufficient and inadequate descriptors), we do believe that application of QSAR methodology will significantly support nanoscience in the near future. Development of reliable nano-QSARs can be considered as the next challenging task for the QSAR community.

  8. The Danish Hip Arthroplasty Register

    PubMed Central

    Gundtoft, Per Hviid; Varnum, Claus; Pedersen, Alma Becic; Overgaard, Søren

    2016-01-01

    Aim of database The aim of the Danish Hip Arthroplasty Register (DHR) is to continuously monitor and improve the quality of treatment of primary and revision total hip arthroplasty (THA) in Denmark. Study population The DHR is a Danish nationwide arthroplasty register established in January 1995. All Danish orthopedic departments – both public and private – report to the register, and registration is compulsory. Main variables The main variables in the register include civil registration number, indication for primary and revision surgery, operation date and side, and postoperative complications. Completeness of primary and revision surgery is evaluated annually and validation of a number of variables has been carried out. Descriptive data A total of 139,525 primary THAs and 22,118 revisions have been registered in the DHR between January 1, 1995 and December 31, 2014. Since 1995, completeness of procedure registration has been high, being 97.8% and 92.0% in 2014 for primary THAs and revisions, respectively. Several risk factors, such as comorbidity, age, specific primary diagnosis and fixation types for failure of primary THAs, and postoperative complications, have been identified through the DHR. Approximately 9,000 primary THAs and 1,500 revisions are reported to the register annually. Conclusion The DHR is important for monitoring and improvement of treatment with THA and is a valuable tool for research in THA surgery due to the high quality of prospective collected data with long-term follow-up and high completeness. The register can be used for population-based epidemiology studies of THA surgery and can be linked to a range of other national databases. PMID:27822092

  9. The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

    PubMed

    Li, Jiazhong; Gramatica, Paola

    2010-11-01

    Quantitative structure-activity relationship (QSAR) methodology aims to explore the relationship between molecular structures and experimental endpoints, producing a model for the prediction of new data; the predictive performance of the model must be checked by external validation. Clearly, the qualities of chemical structure information and experimental endpoints, as well as the statistical parameters used to verify the external predictivity have a strong influence on QSAR model reliability. Here, we emphasize the importance of these three aspects by analyzing our models on estrogen receptor binders (Endocrine disruptor knowledge base (EDKB) database). Endocrine disrupting chemicals, which mimic or antagonize the endogenous hormones such as estrogens, are a hot topic in environmental and toxicological sciences. QSAR shows great values in predicting the estrogenic activity and exploring the interactions between the estrogen receptor and ligands. We have verified our previously published model for additional external validation on new EDKB chemicals. Having found some errors in the used 3D molecular conformations, we redevelop a new model using the same data set with corrected structures, the same method (ordinary least-square regression, OLS) and DRAGON descriptors. The new model, based on some different descriptors, is more predictive on external prediction sets. Three different formulas to calculate correlation coefficient for the external prediction set (Q2 EXT) were compared, and the results indicated that the new proposal of Consonni et al. had more reasonable results, consistent with the conclusions from regression line, Williams plot and root mean square error (RMSE) values. Finally, the importance of reliable endpoints values has been highlighted by comparing the classification assignments of EDKB with those of another estrogen receptor binders database (METI): we found that 16.1% assignments of the common compounds were opposite (20 among 124 common

  10. Towards interoperable and reproducible QSAR analyses: Exchange of datasets.

    PubMed

    Spjuth, Ola; Willighagen, Egon L; Guha, Rajarshi; Eklund, Martin; Wikberg, Jarl Es

    2010-06-30

    QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets and hence work collectively, but

  11. Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening.

    PubMed

    Taha, Mutasem O; Bustanji, Yasser; Al-Bakri, Amal G; Yousef, Al-Motassem; Zalloum, Waleed A; Al-Masri, Ihab M; Atallah, Naji

    2007-03-01

    A pharmacophoric model was developed for human protein tyrosine phosphatase 1B (h-PTP 1B) inhibitors utilizing the HipHop-REFINE module of CATALYST software. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of physicochemical descriptors and pharmacophore hypothesis that yield consistent QSAR equation of good predictive potential (r = 0.87,F-statistic = 69.13,r(BS)2 = 0.76,r(LOO)2 = 0.68). The validity of the QSAR equation and the associated pharmacophoric hypothesis was experimentally established by the identification of five new h-PTP 1B inhibitors retrieved from the National Cancer Institute (NCI) database.

  12. The Danish National Multiple Myeloma Registry

    PubMed Central

    Gimsing, Peter; Holmström, Morten O; Klausen, Tobias Wirenfelt; Andersen, Niels Frost; Gregersen, Henrik; Pedersen, Robert Schou; Plesner, Torben; Pedersen, Per Trøllund; Frederiksen, Mikael; Frølund, Ulf; Helleberg, Carsten; Vangsted, Annette; de Nully Brown, Peter; Abildgaard, Niels

    2016-01-01

    Aim The Danish National Multiple Myeloma Registry (DMMR) is a population-based clinical quality database established in January 2005. The primary aim of the database is to ensure that diagnosis and treatment of plasma cell dyscrasia are of uniform quality throughout the country. Another aim is to support research. Patients are registered with their unique Danish personal identification number, and the combined use of DMMR, other Danish National registries, and the Danish National Cancer Biobank offers a unique platform for population-based translational research. Study population All newly diagnosed patients with multiple myeloma (MM), smoldering MM, solitary plasmacytomas, and plasma cell leukemia in Denmark are registered annually; ~350 patients. Amyloid light-chain amyloidosis, POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes syndrome), monoclonal gammopathy of undetermined significance and monoclonal gammopathy of undetermined significance with polyneuropathy have been registered since 2014. Main variables The main registered variables at diagnosis are patient demographics, baseline disease characteristics, myeloma-defining events, clinical complications, prognostics, first- and second-line treatments, treatment responses, progression free, and overall survival. Descriptive data Up to June 2015, 2,907 newly diagnosed patients with MM, 485 patients with smoldering MM, 64 patients with plasma cell leukemia, and 191 patients with solitary plasmacytomas were registered. Registration completeness of new patients is ~100%. A data validation study performed in 2013–2014 by the Danish Myeloma Study Group showed >95% data correctness. Conclusion The DMMR is a population-based data validated database eligible for clinical, epidemiological, and translational research. PMID:27822103

  13. 3D QSAR studies, pharmacophore modeling and virtual screening on a series of steroidal aromatase inhibitors.

    PubMed

    Xie, Huiding; Qiu, Kaixiong; Xie, Xiaoguang

    2014-11-14

    Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models. The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q² = 0.636, r²(ncv) = 0.988, r²(pred) = 0.658; CoMSIA: q² = 0.843, r²(ncv) = 0.989, r²(pred) = 0.601). This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs. In addition, Genetic algorithm with linear assignment of hypermolecular alignment of database (GALAHAD) was used to derive 3D pharmacophore models. The selected pharmacophore model contains two acceptor atoms and four hydrophobic centers, which was used as a 3D query for virtual screening against NCI2000 database. Six hit compounds were obtained and their biological activities were further predicted by the CoMFA and CoMSIA models, which are expected to design potent and novel SAIs.

  14. SEDIMENT-ASSOCIATED REACTIONS OF AROMATIC AMINES: QSAR DEVELOPMENT

    EPA Science Inventory

    Despite the common occurrence of the aromatic amine functional group in environmental contaminants, few quantitative structure-activity relationships (QSARs) have been developed to predict sorption kinetics for aromatic amines in natural soils and sediments. Towards the goal of d...

  15. Using Toxicological Evidence from QSAR Models in Practice

    EPA Science Inventory

    The new generation of QSAR models provides supporting documentation in addition to the predicted toxicological value. Such information enables the toxicologist to explore the properties of chemical substances and to review and increase the reliability of toxicity predictions. Thi...

  16. Using Toxicological Evidence from QSAR Models in Practice

    EPA Science Inventory

    The new generation of QSAR models provides supporting documentation in addition to the predicted toxicological value. Such information enables the toxicologist to explore the properties of chemical substances and to review and increase the reliability of toxicity predictions. Thi...

  17. Tuning HERG out: antitarget QSAR models for drug development.

    PubMed

    Braga, Rodolpho C; Alves, Vinicius M; Silva, Meryck F B; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander; Andrade, Carolina H

    2014-01-01

    Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDArequired procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure-activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83-0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg).

  18. Quantitative Structure-Activity Relationships (QSARs) - Applications and Methodology

    NASA Astrophysics Data System (ADS)

    Cronin, Mark T. D.

    The aim of this introduction is to describe briefly the applications and methodologies involved in (Q)SAR and relate these to the various chapters in this volume. This chapter gives the reader an overview of how, why and where in silico methods, including (Q)SAR, have been utilized to predict endpoints as diverse as those from pharmacology and toxicology. It provides an illustration of how all the various topics in this book interweave to form a single coherent area of science.

  19. The importance of data curation on QSAR Modeling - PHYSPROP open data as a case study. (QSAR 2016)

    EPA Science Inventory

    During the last few decades many QSAR models and tools have been developed at the US EPA, including the widely used EPISuite. During this period the arsenal of computational capabilities supporting cheminformatics has broadened dramatically with multiple software packages. These ...

  20. The Odense University Pharmacoepidemiological Database (OPED)

    Cancer.gov

    The Odense University Pharmacoepidemiological Database is one of two large prescription registries in Denmark and covers a stable population that is representative of the Danish population as a whole.

  1. 3D-QSAR studies on triclosan derivatives as Plasmodium falciparum enoyl acyl carrier reductase inhibitors.

    PubMed

    Shah, P; Siddiqi, M I

    2010-07-01

    3D-QSAR studies were carried out on a training set of 53 structurally highly diverse analogues of triclosan to investigate the correlation of the structural properties of triclosan derivatives with the inhibition of the activity of enoyl acyl carrier protein reductase in Plasmodium falciparum (PfENR) by employing Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). The crystal structure bound conformation of triclosan, was used as a template for aligning molecules. The probable binding mode conformations of other inhibitors were explored according to molecular docking and molecular mechanics poisson-boltzmann surface area (MM/PBSA) solvation free energy estimation methods using grid based linear Poisson-Boltzmann calculations. Predictive 3D-QSAR models, established using routine database alignment rule based on crystallographic-bound conformation of template molecule, produced statistically significant results with cross-validated r2 cv values of 0.64 and 0.54 and non-cross-validated r2 ncv values of 0.96 and 0.97 for CoMFA and CoMSIA models, respectively. The statistically significant models were validated by a test set of nine compounds with predictive r(2) values of 0.534 and 0.765 for CoMFA and CoMSIA respectively. Our QSAR model is able to successfully explain the geometric and electrostatic complementarities between ligands and receptor and provides useful guidelines to design novel triclosan derivatives as Plasmodium falciparum enoyl acyl carrier reductase inhibitors.

  2. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods

    SciTech Connect

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    2014-10-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure–Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R{sup 2} = 0.55 and CCR = 0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R{sup 2} = 0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. - Highlights: • This is the largest curated dataset for ligand binding domain (LBD) of the THRβ. • We report the first QSAR model for antagonists of AF-2 domain of THRβ. • A combination of QSAR and docking enables

  3. Megavariate analysis of hierarchical QSAR data

    NASA Astrophysics Data System (ADS)

    Eriksson, Lennart; Johansson, Erik; Lindgren, Fredrik; Sjöström, Michael; Wold, Svante

    2002-10-01

    Multivariate PCA- and PLS-models involving many variables are often difficult to interpret, because plots and lists of loadings, coefficients, VIPs, etc, rapidly become messy and hard to overview. There may then be a strong temptation to eliminate variables to obtain a smaller data set. Such a reduction of variables, however, often removes information and makes the modelling efforts less reliable. Model interpretation may be misleading and predictive power may deteriorate. A better alternative is usually to partition the variables into blocks of logically related variables and apply hierarchical data analysis. Such blocked data may be analyzed by PCA and PLS. This modelling forms the base-level of the hierarchical modelling set-up. On the base-level in-depth information is extracted for the different blocks. The score vectors formed on the base-level, here called `super variables', may be linked together in new matrices on the top-level. On the top-level superficial relationships between the X- and the Y-data are investigated. In this paper the basic principles of hierarchical modelling by means of PCA and PLS are reviewed. One objective of the paper is to disseminate this concept to a broader QSAR audience. The hierarchical methods are used to analyze a set of 10 haloalkanes for which K = 30 chemical descriptors and M = 255 biological responses have been gathered. Due to the complexity of the biological data, they are sub-divided in four blocks. All the modelling steps on the base-level and the top-level are reported and the final QSAR model is interpreted thoroughly.

  4. The Danish Neuro-Oncology Registry

    PubMed Central

    Hansen, Steinbjørn

    2016-01-01

    Aim of database The Danish Neuro-Oncology Registry (DNOR) was established by the Danish Neuro-Oncology Group as a national clinical database. It was established for the purpose of supporting research and development in adult patients with primary brain tumors in Denmark. Study population DNOR has registered clinical data on diagnostics and treatment of all adult patients diagnosed with glioma since January 1, 2009, which numbers approximately 400 patients each year. Main variables The database contains information about symptoms, presurgical magnetic resonance imaging (MRI) characteristics, performance status, surgical procedures, residual tumor on postsurgical MRI, postsurgical complications, diagnostic and histology codes, radiotherapy, and chemotherapy. Descriptive data DNOR publishes annual reports on descriptive data. During the period of registration, postoperative MRI is performed in a higher proportion of the patients (Indicator II), and a higher proportion of patients have no residual tumor after surgical resection of the primary tumor (Indicator IV). Further data are available in the annual reports. The indicators reflect only minor elements of handling brain tumor patients. Another advantage of reporting indicators is the related multidisciplinary discussions giving a better understanding of what actually is going on, thereby facilitating the work on adjusting the national guidelines in the Danish Neuro-Oncology Group. Conclusion The establishment of DNOR has optimized the quality in handling primary brain tumor patients in Denmark by reporting indicators and facilitating a better multidisciplinary collaboration at a national level. DNOR provides a valuable resource for research. PMID:27822109

  5. Use of QSARs in international decision-making frameworks to predict health effects of chemical substances.

    PubMed Central

    Cronin, Mark T D; Jaworska, Joanna S; Walker, John D; Comber, Michael H I; Watts, Christopher D; Worth, Andrew P

    2003-01-01

    This article is a review of the use of quantitative (and qualitative) structure-activity relationships (QSARs and SARs) by regulatory agencies and authorities to predict acute toxicity, mutagenicity, carcinogenicity, and other health effects. A number of SAR and QSAR applications, by regulatory agencies and authorities, are reviewed. These include the use of simple QSAR analyses, as well as the use of multivariate QSARs, and a number of different expert system approaches. PMID:12896862

  6. In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening.

    PubMed

    Chaudhari, Prashant; Bari, Sanjay

    2016-02-01

    c-KIT is a component of the platelet-derived growth factor receptor family, classified as type-III receptor tyrosine kinase. c-KIT has been reported to be involved in, small cell lung cancer, other malignant human cancers, and inflammatory and autoimmune diseases associated with mast cells. Available c-KIT inhibitors suffer from tribulations of growing resistance or cardiac toxicity. A combined in silico pharmacophore and structure-based virtual screening was performed to identify novel potential c-KIT inhibitors. In the present study, five molecules from the ZINC database were retrieved as new potential c-KIT inhibitors, using Schrödinger's Maestro 9.0 molecular modeling suite. An atom-featured 3D QSAR model was built using previously reported c-KIT inhibitors containing the indolin-2-one scaffold. The developed 3D QSAR model ADHRR.24 was found to be significant (R2 = 0.9378, Q2 = 0.7832) and instituted to be sufficiently robust with good predictive accuracy, as confirmed through external validation approaches, Y-randomization and GH approach [GH score 0.84 and Enrichment factor (E) 4.964]. The present QSAR model was further validated for the OECD principle 3, in that the applicability domain was calculated using a "standardization approach." Molecular docking of the QSAR dataset molecules and final ZINC hits were performed on the c-KIT receptor (PDB ID: 3G0E). Docking interactions were in agreement with the developed 3D QSAR model. Model ADHRR.24 was explored for ligand-based virtual screening followed by in silico ADME prediction studies. Five molecules from the ZINC database were obtained as potential c-KIT inhibitors with high in -silico predicted activity and strong key binding interactions with the c-KIT receptor.

  7. National Database of Geriatrics

    PubMed Central

    Kannegaard, Pia Nimann; Vinding, Kirsten L; Hare-Bruun, Helle

    2016-01-01

    Aim of database The aim of the National Database of Geriatrics is to monitor the quality of interdisciplinary diagnostics and treatment of patients admitted to a geriatric hospital unit. Study population The database population consists of patients who were admitted to a geriatric hospital unit. Geriatric patients cannot be defined by specific diagnoses. A geriatric patient is typically a frail multimorbid elderly patient with decreasing functional ability and social challenges. The database includes 14–15,000 admissions per year, and the database completeness has been stable at 90% during the past 5 years. Main variables An important part of the geriatric approach is the interdisciplinary collaboration. Indicators, therefore, reflect the combined efforts directed toward the geriatric patient. The indicators include Barthel index, body mass index, de Morton Mobility Index, Chair Stand, percentage of discharges with a rehabilitation plan, and the part of cases where an interdisciplinary conference has taken place. Data are recorded by doctors, nurses, and therapists in a database and linked to the Danish National Patient Register. Descriptive data Descriptive patient-related data include information about home, mobility aid, need of fall and/or cognitive diagnosing, and categorization of cause (general geriatric, orthogeriatric, or neurogeriatric). Conclusion The National Database of Geriatrics covers ∼90% of geriatric admissions in Danish hospitals and provides valuable information about a large and increasing patient population in the health care system. PMID:27822120

  8. Structure–activity relationships study of mTOR kinase inhibition using QSAR and structure-based drug design approaches

    PubMed Central

    Lakhlili, Wiame; Yasri, Abdelaziz; Ibrahimi, Azeddine

    2016-01-01

    The discovery of clinically relevant inhibitors of mammalian target of rapamycin (mTOR) for anticancer therapy has proved to be a challenging task. The quantitative structure–activity relationship (QSAR) approach is a very useful and widespread technique for ligand-based drug design, which can be used to identify novel and potent mTOR inhibitors. In this study, we performed two-dimensional QSAR tests, and molecular docking validation tests of a series of mTOR ATP-competitive inhibitors to elucidate their structural properties associated with their activity. The QSAR tests were performed using partial least square method with a correlation coefficient of r2=0.799 and a cross-validation of q2=0.714. The chemical library screening was done by associating ligand-based to structure-based approach using the three-dimensional structure of mTOR developed by homology modeling. We were able to select 22 compounds from two databases as inhibitors of the mTOR kinase active site. We believe that the method and applications highlighted in this study will help future efforts toward the design of selective ATP-competitive inhibitors. PMID:27980424

  9. Azolium analogues as CDK4 inhibitors: Pharmacophore modeling, 3D QSAR study and new lead drug discovery

    NASA Astrophysics Data System (ADS)

    Rondla, Rohini; Padma Rao, Lavanya Souda; Ramatenki, Vishwanath; Vadija, Rajender; Mukkera, Thirupathi; Potlapally, Sarita Rajender; Vuruputuri, Uma

    2017-04-01

    The cyclin-dependent kinase 4 (CDK4) enzyme is a key regulator in cell cycle G1 phase progression. It is often overexpressed in variety of cancer cells, which makes it an attractive therapeutic target for cancer treatment. A number of chemical scaffolds have been reported as CDK4 inhibitors in the literature, and in particular azolium scaffolds as potential inhibitors. Here, a ligand based pharmacophore modeling and an atom based 3D-QSAR analyses for a series of azolium based CDK4 inhibitors are presented. A five point pharmacophore hypothesis, i.e. APRRR with one H-bond acceptor (A), one positive cationic feature (P) and three ring aromatic sites (R) is developed, which yielded an atom based 3D-QSAR model that shows an excellent correlation coefficient value- R2 = 0.93, fisher ratio- F = 207, along with good predictive ability- Q2 = 0.79, and Pearson R value = 0.89. The visual inspection of the 3D-QSAR model, with the most active and the least active ligands, demonstrates the favorable and unfavorable structural regions for the activity towards CDK4. The roles of positively charged nitrogen, the steric effect, ligand flexibility, and the substituents on the activity are in good agreement with the previously reported experimental results. The generated 3D QSAR model is further applied as query for a 3D database screening, which identifies 23 lead drug candidates with good predicted activities and diverse scaffolds. The ADME analysis reveals that, the pharmacokinetic parameters of all the identified new leads are within the acceptable range.

  10. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure based modeling methods

    PubMed Central

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    2016-01-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure-Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R2=0.55 and CCR=0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R2=0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. PMID:25058446

  11. 3D-QSAR and virtual screening studies of thiazolidine-2,4-dione analogs: Validation of experimental inhibitory potencies towards PIM-1 kinase

    NASA Astrophysics Data System (ADS)

    Asati, Vivek; Bharti, Sanjay Kumar; Budhwani, Ashok Kumar

    2017-04-01

    The proviral insertion site in moloney murine leukemia virus (PIM) is a family of serine/threonine kinase of Ca2+-calmodulin-dependent protein kinase (CAMK) group which is responsible for the activation and regulation of cellular transcription and translation. The three isoforms of PIM kinase (PIM-1, PIM-2 and PIM-3) share high homology and functional idleness are widely expressed and involved in a variety of biological processes including cell survival, proliferation, differentiation and apoptosis. Altered expression of PIM-1 kinase correlated with hematologic malignancies and solid tumors. In the present study, atom-based 3D-QSAR, docking and virtual screening studies have been performed on a series of thiazolidine-2,4-dione derivatives as PIM-1 kinase inhibitors. 3D-QSAR and docking approach has shortlisted the most active thiazolidine-2,4-dione derivatives such as 28, 31, 33 and 35 with the incorporation of more than one structural feature in a single molecule. External validations by various parameters and molecular docking studies at the active site of PIM-1 kinase have proved the reliability of the developed 3D-QSAR model. The generated pharmacophore (AADHR.33) from 3D-QSAR study was used for screening of drug like compounds from ZINC database, where ZINC15056464 and ZINC83292944 showed potential binding affinities at the active site amino acid residues (LYS67, GLU171, ASP128 and ASP186) of PIM-1 kinase (PDB ID: "pdb:4DTK").

  12. Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

    PubMed Central

    Zhou, Nannan; Xu, Yuan; Liu, Xian; Wang, Yulan; Peng, Jianlong; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang

    2015-01-01

    The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson’s correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors. PMID:26110383

  13. QSAR models for anti-malarial activity of 4-aminoquinolines.

    PubMed

    Masand, Vijay H; Toropov, Andrey A; Toropova, Alla P; Mahajan, Devidas T

    2014-03-01

    In the present study, predictive quantitative structure - activity relationship (QSAR) models for anti-malarial activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet (http://www.insilico.eu/coral), has been used as a tool of QSAR analysis to establish statistically robust QSAR model of anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r(2)> 0.85 and r(2)> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q(2) = 0.83, R(2) = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen at a topological distance of six.

  14. From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

    PubMed Central

    Zhu, Hao

    2017-01-01

    Quantitative Structure Activity Relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to Quantitative Structure In vitro-In vivo Relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment. PMID:23086837

  15. National Database of Geriatrics.

    PubMed

    Kannegaard, Pia Nimann; Vinding, Kirsten L; Hare-Bruun, Helle

    2016-01-01

    The aim of the National Database of Geriatrics is to monitor the quality of interdisciplinary diagnostics and treatment of patients admitted to a geriatric hospital unit. The database population consists of patients who were admitted to a geriatric hospital unit. Geriatric patients cannot be defined by specific diagnoses. A geriatric patient is typically a frail multimorbid elderly patient with decreasing functional ability and social challenges. The database includes 14-15,000 admissions per year, and the database completeness has been stable at 90% during the past 5 years. An important part of the geriatric approach is the interdisciplinary collaboration. Indicators, therefore, reflect the combined efforts directed toward the geriatric patient. The indicators include Barthel index, body mass index, de Morton Mobility Index, Chair Stand, percentage of discharges with a rehabilitation plan, and the part of cases where an interdisciplinary conference has taken place. Data are recorded by doctors, nurses, and therapists in a database and linked to the Danish National Patient Register. Descriptive patient-related data include information about home, mobility aid, need of fall and/or cognitive diagnosing, and categorization of cause (general geriatric, orthogeriatric, or neurogeriatric). The National Database of Geriatrics covers ∼90% of geriatric admissions in Danish hospitals and provides valuable information about a large and increasing patient population in the health care system.

  16. On the development and validation of QSAR models.

    PubMed

    Gramatica, Paola

    2013-01-01

    The fundamental and more critical steps that are necessary for the development and validation of QSAR models are presented in this chapter as best practices in the field. These procedures are discussed in the context of predictive QSAR modelling that is focused on achieving models of the highest statistical quality and with external predictive power. The most important and most used statistical parameters needed to verify the real performances of QSAR models (of both linear regression and classification) are presented. Special emphasis is placed on the validation of models, both internally and externally, as well as on the need to define model applicability domains, which should be done when models are employed for the prediction of new external compounds.

  17. QSAR Modeling: Where have you been? Where are you going to?

    PubMed Central

    Cherkasov, Artem; Muratov, Eugene N.; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I.; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C.; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E.; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-01-01

    Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making. PMID:24351051

  18. QSAR modeling: where have you been? Where are you going to?

    PubMed

    Cherkasov, Artem; Muratov, Eugene N; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-06-26

    Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

  19. coral Software: QSAR for Anticancer Agents.

    PubMed

    Benfenati, Emilio; Toropov, Andrey A; Toropova, Alla P; Manganaro, Alberto; Gonella Diaza, Rodolfo

    2011-06-01

    CORrelations And Logic (coral at http://www.insilico.eu/coral) is freeware aimed at establishing a quantitative structure - property/activity relationships (QSPR/QSAR). Simplified molecular input line entry system (SMILES) is used to represent the molecular structure. In fact, symbols in SMILES nomenclatures are indicators of the presence of defined molecular fragments. By means of the calculation with Monte Carlo optimization of the so called correlation weights (contributions) for the above-mentioned molecular fragments, one can define optimal SMILES-based descriptors, which are correlated with an endpoint for the training set. The predictability of these descriptors for an external validation set can be estimated. A collection of SMILES-based models of anticancer activity of 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines for different splits into training and validation set which are calculated with the coral are examined and discussed. Good performance has been obtained for three splits: the r(2) ranged between 0.778 and 0.829 for the sub-training set, between 0.828 and 0.933 for the calibration set, and between 0.807 and 0.931 for the validation set. © 2011 John Wiley & Sons A/S.

  20. Substructural QSAR approaches and topological pharmacophores.

    PubMed Central

    Franke, R; Huebel, S; Streich, W J

    1985-01-01

    For large and diverse data sets, simple QSAR methods based on linear and additive models can no longer be applied. In such cases topological methods using descriptors directly derivable from two-dimensional chemical structures provide a useful alternative. The results of such analyses can be used for lead optimization, to guide biological testing and even aid in the design of novel compounds. Various types of topological descriptors and algorithms are briefly discussed. Which of those is to be selected depends on the objective of the investigation and the properties of the data set. Two new methods, LOGANA and LOCON, are discussed in some more detail. With the help of these methods, substructural patterns ("topological pharmacophores") characteristic of compounds possessing a certain biological property can be evaluated. Both methods are designed in such a way that full use can be made of the data handling capacity of computers while maintaining an optimal impact of the experience of the researcher. They are model-free and do not require any mathematical knowledge. While LOGANA deals with semiquantitative or even qualitative biological data, LOCON can be applied to activity data on a continuous scale. The basic procedure in both cases consists in the stepwise combination of substructural descriptors by the logical operations "and," "or" and "not." With a simple example the utility of the methods is demonstrated. PMID:3905376

  1. Robust cross-validation of linear regression QSAR models.

    PubMed

    Konovalov, Dmitry A; Llewellyn, Lyndon E; Vander Heyden, Yvan; Coomans, Danny

    2008-10-01

    A quantitative structure-activity relationship (QSAR) model is typically developed to predict the biochemical activity of untested compounds from the compounds' molecular structures. "The gold standard" of model validation is the blindfold prediction when the model's predictive power is assessed from how well the model predicts the activity values of compounds that were not considered in any way during the model development/calibration. However, during the development of a QSAR model, it is necessary to obtain some indication of the model's predictive power. This is often done by some form of cross-validation (CV). In this study, the concepts of the predictive power and fitting ability of a multiple linear regression (MLR) QSAR model were examined in the CV context allowing for the presence of outliers. Commonly used predictive power and fitting ability statistics were assessed via Monte Carlo cross-validation when applied to percent human intestinal absorption, blood-brain partition coefficient, and toxicity values of saxitoxin QSAR data sets, as well as three known benchmark data sets with known outlier contamination. It was found that (1) a robust version of MLR should always be preferred over the ordinary-least-squares MLR, regardless of the degree of outlier contamination and that (2) the model's predictive power should only be assessed via robust statistics. The Matlab and java source code used in this study is freely available from the QSAR-BENCH section of www.dmitrykonovalov.org for academic use. The Web site also contains the java-based QSAR-BENCH program, which could be run online via java's Web Start technology (supporting Windows, Mac OSX, Linux/Unix) to reproduce most of the reported results or apply the reported procedures to other data sets.

  2. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

    PubMed Central

    Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

    2015-01-01

    Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using random forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers were 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the ScoreCard database of possible skin or sense organ toxicants as primary candidates for experimental validation. PMID:25560674

  3. QSAR and docking studies of anthraquinone derivatives by similarity cluster prediction.

    PubMed

    Harsa, Alexandra M; Harsa, Teodora E; Diudea, Mircea V

    2016-01-01

    Forty anthraquinone derivatives have been downloaded from PubChem database and investigated in a quantitative structure-activity relationships (QSAR) study. The models describing log P and LD50 of this set were built up on the hypermolecule scheme that mimics the investigated receptor space; the models were validated by the leave-one-out procedure, in the external test set and in a new version of prediction by using similarity clusters. Molecular docking approach using Lamarckian Genetic Algorithm was made on this class of anthraquinones with respect to 3Q3B receptor. The best scored molecules in the docking assay were used as leaders in the similarity clustering procedure. It is demonstrated that the LD50 data of this set of anthraquinones are related to the binding energies of anthraquinone ligands to the 3Q3B receptor.

  4. BioPPSy: An Open-Source Platform for QSAR/QSPR Analysis

    PubMed Central

    Walker, Michael L.

    2016-01-01

    The reliability of quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) models is often difficult to assess due to the problems of accessing the tools and data used to build the models. We present here BioPPSy, which aims to fill this gap by providing an easy-to-use open-source software platform. We demonstrate the program capabilities by calculating three key properties used in drug discovery, aqueous solubility, Caco-2 cell permeability and blood-brain barrier permeability. A comparison is made with a number of previously reported methods, taken from the literature, for each property. The software, including source code, current models and databases, is available from https://sourceforge.net/projects/bioppsy/. PMID:27832156

  5. Development and implementation of (Q)SAR modeling within the CHARMMing Web-user interface

    PubMed Central

    Weidlich, Iwona E.; Pevzner, Yuri; Miller, Benjamin T.; Filippov, Igor V.; Woodcock, H. Lee; Brooks, Bernard R.

    2014-01-01

    Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a Web-based tool for SAR and QSAR modeling to add to the services provided by CHARMMing (www.charmming.org). This new module implements some of the most recent advances in modern machine learning algorithms – Random Forest, Support Vector Machine (SVM), Stochastic Gradient Descent, Gradient Tree Boosting etc. A user can import training data from Pubchem Bioassay data collections directly from our interface or upload his or her own SD files which contain structures and activity information to create new models (either categorical or numerical). A user can then track the model generation process and run models on new data to predict activity. PMID:25362883

  6. Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

    SciTech Connect

    Valerio, Luis G. . E-mail: luis.valerio@FDA.HHS.gov; Arvidson, Kirk B.; Chanderbhan, Ronald F.; Contrera, Joseph F.

    2007-07-01

    , comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent.

  7. Spoken Danish. Book Two.

    ERIC Educational Resources Information Center

    Dearden, Jeannette; Stig-Nielsen, Karin

    This is one of a series of self-teaching textbooks initially prepared for the Armed Forces and now offered to the public. The text is designed to be used with a native speaker of Danish or with the accompanying recordings. The textbook is divided into three major sections, each consisting of five learning units and one unit for review. Each unit…

  8. The Danish System Revisited.

    ERIC Educational Resources Information Center

    Whitehead, John S.

    The paper is a supplement to an earlier paper in the same series which reviews Danish higher education until 1977. Expansion in higher education in the last 20 years, approaching the scale of mass higher education, culminated in a crisis in 1977. At that time, a trend toward self-government and participatory governing boards was seen as the end of…

  9. The advancement of multidimensional QSAR for novel drug discovery - where are we headed?

    PubMed

    Wang, Tao; Yuan, Xin-Song; Wu, Mian-Bin; Lin, Jian-Ping; Yang, Li-Rong

    2017-08-01

    The Multidimensional quantitative structure-activity relationship (multidimensional-QSAR) method is one of the most popular computational methods employed to predict interesting biochemical properties of existing or hypothetical molecules. With continuous progress, the QSAR method has made remarkable success in various fields, such as medicinal chemistry, material science and predictive toxicology. Areas covered: In this review, the authors cover the basic elements of multidimensional -QSAR including model construction, validation and application. It includes and emphasizes the very recent developments of multidimensional -QSAR such as: HQSAR, G-QSAR, MIA-QSAR, multi-target QSAR. The advantages and disadvantages of each method are also discussed and typical examples of their application are detailed. Expert opinion: Although there are defects in multidimensional-QSAR modeling, it is still of enormous help to chemists, biologists and other researchers in various fields. In the authors' opinion, the latest more precise and feasible QSAR models should be further developed by integrating new descriptors, algorithms and other relevant computational techniques. Apart from being applied in traditional fields (e.g. lead optimization and predictive risk assessment), QSAR should be used more widely as a routine method in other emerging research fields including the modeling of nanoparticles(NPs), mixture toxicity and peptides.

  10. Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs.

    PubMed

    Nicolotti, Orazio; Gillet, Valerie J; Fleming, Peter J; Green, Darren V S

    2002-11-07

    Deriving quantitative structure-activity relationship (QSAR) models that are accurate, reliable, and easily interpretable is a difficult task. In this study, two new methods have been developed that aim to find useful QSAR models that represent an appropriate balance between model accuracy and complexity. Both methods are based on genetic programming (GP). The first method, referred to as genetic QSAR (or GPQSAR), uses a penalty function to control model complexity. GPQSAR is designed to derive a single linear model that represents an appropriate balance between the variance and the number of descriptors selected for the model. The second method, referred to as multiobjective genetic QSAR (MoQSAR), is based on multiobjective GP and represents a new way of thinking of QSAR. Specifically, QSAR is considered as a multiobjective optimization problem that comprises a number of competitive objectives. Typical objectives include model fitting, the total number of terms, and the occurrence of nonlinear terms. MoQSAR results in a family of equivalent QSAR models where each QSAR represents a different tradeoff in the objectives. A practical consideration often overlooked in QSAR studies is the need for the model to promote an understanding of the biochemical response under investigation. To accomplish this, chemically intuitive descriptors are needed but do not always give rise to statistically robust models. This problem is addressed by the addition of a further objective, called chemical desirability, that aims to reward models that consist of descriptors that are easily interpretable by chemists. GPQSAR and MoQSAR have been tested on various data sets including the Selwood data set and two different solubility data sets. The study demonstrates that the MoQSAR method is able to find models that are at least as good as models derived using standard statistical approaches and also yields models that allow a medicinal chemist to trade statistical robustness for chemical

  11. SAR/QSAR methods in public health practice.

    PubMed

    Demchuk, Eugene; Ruiz, Patricia; Chou, Selene; Fowler, Bruce A

    2011-07-15

    Methods of (Quantitative) Structure-Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protect populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice.

  12. Are the Chemical Structures in your QSAR Correct?

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are used to predict many different endpoints, utilize hundreds and even thousands of different parameters (or descriptors), and are created using a variety of approaches. The one thing they all have in common is the assumptio...

  13. QSAR modelling of water quality indices of alkylphenol pollutants.

    PubMed

    Kim, J H; Gramatica, P; Kim, M G; Kim, D; Tratnyek, P G

    2007-01-01

    The aim of this study was to determine the degradability of 26 Alkylphenols (APs) by Chemical Oxygen Demand (COD) and/or 5-day Biochemical Oxygen Demand (BOD(5)), and to describe these data from Quantitative Structure-activity Relationships (QSARs). Statistical analysis techniques, such as Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least-Squares (PLS) Regression and Neural Network (NN) were carried out to calibrate and validate four-descriptor QSAR models using two different types of descriptor sets. Stable MLR-QSAR models using Leave-One-Out (LOO) were obtained with high predictability performance: r(2) = 0.924, Q(2)(cv) =0.854 for log (1/BOD) model on 24 APs and r(2) = 0.888, Q(2)(cv) = 0.818 for log (1/COD) on all the studied APs. The MLR models, built with four Dragon descriptors selected by Genetic Algorithm (GA), presented the following performances on 24 APs: r(2) = 0.889, Q(2)(cv) = 0.848 for log (1/BOD(5)) and r(2) = 0.885, Q(2)(cv) = 0.834 for log (1/COD) on 26 compounds. From these results, it is expected that the QSAR models generated could be successfully expanded to predict the biological and chemical activities of structurally diverse AP compounds.

  14. Are the Chemical Structures in your QSAR Correct?

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are used to predict many different endpoints, utilize hundreds and even thousands of different parameters (or descriptors), and are created using a variety of approaches. The one thing they all have in common is the assumptio...

  15. AQUATIC TOXICITY MODE OF ACTION STUDIES APPLIED TO QSAR DEVELOPMENT

    EPA Science Inventory

    A series of QSAR models for predicting fish acute lethality were developed using systematically collected data on more than 600 chemicals. These models were developed based on the assumption that chemicals producing toxicity through a common mechanism will have commonality in the...

  16. AQUATIC TOXICITY MODE OF ACTION STUDIES APPLIED TO QSAR DEVELOPMENT

    EPA Science Inventory

    A series of QSAR models for predicting fish acute lethality were developed using systematically collected data on more than 600 chemicals. These models were developed based on the assumption that chemicals producing toxicity through a common mechanism will have commonality in the...

  17. SAR/QSAR methods in public health practice

    SciTech Connect

    Demchuk, Eugene Ruiz, Patricia; Chou, Selene; Fowler, Bruce A.

    2011-07-15

    Methods of (Quantitative) Structure-Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protect populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice.

  18. Global QSAR models of skin sensitisers for regulatory purposes.

    PubMed

    Chaudhry, Qasim; Piclin, Nadège; Cotterill, Jane; Pintore, Marco; Price, Nick R; Chrétien, Jacques R; Roncaglioni, Alessandra

    2010-07-29

    The new European Regulation on chemical safety, REACH, (Registration, Evaluation, Authorisation and Restriction of CHemical substances), is in the process of being implemented. Many chemicals used in industry require additional testing to comply with the REACH regulations. At the same time EU member states are attempting to reduce the number of animals used in experiments under the 3 Rs policy, (refining, reducing, and replacing the use of animals in laboratory procedures). Computational techniques such as QSAR have the potential to offer an alternative for generating REACH data. The FP6 project CAESAR was aimed at developing QSAR models for 5 key toxicological endpoints of which skin sensitisation was one. This paper reports the development of two global QSAR models using two different computational approaches, which contribute to the hybrid model freely available online. The QSAR models for assessing skin sensitisation have been developed and tested under stringent quality criteria to fulfil the principles laid down by the OECD. The final models, accessible from CAESAR website, offer a robust and reliable method of assessing skin sensitisation for regulatory use.

  19. Chemical domain of QSAR models from atom-centered fragments.

    PubMed

    Kühne, Ralph; Ebert, Ralf-Uwe; Schüürmann, Gerrit

    2009-12-01

    A methodology to characterize the chemical domain of qualitative and quantitative structure-activity relationship (QSAR) models based on the atom-centered fragment (ACF) approach is introduced. ACFs decompose the molecule into structural pieces, with each non-hydrogen atom of the molecule acting as an ACF center. ACFs vary with respect to their size in terms of the path length covered in each bonding direction starting from a given central atom and how comprehensively the neighbor atoms (including hydrogen) are described in terms of element type and bonding environment. In addition to these different levels of ACF definitions, the ACF match mode as degree of strictness of the ACF comparison between a test compound and a given ACF pool (such as from a training set) has to be specified. Analyses of the prediction statistics of three QSAR models with their training sets as well as with external test sets and associated subsets demonstrate a clear relationship between the prediction performance and the levels of ACF definition and match mode. The findings suggest that second-order ACFs combined with a borderline match mode may serve as a generic and at the same time a mechanistically sound tool to define and evaluate the chemical domain of QSAR models. Moreover, four standard categories of the ACF-based membership to a given chemical domain (outside, borderline outside, borderline inside, inside) are introduced that provide more specific information about the expected QSAR prediction performance. As such, the ACF-based characterization of the chemical domain appears to be particularly useful for QSAR applications in the context of REACH and other regulatory schemes addressing the safety evaluation of chemical compounds.

  20. A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors.

    PubMed

    Xie, Huiding; Chen, Lijun; Zhang, Jianqiang; Xie, Xiaoguang; Qiu, Kaixiong; Fu, Jijun

    2015-05-29

    B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q(2) = 0.621, r(2)(pred) = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.

  1. A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors

    PubMed Central

    Xie, Huiding; Chen, Lijun; Zhang, Jianqiang; Xie, Xiaoguang; Qiu, Kaixiong; Fu, Jijun

    2015-01-01

    B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q2 = 0.621, r2pred = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained. PMID:26035757

  2. The Danish Adoption Register.

    PubMed

    Petersen, Liselotte; Sørensen, Thorkild I A

    2011-07-01

    The Danish Adoption Register was established in 1963-1964 to explore the genetic and environmental contribution to familial aggregation of schizophrenia. The register encompass information on all 14,425 non-familial adoptions of Danish children legally granted in Denmark 1924-1947. It includes name and date of birth of each adoptee and his or her biological and adoptive parents, date of transfer to adoptive parents and date of formal adoption. The linkage to biological and adoptive parents is close to complete, even biological fathers are registered for 91.4% of the adoptees. Adoption registers are a unique source allowing disentangling of genetic and familial environmental influences on traits, risk of diseases, and mortality.

  3. Danish auroral science history

    NASA Astrophysics Data System (ADS)

    Stauning, P.

    2011-01-01

    Danish auroral science history begins with the early auroral observations made by the Danish astronomer Tycho Brahe during the years from 1582 to 1601 preceding the Maunder minimum in solar activity. Included are also the brilliant observations made by another astronomer, Ole Rømer, from Copenhagen in 1707, as well as the early auroral observations made from Greenland by missionaries during the 18th and 19th centuries. The relations between auroras and geomagnetic variations were analysed by H. C. Ørsted, who also played a vital role in the development of Danish meteorology that came to include comprehensive auroral observations from Denmark, Iceland and Greenland as well as auroral and geomagnetic research. The very important auroral investigations made by Sophus Tromholt are outlined. His analysis from 1880 of auroral observations from Greenland prepared for the significant contributions from the Danish Meteorological Institute, DMI, (founded in 1872) to the first International Polar Year 1882/83, where an expedition headed by Adam Paulsen was sent to Greenland to conduct auroral and geomagnetic observations. Paulsen's analyses of the collected data gave many important results but also raised many new questions that gave rise to auroral expeditions to Iceland in 1899 to 1900 and to Finland in 1900 to 1901. Among the results from these expeditions were 26 unique paintings of the auroras made by the artist painter, Harald Moltke. The expedition to Finland was headed by Dan la Cour, who later as director of the DMI came to be in charge of the comprehensive international geomagnetic and auroral observations made during the Second International Polar Year in 1932/33. Finally, the article describes the important investigations made by Knud Lassen during, among others, the International Geophysical Year 1957/58 and during the International Quiet Sun Year (IQSY) in 1964/65. With his leadership the auroral and geomagnetic research at DMI reached a high international

  4. The Role of Qsar Methodology in the Regulatory Assessment of Chemicals

    NASA Astrophysics Data System (ADS)

    Worth, Andrew Paul

    The aim of this chapter is to outline the different ways in which quantitative structure-activity relationship (QSAR) methods can be used in the regulatory assessment of chemicals. The chapter draws on experience gained in the European Union in the assessment of industrial chemicals, as well as recently developed guidance for the use of QSARs within specific legislative frameworks such as REACH and the Water Framework Directive. This chapter reviews the concepts of QSAR validity, applicability, and acceptability and emphasises that the use of individual QSAR estimates is highly context-dependent, which has implications in terms of the confidence needed in the model validity. In addition to the potential use of QSAR models as stand-alone estimation methods, it is expected that QSARs will be used within the context of broader weight-of-evidence approaches, such as chemical categories and integrated testing strategies; therefore, the role of (Q)SARs within these approaches is explained. This chapter also refers to a range of freely available software tools being developed to facilitate the use of QSARs for regulatory purposes. Finally, some conclusions are drawn concerning current needs for the further development and uptake of QSARs

  5. Per- and polyfluoro toxicity (LC(50) inhalation) study in rat and mouse using QSAR modeling.

    PubMed

    Bhhatarai, Barun; Gramatica, Paola

    2010-03-15

    Fully or partially fluorinated compounds, known as per- and polyfluorinated chemicals are widely distributed in the environment and released because of their use in different household and industrial products. Few of these long chain per- and polyfluorinated chemicals are classified as emerging pollutants, and their environmental and toxicological effects are unveiled in the literature. This has diverted the production of long chain compounds, considered as more toxic, to short chains, but concerns regarding the toxicity of both types of per- and polyfluorinated chemicals are alarming. There are few experimental data available on the environmental behavior and toxicity of these compounds, and moreover, toxicity profiles are found to be different for the types of animals and species used. Quantitative structure-activity relationship (QSAR) is applied to a combination of short and long chain per- and polyfluorinated chemicals, for the first time, to model and predict the toxicity on two species of rodents, rat (Rattus) and mouse (Mus), by modeling inhalation (LC(50)) data. Multiple linear regression (MLR) models using the ordinary-least-squares (OLS) method, based on theoretical molecular descriptors selected by genetic algorithm (GA), were used for QSAR studies. Training and prediction sets were prepared a priori, and these sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the model was verified on a larger set of per- and polyfluorinated chemicals retrieved from different databases and journals. The descriptors involved, the similarities, and the differences observed between models pertaining to the toxicity related to the two species are discussed. Chemometric methods such as principal component analysis (PCA) and multidimensional scaling (MDS) were used to select most toxic compounds from those within the AD of both models, which will be subjected to experimental tests

  6. Danish Cultural Identity and the Teaching of Danish to Foreigners

    ERIC Educational Resources Information Center

    Reuter, Hedwig

    2006-01-01

    Danish as a second language textbooks published over the last 15 years have presented the Danish cultural identity as a homogenous and purely national phenomenon. Research into teaching theory, on the other hand, has been more broad-minded, and is based on interactivity. The aim of this paper is to explain this divergence. (Contains 2 notes.)

  7. From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markov-spectral moments indices.

    PubMed

    Riera-Fernández, Pablo; Martín-Romalde, Raquel; Prado-Prado, Francisco J; Escobar, Manuel; Munteanu, Cristian R; Concu, Riccardo; Duardo-Sanchez, Aliuska; González-Díaz, Humberto

    2012-01-01

    Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks irrespective the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. The goal of this work, in first instance, is to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and

  8. Surveyance of disease frequency in a population by linkage to diagnostic laboratory databases. A system for monitoring the incidences of hyper- and hypothyroidism as part of the Danish iodine supplementation program.

    PubMed

    Pedersen, Inge Bülow; Laurberg, Peter; Arnfred, Terkel; Knudsen, Nils; Jørgensen, Torben; Perrild, Hans; Ovesen, Lars

    2002-03-01

    In Denmark an increase in iodine intake through salt iodization has been introduced in 1998. In parallel a program for surveyance of thyroid diseases in the population was developed as recommended by UNICEF and WHO. To develop and evaluate a computer based system to identify and register new cases of hyper- and hypothyroidism in a well defined cohort, by linkage to diagnostic laboratory databases. (1) Two sub cohorts for monitoring were defined (n=535,859), and evaluated to minimize loss of new cases. Collaboration was established with laboratories covering thyroid hormone analyses in the cohort; (2) a diagnostic algorithm was defined and evaluated against clinical practice; (3) evaluation of the laboratory methods employed by the four participating laboratories, to ensure they would reach the same diagnosis in a patient; (4) a register database was developed which used data imported from the laboratory databases to automatically identify previously unknown cases of hyper- and hypothyroidism and record diagnostic activity in the area. All parts of the registration were carefully evaluated. We describe for the first time a computer based system for prospective measuring the incidence rate of hyper- and hypothyroidism. The system is particularly useful for monitoring of iodine supplementation programmes.

  9. A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology.

    PubMed

    Gramatica, Paola; Sangion, Alessandro

    2016-06-27

    In the last years, external validation of QSAR models was the subject of intensive debate in the scientific literature. Different groups have proposed different metrics to find "the best" parameter to characterize the external predictivity of a QSAR model. This editorial summarizes the history of parameter development for the external QSAR model validation and suggests, once again, the concurrent use of several different metrics to assess the real predictive capability of QSAR models.

  10. An examination of data quality on QSAR Modeling in regards ...

    EPA Pesticide Factsheets

    The development of QSAR models is critically dependent on the quality of available data. As part of our efforts to develop public platforms to provide access to predictive models, we have attempted to discriminate the influence of the quality versus quantity of data available to develop and validate QSAR models. We have focused our efforts on the widely used EPISuite software that was initially developed over two decades ago and, specifically, on the PHYSPROP dataset used to train the EPISuite prediction models. This presentation will review our approaches to examining key datasets, the delivery of curated data and the development of machine-learning models for thirteen separate property endpoints of interest to environmental science. We will also review how these data will be made freely accessible to the community via a new “chemistry dashboard”. This abstract does not reflect U.S. EPA policy. presentation at UNC-CH.

  11. Atomic softness-based QSAR study of testosterone

    NASA Astrophysics Data System (ADS)

    Srivastava, H. K.; Pasha, F. A.; Singh, P. P.

    Ionization potential of an atom in a molecule, electron affinity of an atom in a molecule, and quantum chemical descriptor atomic softness values En‡-based quantitative structure-activity relationship (QSAR) study of testosterone derivatives have been done with the help of PM3 calculations on WinMOPAC 7.21 software. The 3D modeling and geometry optimization of all the compounds have been done with the help of PCMODEL software. The biological activities of testosterone derivatives have been taken from literature. The predicted values of biological activity with the help of multiple linear regression (MLR) analysis is very close to observed biological activity. The cross-validation coefficient and correlation coefficient also indicate that the QSAR model is valuable. Regression analysis shows a very good relationship with biological activity and En‡ values. With the help of these values, prediction of the biological activity of any unknown compound is possible.

  12. Review on lazy learning regressors and their applications in QSAR.

    PubMed

    Kulkarni, Abhijit J; Jayaraman, Valadi K; Kulkarni, Bhaskar D

    2009-05-01

    Building accurate quantitative structure-activity relationships (QSAR) is important in drug design, environmental modeling, toxicology, and chemical property prediction. QSAR methods can be utilized to solve mainly two types of problems viz., pattern recognition, (or classification) where output is discrete (i.e. class information), e.g., active or non-active molecule, binding or non-binding molecule etc., and function approximation, (i.e. regression) where the output is continuous (e.g., actual activity prediction). The present review deals with the second type of problem (regression) with specific attention to one of the most effective machine learning procedures, viz. lazy learning. The methodologies of the algorithm along with the relevant technical information are discussed in detail. We also present three real-life case studies to briefly outline the typical characteristics of the modeling formalism.

  13. Elaborate ligand-based modeling coupled with multiple linear regression and k nearest neighbor QSAR analyses unveiled new nanomolar mTOR inhibitors.

    PubMed

    Khanfar, Mohammad A; Taha, Mutasem O

    2013-10-28

    The mammalian target of rapamycin (mTOR) has an important role in cell growth, proliferation, and survival. mTOR is frequently hyperactivated in cancer, and therefore, it is a clinically validated target for cancer therapy. In this study, we combined exhaustive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent mTOR inhibitors employing 210 known mTOR ligands. Genetic function algorithm (GFA) coupled with k nearest neighbor (kNN) and multiple linear regression (MLR) analyses were employed to build self-consistent and predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. Optimal QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of several new promising mTOR inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hit illustrated an IC50 value of 48 nM.

  14. Quantum chemical parameters in QSAR: what do I use when?

    USGS Publications Warehouse

    Hickey, James P.; Ostrander, Gary K.

    1996-01-01

    This chapter provides a brief overview of the numerous quantum chemical parameters that have been/are currently being used in quantitative structure activity relationships (QSAR), along with a representative bibliography. The parameters will be grouped according to their mechanistic interpretations, and representative biological and physical chemical applications will be mentioned. Parmater computation methods and the appropriate software are highlighted, as are sources for software.

  15. Kinase inhibitor recognition by use of a multivariable QSAR model.

    PubMed

    Sprous, D G; Zhang, John; Zhang, Lei; Wang, Zhaolin; Tepper, M A

    2006-01-01

    We have applied a retrosynthetic program to determine the scaffold and R-group chemical space seen within a library of known kinase inhibitors and non-kinase drug-like molecules. Comparison of the differences quickly revealed that kinase inhibitors are distinct in several chemical fragment and physical properties. We then applied these descriptors in a multivariable quantitative structure-activity relationship (QSAR) model with the goal to distinguish kinase inhibitors from non-kinase drug-like molecules. This model is heuristic in that it was trained over a dataset of 258 known kinase inhibitors and 230 non-kinase drug molecules. The final model recognized 98% of the training set as being kinase inhibitors and had a false positive rate of 15%. This trait for false positives was accepted out of a desire to maintain diversity and not miss possible good kinase inhibitors for screening. The model was validated by reserving a portion of the datasets as test sets, which were not included in the QSAR model building stage. This was done repetitively for different percentiles of the total dataset population. It was seen that model recognition and false positive were only slightly damaged well down to a 70% reserve (30% dataset used for QSAR model training while 70% used for reserve test set). Beyond 70%, the QSAR models were inconsistent, signifying that the training sets were inadequately diverse to represent the greater reserve test sets. We applied this model to evaluate the commercial kinase libraries available from Asinex, BioFocus, ChemDiv and LifeChemicals to facilitate purchase decisions for compounds for HTS for lead compounds. We observed that there are significant differences in populations of recognizable kinase inhibitors across the vendors analyzed, with BioFocus showing the greatest population of kinase like molecules.

  16. Acute toxicity and QSAR of chlorophenols on Daphnia magna

    SciTech Connect

    Devillers, J.; Chambon, P.

    1986-10-01

    Chlorophenols which are released into natural waters from various industrial processes and from agricultural uses have been recognized as a group of chemical substances potentially hazardous to the aquatic environment. Therefore it is important to estimate their toxic impact on biota. Thus, the scope of this research was to obtain acute toxicity data for seventeen chlorophenols towards Daphnia magna and to explore the possibilities of deriving QSAR's (quantitative structure-activity relationship) from the above values.

  17. Exhaustive Structure Generation for Inverse-QSPR/QSAR.

    PubMed

    Miyao, Tomoyuki; Arakawa, Masamoto; Funatsu, Kimito

    2010-01-12

    Chemical structure generation based on quantitative structure property relationship (QSPR) or quantitative structure activity relationship (QSAR) models is one of the central themes in the field of computer-aided molecular design. The objective of structure generation is to find promising molecules, which according to statistical models, are considered to have desired properties. In this paper, a new method is proposed for the exhaustive generation of chemical structures based on inverse-QSPR/QSAR. In this method, QSPR/QSAR models are constructed by multiple linear regression method, and then the conditional distribution of explanatory variables given the desired properties is estimated by inverse analysis of the models using the framework of a linear Gaussian model. Finally, chemical structures are exhaustively generated by a sophisticated algorithm that is based on a canonical construction path method. The usefulness of the proposed method is demonstrated using a dataset of the boiling points of acyclic hydrocarbons containing up to 12 carbon atoms. The QSPR model was constructed with 600 hydrocarbons and their boiling points. Using the proposed method, chemical structures which had boiling points of 100, 150, or 200 °C were exhaustively generated.

  18. Ranking of aquatic toxicity of esters modelled by QSAR.

    PubMed

    Papa, Ester; Battaini, Francesca; Gramatica, Paola

    2005-02-01

    Alternative methods like predictions based on Quantitative Structure-Activity Relationships (QSARs) are now accepted to fill data gaps and define priority lists for more expensive and time consuming assessments. A heterogeneous data set of 74 esters was studied for their aquatic toxicity, and available experimental toxicity data on algae, Daphnia and fish were used to develop statistically validated QSAR models, obtained using multiple linear regression (MLR) by the OLS (Ordinary Least Squares) method and GA-VSS (Variable Subset Selection by Genetic Algorithms) to predict missing values. An ESter Aquatic Toxicity INdex (ESATIN) was then obtained by combining, by PCA, experimental and predicted toxicity data, from which model outliers and esters highly influential due to their structure had been eliminated. Finally this integrated aquatic toxicity index, defined by the PC1 score, was modelled using only a few theoretical molecular descriptors. This last QSAR model, statistically validated for its predictive power, could be proposed as a preliminary evaluative method for screening/prioritising esters according to their integrated aquatic toxicity, just starting from their molecular structure.

  19. Alert-QSAR. Implications for Electrophilic Theory of Chemical Carcinogenesis

    PubMed Central

    Putz, Mihai V.; Ionaşcu, Cosmin; Putz, Ana-Maria; Ostafe, Vasile

    2011-01-01

    Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations ASA of electrophilic molecular structures with observed carcinogenic potencies in rats (observed activity, A = Log[1/TD50], i.e., ASA=f(X1SA,X2SA,…)). The present method includes calculation of the recently developed residual correlation of the structural alert models, i.e., ARASA=f(A−ASA,X1SA,X2SA,…). We propose a specific electrophilic ligand-receptor mechanism that combines electronegativity with chemical hardness-associated frontier principles, equality of ligand-reagent electronegativities and ligand maximum chemical hardness for highly diverse toxic molecules against specific receptors in rats. The observed carcinogenic activity is influenced by the induced SA-mutagenic intermediate effect, alongside Hansch indices such as hydrophobicity (LogP), polarizability (POL) and total energy (Etot), which account for molecular membrane diffusion, ionic deformation, and stericity, respectively. A possible QSAR mechanistic interpretation of mutagenicity as the first step in genotoxic carcinogenesis development is discussed using the structural alert chemoinformation and in full accordance with the Organization for Economic Co-operation and Development QSAR guidance principles. PMID:21954348

  20. QSAR studies on imidazopyrazine derivatives as Aurora A kinase inhibitors.

    PubMed

    Leng, Y; Lu, T; Yuan, H L; Liu, H C; Lu, S; Zhang, W W; Jiang, Y L; Chen, Y D

    2012-10-01

    Aurora kinases have emerged as attractive targets for the development of novel anti-cancer agents. A combined study of molecular docking, pharmacophore modelling and 3D-QSAR was performed on a series of imidazo [1, 2-a] pyrazines as novel Aurora kinase inhibitors to gain insights into the structural determinants and their structure-activity relationship. An ensemble of conformations based on molecular docking was used for PHASE pharmacophore studies. The developed best-fitted pharmacophore model was validated by diverse chemotypes of Aurora A kinase inhibitors and was consistent with the structural requirements for the docked binding mechanism. Subsequently, the pharmacophore-based alignment was used to develop PHASE and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models. The best CoMSIA model showed good statistics (q (2 )= 0.567, r (2 )= 0.992), and the predictive ability of the model was validated using an external test set of 13 compounds giving a satisfactory prediction ([Formula: see text]). The 3D contour maps provided insight into the binding mechanism and highlighted key structural features that are essential to the inhibitory activity. Based on the PHASE and CoMSIA 3D-QSAR results, a set of novel Aurora A inhibitors were designed that showed excellent potencies.

  1. Comparative QSAR analysis of cyclo-oxygenase2 inhibiting drugs.

    PubMed

    Mohanapriya, Arumugam; Achuthan, Dayalan

    2012-01-01

    Cyclo-oxygenase 2 (COX2) inhibiting drugs were subjected to comparative quantitative structure activity relationship (QSAR) analysis with an attempt to derive and to understand the relationship between the biological activity and molecular descriptors by multiple regression analysis. The different drugs that inhibit cyclo-oxygenase 2 enzyme were compared instead of subjecting one drug and its derivatives to QSAR analysis. The study was conducted to look for the common structural features between the drugs which confer to a good biological activity. Based on the regression analysis the following descriptors were finalized as the components fitting best in the regression equations: Ss, SCBO, RBN, nN, SIC0, IC1, and H-055. These descriptors belong to constitution (Ss, SCBO, RBN, nN), information indices (SIC0, IC1) and atom centered fragments (H-055) category. Based on these descriptors QSAR models were generated and evaluated for best structure-activity correlation. The model generated from constitution and information indices descriptors corresponds to the essential structural features of the drugs and are found to have significant correlation with COX2 inhibiting activity. This study shall help in rational drug design and synthesis of new selective cyclo-oxygenase 2 inhibitors with predetermined affinity and activity.

  2. Rate constants of hydroxyl radical oxidation of polychlorinated biphenyls in the gas phase: A single-descriptor based QSAR and DFT study.

    PubMed

    Yang, Zhihui; Luo, Shuang; Wei, Zongsu; Ye, Tiantian; Spinney, Richard; Chen, Dong; Xiao, Ruiyang

    2016-04-01

    The second-order rate constants (k) of hydroxyl radical (·OH) with polychlorinated biphenyls (PCBs) in the gas phase are of scientific and regulatory importance for assessing their global distribution and fate in the atmosphere. Due to the limited number of measured k values, there is a need to model the k values for unknown PCBs congeners. In the present study, we developed a quantitative structure-activity relationship (QSAR) model with quantum chemical descriptors using a sequential approach, including correlation analysis, principal component analysis, multi-linear regression, validation, and estimation of applicability domain. The result indicates that the single descriptor, polarizability (α), plays an important role in determining the reactivity with a global standardized function of lnk = -0.054 × α ‒ 19.49 at 298 K. In order to validate the QSAR predicted k values and expand the current k value database for PCBs congeners, an independent method, density functional theory (DFT), was employed to calculate the kinetics and thermodynamics of the gas-phase ·OH oxidation of 2,4',5-trichlorobiphenyl (PCB31), 2,2',4,4'-tetrachlorobiphenyl (PCB47), 2,3,4,5,6-pentachlorobiphenyl (PCB116), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB169), and 2,3,3',4,5,5',6-heptachlorobiphenyl (PCB192) at 298 K at B3LYP/6-311++G**//B3LYP/6-31 + G** level of theory. The QSAR predicted and DFT calculated k values for ·OH oxidation of these PCB congeners exhibit excellent agreement with the experimental k values, indicating the robustness and predictive power of the single-descriptor based QSAR model we developed.

  3. The Danish Neuro-Oncology Registry: establishment, completeness and validity.

    PubMed

    Hansen, Steinbjørn; Nielsen, Jan; Laursen, René J; Rasmussen, Birthe Krogh; Nørgård, Bente Mertz; Gradel, Kim Oren; Guldberg, Rikke

    2016-08-30

    The Danish Neuro-Oncology Registry (DNOR) is a nationwide clinical cancer database that has prospectively registered data on patients with gliomas since January 2009. The purpose of this study was to describe the establishment of the DNOR and further to evaluate the database completeness of patient registration and validity of data. The completeness of the number of patients registered in the database was evaluated in the study period from January 2009 through December 2014 by comparing cases reported to the DNOR with the Danish National Patient Registry and the Danish Pathology Registry. The data validity of important clinical variables was evaluated by a random sample of 100 patients from the DNOR using the medical records as reference. A total of 2241 patients were registered in the DNOR by December 2014 with an overall patient completeness of 92 %, which increased during the study period (from 78 % in 2009 to 96 % in 2014). Medical records were available for all patients in the validity analyses. Most variables showed a high agreement proportion (56-100 %), with a fair to good chance-corrected agreement (k = 0.43-1.0). The completeness of patient registration was very high (92 %) and the validity of the most important patient data was good. The DNOR is a newly established national database, which is a reliable source for future scientific studies and clinical quality assessments among patients with gliomas.

  4. Comprehensive 3D-QSAR and binding mode of BACE-1 inhibitors using R-group search and molecular docking.

    PubMed

    Huang, Dandan; Liu, Yonglan; Shi, Bozhi; Li, Yueting; Wang, Guixue; Liang, Guizhao

    2013-09-01

    The β-enzyme (BACE), which takes an active part in the processing of amyloid precursor protein, thereby leads to the production of amyloid-β (Aβ) in the brain, is a major therapeutic target against Alzheimer's disease. The present study is aimed at studying 3D-QSAR of BACE-1 inhibitors and their binding mode. We build a 3D-QSAR model involving 99 training BACE-1 inhibitors based on Topomer CoMFA, and 26 molecules are employed to validate the external predictive power of the model obtained. The multiple correlation coefficients of fitting modeling, leave one out cross validation, and external validation are 0.966, 0.767 and 0.784, respectively. Topomer search is used as a tool for virtual screening in lead-like compounds of ZINC databases (2012); as a result, we successfully design 30 new molecules with higher activity than that of all training and test inhibitors. Besides, Surflex-dock is employed to explore binding mode of the inhibitors studied when acting with BACE-1 enzyme. The result shows that the inhibitors closely interact with the key sites related to ASP93, THR133, GLN134, ASP289, GLY291, THR292, THR293, ASN294, ARG296 and SER386 of BACE-1.

  5. Developing a QSAR model for hepatotoxicity screening of the active compounds in traditional Chinese medicines.

    PubMed

    Huang, Shan-Han; Tung, Chun-Wei; Fülöp, Ferenc; Li, Jih-Heng

    2015-04-01

    The perception that natural substances are deemed safe has made traditional Chinese medicine (TCM) popular in the treatment and prevention of disease globally. However, such an assumption is often misleading owing to a lack of scientific validation. To assess the safety of TCM, in silico screening provides major advantages over the classical laboratory approaches in terms of resource- and time-saving and full reproducibility. To screen the hepatotoxicity of the active compounds of TCMs, a quantitative structure-activity relationship (QSAR) model was firstly established by utilizing drugs from the Liver Toxicity Knowledge Base. These drugs were annotated with drug-induced liver injury information obtained from clinical trials and post-marketing surveillance. The performance of the model after nested 10-fold cross-validation was 79.1%, 91.2%, 53.8% for accuracy, sensitivity, and specificity, respectively. The external validation of 91 well-known ingredients of common herbal medicines yielded a high accuracy (87%). After screening the TCM Database@Taiwan, the world's largest TCM database, a total of 6853 (74.8%) ingredients were predicted to have hepatotoxic potential. The one-hundred chemical ingredients predicted to have the highest hepatotoxic potential by our model were further verified by published literatures. Our study indicated that this model can serve as a complementary tool to evaluate the safety of TCM.

  6. A MODE-OF-ACTION-BASED QSAR APPROACH TO IMPROVE UNDERSTANDING OF DEVELOPMENTAL TOXICITY

    EPA Science Inventory

    QSAR models of developmental toxicity (devtox) have met with limited regulatory acceptance due to the use of ill-defined endpoints, lack of biological interpretability, and poor model performance. More generally, the lack of biological inference of many QSAR models is often due t...

  7. A MODE-OF-ACTION-BASED QSAR APPROACH TO IMPROVE UNDERSTANDING OF DEVELOPMENTAL TOXICITY

    EPA Science Inventory

    QSAR models of developmental toxicity (devtox) have met with limited regulatory acceptance due to the use of ill-defined endpoints, lack of biological interpretability, and poor model performance. More generally, the lack of biological inference of many QSAR models is often due t...

  8. ITERATIVE PROCESS OF QSAR BUILDING AND STRATEGIC TESTING: PREDICTING ER BINDING AFFINITY

    EPA Science Inventory

    Basic principles of QSAR model development and application are discussed. The most difficult step in QSAR application for regulatory use may be determining when a model is sufficiently improved to provide predictions for a specified chemical domain of regulatory concern. The iter...

  9. QSAR modeling for anti-human African trypanosomiasis activity of substituted 2-Phenylimidazopyridines

    NASA Astrophysics Data System (ADS)

    Masand, Vijay H.; El-Sayed, Nahed N. E.; Mahajan, Devidas T.; Mercader, Andrew G.; Alafeefy, Ahmed M.; Shibi, I. G.

    2017-02-01

    In the present work, sixty substituted 2-Phenylimidazopyridines previously reported with potent anti-human African trypanosomiasis (HAT) activity were selected to build genetic algorithm (GA) based QSAR models to determine the structural features that have significant correlation with the activity. Multiple QSAR models were built using easily interpretable descriptors that are directly associated with the presence or the absence of a structural scaffold, or a specific atom. All the QSAR models have been thoroughly validated according to the OECD principles. All the QSAR models are statistically very robust (R2 = 0.80-0.87) with high external predictive ability (CCCex = 0.81-0.92). The QSAR analysis reveals that the HAT activity has good correlation with the presence of five membered rings in the molecule.

  10. Modeling robust QSAR 3: SOM-4D-QSAR with iterative variable elimination IVE-PLS: application to steroid, azo dye, and benzoic acid series.

    PubMed

    Bak, Andrzej; Polanski, Jaroslaw

    2007-01-01

    In the current paper we present a receptor-independent 4D-QSAR method based on self-organizing mapping (SOM-4D-QSAR) and in particular focus on its pharmacophore mapping ability. We use a novel stochastic procedure to verify the predictive ability of the method for a large population of 4D-QSAR models generated. This systematic study was conducted on a series of benzoic acids, azo dyes, and steroids that bind aromatase. We show that the 4D-QSAR method coupled with IVE-PLS provides a very stable and predictive modeling technique. The method enables us to identify the molecular motifs contributing the most to the fiber-dye affinity and the aromatase enzyme binding activity of the steroid. However, the method appeared much less effective for the benzoic acid series, in which the efficacy was limited by electronic effects strictly correlated to a single conformer.

  11. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

    NASA Astrophysics Data System (ADS)

    Jagiello, Karolina; Grzonkowska, Monika; Swirog, Marta; Ahmed, Lucky; Rasulev, Bakhtiyor; Avramopoulos, Aggelos; Papadopoulos, Manthos G.; Leszczynski, Jerzy; Puzyn, Tomasz

    2016-09-01

    In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure-Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure-Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide the recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.

  12. Hierarchical QSAR technology based on the Simplex representation of molecular structure

    NASA Astrophysics Data System (ADS)

    Kuz'min, V. E.; Artemenko, A. G.; Muratov, E. N.

    2008-06-01

    This article is about the hierarchical quantitative structure-activity relationship technology (HiT QSAR) based on the Simplex representation of molecular structure (SiRMS) and its application for different QSAR/QSP(property)R tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) to the QSAR problem by the series of enhanced models of molecular structure description [from one dimensional (1D) to four dimensional (4D)]. It is a system of permanently improved solutions. In the SiRMS approach, every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality and symmetry). The level of simplex descriptors detailing increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach reported here are the absence of "molecular alignment" problems, consideration of different physical-chemical properties of atoms (e.g. charge, lipophilicity, etc.), the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of the HiT QSAR approach is demonstrated by comparing it with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D-4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as predictive virtual screening tools and their ability to serve as the base of directed drug design was validated by subsequent synthetic and biological experiments, among others. The HiT QSAR is realized as a complex of computer programs known as HiT QSAR software that also includes a powerful statistical block and a number of useful utilities.

  13. The Danish Multiple Sclerosis Treatment Register

    PubMed Central

    Magyari, Melinda; Koch-Henriksen, Nils; Sørensen, Per Soelberg

    2016-01-01

    Aim of the database The Danish Multiple Sclerosis Treatment Register (DMSTR) serves as a clinical quality register, enabling the health authorities to monitor the quality of the disease-modifying treatment, and it is an important data source for epidemiological research. Study population The DMSTR includes all patients with multiple sclerosis who had been treated with disease-modifying drugs since 1996. At present, more than 8,400 patients have been registered in this database. Data are continuously entered online into a central database from all sites in Denmark at start and at regular visits. Main variables Include age, sex, onset year and year of the diagnosis, basic clinical information, and information about treatment, side effects, and relapses. Descriptive data Notification is done at treatment start, and thereafter at every scheduled clinical visit 3 months after treatment start, and thereafter every 6 months. The longitudinally collected information about the disease activity and side effects made it possible to investigate the clinical efficacy and adverse events of different disease-modifying therapies. Conclusion The database contributed to a certain harmonization of treatment procedures in Denmark and will continue to be a major factor in terms of quality in clinical praxis, research and monitoring of adverse events, and plays an important role in research. PMID:27822098

  14. The Danish Heart Registry

    PubMed Central

    Özcan, Cengiz; Juel, Knud; Flensted Lassen, Jens; von Kappelgaard, Lene Mia; Mortensen, Poul Erik; Gislason, Gunnar

    2016-01-01

    Aim The Danish Heart Registry (DHR) seeks to monitor nationwide activity and quality of invasive diagnostic and treatment strategies in patients with ischemic heart disease as well as valvular heart disease and to provide data for research. Study population All adult (≥15 years) patients undergoing coronary angiography (CAG), percutaneous coronary intervention (PCI), coronary artery bypass grafting, and heart valve surgery performed across all Danish hospitals were included. Main variables The DHR contains a subset of the data stored in the Eastern and Western Denmark Heart Registries (EDHR and WDHR). For each type of procedure, up to 70 variables are registered in the DHR. Since 2010, the data quality protocol encompasses fulfillment of web-based validation rules of daily-submitted records and yearly approval of the data by the EDHR and WDHR. Descriptive data The data collection on procedure has been complete for PCI and surgery since 2000, and for CAG as of 2006. From 2000 to 2014, the number of CAG, PCI, and surgical procedures changed by 231%, 193%, and 99%, respectively. Until the end of 2014, a total of 357,476 CAG, 131,309 PCI, and 60,831 surgical procedures had been performed, corresponding to 249,445, 100,609, and 55,539 first-time patients, respectively. The DHR generally has a high level of completeness (1–missing) of each procedure (>90%) when compared to the National Patient Registry. Variables important for assessing the quality of care have a high level of completeness for surgery since 2000, and for CAG and PCI since 2010. Conclusion The DHR contains valuable data on cardiac invasive procedures, which makes it an important national monitoring and quality system and at the same time serves as a platform for research projects in the cardiovascular field. PMID:27822091

  15. Isothiazolinones in commercial products at Danish workplaces.

    PubMed

    Friis, Ulrik Fischer; Menné, Torkil; Flyvholm, Mari-Ann; Bonde, Jens Peter Ellekilde; Lepoittevin, Jean-Pierre; Le Coz, Christophe J; Johansen, Jeanne Duus

    2014-08-01

    In recent years, a steep increase in the frequency of occupational contact allergy to isothiazolinones has been reported from several European countries. To examine the extent and occurrence of isothiazolinones in different types of product at Danish workplaces. Seven different isothiazolinones were identified in the Dictionary of Contact Allergens: Chemical Structures, Sources, and References from Kanerva's Occupational Dermatitis. By use of the chemical names and Chemical Abstracts Service numbers for these chemicals, information on products registered in the Danish Product Register Database (PROBAS) was obtained. All seven isothiazolinones were registered in PROBAS. The top three isothiazolinones registered were: benzisothiazolinone (BIT), registered in 985 products, methylisothiazolinone (MI), registered in 884 products, and methylchloroisothiazolinone (MCI)/MI, registered in 611 products. The concentration ranges were 0.01 ppm to 45% for BIT, 0.01 ppm to 10% for MI, and 0.01 ppm to 14.1% for MCI/MI. The most common product type was 'paint and varnish'; five of the seven isothiazolinones were registered in this type of product. Isothiazolinones are present in multiple products registered for use at workplaces, and may occur in high concentrations. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  16. QSAR Modeling of Rat Acute Toxicity by Oral Exposure

    PubMed Central

    Zhu, Hao; Martin, Todd M.; Ye, Lin; Sedykh, Alexander; Young, Douglas M.; Tropsha, Alexander

    2009-01-01

    Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. In this study, a comprehensive dataset of 7,385 compounds with their most conservative lethal dose (LD50) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire dataset was selected that included all 3,472 compounds used in the TOPKAT’s training set. The remaining 3,913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R2 of linear regression between actual and predicted LD50 values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R2 ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD50 for every compound using all 5 models. The consensus models afforded higher prediction accuracy for the external validation dataset with the higher coverage as compared to individual constituent models. The validated consensus LD50 models developed in this study can be used as reliable computational predictors of in vivo acute toxicity. PMID:19845371

  17. Development of a QSAR Model for Thyroperoxidase Inhbition ...

    EPA Pesticide Factsheets

    hyroid hormones (THs) are involved in multiple biological processes and are critical modulators of fetal development. Even moderate changes in maternal or fetal TH levels can produce irreversible neurological deficits in children, such as lower IQ. The enzyme thyroperoxidase (TPO) plays a key role in the synthesis of THs, and inhibition of TPO by xenobiotics results in decreased TH synthesis. Recently, a high-throughput screening assay for TPO inhibition (AUR-TPO) was developed and used to test the ToxCast Phase I and II chemicals. In the present study, we used the results from AUR-TPO to develop a Quantitative Structure-Activity Relationship (QSAR) model for TPO inhibition. The training set consisted of 898 discrete organic chemicals: 134 inhibitors and 764 non-inhibitors. A five times two-fold cross-validation of the model was performed, yielding a balanced accuracy of 78.7%. More recently, an additional ~800 chemicals were tested in the AUR-TPO assay. These data were used for a blinded external validation of the QSAR model, demonstrating a balanced accuracy of 85.7%. Overall, the cross- and external validation indicate a robust model with high predictive performance. Next, we used the QSAR model to predict 72,526 REACH pre-registered substances. The model could predict 49.5% (35,925) of the substances in its applicability domain and of these, 8,863 (24.7%) were predicted to be TPO inhibitors. Predictions from this screening can be used in a tiered approach to

  18. A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities

    SciTech Connect

    Valencia, Antoni; Prous, Josep; Mora, Oscar; Sadrieh, Nakissa; Valerio, Luis G.

    2013-12-15

    As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry℠, a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90% was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84 ± 1% sensitivity, 81 ± 1% specificity, 83 ± 1% concordance and 79 ± 1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity. - Highlights: • A new in silico QSAR model to predict Ames mutagenicity is described. • The model is extensively validated with chemicals from the FDA and the public domain. • Validation tests

  19. QSAR of heterocyclic antifungal agents by flip regression

    NASA Astrophysics Data System (ADS)

    Deeb, Omar; Clare, Brian W.

    2008-12-01

    QSAR analysis of a set of 96 heterocyclics with antifungal activity was performed. The results reveals that a pyridine ring is more favorable than benzene as the 6-membered ring, for high activity, but thiazole is unfavorable as the 5-membered ring relative to imidazole or oxazole. Methylene is the spacer leading to the highest activity. The descriptors used are indicator variables, which account for identity of substituent, lipophilicity and volume of substituent, and total polarizability. Unlike previously reported results for this data set, our fits do not exceed the limitations set by the nature of the data itself.

  20. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis.

    PubMed

    Zhu, Hao; Tropsha, Alexander; Fourches, Denis; Varnek, Alexandre; Papa, Ester; Gramatica, Paola; Oberg, Tomas; Dao, Phuong; Cherkasov, Artem; Tetko, Igor V

    2008-04-01

    Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs2). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI2) and from 0.38 to 0.83 (linear regression coefficient R absII2), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the

  1. Receptor-based 3D-QSAR in Drug Design: Methods and Applications in Kinase Studies.

    PubMed

    Fang, Cheng; Xiao, Zhiyan

    2016-01-01

    Receptor-based 3D-QSAR strategy represents a superior integration of structure-based drug design (SBDD) and three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. It combines the accurate prediction of ligand poses by the SBDD approach with the good predictability and interpretability of statistical models derived from the 3D-QSAR approach. Extensive efforts have been devoted to the development of receptor-based 3D-QSAR methods and two alternative approaches have been exploited. One associates with computing the binding interactions between a receptor and a ligand to generate structure-based descriptors for QSAR analyses. The other concerns the application of various docking protocols to generate optimal ligand poses so as to provide reliable molecular alignments for the conventional 3D-QSAR operations. This review highlights new concepts and methodologies recently developed in the field of receptorbased 3D-QSAR, and in particular, covers its application in kinase studies.

  2. A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities.

    PubMed

    Valencia, Antoni; Prous, Josep; Mora, Oscar; Sadrieh, Nakissa; Valerio, Luis G

    2013-12-15

    As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry(SM), a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90% was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84±1% sensitivity, 81±1% specificity, 83±1% concordance and 79±1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity.

  3. The Danish Multiple Sclerosis Treatment Register.

    PubMed

    Magyari, Melinda; Koch-Henriksen, Nils; Sørensen, Per Soelberg

    2016-01-01

    The Danish Multiple Sclerosis Treatment Register (DMSTR) serves as a clinical quality register, enabling the health authorities to monitor the quality of the disease-modifying treatment, and it is an important data source for epidemiological research. The DMSTR includes all patients with multiple sclerosis who had been treated with disease-modifying drugs since 1996. At present, more than 8,400 patients have been registered in this database. Data are continuously entered online into a central database from all sites in Denmark at start and at regular visits. Include age, sex, onset year and year of the diagnosis, basic clinical information, and information about treatment, side effects, and relapses. Notification is done at treatment start, and thereafter at every scheduled clinical visit 3 months after treatment start, and thereafter every 6 months. The longitudinally collected information about the disease activity and side effects made it possible to investigate the clinical efficacy and adverse events of different disease-modifying therapies. The database contributed to a certain harmonization of treatment procedures in Denmark and will continue to be a major factor in terms of quality in clinical praxis, research and monitoring of adverse events, and plays an important role in research.

  4. A 4D-QSAR study on anti-HIV HEPT analogues.

    PubMed

    Bak, Andrzej; Polanski, Jaroslaw

    2006-01-01

    We used the 4D-QSAR method coupled with the PLS analysis and uninformative variable elimination or its variants for the investigations of the antiviral activity of HEPT, a series of conformationally flexible molecules that bind HIV-1 reverse transcriptase. An analysis of several Hopfinger's and SOM-4D-QSAR models indicated that both methods yield comparable results. Generally, charge descriptors provide better modeling efficiency. We have shown that the method properly indicates the mode of interaction revealed by X-ray studies. It also allows us to calculate highly predictive QSAR models.

  5. Application of the modelling power approach to variable subset selection for GA-PLS QSAR models.

    PubMed

    Sagrado, Salvador; Cronin, Mark T D

    2008-02-25

    A previously developed function, the Modelling Power Plot, has been applied to QSARs developed using partial least squares (PLS) following variable selection from a genetic algorithm (GA). Modelling power (Mp) integrates the predictive and descriptive capabilities of a QSAR. With regard to QSARs for narcotic toxic potency, Mp was able to guide the optimal selection of variables using a GA. The results emphasise the importance of Mp to assess the success of the variable selection and that techniques such as PLS are more robust following variable selection.

  6. Compatibility of functional groups in K[sup ow]-based QSARs: Application to nitro compounds

    SciTech Connect

    Banerjee, S.; Williams, C.L. )

    1993-10-01

    Nitro compounds are particular difficult to handle in simple K[sup ow]-based QSARs, owing to differences in their lipid-phase activity coefficients. These differences can be corrected, in part, through inclusion of a term in octanol solubility. A procedure for identifying potentially incompatible groups in a given QSAR is suggested. The quality of a QSAR is best if the interactions of the functional groups involved with octanol fall within a narrow range. These interactions are easily calculated by the UNIFAC method.

  7. QSAR classification of estrogen receptor binders and pre-screening of potential pleiotropic EDCs.

    PubMed

    Li, J; Gramatica, P

    2010-10-01

    Endocrine disrupting chemicals (EDCs) are suspected of posing serious threats to human and wildlife health through a variety of mechanisms, these being mainly receptor-mediated modes of action. It is reported that some EDCs exhibit dual activities as estrogen receptor (ER) and androgen receptor (AR) binders. Indeed, such compounds can affect the normal endocrine system through a dual complex mechanism, so steps should be taken not only to identify them a priori from their chemical structure, but also to prioritize them for experimental tests in order to reduce and even forbid their usage. To date, very few EDCs with dual activities have been identified. The present research uses QSARs, to investigate what, so far, is the largest and most heterogeneous ER binder data set (combined METI and EDKB databases). New predictive classification models were derived using different modelling methods and a consensus approach, and these were used to virtually screen a large AR binder data set after strict validation. As a result, 46 AR antagonists were predicted from their chemical structure to also have potential ER binding activities, i.e. pleiotropic EDCs. In addition, 48 not yet recognized ER binders were in silico identified, which increases the number of potential EDCs that are substances of very high concern (SVHC) in REACH. Thus, the proposed screening models, based only on structure information, have the main aim to prioritize experimental tests for the highlighted compounds with potential estrogenic activities and also to design safer alternatives.

  8. Subjectless sentences in child Danish.

    PubMed

    Hamann, C; Plunkett, K

    1998-11-01

    Three alternative accounts of subject omission, pragmatic, processing and grammatical, are considered from the perspective of child Danish. Longitudinal data for two Danish children are analyzed for subject omission, finite and infinitival verb usage and discourse anchorage of sentence subjects (overt and missing). The data exhibit a well-defined phase of subject omission which coincides with a well-defined phase of infinitival verbal utterances. No evidence is found for input driven accounts of subject omission. Danish adults rarely omit subjects from utterance initial position. Neither is there any evidence to support the claim that omitted subjects are anchored in previous discourse. Evidence supporting a processing constraint explanation of missing subjects is equivocal. The pattern of subject omission, infinitival usage and third person pronoun and past tense usage points to a grammatical explanation of the phenomenon. However, current grammatical accounts have difficulty accommodating several aspects of the data reported. Contrary to structure building theories, the Danish children do not exhibit a phase of development where only uninflected verb forms are used. Danish children also omit subjects from finite utterances. Furthermore, the decline of subject omissions in finite utterances coincides with decline in usage of infinitival utterances. These findings challenge tense-based accounts of children's subject omission. Finally, Danish children exhibit an asymmetry in subject omission according to verb type; subjects are omitted from main verb utterances more frequently than from copula utterances. Given the language typology associated with Danish, this asymmetry is difficult to accommodate within truncation and tense or number-based accounts of subject omission. We suggest that a proper treatment of child subject omission will involve an integration of grammatical and discourse-based approaches.

  9. A QSAR Model for Thyroperoxidase Inhibition and Screening ...

    EPA Pesticide Factsheets

    Thyroid hormones (THs) are critical modulators of a wide range of biological processes from neurodevelopment to metabolism. Well regulated levels of THs are critical during development and even moderate changes in maternal or fetal TH levels produce irreversible neurological deficits in children. The enzyme thyroperoxidase (TPO) plays a key role in the synthesis of THs. Inhibition of TPO by xenobiotics leads to decreased TH synthesis and, depending on the degree of synthesis inhibition, may result in adverse developmental outcomes. Recently, a high-throughput screening assay for TPO inhibition (AUR-TPO) was developed and used to screen the ToxCast Phase I and II chemicals. In the present study, we used the results from the AUR-TPO screening to develop a Quantitative Structure-Activity Relationship (QSAR) model for TPO inhibition in Leadscope®. The training set consisted of 898 discrete organic chemicals: 134 positive and 764 negative for TPO inhibition. A 10 times two-fold 50% cross-validation of the model was performed, yielding a balanced accuracy of 78.7% within its defined applicability domain. More recently, an additional ~800 chemicals from the US EPA Endocrine Disruption Screening Program (EDSP21) were screened using the AUR-TPO assay. This data was used for external validation of the QSAR model, demonstrating a balanced accuracy of 85.7% within its applicability domain. Overall, the cross- and external validations indicate a model with a high predictiv

  10. Hologram QSAR studies of antiprotozoal activities of sesquiterpene lactones.

    PubMed

    Trossini, Gustavo H G; Maltarollo, Vinícius G; Schmidt, Thomas J

    2014-07-18

    Infectious diseases such as trypanosomiasis and leishmaniasis are considered neglected tropical diseases due the lack for many years of research and development into new drug treatments besides the high incidence of mortality and the lack of current safe and effective drug therapies. Natural products such as sesquiterpene lactones have shown activity against T. brucei and L. donovani, the parasites responsible for these neglected diseases. To evaluate structure activity relationships, HQSAR models were constructed to relate a series of 40 sesquiterpene lactones (STLs) with activity against T. brucei, T. cruzi, L. donovani and P. falciparum and also with their cytotoxicity. All constructed models showed good internal (leave-one-out q2 values ranging from 0.637 to 0.775) and external validation coefficients (r2test values ranging from 0.653 to 0.944). From HQSAR contribution maps, several differences between the most and least potent compounds were found. The fragment contribution of PLS-generated models confirmed the results of previous QSAR studies that the presence of α,β-unsatured carbonyl groups is fundamental to biological activity. QSAR models for the activity of these compounds against T. cruzi, L. donovani and P. falciparum are reported here for the first time. The constructed HQSAR models are suitable to predict the activity of untested STLs.

  11. QSARs for aromatic hydrocarbons at several trophic levels.

    PubMed

    Di Marzio, Walter; Saenz, Maria Elena

    2006-04-01

    Quantitative structure-activity relationships (QSARs) with aromatic hydrocarbons were obtained. Biological response was measured by acute toxicity of several aquatic trophic levels. The chemicals assayed were benzene, toluene, ethylbenzene, o-xylene, m-xylene, p-xylene, isopropylbenzene, n-propylbenzene, and butylbenzene. Acute toxicity tests were carried out with Scenedesmus quadricauda, as representative of primary producers; Daphnia spinulata, a zooplanctonic cladoceran; Hyalella curvispina, a benthic macroinvertebrate; and Bryconamericus iheringii, an omnivorous native fish. The EC50 or LC50 was calculated from analytical determinations of aromatic hydrocarbons. Nonlinear regression analysis between the logarithm of the octanol-water partition coefficient (log Kow) of each compounds and the toxicity end points was performed. QSARs were positively related to increases in log Kow at all trophic levels. Intertaxonomic differences were found in comparisons of algae with animals and of invertebrates with vertebrates. We observed that these differences were not significant with a log Kow higher than 3 for all organisms. Aromatic hydrocarbons with log Kow values of less than 3 showed different toxicity responses, with algae more resistant than fish and invertebrates. We concluded that this was a result of the narcotic mode of action related to liposolubility and the ability of the compound to reach its target site in the cell. The bioconcentration factor (BCF) achieved to start nonpolar narcosis fell almost 1 order of magnitude below the BCF expected from the log Kow. Predicted critical body residues for nonpolar narcosis ranged between 2 and 1 mM. Copyright 2006 Wiley Periodicals, Inc.

  12. Correlating metal ionic characteristics with biosorption capacity using QSAR model.

    PubMed

    Can, Chen; Jianlong, Wang

    2007-11-01

    The relationship between metal ionic characteristics and the maximum biosorption capacity (q(max)) was established using QSAR model based on the classification of metal ions (soft, hard and borderline ions). Ten kinds of metal ions (Ag(+), Cs(+), Zn(2+), Pb(2+), N(i2+), Cu(2+), Co(2+), Sr(2+), Cd(2+), Cr(3+)) were selected and the waste biomass of Saccharomyces cerevisiae obtained from a local brewery was used as biosorbent. Eighteen parameters of physiochemical characteristics of metal ions were selected and correlated with q(max). Classification of metal ions could improve the QSAR models and different characteristics were significant in correlating with q(max), such as polarizing power Z(2)/r or the first hydrolysis constant |logK(OH)| or ionization potential IP. X(m)(2)r seemed to be suitable for metal ions including soft ions, and Z(2)/r, |logK(OH)| and IP suitable for only soft ions or metal ions excluding soft ions. It provided a new way to predict the biosorptive capacity of metal ions.

  13. QSAR Modelling of CYP3A4 Inhibition as a Screening Tool in the Context of DrugDrug Interaction Studies.

    PubMed

    Hamon, Véronique; Horvath, Dragos; Gaudin, Cédric; Desrivot, Julie; Junges, Céline; Arrault, Alban; Bertrand, Marc; Vayer, Philippe

    2012-09-01

    Drugdrug interaction potential (DDI), especially cytochrome P450 (CYP) 3A4 inhibition potential, is one of the most important parameters to be optimized before preclinical and clinical pharmaceutical development as regard to the number of marketed drug metabolized mainly by this CYP and potentially co-administered with the future drug. The present study aims to develop in silico models for CYP3A4 inhibition prediction to help medicinal chemists during the discovery phase and even before the synthesis of new chemical entities (NCEs), focusing on NCEs devoid of any inhibitory potential toward this CYP. In order to find a relevant relationship between CYP3A4 inhibition and chemical features of the screened compounds, we applied a genetic-algorithm-based QSAR exploratory tool SQS (Stochastic QSAR Sampler) in combination with different description approaches comprising alignment-independent Volsurf descriptors, ISIDA fragments and Topological Fuzzy Pharmacophore Triplets. The experimental data used to build models were extracted from an in-house database. We derived a model with good prediction ability that was confirmed on both newly synthesized compound and public dataset retrieved from Pubchem database. This model is a promising efficient tool for filtering out potentially problematic compounds.

  14. Parameters for Pyrethroid Insecticide QSAR and PBPK/PD Models for Human Risk Assessment

    EPA Science Inventory

    This pyrethroid insecticide parameter review is an extension of our interest in developing quantitative structure–activity relationship–physiologically based pharmacokinetic/pharmacodynamic (QSAR-PBPK/PD) models for assessing health risks, which interest started with the organoph...

  15. Parameters for Pyrethroid Insecticide QSAR and PBPK/PD Models for Human Risk Assessment

    EPA Science Inventory

    This pyrethroid insecticide parameter review is an extension of our interest in developing quantitative structure–activity relationship–physiologically based pharmacokinetic/pharmacodynamic (QSAR-PBPK/PD) models for assessing health risks, which interest started with the organoph...

  16. Per aspera ad astra: application of Simplex QSAR approach in antiviral research.

    PubMed

    Muratov, Eugene N; Artemenko, Anatoly G; Varlamova, Ekaterina V; Polischuk, Pavel G; Lozitsky, Victor P; Fedchuk, Alla S; Lozitska, Regina L; Gridina, Tat'yana L; Koroleva, Ludmila S; Sil'nikov, Vladimir N; Galabov, Angel S; Makarov, Vadim A; Riabova, Olga B; Wutzler, Peter; Schmidtke, Michaela; Kuz'min, Victor E

    2010-07-01

    This review explores the application of the Simplex representation of molecular structure (SiRMS) QSAR approach in antiviral research. We provide an introduction to and description of SiRMS, its application in antiviral research and future directions of development of the Simplex approach and the whole QSAR field. In the Simplex approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality and symmetry). The main advantages of SiRMS are consideration of the different physical-chemical properties of atoms, high adequacy and good interpretability of models obtained and clear procedures for molecular design. The reliability of developed QSAR models as predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic and biological experiments. The SiRMS approach is realized as the complex of the computer program 'HiT QSAR', which is available on request.

  17. 3-D QSARS FOR RANKING AND PRIORITIZATION OF LARGE CHEMICAL DATASETS: AN EDC CASE STUDY

    EPA Science Inventory

    The COmmon REactivity Pattern (COREPA) approach is a three-dimensional structure activity (3-D QSAR) technique that permits identification and quantification of specific global and local steroelectronic characteristics associated with a chemical's biological activity. It goes bey...

  18. 3-D QSARS FOR RANKING AND PRIORITIZATION OF LARGE CHEMICAL DATASETS: AN EDC CASE STUDY

    EPA Science Inventory

    The COmmon REactivity Pattern (COREPA) approach is a three-dimensional structure activity (3-D QSAR) technique that permits identification and quantification of specific global and local steroelectronic characteristics associated with a chemical's biological activity. It goes bey...

  19. PREDICTING TOXICOLOGICAL ENDPOINTS OF CHEMICALS USING QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSARS)

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are being developed to predict the toxicological endpoints for untested chemicals similar in structure to chemicals that have known experimental toxicological data. Based on a very large number of predetermined descriptors, a...

  20. PREDICTING TOXICOLOGICAL ENDPOINTS OF CHEMICALS USING QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSARS)

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are being developed to predict the toxicological endpoints for untested chemicals similar in structure to chemicals that have known experimental toxicological data. Based on a very large number of predetermined descriptors, a...

  1. Tuning hERG out: Antitarget QSAR Models for Drug Development

    PubMed Central

    Braga, Rodolpho C.; Alves, Vinícius M.; Silva, Meryck F. B.; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander; Andrade, Carolina H.

    2015-01-01

    Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDA-required procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure–activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83–0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg). PMID:24805060

  2. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

    PubMed

    Eriksson, Lennart; Jaworska, Joanna; Worth, Andrew P; Cronin, Mark T D; McDowell, Robert M; Gramatica, Paola

    2003-08-01

    This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

  3. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

    PubMed Central

    Eriksson, Lennart; Jaworska, Joanna; Worth, Andrew P; Cronin, Mark T D; McDowell, Robert M; Gramatica, Paola

    2003-01-01

    This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation. PMID:12896860

  4. 3-D QSAR studies on histone deacetylase inhibitors. A GOLPE/GRID approach on different series of compounds.

    PubMed

    Ragno, Rino; Simeoni, Silvia; Valente, Sergio; Massa, Silvio; Mai, Antonello

    2006-01-01

    Docking simulation and three-dimensional quantitative structure-activity relationships (3D-QSARs) analyses were conducted on four series of HDAC inhibitors. The studies were performed using the GRID/GOLPE combination using structure-based alignment. Twelve 3-D QSAR models were derived and discussed. Compared to previous studies on similar inhibitors, the present 3-D QSAR investigation proved to be of higher statistical value, displaying for the best global model r2, q2, and cross-validated SDEP values of 0.94, 0.83, and 0.41, respectively. A comparison of the 3-D QSAR maps with the structural features of the binding site showed good correlation. The results of 3D-QSAR and docking studies validated each other and provided insight into the structural requirements for anti-HDAC activity. To our knowledge this is the first 3-D QSAR application on a broad molecular diversity training set of HDACIs.

  5. AutoQSAR: an automated machine learning tool for best-practice quantitative structure-activity relationship modeling.

    PubMed

    Dixon, Steven L; Duan, Jianxin; Smith, Ethan; Von Bargen, Christopher D; Sherman, Woody; Repasky, Matthew P

    2016-10-01

    We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.

  6. CheS-Mapper 2.0 for visual validation of (Q)SAR models

    PubMed Central

    2014-01-01

    Background Sound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data. Even though, a number of visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allow the investigation of model validation results are still lacking. Results We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. The approach applies the 3D viewer CheS-Mapper, an open-source application for the exploration of small molecules in virtual 3D space. The present work describes the new functionalities in CheS-Mapper 2.0, that facilitate the analysis of (Q)SAR information and allows the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. The approach is generic: It is model-independent and can handle physico-chemical and structural input features as well as quantitative and qualitative endpoints. Conclusions Visual validation with CheS-Mapper enables analyzing (Q)SAR information in the data and indicates how this information is employed by the (Q)SAR model. It reveals, if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org. Graphical abstract Comparing actual and predicted activity values with CheS-Mapper.

  7. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

    SciTech Connect

    Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh; Zhu, Hao; Afantitis, Antreas; Mouchlis, Varnavas D.; Melagraki, Georgia; Rusyn, Ivan; Tropsha, Alexander

    2013-10-01

    Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure–activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R{sup 2} = 0.71, STL R{sup 2} = 0.73). For ERβ binding affinity, MTL models were significantly more predictive (R{sup 2} = 0.53, p < 0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. - Highlights: • This is the largest curated dataset inclusive of ERα and β (the latter is unique). • New methodology that for the first time affords acceptable ERβ models. • A combination of QSAR and docking enables prediction of affinity and function.

  8. QSAR without arbitrary descriptors: the electron-conformational method.

    PubMed

    Bersuker, Isaac B

    2008-01-01

    The electron-conformational (EC) method in QSAR problems employs a unique (based on first principles) descriptor of molecular properties that incorporates the electronic structure and topology of the molecule and is presented in a digital-matrix form suitable for computer processing, the EC matrix of congruity (ECMC). Its matrix elements have clear-cut physical meanings of interatomic distances, bond orders, and atomic reactivity (interaction indices). By comparing these matrices for several active compounds of the training set a group of matrix elements is revealed that are common for these compounds within a minimum tolerance, the EC submatrix of activity (ECSA). The latter is the numerical pharmacophore for the level of activity and diversity of the tried compounds. The EC method was described in detail and used for pharmacophore identification and quantitative bioactivity prediction elsewhere. In this paper we give further general considerations of its uniqueness and emphasize its advantages as compared with traditional QSAR methods, outlining the following three novel points: (1) The unique, non-arbitrary descriptor employed in the EC method avoids the shortcomings of the arbitrary chosen descriptors and statistical estimation of their weight in the evaluation of the pharmacophore used in traditional QSAR methods. Arbitrary descriptors may be interdependent ("non-orthogonal") and their sets are necessarily incomplete, hence they may lead to chance correlations and artifacts. The EC pharmacophore is void of these failures, thus deemed to be absolutely reliable within the accuracy of the experimental data and the diversity of the molecules used in its evaluation; (2) The tolerances in the matrix elements of the ECSA play a special role reflecting the flexibilities of the pharmacophore parameters and the dependence of the activity on the latter quantitatively; they are obtained in a minimization procedure; by increasing the tolerances one can get pharmacophores

  9. QSAR of Tryptanthrin Analogs via Tunneling Barrier Height Imaging

    NASA Astrophysics Data System (ADS)

    Sriraman, Krishnan

    A new class of potential therapeutic agents, namely indolo[2,1-b]quinazolin-6,12- dione (tryptanthrin), and its analogues, have generated interest due to their broad spectrum of activity against a variety of pathogenic organisms. Little is known about the mechanism of action of tryptanthrins at the cellular and molecular levels. One method that has been employed to understand mechanisms of action and predict biological activities is quantitative structure activity relationship (QSAR). Since previous tryptanthrin studies could not clearly identify the pharmacophore, it was proposed to measure barrier height (BH) energy values for preparing a QSAR vs. IC50 values from the literature. The BH energy values were measured using barrier height spectroscopy which is performed using a scanning tunneling microscope (STM). Topography images (7 x 7 nm size) for tryptanthrin and three of its analogs namely 8-fluorotryptanthrin, 4-aza-8-fluorotryptanthrin, and 4-aza-8-chlorotryptanthrin were collected. Both HOMO and LUMO were collected at an applied bias of +/- 0.8 V and 0.1 nA. Excellent positive bias images (LUMO) of tryptanthrin were collected in which individual molecules and their lobes could be clearly recognized. A comparison with the density functional theory (DFT) calculated image of the LUMO resulted in an excellent match. An interesting outcome of the tryptanthrin LUMO imaging was the arrangement of molecules (parallel alignments) in the image which was explained by considering the intermolecular forces. Excellent BH images with sub-molecular resolution for 4-aza-8-fluorotryptanthrin were observed. BH values were calculated for each of the various lobes in the molecule from the BH image. Preliminary QSAR training sets were constructed using literature values of IC50 for W-2 and D-6 strains of Plasmodium falciparum as well as Leishmanai donovani versus average measured molecular barrier heights. The correlations were found to be fair for all the three pathogens. The

  10. Novel approach to evolutionary neural network based descriptor selection and QSAR model development

    NASA Astrophysics Data System (ADS)

    Debeljak, Željko; Marohnić, Viktor; Srečnik, Goran; Medić-Šarić, Marica

    2005-12-01

    Capability of evolutionary neural network (ENN) based QSAR approach to direct the descriptor selection process towards stable descriptor subset (DS) composition characterized by acceptable generalization, as well as the influence of description stability on QSAR model interpretation have been examined. In order to analyze the DS stability and QSAR model generalization properties multiple random dataset partitions into training and test set were made. Acceptability criteria proposed by Golbraikh et al. [J. Comput.-Aided Mol. Des., 17 (2003) 241] have been chosen for selection of highly predictive QSAR models from a set of all models produced by ENN for each dataset splitting. All QSAR models that pass Golbraikh's filter generated by ENN for each dataset partition were collected. Two final DS forming principles were compared. Standard principle is based on selection of descriptors characterized by highest frequencies among all descriptors that appear in the pool [J. Chem. Inf. Comput. Sci., 43 (2003) 949]. Search across the model pool for DS that are stable against multiple dataset subsampling i.e. universal DS solutions is the basis of novel approach. Based on described principles benzodiazepine QSAR has been proposed and evaluated against results reported by others in terms of final DS composition and model predictive performance.

  11. On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design.

    PubMed

    Roy, Kunal; Mitra, Indrani

    2011-07-01

    Quantitative structure-activity relationships (QSARs) have important applications in drug discovery research, environmental fate modeling, property prediction, etc. Validation has been recognized as a very important step for QSAR model development. As one of the important objectives of QSAR modeling is to predict activity/property/toxicity of new chemicals falling within the domain of applicability of the developed models and QSARs are being used for regulatory decisions, checking reliability of the models and confidence of their predictions is a very important aspect, which can be judged during the validation process. One prime application of a statistically significant QSAR model is virtual screening for molecules with improved potency based on the pharmacophoric features and the descriptors appearing in the QSAR model. Validated QSAR models may also be utilized for design of focused libraries which may be subsequently screened for the selection of hits. The present review focuses on various metrics used for validation of predictive QSAR models together with an overview of the application of QSAR models in the fields of virtual screening and focused library design for diverse series of compounds with citation of some recent examples.

  12. Use and perceived benefits and barriers of QSAR models for REACH: findings from a questionnaire to stakeholders

    PubMed Central

    2012-01-01

    The ORCHESTRA online questionnaire on “benefits and barriers to the use of QSAR methods” addressed the academic, consultant, regulatory and industry communities potentially interested by QSAR methods in the context of REACH. Replies from more than 60 stakeholders produced some insights on the actual application of QSAR methods, and how to improve their use. Respondents state in majority that they have used QSAR methods. All have some future plans to test or use QSAR methods in accordance with their stakeholder role. The stakeholder respondents cited a total of 28 models, methods or software that they have actually applied. The three most frequently cited suites, used moreover by all the stakeholder categories, are the OECD Toolbox, EPISuite and CAESAR; all are free tools. Results suggest that stereotyped assumptions about the barriers to application of QSAR may be incorrect. Economic costs (including potential delays) are not found to be a major barrier. And only one respondent “prefers” traditional, well-known and accepted toxicological assessment methods. Information and guidance may be the keys to reinforcing use of QSAR models. Regulators appear most interested in obtaining clear explanation of the basis of the models, to provide a solid basis for decisions. Scientists appear most interested in the exploration of the scientific capabilities of the QSAR approach. Industry shows interest in obtaining reassurance that appropriate uses of QSAR will be accepted by regulators. PMID:23244245

  13. IDENTIFICATION OF PUTATIVE ESTROGEN RECEPTOR-MEDIATED ENDOCRINE DISRUPTING CHEMICALS USING QSAR- AND STRUCTURE-BASED VIRTUAL SCREENING APPROACHES

    PubMed Central

    Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh; Zhu, Hao; Afantitis, Antreas; Mouchlis, Varnavas D.; Melagraki, Georgia; Rusyn, Ivan; Tropsha, Alexander

    2013-01-01

    Identification of Endocrine Disrupting Chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause Estrogen Receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous Quantitative Structure-Activity Relationships (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R2=0.71, STL R2=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R2=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. PMID:23707773

  14. Prediction of Halocarbon Toxicity from Structure: A Hierarchical QSAR Approach

    SciTech Connect

    Gute, B D; Balasubramanian, K; Geiss, K; Basak, S C

    2003-04-11

    Mathematical structural invariants and quantum theoretical descriptors have been used extensively in quantitative structure-activity relationships (QSARs) for the estimation of pharmaceutical activities, biological properties, physicochemical properties, and the toxicities of chemicals. Recently our research team has explored the relative importance of various levels of chemodescriptors, i.e., topostructural, topochemical, geometrical, and quantum theoretical descriptors, in property estimation. This study examines the contribution of chemodescriptors ranging from topostructural to quantum theoretic calculations up to the Gaussian STO-3G level in the prediction of the toxicity of a set of twenty halocarbons. We also report the results of experimental cell-level toxicity studies on these twenty halocarbons to validate our models.

  15. QSID Tool: a new three-dimensional QSAR environmental tool

    NASA Astrophysics Data System (ADS)

    Park, Dong Sun; Kim, Jae Min; Lee, Young Bok; Ahn, Chang Ho

    2008-12-01

    QSID Tool (Quantitative structure-activity relationship tool for Innovative Discovery) was developed to provide an easy-to-use, robust and high quality environmental tool for 3D QSAR. Predictive models developed with QSID Tool can accelerate the discovery of lead compounds by enabling researchers to formulate and test hypotheses for optimizing efficacy and increasing drug safety and bioavailability early in the process of drug discovery. QSID Tool was evaluated by comparison with SYBYL® using two different datasets derived from the inhibitors of Trypsin (Böhm et al., J Med Chem 42:458, 1999) and p38-MAPK (Liverton et al., J Med Chem 42:2180, 1999; Romeiro et al., J Comput Aided Mol Des 19:385, 2005; Romeiro et al., J Mol Model 12:855, 2006). The results suggest that QSID Tool is a useful model for the prediction of new analogue activities.

  16. Molecular design and QSARs/QSPRs with molecular descriptors family.

    PubMed

    Bolboacă, Sorana D; Jäntschi, Lorentz; Diudea, Mircea V

    2013-06-01

    The aim of the present paper is to present the methodology of the molecular descriptors family (MDF) as an integrative tool in molecular modeling and its abilities as a multivariate QSAR/QSPR modeling tool. An algorithm for extracting useful information from the topological and geometrical representation of chemical compounds was developed and integrated to calculate MDF members. The MDF methodology was implemented and the software is available online (http://l.academicdirect.org/Chemistry/SARs/MDF_SARs/). This integrative tool was developed in order to maximize performance, functionality, efficiency and portability. The MDF methodology is able to provide reliable and valid multiple linear regression models. Furthermore, in many cases, the MDF models were better than the published results in the literature in terms of correlation coefficients (statistically significant Steiger's Z test at a significance level of 5%) and/or in terms of values of information criteria and Kubinyi function. The MDF methodology developed and implemented as a platform for investigating and characterizing quantitative relationships between the chemical structure and the activity/property of active compounds was used on more than 50 study cases. In almost all cases, the methodology allowed obtaining of QSAR/QSPR models improved in explanatory power of structure-activity and structure-property relationships. The algorithms applied in the computation of geometric and topological descriptors (useful in modeling physicochemical or biological properties of molecules) and those used in searching for reliable and valid multiple linear regression models certain enrich the pool of low-cost low-time drug design tools.

  17. Comprehensive Network Map of ADME-Tox Databases.

    PubMed

    Canault, Baptiste; Bourg, Stéphane; Vayer, Philippe; Bonnet, Pascal

    2017-06-29

    In the last decade, many statistical-based approaches have been developed to improve poor pharmacokinetics (PK) and to reduce toxicity of lead compounds, which are one of the main causes of high failure rate in drug development. Predictive QSAR models are not always very efficient due to the low number of available biological data and the differences in the experimental protocols. Fortunately, the number of available databases continues to grow every year. However, it remains a challenge to determine the source and the quality of the original data. The main goal is to identify the relevant databases required to generate the most robust predictive models. In this study, an interactive network of databases was proposed to easily find online data sources related to ADME-Tox parameters data. In this map, relevant information regarding scope of application, data availability and data redundancy can be obtained for each data source. To illustrate the usage of data mining from the network, a dataset on plasma protein binding is selected based on various sources such as DrugBank, PubChem and ChEMBL databases. A total of 2,606 unique molecules with experimental values of PPB were extracted and can constitute a consistent dataset for QSAR modeling. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Biofuel Database

    National Institute of Standards and Technology Data Gateway

    Biofuel Database (Web, free access)   This database brings together structural, biological, and thermodynamic data for enzymes that are either in current use or are being considered for use in the production of biofuels.

  19. Database Administrator

    ERIC Educational Resources Information Center

    Moore, Pam

    2010-01-01

    The Internet and electronic commerce (e-commerce) generate lots of data. Data must be stored, organized, and managed. Database administrators, or DBAs, work with database software to find ways to do this. They identify user needs, set up computer databases, and test systems. They ensure that systems perform as they should and add people to the…

  20. FIREMON Database

    Treesearch

    John F. Caratti

    2006-01-01

    The FIREMON database software allows users to enter data, store, analyze, and summarize plot data, photos, and related documents. The FIREMON database software consists of a Java application and a Microsoft® Access database. The Java application provides the user interface with FIREMON data through data entry forms, data summary reports, and other data management tools...

  1. Database Administrator

    ERIC Educational Resources Information Center

    Moore, Pam

    2010-01-01

    The Internet and electronic commerce (e-commerce) generate lots of data. Data must be stored, organized, and managed. Database administrators, or DBAs, work with database software to find ways to do this. They identify user needs, set up computer databases, and test systems. They ensure that systems perform as they should and add people to the…

  2. A Comparison of QSAR Based Thermo and Water Solvation Property Prediction Tools and Experimental Data for Selected Traditional Chemical Warfare Agents and Simulants

    DTIC Science & Technology

    2014-07-01

    software packages were available, including EPI Suite, ACD Labs, ChemAxon’s Marvin, Vega , and ADF COSMO-RS. EPI Suite’s KOWWIN is a QSAR based model ... QSARs are a well established method of property prediction first demonstrated for petroleum components. QSARs are simple mathematical regression models ...Validation and error assessment of the QSAR model is performed with the remaining laboratory measurements that were not included in the original

  3. AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

    PubMed Central

    2011-01-01

    Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of

  4. Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology.

    PubMed

    Singh, Kunwar P; Gupta, Shikha; Kumar, Anuj; Mohan, Dinesh

    2014-05-19

    The research aims to develop multispecies quantitative structure-activity relationships (QSARs) modeling tools capable of predicting the acute toxicity of diverse chemicals in various Organization for Economic Co-operation and Development (OECD) recommended test species of different trophic levels for regulatory toxicology. Accordingly, the ensemble learning (EL) approach based classification and regression QSAR models, such as decision treeboost (DTB) and decision tree forest (DTF) implementing stochastic gradient boosting and bagging algorithms were developed using the algae (P. subcapitata) experimental toxicity data for chemicals. The EL-QSAR models were successfully applied to predict toxicities of wide groups of chemicals in other test species including algae (S. obliguue), daphnia, fish, and bacteria. Structural diversity of the selected chemicals and those of the end-point toxicity data of five different test species were tested using the Tanimoto similarity index and Kruskal-Wallis (K-W) statistics. Predictive and generalization abilities of the constructed QSAR models were compared using statistical parameters. The developed QSAR models (DTB and DTF) yielded a considerably high classification accuracy in complete data of model building (algae) species (97.82%, 99.01%) and ranged between 92.50%-94.26% and 92.14%-94.12% in four test species, respectively, whereas regression QSAR models (DTB and DTF) rendered high correlation (R(2)) between the measured and model predicted toxicity end-point values and low mean-squared error in model building (algae) species (0.918, 0.15; 0.905, 0.21) and ranged between 0.575 and 0.672, 0.18-0.51 and 0.605-0.689 and 0.20-0.45 in four different test species. The developed QSAR models exhibited good predictive and generalization abilities in different test species of varied trophic levels and can be used for predicting the toxicities of new chemicals for screening and prioritization of chemicals for regulation.

  5. Searching for anthranilic acid-based thumb pocket 2 HCV NS5B polymerase inhibitors through a combination of molecular docking, 3D-QSAR and virtual screening.

    PubMed

    Vrontaki, Eleni; Melagraki, Georgia; Mavromoustakos, Thomas; Afantitis, Antreas

    2016-01-01

    A combination of the following computational methods: (i) molecular docking, (ii) 3-D Quantitative Structure Activity Relationship Comparative Molecular Field Analysis (3D-QSAR CoMFA), (iii) similarity search and (iv) virtual screening using PubChem database was applied to identify new anthranilic acid-based inhibitors of hepatitis C virus (HCV) replication. A number of known inhibitors were initially docked into the "Thumb Pocket 2" allosteric site of the crystal structure of the enzyme HCV RNA-dependent RNA polymerase (NS5B GT1b). Then, the CoMFA fields were generated through a receptor-based alignment of docking poses to build a validated and stable 3D-QSAR CoMFA model. The proposed model can be first utilized to get insight into the molecular features that promote bioactivity, and then within a virtual screening procedure, it can be used to estimate the activity of novel potential bioactive compounds prior to their synthesis and biological tests.

  6. Pharmacophore modelling, atom-based 3D-QSAR generation and virtual screening of molecules projected for mPGES-1 inhibitory activity.

    PubMed

    Misra, S; Saini, M; Ojha, H; Sharma, D; Sharma, K

    2017-01-01

    COX-2 inhibitors exhibit anticancer effects in various cancer models but due to the adverse side effects associated with these inhibitors, targeting molecules downstream of COX-2 (such as mPGES-1) has been suggested. Even after calls for mPGES-1 inhibitor design, to date there are only a few published inhibitors targeting the enzyme and displaying anticancer activity. In the present study, we have deployed both ligand and structure-based drug design approaches to hunt novel drug-like candidates as mPGES-1 inhibitors. Fifty-four compounds with tested mPGES-1 inhibitory value were used to develop a model with four pharmacophoric features. 3D-QSAR studies were undertaken to check the robustness of the model. Statistical parameters such as r(2) = 0.9924, q(2) = 0.5761 and F test = 1139.7 indicated significant predictive ability of the proposed model. Our QSAR model exhibits sites where a hydrogen bond donor, hydrophobic group and the aromatic ring can be substituted so as to enhance the efficacy of the inhibitor. Furthermore, we used our validated pharmacophore model as a three-dimensional query to screen the FDA-approved Lopac database. Finally, five compounds were selected as potent mPGES-1 inhibitors on the basis of their docking energy and pharmacokinetic properties such as ADME and Lipinski rule of five.

  7. eCounterscreening: using QSAR predictions to prioritize testing for off-target activities and setting the balance between benefit and risk.

    PubMed

    Sheridan, Robert P; McMasters, Daniel R; Voigt, Johannes H; Wildey, Mary Jo

    2015-02-23

    During drug development, compounds are tested against counterscreens, a panel of off-target activities that would be undesirable for a drug to have. Testing every compound against every counterscreen is generally too costly in terms of time and money, and we need to find a rational way of prioritizing counterscreen testing. Here we present the eCounterscreening paradigm, wherein predictions from QSAR models for counterscreen activity are used to generate a recommendation as to whether a specific compound in a specific project should be tested against a specific counterscreen. The rules behind the recommendations, which can be summarized in a risk-benefit plot specific for a counterscreen/project combination, are based on a previously assembled database of prospective QSAR predictions. The recommendations require two user-defined cutoffs: the level of activity in a specific counterscreen that is considered undesirable and the level of risk the chemist is willing to accept that an undesired counterscreen activity will go undetected. We demonstrate in a simulated prospective experiment that eCounterscreening can be used to postpone a large fraction of counterscreen testing and still have an acceptably low risk of undetected counterscreen activity.

  8. Evaluation of the EVA descriptor for QSAR studies: 3. The use of a genetic algorithm to search for models with enhanced predictive properties (EVA_GA)

    NASA Astrophysics Data System (ADS)

    Turner, David B.; Willett, Peter

    2000-01-01

    The EVA structural descriptor, based upon calculated fundamental molecular vibrational frequencies, has proved to be an effective descriptor for both QSAR and database similarity calculations. The descriptor is sensitive to 3D structure but has an advantage over field-based 3D-QSAR methods inasmuch as structural superposition is not required. The original technique involves a standardisation method wherein uniform Gaussians of fixed standard deviation (σ) are used to smear out frequencies projected onto a linear scale. The smearing function permits the overlap of proximal frequencies and thence the extraction of a fixed dimensional descriptor regardless of the number and precise values of the frequencies. It is proposed here that there exist optimal localised values of σ in different spectral regions; that is, the overlap of frequencies using uniform Gaussians may, at certain points in the spectrum, either be insufficient to pick up relationships where they exist or mix up information to such an extent that significant correlations are obscured by noise. A genetic algorithm is used to search for optimal localised σ values using crossvalidated PLS regression scores as the fitness score to be optimised. The resultant models were then validated against a previously unseen test set of compounds and through data scrambling. The performance of EVA_GA is compared to that of EVA and analogous CoMFA studies; in the latter case a brief evaluation is made of the effect of grid resolution upon the stability of CoMFA PLS scores particularly in relation to test set predictions.

  9. In silico screening for identification of novel β-1,3-glucan synthase inhibitors using pharmacophore and 3D-QSAR methodologies.

    PubMed

    Meetei, Potshangbam Angamba; Rathore, R S; Prabhu, N Prakash; Vindal, Vaibhav

    2016-01-01

    The enzyme β-1,3-glucan synthase, which catalyzes the synthesis of β-1,3-glucan, an essential and unique structural component of the fungal cell wall, has been considered as a promising target for the development of less toxic anti-fungal agents. In this study, a robust pharmacophore model was developed and structure activity relationship analysis of 42 pyridazinone derivatives as β-1,3-glucan synthase inhibitors were carried out. A five-point pharmacophore model, consisting of two aromatic rings (R) and three hydrogen bond acceptors (A) was generated. Pharmacophore based 3D-QSAR model was developed for the same reported data sets. The generated 3D-QSAR model yielded a significant correlation coefficient value (R (2) = 0.954) along with good predictive power confirmed by the high value of cross-validated correlation coefficient (Q (2) = 0.827). Further, the pharmacophore model was employed as a 3D search query to screen small molecules database retrieved from ZINC to select new scaffolds. Finally, ADME studies revealed the pharmacokinetic efficiency of these compounds.

  10. A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors.

    PubMed

    Cao, G P; Arooj, M; Thangapandian, S; Park, C; Arulalapperumal, V; Kim, Y; Kwon, Y J; Kim, H H; Suh, J K; Lee, K W

    2015-01-01

    Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two quantitative structure-activity relationship (QSAR) classification models were developed using K nearest neighbours (KNN) and neighbourhood classifier (NEC). Molecular descriptors were calculated for the data set and database compounds using ADRIANA.Code of Molecular Networks. Principal components analysis (PCA) was used to select the descriptors. The developed models were validated by leave-one-out cross validation (LOO CV). The performances of the developed models were evaluated with an external test set. Highly predictive models were used for database virtual screening. Furthermore, hit compounds were subsequently subject to molecular docking. Five hits were obtained based on consensus scoring function and binding affinity as potential HDAC8 inhibitors. Finally, HDAC8 structures in complex with five hits were also subjected to 5 ns molecular dynamics (MD) simulations to evaluate the complex structure stability. To the best of our knowledge, the NEC classification model used in this study is the first application of NEC to virtual screening for drug discovery.

  11. (Q)SAR assessments of potentially mutagenic impurities: a regulatory perspective on the utility of expert knowledge and data submission.

    PubMed

    Powley, Mark W

    2015-03-01

    (Quantitative) structure activity relationship [(Q)SAR] modeling is the primary tool used to evaluate the mutagenic potential associated with drug impurities. General recommendations regarding the use of (Q)SAR in regulatory decision making have recently been provided in the ICH M7 guideline. Although (Q)SAR alone is capable of achieving reasonable sensitivity and specificity, reliance on a simple positive or negative prediction can be problematic. The key to improving (Q)SAR performance is to integrate supporting information, also referred to as expert knowledge, into the final conclusion. In the regulatory context, expert knowledge is intended to (1) maximize confidence in a (Q)SAR prediction, (2) provide rationale to supersede a positive or negative (Q)SAR prediction, or (3) provide a basis for assessing mutagenicity in absence of a (Q)SAR prediction. Expert knowledge is subjective and is associated with great variability in regards to content and quality. However, it is still a critical component of impurity evaluations and its utility is acknowledged in the ICH M7 guideline. The current paper discusses the use of expert knowledge to support regulatory decision making, describes case studies, and provides recommendations for reporting data from (Q)SAR evaluations. Published by Elsevier Inc.

  12. An Early Danish Computer Game

    NASA Astrophysics Data System (ADS)

    Jørgensen, Anker Helms

    This paper reports on the development of Nimbi, which is an early computer game implemented at the Danish Computer Company Regnecentralen in 1962-63. Nimbi is a variant of the ancient game Nim. The paper traces the primary origins of the development of Nimbi. These include a mathematical analysis from 1901 of Nim that “killed the game” as the outcome could be predicted quite easily; the desire of the Danish inventor Piet Hein to make a game that eluded such analyses; and the desire of Piet Hein to have computers play games against humans. The development of Nimbi was successful in spite of considerable technical obstacles. However, it seems that the game was not used for publicizing the capabilities of computers - at least not widely - as was the case with earlier Nim implementations, such as the British Nim-playing computer Nimrod in 1951.

  13. Exploring conformational search protocols for ligand-based virtual screening and 3-D QSAR modeling

    NASA Astrophysics Data System (ADS)

    Cappel, Daniel; Dixon, Steven L.; Sherman, Woody; Duan, Jianxin

    2015-02-01

    3-D ligand conformations are required for most ligand-based drug design methods, such as pharmacophore modeling, shape-based screening, and 3-D QSAR model building. Many studies of conformational search methods have focused on the reproduction of crystal structures (i.e. bioactive conformations); however, for ligand-based modeling the key question is how to generate a ligand alignment that produces the best results for a given query molecule. In this work, we study different conformation generation modes of ConfGen and the impact on virtual screening (Shape Screening and e-Pharmacophore) and QSAR predictions (atom-based and field-based). In addition, we develop a new search method, called common scaffold alignment, that automatically detects the maximum common scaffold between each screening molecule and the query to ensure identical coordinates of the common core, thereby minimizing the noise introduced by analogous parts of the molecules. In general, we find that virtual screening results are relatively insensitive to the conformational search protocol; hence, a conformational search method that generates fewer conformations could be considered "better" because it is more computationally efficient for screening. However, for 3-D QSAR modeling we find that more thorough conformational sampling tends to produce better QSAR predictions. In addition, significant improvements in QSAR predictions are obtained with the common scaffold alignment protocol developed in this work, which focuses conformational sampling on parts of the molecules that are not part of the common scaffold.

  14. Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors.

    PubMed

    Živković, Jelena V; Trutić, Nataša V; Veselinović, Jovana B; Nikolić, Goran M; Veselinović, Aleksandar M

    2015-09-01

    The Monte Carlo method was used for QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors. The first QSAR model was developed for a series of 74 3-anilino-4-arylmaleimide derivatives. The second QSAR model was developed for a series of 177 maleimide derivatives. QSAR models were calculated with the representation of the molecular structure by the simplified molecular input-line entry system. Two splits have been examined: one split into the training and test set for the first QSAR model, and one split into the training, test and validation set for the second. The statistical quality of the developed model is very good. The calculated model for 3-anilino-4-arylmaleimide derivatives had following statistical parameters: r(2)=0.8617 for the training set; r(2)=0.8659, and r(m)(2)=0.7361 for the test set. The calculated model for maleimide derivatives had following statistical parameters: r(2)=0.9435, for the training, r(2)=0.9262 and r(m)(2)=0.8199 for the test and r(2)=0.8418, r(av)(m)(2)=0.7469 and ∆r(m)(2)=0.1476 for the validation set. Structural indicators considered as molecular fragments responsible for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new potential glycogen synthase kinase-3β inhibitors has been presented by using defined structural alerts.

  15. Imidazolium Ionic Liquids as Potential Anti-Candida Inhibitors: QSAR Modeling and Experimental Studies.

    PubMed

    Hodyna, Diana; Kovalishyn, Vasyl; Rogalsky, Sergiy; Blagodatnyi, Volodymyr; Metelytsia, Larisa

    2016-01-01

    Quantitative structure-activity relationships (QSAR) of imidazolium ionic liquids (ILs) as inhibitors of C. albicans collection strains (IOA-109, KCTC 1940, ATCC 10231) have been studied. Predictive QSAR models were built using different descriptor sets for a set of 88 ionic liquids with known minimum inhibitory concentrations (MIC) against C. albicans. We applied the state-of-the-art QSAR methodologies such as WEKA Random Forest (RF) as a binary classifier, Associative Neural Networks (ASNN) and k-Nearest Neighbors (k-NN) to build continuum non-linear regression models. The obtained models were validated using a 5-fold cross-validation approach and resulted in the prediction accuracies of 80% ± 5.0 for the classification models and q2 = 0.73-0.87 for the non-linear regression models. Biological testing of newly synthesized 1,3-dialkylimidazolium ionic liquids with predicted activity was performed by disco-diffusion method against C. albicans ATCC 10231 M885 strain and clinical isolates C. albicans, C. krusei and C. glabrata strains. The high percentage of coincidence between the QSAR predictions and the experimental results confirmed the high predictive power of the developed QSAR models within the applicability domain of new imidazolium ionic liquids.

  16. The receptor-dependent LQTA-QSAR: application to a set of trypanothione reductase inhibitors

    NASA Astrophysics Data System (ADS)

    Barbosa, Euzébio G.; Pasqualoto, Kerly Fernanda M.; Ferreira, Márcia M. C.

    2012-09-01

    A new Receptor- Dependent LQTA- QSAR approach, RD- LQTA- QSAR, is proposed as a new 4D-QSAR method. It is an evolution of receptor independent LQTA-QSAR. This approach uses the free GROMACS package to carry out molecular dynamics simulations and generates a conformational ensemble profile for each compound. Such an ensemble is used to build molecular interaction field-based QSAR models, as in CoMFA. To show the potential of this methodology, a set of 38 phenothiazine derivatives that are specific competitive T. cruzi trypanothione reductase inhibitors, was chosen. Using a combination of molecular docking and molecular dynamics simulations, the binding mode of the phenotiazine derivatives was evaluated in a simulated induced fit approach. The ligands alignments were performed using both ligand and binding site atoms, enabling unbiased alignment. The models obtained were extensively validated by leave- N-out cross-validation and y-randomization techniques to test for their robustness and absence of chance correlation. The final model presented Q 2 LOO of 0.87 and R² of 0.92 and a suitable external prediction of Q_{ext}2 = 0.78. The adapted binding site obtained is useful to perform virtual screening and ligand structure-based design and the descriptors in the final model can aid in the design new inhibitors.

  17. Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.

    PubMed

    Winkler, David A; Le, Tu C

    2017-01-01

    Neural networks have generated valuable Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems have been overcome by modern mathematical techniques. QSAR studies have almost always used so-called "shallow" neural networks in which there is a single hidden layer between the input and output layers. Recently, a new and potentially paradigm-shifting type of neural network based on Deep Learning has appeared. Deep learning methods have generated impressive improvements in image and voice recognition, and are now being applied to QSAR and QSAR modelling. This paper describes the differences in approach between deep and shallow neural networks, compares their abilities to predict the properties of test sets for 15 large drug data sets (the kaggle set), discusses the results in terms of the Universal Approximation theorem for neural networks, and describes how DNN may ameliorate or remove troublesome "activity cliffs" in QSAR data sets. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Aptamer Database

    PubMed Central

    Lee, Jennifer F.; Hesselberth, Jay R.; Meyers, Lauren Ancel; Ellington, Andrew D.

    2004-01-01

    The aptamer database is designed to contain comprehensive sequence information on aptamers and unnatural ribozymes that have been generated by in vitro selection methods. Such data are not normally collected in ‘natural’ sequence databases, such as GenBank. Besides serving as a storehouse of sequences that may have diagnostic or therapeutic utility, the database serves as a valuable resource for theoretical biologists who describe and explore fitness landscapes. The database is updated monthly and is publicly available at http://aptamer.icmb.utexas.edu/. PMID:14681367

  19. A three-tier QSAR modeling strategy for estimating eye irritation potential of diverse chemicals in rabbit for regulatory purposes.

    PubMed

    Basant, Nikita; Gupta, Shikha; Singh, Kunwar P

    2016-06-01

    Experimental determination of the eye irritation potential (EIP) of chemicals is not only tedious, time and resource intensive, it involves cruelty to test animals. In this study, we have established a three-tier QSAR modeling strategy for estimating the EIP of chemicals for the use of pharmaceutical industry and regulatory agencies. Accordingly, a qualitative (binary classification: irritating, non-irritating), semi-quantitative (four-category classification), and quantitative (regression) QSAR models employing the SDT, DTF, and DTB methods were developed for predicting the EIP of chemicals in accordance with the OECD guidelines. Structural features of chemicals responsible for eye irritation were extracted and used in QSAR analysis. The external predictive power of the developed QSAR models were evaluated through the internal and external validation procedures recommended in QSAR literature. In test data, the two and four category classification QSAR models (DTF, DTB) rendered accuracy of >93%, while the regression QSAR models (DTF, DTB) yielded correlation (R(2)) of >0.92 between the measured and predicted EIPs. Values of various statistical validation coefficients derived for the test data were above their respective threshold limits (except rm(2) in DTF), thus put a high confidence in this analysis. The applicability domain of the constructed QSAR models were defined using the descriptors range and leverage approaches. The QSAR models in this study performed better than any of the previous studies. The results suggest that the developed QSAR models can reliably predict the EIP of diverse chemicals and can be useful tools for screening of candidate molecules in the drug development process. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Free energy force field (FEFF) 3D-QSAR analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors.

    PubMed

    Santos-Filho, O A; Mishra, R K; Hopfinger, A J

    2001-09-01

    Free energy force field (FEFF) 3D-QSAR analysis was used to construct ligand-receptor binding models for a set of 18 structurally diverse antifolates including pyrimethamine, cycloguanil, methotrexate, aminopterin and trimethoprim, and 13 pyrrolo[2,3-d]pyrimidines. The molecular target ('receptor') used was a 3D-homology model of a specific mutant type of Plasmodium falciparum (Pf) dihydrofolate reductase (DHFR). The dependent variable of the 3D-QSAR models is the IC50 inhibition constant for the specific mutant type of PfDHFR. The independent variables of the 3D-QSAR models (the descriptors) are scaled energy terms of a modified first-generation AMBER force field combined with a hydration shell aqueous solvation model and a collection of 2D-QSAR descriptors often used in QSAR studies. Multiple temperature molecular dynamics simulation (MDS) and the genetic function approximation (GFA) were employed using partial least square (PLS) and multidimensional linear regressions as the fitting functions to develop FEFF 3D-QSAR models for the binding process. The significant FEFF energy terms in the best 3D-QSAR models include energy contributions of the direct ligand-receptor interaction. Some changes in conformational energy terms of the ligand due to binding to the enzyme are also found to be important descriptors. The FEFF 3D-QSAR models indicate some structural features perhaps relevant to the mechanism of resistance of the PfDHFR to current antimalarials. The FEFF 3D-QSAR models are also compared to receptor-independent (RI) 4D-QSAR models developed in an earlier study and subsequently refined using recently developed generalized alignment rules.

  1. Free energy force field (FEFF) 3D-QSAR analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors

    NASA Astrophysics Data System (ADS)

    Santos-Filho, Osvaldo A.; Mishra, Rama K.; Hopfinger, A. J.

    2001-09-01

    Free energy force field (FEFF) 3D-QSAR analysis was used to construct ligand-receptor binding models for a set of 18 structurally diverse antifolates including pyrimethamine, cycloguanil, methotrexate, aminopterin and trimethoprim, and 13 pyrrolo[2,3-d]pyrimidines. The molecular target (`receptor') used was a 3D-homology model of a specific mutant type of Plasmodium falciparum (Pf) dihydrofolate reductase (DHFR). The dependent variable of the 3D-QSAR models is the IC50 inhibition constant for the specific mutant type of PfDHFR. The independent variables of the 3D-QSAR models (the descriptors) are scaled energy terms of a modified first-generation AMBER force field combined with a hydration shell aqueous solvation model and a collection of 2D-QSAR descriptors often used in QSAR studies. Multiple temperature molecular dynamics simulation (MDS) and the genetic function approximation (GFA) were employed using partial least square (PLS) and multidimensional linear regressions as the fitting functions to develop FEFF 3D-QSAR models for the binding process. The significant FEFF energy terms in the best 3D-QSAR models include energy contributions of the direct ligand-receptor interaction. Some changes in conformational energy terms of the ligand due to binding to the enzyme are also found to be important descriptors. The FEFF 3D-QSAR models indicate some structural features perhaps relevant to the mechanism of resistance of the PfDHFR to current antimalarials. The FEFF 3D-QSAR models are also compared to receptor-independent (RI) 4D-QSAR models developed in an earlier study and subsequently refined using recently developed generalized alignment rules.

  2. Recent advances in QSAR-based identification and design of anti-tubercular agents.

    PubMed

    Nidhi; Siddiqi, Mohammad Imran

    2014-01-01

    Increasing worldwide incidence and the advent of multi drug resistant and extensively drug resistant tuberculosis raise the need of new drugs for the treatment of tuberculosis soon. To meet the required pace QSAR-based rational approaches may prove fruitful as they render rapid and cost-efficient design and optimization of new drug candidates. This review presents a comprehensive overview of QSAR studies reported for newer anti-tubercular agents including nitroimidazoles, fluoroquinolones, quinoxalines, carboxamides and other classes of molecules. The article includes review of 2D and 3D-QSAR approaches and the recent trend of integration of these methods with virtual screening using 3D pharmacophore and molecular docking approaches for the identification and design of novel anti-tubercular agents.

  3. Antiprotozoal Nitazoxanide Derivatives: Synthesis, Bioassays and QSAR Study Combined with Docking for Mechanistic Insight

    PubMed Central

    Scior, Thomas; Lozano-Aponte, Jorge; Ajmani, Subhash; Hernández-Montero, Eduardo; Chávez-Silva, Fabiola; Hernández-Núñez, Emanuel; Moo-Puc, Rosa; Fraguela-Collar, Andres; Navarrete-Vázquez, Gabriel

    2015-01-01

    In view of the serious health problems concerning infectious diseases in heavily populated areas, we followed the strategy of lead compound diversification to evaluate the near-by chemical space for new organic compounds. To this end, twenty derivatives of nitazoxanide (NTZ) were synthesized and tested for activity against Entamoeba histolytica parasites. To ensure drug-likeliness and activity relatedness of the new compounds, the synthetic work was assisted by a quantitative structure-activity relationships study (QSAR). Many of the inherent downsides – well-known to QSAR practitioners – we circumvented thanks to workarounds which we proposed in prior QSAR publication. To gain further mechanistic insight on a molecular level, ligand-enzyme docking simulations were carried out since NTZ is known to inhibit the protozoal pyruvate ferredoxin oxidoreductase (PFOR) enzyme as its biomolecular target. PMID:25872791

  4. Substituted naphthalen-1-yl-acetic acid hydrazides: synthesis, antimicrobial evaluation and QSAR analysis.

    PubMed

    Narang, Rakesh; Narasimhan, Balasubramanian; Sharma, Sunil; De Clercq, Erik; Pannecouque, Christophe; Balzarini, Jan

    2013-03-01

    A series of naphthalen-1-yl-acetic acid benzylidene/(1-phenyl-ethylidene)-hydrazides (1-36) was synthesized and tested, in vitro, for antiviral, antibacterial and antifungal activities. The antibacterial and antifungal screening results indicated that compounds having o-bromo, methoxy and hydroxy substitutents were the most active ones. The results of antiviral evaluation showed that none of the synthesized derivatives inhibited the viral infection at subtoxic concentrations. QSAR investigations revealed that the multi-target QSAR model was more effective in describing the antimicrobial activity than the one-target QSAR models. Further, it revealed the importance of the partition coefficient (log P) followed by energies of the highest occupied molecular orbital (HOMO) and topological parameters, molecular connectivity indices (1χ, 3χ and 3χv) in describing the antimicrobial activity of substituted hydrazides.

  5. Maize databases

    USDA-ARS?s Scientific Manuscript database

    This chapter is a succinct overview of maize data held in the species-specific database MaizeGDB (the Maize Genomics and Genetics Database), and selected multi-species data repositories, such as Gramene/Ensembl Plants, Phytozome, UniProt and the National Center for Biotechnology Information (NCBI), ...

  6. Database Manager

    ERIC Educational Resources Information Center

    Martin, Andrew

    2010-01-01

    It is normal practice today for organizations to store large quantities of records of related information as computer-based files or databases. Purposeful information is retrieved by performing queries on the data sets. The purpose of DATABASE MANAGER is to communicate to students the method by which the computer performs these queries. This…

  7. Image Databases.

    ERIC Educational Resources Information Center

    Pettersson, Rune

    Different kinds of pictorial databases are described with respect to aims, user groups, search possibilities, storage, and distribution. Some specific examples are given for databases used for the following purposes: (1) labor markets for artists; (2) document management; (3) telling a story; (4) preservation (archives and museums); (5) research;…

  8. Database Manager

    ERIC Educational Resources Information Center

    Martin, Andrew

    2010-01-01

    It is normal practice today for organizations to store large quantities of records of related information as computer-based files or databases. Purposeful information is retrieved by performing queries on the data sets. The purpose of DATABASE MANAGER is to communicate to students the method by which the computer performs these queries. This…

  9. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors.

    PubMed

    Briard, Jennie G; Fernandez, Michael; De Luna, Phil; Woo, Tom K; Ben, Robert N

    2016-05-24

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

  10. Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity.

    PubMed

    Oksel, Ceyda; Ma, Cai Y; Liu, Jing J; Wilkins, Terry; Wang, Xue Z

    2017-01-01

    Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.

  11. Prediction of PAH mutagenicity in human cells by QSAR classification.

    PubMed

    Papa, E; Pilutti, P; Gramatica, P

    2008-01-01

    Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants of high environmental concern. The experimental data of a mutagenicity test on human B-lymphoblastoid cells (alternative to the Ames bacterial test) for a set of 70 oxo-, nitro- and unsubstituted PAHs, detected in particulate matter (PM), were modelled by Quantitative Structure-Activity Relationships (QSAR) classification methods (k-NN, k-Nearest Neighbour, and CART, Classification and Regression Tree) based on different theoretical molecular descriptors selected by Genetic Algorithms. The best models were validated for predictivity both externally and internally. For external validation, Self Organizing Maps (SOM) were applied to split the original data set. The best models, developed on the training set alone, show good predictive performance also on the prediction set chemicals (sensitivity 69.2-87.1%, specificity 62.5-87.5%). The classification of PAHs according to their mutagenicity, based only on a few theoretical molecular descriptors, allows a preliminary assessment of the human health risk, and the prioritisation of these compounds.

  12. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors

    NASA Astrophysics Data System (ADS)

    Briard, Jennie G.; Fernandez, Michael; de Luna, Phil; Woo, Tom. K.; Ben, Robert N.

    2016-05-01

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

  13. Ecotoxicity and QSAR studies of glycerol ethers in Daphnia magna.

    PubMed

    Perales, Eduardo; García, Jose Ignacio; Pires, Elisabet; Aldea, Luis; Lomba, Laura; Giner, Beatriz

    2017-09-01

    Glycerol is currently considered a raw, renewable material, which can be used to synthesize new glycerol derivatives that may be used as green solvents. However, these compounds must be environmentally evaluated before their use. The acute ecotoxicity of a series of mono-, di-, and trialkyl ethers synthesized from glycerol for the crustacean Daphnia magna has been studied. The EC50 values of these ethers after 24 h of exposure were determined according to the OECD 202 protocol. Their possible structural-toxicity relationships according to different alkyl substituents have been discussed after applying different QSAR models (with the DARC-PELCO approach and topological parameters). The results of the immobilization test show that most of the glycerol derivatives studied exhibit relatively low ecotoxicity. There is a correlation between the lipophilicity and the increase of the toxic effect in the crustacean biomodel. Furthermore, the length and the number of the alkyl substituents and ecotoxicity are highly related. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. A new strategy of outlier detection for QSAR/QSPR.

    PubMed

    Cao, Dong-Sheng; Liang, Yi-Zeng; Xu, Qing-Song; Li, Hong-Dong; Chen, Xian

    2010-02-01

    The crucial step of building a high performance QSAR/QSPR model is the detection of outliers in the model. Detecting outliers in a multivariate point cloud is not trivial, especially when several outliers coexist in the model. The classical identification methods do not always identify them, because they are based on the sample mean and covariance matrix influenced by the outliers. Moreover, existing methods only lay stress on some type of outliers but not all the outliers. To avoid these problems and detect all kinds of outliers simultaneously, we provide a new strategy based on Monte-Carlo cross-validation, which was termed as the MC method. The MC method inherently provides a feasible way to detect different kinds of outliers by establishment of many cross-predictive models. With the help of the distribution of predictive residuals such obtained, it seems to be able to reduce the risk caused by the masking effect. In addition, a new display is proposed, in which the absolute values of mean value of predictive residuals are plotted versus standard deviations of predictive residuals. The plot divides the data into normal samples, y direction outliers and X direction outliers. Several examples are used to demonstrate the detection ability of MC method through the comparison of different diagnostic methods.

  15. Quantitative structure-activity relationships (QSARs) for the transformation of organic micropollutants during oxidative water treatment.

    PubMed

    Lee, Yunho; von Gunten, Urs

    2012-12-01

    Various oxidants such as chlorine, chlorine dioxide, ferrate(VI), ozone, and hydroxyl radicals can be applied for eliminating organic micropollutant by oxidative transformation during water treatment in systems such as drinking water, wastewater, and water reuse. Over the last decades, many second-order rate constants (k) have been determined for the reaction of these oxidants with model compounds and micropollutants. Good correlations (quantitative structure-activity relationships or QSARs) are often found between the k-values for an oxidation reaction of closely related compounds (i.e. having a common organic functional group) and substituent descriptor variables such as Hammett or Taft sigma constants. In this study, we developed QSARs for the oxidation of organic and some inorganic compounds and organic micropollutants transformation during oxidative water treatment. A number of 18 QSARs were developed based on overall 412 k-values for the reaction of chlorine, chlorine dioxide, ferrate, and ozone with organic compounds containing electron-rich moieties such as phenols, anilines, olefins, and amines. On average, 303 out of 412 (74%) k-values were predicted by these QSARs within a factor of 1/3-3 compared to the measured values. For HO(·) reactions, some principles and estimation methods of k-values (e.g. the Group Contribution Method) are discussed. The developed QSARs and the Group Contribution Method could be used to predict the k-values for various emerging organic micropollutants. As a demonstration, 39 out of 45 (87%) predicted k-values were found within a factor 1/3-3 compared to the measured values for the selected emerging micropollutants. Finally, it is discussed how the uncertainty in the predicted k-values using the QSARs affects the accuracy of prediction for micropollutant elimination during oxidative water treatment. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies.

    PubMed

    Nendza, Monika; Müller, Martin; Wenzel, Andrea

    2017-03-22

    Fish acute toxicity studies are required for environmental hazard and risk assessment of chemicals by national and international legislations such as REACH, the regulations of plant protection products and biocidal products, or the GHS (globally harmonised system) for classification and labelling of chemicals. Alternative methods like QSARs (quantitative structure-activity relationships) can replace many ecotoxicity tests. However, complete substitution of in vivo animal tests by in silico methods may not be realistic. For the so-called baseline toxicants, it is possible to predict the fish acute toxicity with sufficient accuracy from log Kow and, hence, valid QSARs can replace in vivo testing. In contrast, excess toxicants and chemicals not reliably classified as baseline toxicants require further in silico, in vitro or in vivo assessments. Thus, the critical task is to discriminate between baseline and excess toxicants. For fish acute toxicity, we derived a scheme based on structural alerts and physicochemical property thresholds to classify chemicals as either baseline toxicants (=predictable by QSARs) or as potential excess toxicants (=not predictable by baseline QSARs). The step-wise approach identifies baseline toxicants (true negatives) in a precautionary way to avoid false negative predictions. Therefore, a certain fraction of false positives can be tolerated, i.e. baseline toxicants without specific effects that may be tested instead of predicted. Application of the classification scheme to a new heterogeneous dataset for diverse fish species results in 40% baseline toxicants, 24% excess toxicants and 36% compounds not classified. Thus, we can conclude that replacing about half of the fish acute toxicity tests by QSAR predictions is realistic to be achieved in the short-term. The long-term goals are classification criteria also for further groups of toxicants and to replace as many in vivo fish acute toxicity tests as possible with valid QSAR predictions.

  17. The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability?

    PubMed

    Toropova, Alla P; Toropov, Andrey A

    2017-05-15

    New criterion of the predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs) is suggested. This criterion is calculated with utilization of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The utilization of this criterion improves the predictive potential of QSAR models of dermal permeability coefficient, logKp (cm/h). Copyright © 2017 Elsevier B.V. All rights reserved.

  18. QSAR study and VolSurf characterization of anti-HIV quinolone library

    NASA Astrophysics Data System (ADS)

    Filipponi, Enrica; Cruciani, Gabriele; Tabarrini, Oriana; Cecchetti, Violetta; Fravolini, Arnaldo

    2001-03-01

    Antiviral quinolones are promising compounds in the search for new therapeutically effective agents for the treatment of AIDS. To rationalize the SAR for this new interesting class of anti-HIV derivatives, we performed a 3D-QSAR study on a library of 101 6-fluoro and 6-desfluoroquinolones, taken either from the literature or synthesized by us. The chemometric procedure involved a fully semiempirical minimization of the molecular structures by the AMSOL program, which takes into account the solvatation effect, and their 3D characterization by the VolSurf/GRID program. The QSAR analysis, based on PCA and PLS methods, shows the key structural features responsible for the antiviral activity.

  19. Computational identification of novel histone deacetylase inhibitors by docking based QSAR.

    PubMed

    Nair, Syam B; Teli, Mahesh Kumar; Pradeep, H; Rajanikant, G K

    2012-06-01

    Histone deacetylases (HDACs) are enzymes that modify chromatin structure and contribute to aberrant gene expression in cancer. A series compounds with well-assigned HDAC inhibitory activity was used for docking based 3D-QSAR analysis. The 3D-QSAR acquired had excellent correlation coefficient value (q2=0.753) and high Fisher ratio (F=300.2). A validated pharmacophore model (AAAPR) was employed for virtual screening. After manual selection, molecular docking and further refinement, six compounds with good absorption, distribution, metabolism, and excretion (ADME) properties were selected as potential HDAC inhibitors. Further, the molecular interactions of these inhibitors with the HDAC active site residues were discussed in detail.

  20. Protonation states and conformational ensemble in ligand-based QSAR modeling.

    PubMed

    De Benedetti, Pier G

    2013-01-01

    Drug affinity and function depend on the different protonation species (present in the biological context) that generate different conformational ensembles with different structural features and, hence, different physico-chemical properties. In the present review article these strongly interdependent structural features will be considered for their crucial role in ligand-based QSAR modeling and drug design by using quantum chemical electronic/reactivity descriptors and molecular shape description. Some selected and relevant examples illustrate the role of these molecular descriptors, computed on the bioactive protonation states and conformers, as determinant factors in mechanistic/causative QSAR analysis.

  1. 3D-QSAR-Assisted Design, Synthesis, and Evaluation of Novobiocin Analogues

    PubMed Central

    2012-01-01

    Hsp90 is an attractive therapeutic target for the treatment of cancer. Extensive structural modifications to novobiocin, the first Hsp90 C-terminal inhibitor discovered, have produced a library of novobiocin analogues and revealed some structure–activity relationships. On the basis of the most potent novobiocin analogues generated from prior studies, a three-dimensional quantitative structure–activity (3D QSAR) model was built. In addition, a new set of novobiocin analogues containing various structural features supported by the 3D QSAR model were synthesized and evaluated against two breast cancer cell lines. Several new inhibitors produced antiproliferative activity at midnanomolar concentrations, which results through Hsp90 inhibition. PMID:23606927

  2. The specificity of the QSAR models for regulatory purposes: the example of the DEMETRA project.

    PubMed

    Benfenati, E

    2007-01-01

    QSAR models have special characteristics depending on the field they are addressing. In the case of QSAR models for regulatory purposes particular requirements should be introduced, to warrant the proper use within a regulatory context. Here the criteria introduced within the European project DEMETRA are presented and discussed. The case discussed here refers to models addressing (eco)toxicological endpoints. Major issues are related to the identification of regulation guidelines, data quality and reproducibility. Further points to be addressed in the model description should be uncertainty and availability of the model, and false negatives, even in the case of regression models.

  3. Genome databases

    SciTech Connect

    Courteau, J.

    1991-10-11

    Since the Genome Project began several years ago, a plethora of databases have been developed or are in the works. They range from the massive Genome Data Base at Johns Hopkins University, the central repository of all gene mapping information, to small databases focusing on single chromosomes or organisms. Some are publicly available, others are essentially private electronic lab notebooks. Still others limit access to a consortium of researchers working on, say, a single human chromosome. An increasing number incorporate sophisticated search and analytical software, while others operate as little more than data lists. In consultation with numerous experts in the field, a list has been compiled of some key genome-related databases. The list was not limited to map and sequence databases but also included the tools investigators use to interpret and elucidate genetic data, such as protein sequence and protein structure databases. Because a major goal of the Genome Project is to map and sequence the genomes of several experimental animals, including E. coli, yeast, fruit fly, nematode, and mouse, the available databases for those organisms are listed as well. The author also includes several databases that are still under development - including some ambitious efforts that go beyond data compilation to create what are being called electronic research communities, enabling many users, rather than just one or a few curators, to add or edit the data and tag it as raw or confirmed.

  4. Reducing abortion: the Danish experience.

    PubMed

    Risor, H

    1989-01-01

    In 1987, 20,830 legal abortions were performed in Denmark. 2,845 involved women below the age of 20, and 532 involved women terminating pregnancy after the 12th week. Danish law permits all of its female citizens to have an abortion free-of-charge before the 12th week of pregnancy. After the 12th week, the abortion must be applied for through a committee of 3 members, and all counties in Denmark have a committee. It is felt in Denmark that a woman has a right to an abortion if she decides to have one. It she makes that choice, doctors and nurses are supportive. Since 1970, sex education has been mandatory in Danish schools. Teachers often collaborate closely with school doctors and nurses in this education. All counties are required to have at least 1 clinic that provides contraceptive counselling. It was recently found that the lowest number of pregnancies among teenaged girls was found in a county in Jutland where all 9th grade students visit the county clinic to learn about contraceptives, pregnancy, and abortion. Within 1 year after Copenhagen had adopted this practice, the number of abortions among teenagers declined by 20%. One fourth of all pharmacies also collaborate with schools to promote sex education, instructing students about contraceptives and pregnancy tests. The Danish Family Planning Association has produced a film on abortion, and plans to produce videos on abortion for use in schools. The organization also holds training programs for health care personnel on contraception, pregnancy, and abortion. By means of the practices described above, it is hoped that the number of abortions and unwanted pregnancies in Denmark will be reduced.

  5. Nonlinear QSAR modeling for predicting cytotoxicity of ionic liquids in leukemia rat cell line: an aid to green chemicals designing.

    PubMed

    Gupta, Shikha; Basant, Nikita; Singh, Kunwar P

    2015-08-01

    Safety assessment and designing of safer ionic liquids (ILs) are among the priorities of the chemists and toxicologists today. Computational approaches have been considered as appropriate methods for prior safety assessment of chemicals and tools to aid in structural designing. The present study is an attempt to investigate the chemical attributes of a wide variety of ILs towards their cytotoxicity in leukemia rat cell line IPC-81 through the development of nonlinear quantitative structure-activity relationship (QSAR) models in the light of the OECD principles for QSAR development. Here, the cascade correlation network (CCN), probabilistic neural network (PNN), and generalized regression neural networks (GRNN) QSAR models were established for the discrimination of ILs in four categories of cytotoxicity and their end-point prediction using few simple descriptors. The diversity and nonlinearity of the considered dataset were evaluated through computing the Euclidean distance and Brock-Dechert-Scheinkman statistics. The constructed QSAR models were validated with external test data. The predictive power of these models was established through a variety of stringent parameters recommended in QSAR literature. The classification QSARs rendered the accuracy of >86%, and the regression models yielded correlation (R(2)) of >0.90 in test data. The developed QSAR models exhibited high statistical confidence and identified the structural elements of the ILs responsible for their cytotoxicity and, hence, could be useful tools in structural designing of safer and green ILs.

  6. QSAR analyses of organophosphates for insecticidal activity and its in-silico validation using molecular docking study.

    PubMed

    Niraj, Ravi Ranjan Kumar; Saini, Vandana; Kumar, Ajit

    2015-11-01

    The present work was carried out to design and develop novel QSAR models using 2D-QSAR and 3D-QSAR with CoMFA methodology for prediction of insecticidal activity of organophosphate (OP) molecules. The models were validated on an entirely different external dataset of in-house generated combinatorial library of OPs, by completely different computational approach of molecular docking against the target AChE protein of Musca domestica. The dock scores were observed to be in good correlation with 2D-QSAR and 3D-QSAR with CoMFA predicted activities and had the correlation coefficients (r(2)) of -0.62 and -0.63, respectively. The activities predicted by 2D-QSAR and 3D-QSAR with CoMFA were also observed to be highly correlated with r(2)=0.82. Also, the combinatorial library molecules were screened for toxicity in non-target organisms and degradability using USEPA-EPI Suite. The work was first step towards computer aided design and development of novel OP pesticide candidates with good insecticidal property but lower toxicity in non-targeted organisms and having biodegradation potential.

  7. QSAR as a random event: a case of NOAEL.

    PubMed

    Toropova, Alla P; Toropov, Andrey A; Veselinović, Jovana B; Veselinović, Aleksandar M

    2015-06-01

    Quantitative structure-activity relationships (QSAR) for no observed adverse effect levels (NOAEL, mmol/kg/day, in logarithmic units) are suggested. Simplified molecular input line entry systems (SMILES) were used for molecular structure representation. Monte Carlo method was used for one-variable models building up for three different splits into the "visible" training set and "invisible" validation. The statistical quality of the models for three random splits are the following: split 1 n = 180, r (2) = 0.718, q (2) = 0.712, s = 0.403, F = 454 (training set); n = 17, r (2) = 0.544, s = 0.367 (calibration set); n = 21, r (2) = 0.61, s = 0.44, r m (2) = 0.61 (validation set); split 2 n = 169, r (2) = 0.711, q (2) = 0.705, s = 0.409, F = 411 (training set); n = 27, r (2) = 0.512, s = 0.461 (calibration set); n = 22, r (2) = 0.669, s = 0.360, r m (2) = 0.63 (validation set); split 3 n = 172, r (2) = 0.679, q (2) = 0.672, s = 0.420, F = 360 (training set); n = 19, r (2) = 0.617, s = 0.582 (calibration set); n = 21, r (2) = 0.627, s = 0.367, r m (2) = 0.54 (validation set). All models are built according to OCED principles.

  8. Prediction of biodegradability of aromatics in water using QSAR modeling.

    PubMed

    Cvetnic, Matija; Juretic Perisic, Daria; Kovacic, Marin; Kusic, Hrvoje; Dermadi, Jasna; Horvat, Sanja; Bolanca, Tomislav; Marin, Vedrana; Karamanis, Panaghiotis; Loncaric Bozic, Ana

    2017-05-01

    The study was aimed at developing models for predicting the biodegradability of aromatic water pollutants. For that purpose, 36 single-benzene ring compounds, with different type, number and position of substituents, were used. The biodegradability was estimated according to the ratio of the biochemical (BOD5) and chemical (COD) oxygen demand values determined for parent compounds ((BOD5/COD)0), as well as for their reaction mixtures in half-life achieved by UV-C/H2O2 process ((BOD5/COD)t1/2). The models correlating biodegradability and molecular structure characteristics of studied pollutants were derived using quantitative structure-activity relationship (QSAR) principles and tools. Upon derivation of the models and calibration on the training and subsequent testing on the test set, 3- and 5-variable models were selected as the most predictive for (BOD5/COD)0 and (BOD5/COD)t1/2, respectively, according to the values of statistical parameters R(2) and Q(2). Hence, 3-variable model predicting (BOD5/COD)0 possessed R(2)=0.863 and Q(2)=0.799 for training set, and R(2)=0.710 for test set, while 5-variable model predicting (BOD5/COD)1/2 possessed R(2)=0.886 and Q(2)=0.788 for training set, and R(2)=0.564 for test set. The selected models are interpretable and transparent, reflecting key structural features that influence targeted biodegradability and can be correlated with the degradation mechanisms of studied compounds by UV-C/H2O2.

  9. Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances.

    PubMed Central

    Cronin, Mark T D; Walker, John D; Jaworska, Joanna S; Comber, Michael H I; Watts, Christopher D; Worth, Andrew P

    2003-01-01

    This article is a review of the use, by regulatory agencies and authorities, of quantitative structure-activity relationships (QSARs) to predict ecologic effects and environmental fate of chemicals. For many years, the U.S. Environmental Protection Agency has been the most prominent regulatory agency using QSARs to predict the ecologic effects and environmental fate of chemicals. However, as increasing numbers of standard QSAR methods are developed and validated to predict ecologic effects and environmental fate of chemicals, it is anticipated that more regulatory agencies and authorities will find them to be acceptable alternatives to chemical testing. PMID:12896861

  10. Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow).

    PubMed

    Papa, Ester; Villa, Fulvio; Gramatica, Paola

    2005-01-01

    The use of Quantitative Structure-Activity Relationships in assessing the potential negative effects of chemicals plays an important role in ecotoxicology. (LC50)(96h) in Pimephales promelas (Duluth database) is widely modeled as an aquatic toxicity end-point. The object of this study was to compare different molecular descriptors in the development of new statistically validated QSAR models to predict the aquatic toxicity of chemicals classified according to their MOA and in a unique general model. The applied multiple linear regression approach (ordinary least squares) is based on theoretical molecular descriptor variety (1D, 2D, and 3D, from DRAGON package, and some calculated logP). The best combination of modeling descriptors was selected by the Genetic Algorithm-Variable Subset Selection procedure. The robustness and the predictive performance of the proposed models was verified using both internal (cross-validation by LOO, bootstrap, Y-scrambling) and external statistical validations (by splitting the original data set into training and validation sets by Kohonen-artificial neural networks (K-ANN)). The model applicability domain (AD) was checked by the leverage approach to verify prediction reliability.

  11. Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?

    EPA Science Inventory

    Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external dataset, the best way to validate the predictive ability of a model is to perform its s...

  12. The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study

    NASA Astrophysics Data System (ADS)

    Kuz'min, Victor E.; Muratov, Eugene N.; Artemenko, Anatoly G.; Gorb, Leonid; Qasim, Mohammad; Leszczynski, Jerzy

    2008-10-01

    The present study applies the Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) for (i) evaluation of the influence of the characteristics of 28 nitroaromatic compounds (some of which belong to a widely known class of explosives) as to their toxicity; (ii) prediction of toxicity for new nitroaromatic derivatives; (iii) analysis of the effects of substituents in nitroaromatic compounds on their toxicity in vivo. The 50% lethal dose concentration for rats (LD50) was used to develop the QSAR models based on simplex representation of molecular structure. The preliminary 1D QSAR results show that even the information on the composition of molecules reveals the main tendencies of changes in toxicity. The statistic characteristics for partial least squares 2D QSAR models are quite satisfactory ( R 2 = 0.96-0.98; Q 2 = 0.91-0.93; R 2 test = 0.89-0.92), which allows us to carry out the prediction of activity for 41 novel compounds designed by the application of new combinations of substituents represented in the training set. The comprehensive analysis of toxicity changes as a function of substituent position and nature was carried out. Molecular fragments that promote and interfere with toxicity were defined on the basis of the obtained models. It was shown that the mutual influence of substituents in the benzene ring plays a crucial role regarding toxicity. The influence of different substituents on toxicity can be mediated via different C-H fragments of the aromatic ring.

  13. QSAR and molecular docking studies on oxindole derivatives as VEGFR-2 tyrosine kinase inhibitors.

    PubMed

    Kang, Cong-Min; Liu, Dong-Qing; Zhao, Xu-Hao; Dai, Ying-Jie; Cheng, Jia-Gao; Lv, Ying-Tao

    2016-01-01

    The three-dimensional quantitative structure-activity relationships (3D-QSAR) were established for 30 oxindole derivatives as vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase inhibitors by using comparative molecular field analysis (CoMFA) and comparative similarity indices analysis comparative molecular similarity indices analysis (CoMSIA) techniques. With the CoMFA model, the cross-validated value (q(2)) was 0.777, the non-cross-validated value (R(2)) was 0.987, and the external cross-validated value ([Formula: see text]) was 0.72. And with the CoMSIA model, the corresponding q(2), R(2) and [Formula: see text] values were 0.710, 0.988 and 0.78, respectively. Docking studies were employed to bind the inhibitors into the active site to determine the probable binding conformation. The binding mode obtained by molecular docking was in good agreement with the 3D-QSAR results. Based on the QSAR models and the docking binding mode, a set of new VEGFR-2 tyrosine kinase inhibitors were designed, which showed excellent predicting inhibiting potencies. The result revealed that both QSAR models have good predictive capability to guide the design and structural modification of homologic compounds. It is also helpful for further research and development of new VEGFR-2 tyrosine kinase inhibitors.

  14. Consensus hologram QSAR modeling for the prediction of human intestinal absorption.

    PubMed

    Moda, Tiago L; Andricopulo, Adriano D

    2012-04-15

    Consistent in silico models for ADME properties are useful tools in early drug discovery. Here, we report the hologram QSAR modeling of human intestinal absorption using a dataset of 638 compounds with experimental data associated. The final validated models are consistent and robust for the consensus prediction of this important pharmacokinetic property and are suitable for virtual screening applications.

  15. Application of 3D-QSAR in the rational design of receptor ligands and enzyme inhibitors.

    PubMed

    Mor, Marco; Rivara, Silvia; Lodola, Alessio; Lorenzi, Simone; Bordi, Fabrizio; Plazzi, Pier Vincenzo; Spadoni, Gilberto; Bedini, Annalida; Duranti, Andrea; Tontini, Andrea; Tarzia, Giorgio

    2005-11-01

    Quantitative structure-activity relationships (QSARs) are frequently employed in medicinal chemistry projects, both to rationalize structure-activity relationships (SAR) for known series of compounds and to help in the design of innovative structures endowed with desired pharmacological actions. As a difference from the so-called structure-based drug design tools, they do not require the knowledge of the biological target structure, but are based on the comparison of drug structural features, thus being defined ligand-based drug design tools. In the 3D-QSAR approach, structural descriptors are calculated from molecular models of the ligands, as interaction fields within a three-dimensional (3D) lattice of points surrounding the ligand structure. These descriptors are collected in a large X matrix, which is submitted to multivariate analysis to look for correlations with biological activity. Like for other QSARs, the reliability and usefulness of the correlation models depends on the validity of the assumptions and on the quality of the data. A careful selection of compounds and pharmacological data can improve the application of 3D-QSAR analysis in drug design. Some examples of the application of CoMFA and CoMSIA approaches to the SAR study and design of receptor or enzyme ligands is described, pointing the attention to the fields of melatonin receptor ligands and FAAH inhibitors.

  16. DFT-based QSAR study and molecular design of AHMA derivatives as potent anticancer agents

    NASA Astrophysics Data System (ADS)

    Chen, Jincan; Shen, Yong; Liao, Siyan; Chen, Lanmei; Zheng, Kangcheng

    A quantitative structure-activity relationship (QSAR) of 3-(9-acridinylamino)-5-hydroxymethylaniline (AHMA) derivatives and their alkylcarbamates as potent anticancer agents has been studied using density functional theory (DFT), molecular mechanics (MM+), and statistical methods. In the best established QSAR equation, the energy (ENL) of the next lowest unoccupied molecular orbital (NLUMO) and the net charges (QFR) of the first atom of the substituent R, as well as the steric parameter (MR2) of subsituent R2 are the main independent factors contributing to the anticancer activity of the compounds. A new scheme determining outliers by ?leave-one-out? (LOO) cross-validation coefficient (q2n-i) was suggested and successfully used. The fitting correlation coefficient (R2) and the ?LOO? cross-validation coefficient (q2) values for the training set of 25 compounds are 0.881 and 0.829, respectively. The predicted activities of 5 compounds in the test set using this QSAR model are in good agreement with their experimental values, indicating that this model has excellent predictive ability. Based on the established QSAR equation, 10 new compounds with rather high anticancer activity much greater than that of 34 compounds have been designed and await experimental verification.

  17. AutoWeka: toward an automated data mining software for QSAR and QSPR studies.

    PubMed

    Nantasenamat, Chanin; Worachartcheewan, Apilak; Jamsak, Saksiri; Preeyanon, Likit; Shoombuatong, Watshara; Simeon, Saw; Mandi, Prasit; Isarankura-Na-Ayudhya, Chartchalerm; Prachayasittikul, Virapong

    2015-01-01

    In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predictive modeling. The latter is based on the concept of quantitative structure-activity/property relationship (QSAR/QSPR) when applied in modeling the biological activity and chemical properties, respectively, of compounds. Data mining is a powerful technology underlying QSAR/QSPR as it harnesses knowledge from large volumes of high-dimensional data via multivariate analysis. Although extremely useful, the technicalities of data mining may overwhelm potential users, especially those in the life sciences. Herein, we aim to lower the barriers to access and utilization of data mining software for QSAR/QSPR studies. AutoWeka is an automated data mining software tool that is powered by the widely used machine learning package Weka. The software provides a user-friendly graphical interface along with an automated parameter search capability. It employs two robust and popular machine learning methods: artificial neural networks and support vector machines. This chapter describes the practical usage of AutoWeka and relevant tools in the development of predictive QSAR/QSPR models. The software is freely available at http://www.mt.mahidol.ac.th/autoweka.

  18. SAR/QSAR MODELS FOR TOXICITY PREDICTION: APPROACHES AND NEW DIRECTIONS

    EPA Science Inventory

    Abstract

    SAR/QSAR MODELS FOR TOXICITY PREDICTION: APPROACHES AND NEW DIRECTIONS

    Risk assessment typically incorporates some relevant toxicity information upon which to base a sound estimation for a chemical of concern. However, there are many circumstances in whic...

  19. Developing sensor activity relationships for the JPL electronic nose sensors using molecular modeling and QSAR techniques

    NASA Technical Reports Server (NTRS)

    Shevade, A. V.; Ryan, M. A.; Homer, M. L.; Jewell, A. D.; Zhou, H.; Manatt, K.; Kisor, A. K.

    2005-01-01

    We report a Quantitative Structure-Activity Relationships (QSAR) study using Genetic Function Approximations (GFA) to describe the polymer-carbon composite sensor activities in the JPL Electronic Nose, when exposed to chemical vapors at parts-per-million concentration levels.

  20. QSAR study of selective ligands for the thyroid hormone receptor beta.

    PubMed

    Liu, Huanxiang; Gramatica, Paola

    2007-08-01

    In this paper, an accurate and reliable QSAR model of 87 selective ligands for the thyroid hormone receptor beta 1 (TRbeta1) was developed, based on theoretical molecular descriptors to predict the binding affinity of compounds with receptor. The structural characteristics of compounds were described wholly by a large amount of molecular structural descriptors calculated by DRAGON. Six most relevant structural descriptors to the studied activity were selected as the inputs of QSAR model by a robust optimization algorithm Genetic Algorithm. The built model was fully assessed by various validation methods, including internal and external validation, Y-randomization test, chemical applicability domain, and all the validations indicate that the QSAR model we proposed is robust and satisfactory. Thus, the built QSAR model can be used to fast and accurately predict the binding affinity of compounds (in the defined applicability domain) to TRbeta1. At the same time, the model proposed could also identify and provide some insight into what structural features are related to the biological activity of these compounds and provide some instruction for further designing the new selective ligands for TRbeta1 with high activity.

  1. Molecular docking and QSAR of aplyronine A and analogues: potent inhibitors of actin

    NASA Astrophysics Data System (ADS)

    Hussain, Abrar; Melville, James L.; Hirst, Jonathan D.

    2010-01-01

    Actin-binding natural products have been identified as a potential basis for the design of cancer therapeutic agents. We report flexible docking and QSAR studies on aplyronine A analogues. Our findings show the macrolide `tail' to be fundamental for the depolymerisation effect of actin-binding macrolides and that it is the tail which forms the initial interaction with the actin rather than the macrocycle, as previously believed. Docking energy scores for the compounds were highly correlated with actin depolymerisation activity. The 3D-QSAR models were predictive, with the best model giving a q 2 value of 0.85 and a r 2 of 0.94. Results from the docking simulations and the interpretation from QSAR "coeff*stdev" contour maps provide insight into the binding mechanism of each analogue and highlight key features that influence depolymerisation activity. The results herein may aid the design of a putative set of analogues that can help produce efficacious and tolerable anti-tumour agents. Finally, using the best QSAR model, we have also made genuine predictions for an independent set of recently reported aplyronine analogues.

  2. QSAR study on the antibacterial activity of some sulfa drugs: building blockers of Mannich bases.

    PubMed

    Mandloi, Dheeraj; Joshi, Sheela; Khadikar, Padmakar V; Khosla, Navita

    2005-01-17

    Sulfa drugs are building blockers of several types of Mannich bases. Consequently, the antibacterial activities of sulfa drugs are reported in this paper, which will help in explaining and understanding antibacterial activities of Mannich bases. Reported QSAR is carried out using distance-based topological indices and discussed critically on the basis of statistical parameters.

  3. Probability issues in molecular design: predictive and modeling ability in 3D-QSAR schemes.

    PubMed

    Polanski, Jaroslaw; Gieleciak, Rafal; Bak, Andrzej

    2004-12-01

    In the current work we investigated 3D-QSAR data by the use of the coupled leave-several-out (LSO) and leave-one-out (LOO) cross-validation (CV) procedures. We verified the above mentioned scheme using both simulated data and real 3D QSAR data describing a series of CoMFA steroids, heterocyclic azo dyes and styrylquinoline HIV integrase inhibitors. Unlike in standard analyses, this technique characterizes individual method not by a single performance metrics but screens a whole possible modeling space by sampling different molecules into the training and test sets, respectively. This allowed us for the discussion of the information included in the estimators validating cross-validation procedures, as well as the comparison of the efficiency of several 3D QSAR schemes, in particular, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Surface Analysis (CoMSA). Moreover, it allows one to acquire some general knowledge about predictive and modeling ability in 3D QSAR method.

  4. Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?

    EPA Science Inventory

    Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external dataset, the best way to validate the predictive ability of a model is to perform its s...

  5. Receptor Guided 3D-QSAR: A Useful Approach for Designing of IGF-1R Inhibitors

    PubMed Central

    Muddassar, M.; Pasha, F. A.; Chung, H. W.; Yoo, K. H.; Oh, C. H.; Cho, S. J.

    2008-01-01

    Research by other investigators has established that insulin-like growth factor‐1 receptor (IGF-1R) is a key oncological target, and that derivatives of 1, 3-disubstituted-imidazo[1,5-α] pyrazine are potent IGF-1R inhibitors. In this paper, we report on our three-dimensional quantitative structure activity relationship (3D-QSAR) studies for this series of compounds. We validated the 3D-QSAR models by the comparison of two major alignment schemes, namely, ligand-based (LB) and receptor-guided (RG) alignment schemes. The latter scheme yielded better 3D-QSAR models for both comparative molecular field analysis (CoMFA) (q2 = 0.35, r2 = 0.95) and comparative molecular similarity indices analysis (CoMSIA) (q2 = 0.51, r2 = 0.86). We submit that this might arise from the more accurate inhibitor alignment that results from using the structural information of the active site. We conclude that the receptor-guided 3D-QSAR may be helpful to design more potent IGF-1R inhibitors, as well as to understand their binding affinity with the receptor. PMID:18385815

  6. Receptor guided 3D-QSAR: a useful approach for designing of IGF-1R inhibitors.

    PubMed

    Muddassar, M; Pasha, F A; Chung, H W; Yoo, K H; Oh, C H; Cho, S J

    2008-01-01

    Research by other investigators has established that insulin-like growth factor-1 receptor (IGF-1R) is a key oncological target, and that derivatives of 1, 3-disubstituted-imidazo[1,5-alpha] pyrazine are potent IGF-1R inhibitors. In this paper, we report on our three-dimensional quantitative structure activity relationship (3D-QSAR) studies for this series of compounds. We validated the 3D-QSAR models by the comparison of two major alignment schemes, namely, ligand-based (LB) and receptor-guided (RG) alignment schemes. The latter scheme yielded better 3D-QSAR models for both comparative molecular field analysis (CoMFA) (q(2) = 0.35, r(2) = 0.95) and comparative molecular similarity indices analysis (CoMSIA) (q(2) = 0.51, r(2) = 0.86). We submit that this might arise from the more accurate inhibitor alignment that results from using the structural information of the active site. We conclude that the receptor-guided 3D-QSAR may be helpful to design more potent IGF-1R inhibitors, as well as to understand their binding affinity with the receptor.

  7. Antibacterial Activity of Imidazolium-Based Ionic Liquids Investigated by QSAR Modeling and Experimental Studies.

    PubMed

    Hodyna, Diana; Kovalishyn, Vasyl; Rogalsky, Sergiy; Blagodatnyi, Volodymyr; Petko, Kirill; Metelytsia, Larisa

    2016-09-01

    Predictive QSAR models for the inhibitors of B. subtilis and Ps. aeruginosa among imidazolium-based ionic liquids were developed using literary data. The regression QSAR models were created through Artificial Neural Network and k-nearest neighbor procedures. The classification QSAR models were constructed using WEKA-RF (random forest) method. The predictive ability of the models was tested by fivefold cross-validation; giving q(2) = 0.77-0.92 for regression models and accuracy 83-88% for classification models. Twenty synthesized samples of 1,3-dialkylimidazolium ionic liquids with predictive value of activity level of antimicrobial potential were evaluated. For all asymmetric 1,3-dialkylimidazolium ionic liquids, only compounds containing at least one radical with alkyl chain length of 12 carbon atoms showed high antibacterial activity. However, the activity of symmetric 1,3-dialkylimidazolium salts was found to have opposite relationship with the length of aliphatic radical being maximum for compounds based on 1,3-dioctylimidazolium cation. The obtained experimental results suggested that the application of classification QSAR models is more accurate for the prediction of activity of new imidazolium-based ILs as potential antibacterials. © 2016 John Wiley & Sons A/S.

  8. Structure Modification Toward Applicability Domain of a QSAR/QSPR Model Considering Activity/Property.

    PubMed

    Ochi, Shoki; Miyao, Tomoyuki; Funatsu, Kimito

    2017-08-16

    In drug and material design, the activity and property values of the designed chemical structures can be predicted by quantitative structure-activity and structure-property relationship (QSAR/QSPR) models. When a QSAR/QSPR model is applied to chemical structures, its applicability domain (AD) must be considered. The predicted activity/property values are only reliable for chemical structures inside the AD. Chemical structures outside the AD are usually neglected, as the predicted values are unreliable. The purpose of this study is to develop a methodology for obtaining novel chemical structures with the desired activity or property based on a QSAR/QSPR model by making use of the neglected structures. We propose a structure modification strategy for the AD that considers the activity and property simultaneously. The AD is defined by a one-class support vector machine and the structure modification is guided by a partial derivative of the AD model and matched molecular pairs analysis. Three proof-of-concept case studies generate novel chemical structures inside the AD that exhibit preferable activity/property values according to the QSAR/QSPR model. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. Synthesis, in vitro antitubercular activity and 3D-QSAR study of 1,4-dihydropyridines.

    PubMed

    Manvar, Atul T; Pissurlenkar, Raghuvir R S; Virsodia, Vijay R; Upadhyay, Kuldip D; Manvar, Dinesh R; Mishra, Arun K; Acharya, Hrishikesh D; Parecha, Alpesh R; Dholakia, Chintan D; Shah, Anamik K; Coutinho, Evans C

    2010-05-01

    In continuation of our research program on new antitubercular agents, this article is a report of the synthesis of 97 various symmetrical, unsymmetrical, and N-substituted 1,4-dihydropyridines. The synthesized molecules were tested for their activity against M. tuberculosis H (37)Rv strain with rifampin as the standard drug. The percentage inhibition was found in the range 3-93%. In an effort to understand the relationship between structure and activity, 3D-QSAR studies were also carried out on a subset that is representative of the molecules synthesized. For the generation of the QSAR models, a training set of 35 diverse molecules representing the synthesized molecules was utilized. The molecules were aligned using the atom-fit technique. The CoMFA and CoMSIA models generated on the molecules aligned by the atom-fit method show a correlation coefficient (r (2)) of 0.98 and 0.95 with cross-validated r (2)(q (2)) of 0.56 and 0.62, respectively. The 3D-QSAR models were externally validated against a test set of 19 molecules (aligned previously with the training set) for which the predictive r(2)(r(r)(pred)) is recorded as 0.74 and 0.69 for the CoMFA and CoMSIA models, respectively. The models were checked for chance correlation through y-scrambling. The QSAR models revealed the importance of the conformational flexibility of the substituents in antitubercular activity.

  10. Experiment Databases

    NASA Astrophysics Data System (ADS)

    Vanschoren, Joaquin; Blockeel, Hendrik

    Next to running machine learning algorithms based on inductive queries, much can be learned by immediately querying the combined results of many prior studies. Indeed, all around the globe, thousands of machine learning experiments are being executed on a daily basis, generating a constant stream of empirical information on machine learning techniques. While the information contained in these experiments might have many uses beyond their original intent, results are typically described very concisely in papers and discarded afterwards. If we properly store and organize these results in central databases, they can be immediately reused for further analysis, thus boosting future research. In this chapter, we propose the use of experiment databases: databases designed to collect all the necessary details of these experiments, and to intelligently organize them in online repositories to enable fast and thorough analysis of a myriad of collected results. They constitute an additional, queriable source of empirical meta-data based on principled descriptions of algorithm executions, without reimplementing the algorithms in an inductive database. As such, they engender a very dynamic, collaborative approach to experimentation, in which experiments can be freely shared, linked together, and immediately reused by researchers all over the world. They can be set up for personal use, to share results within a lab or to create open, community-wide repositories. Here, we provide a high-level overview of their design, and use an existing experiment database to answer various interesting research questions about machine learning algorithms and to verify a number of recent studies.

  11. QSAR models for predicting in vivo aquatic toxicity of chlorinated alkanes to fish.

    PubMed

    Zvinavashe, Elton; van den Berg, Hans; Soffers, Ans E M F; Vervoort, Jacques; Freidig, Andreas; Murk, Albertinka J; Rietjens, Ivonne M C M

    2008-03-01

    Quantitative structure-activity relationship (QSAR) models are expected to play a crucial role in reducing the number of animals to be used for toxicity testing resulting from the adoption of the new European Union chemical control system called Registration, Evaluation, and Authorization of Chemicals (REACH). The objective of the present study was to generate in vitro acute toxicity data that could be used to develop a QSAR model to describe acute in vivo toxicity of chlorinated alkanes. Cytotoxicity of a series of chlorinated alkanes to Chinese hamster ovary (CHO) cells was observed at concentrations similar to those that have been shown previously to be toxic to fish. Strong correlations exist between the acute in vitro toxicity of the chlorinated alkanes and (i) hydrophobicity [modeled by the calculated log K ow (octanol-water partition coefficient); r (2) = 0.883 and r int (2) = 0.854] and (ii) in vivo acute toxicity to fish ( r (2) = 0.758). A QSAR model has been developed to predict in vivo acute toxicity to fish, based on the in vitro data and even on in silico log K ow data only. The developed QSAR model is applicable to chlorinated alkanes with up to 10 carbon atoms, up to eight chlorine atoms, and log K ow values lying within the range from 1.71 to 5.70. Out of the 100204 compounds on the European Inventory of Existing Chemicals (EINECS), our QSAR model covers 77 (0.1%) of them. Our findings demonstrate that in vitro experiments and even in silico calculations can replace animal experiments in the prediction of the acute toxicity of chlorinated alkanes.

  12. Review of synthesis, biological assay and QSAR studies of β-secretase inhibitors.

    PubMed

    Niño, Helena; García-Pintos, Isela; Rodríguez-Borges, José E; Escobar-Cubiella, Manolo; García-Mera, Xerardo; Prado-Prado, Francisco

    2011-12-01

    Alzheimer's disease (AD) is highly complex. While several pathologies characterize this disease, amyloid plaques, composed of the β-amyloid peptide, are hallmark neuropathological lesions in Alzheimer's disease brain. Indeed, a wealth of evidence suggests that β-amyloid is central to the pathophysiology of AD and is likely to play an early role in this intractable neurodegenerative disorder. The BACE-1 enzyme is essential for the generation of β-amyloid. BACE-1 knockout mice do not produce β-amyloid and are free from Alzheimer's associated pathologies, including neuronal loss and certain memory deficits. The fact that BACE-1 initiates the formation of β-amyloid, and the observation that BACE-1 levels are elevated in this disease provide direct and compelling reasons to develop therapies directed at BACE-1 inhibition, thus reducing β-amyloid and its associated toxicities. In this sense, quantitative structure-activity relationships (QSAR) could play an important role in studying these β-secretase inhibitors. QSAR models are necessary in order to guide the β-secretase synthesis. This work is aimed at reviewing different design and synthesis and computational studies for a very large and heterogeneous series of β-secretase inhibitors. First, we review design, synthesis, and Biological assay of β-secretase inhibitors. Next, we review 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compounds to find out the structural requirements. Next, we review QSAR studies using the method of Linear Discriminant Analysis (LDA) in order to understand the essential structural requirement for receptor binding for β- secretase inhibitors.

  13. 2D-QSAR study of fullerene nanostructure derivatives as potent HIV-1 protease inhibitors

    NASA Astrophysics Data System (ADS)

    Barzegar, Abolfazl; Jafari Mousavi, Somaye; Hamidi, Hossein; Sadeghi, Mehdi

    2017-09-01

    The protease of human immunodeficiency virus1 (HIV-PR) is an essential enzyme for antiviral treatments. Carbon nanostructures of fullerene derivatives, have nanoscale dimension with a diameter comparable to the diameter of the active site of HIV-PR which would in turn inhibit HIV. In this research, two dimensional quantitative structure-activity relationships (2D-QSAR) of fullerene derivatives against HIV-PR activity were employed as a powerful tool for elucidation the relationships between structure and experimental observations. QSAR study of 49 fullerene derivatives was performed by employing stepwise-MLR, GAPLS-MLR, and PCA-MLR models for variable (descriptor) selection and model construction. QSAR models were obtained with higher ability to predict the activity of the fullerene derivatives against HIV-PR by a correlation coefficient (R2training) of 0.942, 0.89, and 0.87 as well as R2test values of 0.791, 0.67and 0.674 for stepwise-MLR, GAPLS-MLR, and PCA -MLR models, respectively. Leave-one-out cross-validated correlation coefficient (R2CV) and Y-randomization methods confirmed the models robustness. The descriptors indicated that the HIV-PR inhibition depends on the van der Waals volumes, polarizability, bond order between two atoms and electronegativities of fullerenes derivatives. 2D-QSAR simulation without needing receptor's active site geometry, resulted in useful descriptors mainly denoting ;C60 backbone-functional groups; and ;C60 functional groups; properties. Both properties in fullerene refer to the ligand fitness and improvement van der Waals interactions with HIV-PR active site. Therefore, the QSAR models can be used in the search for novel HIV-PR inhibitors based on fullerene derivatives.

  14. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

    PubMed

    Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

    2017-06-30

    Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

  15. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

    PubMed Central

    2017-01-01

    Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113

  16. Fingerprint-based clustering applied to define a QSAR model use radius.

    PubMed

    Sprous, D G

    2008-09-01

    In ongoing research, QSAR has been a tool applied to evaluate compound qualities associated with skin permeability and membership in either a druglike class or specific nondruglike type classes. A need that arose from this pursuit was to know the boundaries of the QSAR models within which molecules could be analyzed. To satisfy this need, a method of QSAR model validation was developed which moves away from the simple declaration of correlation to a description of expected correlation as a function of similarity to the training set. This extension of the "validation" and "predictive" concepts to include a border is referred to henceforth as the QSAR model use radius. By defining this metric, it is possible to select for models which have predictivity exterior to their training sets. The heart of this approach is the common use of division into training sets and test sets to demonstrate an ability to successfully predict outside of the training set. The new rigor introduced is to repetitively cluster and systematically increase the permitted dissimilarity within those clusters. The training sets are assembled by taking one and only one compound from each cluster at a specific level of permitted dissimilarity. The QSAR model is developed over these training sets and applied to predict the remaining compounds. In this manner, it is possible to point where there is adequate similarity to predict a compound and where there is not. This method is especially useful for large, chemically redundant systems of greater than 250 compounds where leave-one-out crossvalidation is of limited use. To illustrate this technique, the results of defining the use radius for (a) a skin permeability model (based on 276 compounds), (b) a drug compound and "safe" compound partition (3000 compounds) and (c) a kinase inhibitor and drug compound partition ( approximately 1300 compounds) are discussed.

  17. Solubility Database

    National Institute of Standards and Technology Data Gateway

    SRD 106 IUPAC-NIST Solubility Database (Web, free access)   These solubilities are compiled from 18 volumes (Click here for List) of the International Union for Pure and Applied Chemistry(IUPAC)-NIST Solubility Data Series. The database includes liquid-liquid, solid-liquid, and gas-liquid systems. Typical solvents and solutes include water, seawater, heavy water, inorganic compounds, and a variety of organic compounds such as hydrocarbons, halogenated hydrocarbons, alcohols, acids, esters and nitrogen compounds. There are over 67,500 solubility measurements and over 1800 references.

  18. Agility - The Danish Way (Briefing Charts)

    DTIC Science & Technology

    2014-06-01

    Agility - The Danish Way Dr. William Mitchell Dept. for Joint Operations | C2 & Intelligence | Royal Danish Defence College Ryvangs Allé 1...AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Royal Danish Defence College,Dept...Establish presence in Mogadishu and expand with main effort in Southern Somalia. •MD2- Establish small military presence in Somaliland. • P/SD1-ID Clan

  19. The Danish Registry of Diabetic Retinopathy

    PubMed Central

    Andersen, Nis; Hjortdal, Jesper Østergaard; Schielke, Katja Christina; Bek, Toke; Grauslund, Jakob; Laugesen, Caroline Schmidt; Lund-Andersen, Henrik; Cerqueira, Charlotte; Andresen, Jens

    2016-01-01

    Aim of database To monitor the development of diabetic eye disease in Denmark and to evaluate the accessibility and effectiveness of diabetic eye screening programs with focus on interregional variations. Target population The target population includes all patients diagnosed with diabetes. Denmark (5.5 million inhabitants) has ~320,000 diabetes patients with an annual increase of 27,000 newly diagnosed patients. The Danish Registry of Diabetic Retinopathy (DiaBase) collects data on all diabetes patients aged ≥18 years who attend screening for diabetic eye disease in hospital eye departments and in private ophthalmological practice. In 2014–2015, DiaBase included data collected from 77,968 diabetes patients. Main variables The main variables provide data for calculation of performance indicators to monitor the quality of diabetic eye screening and development of diabetic retinopathy. Data with respect to age, sex, best corrected visual acuity, screening frequency, grading of diabetic retinopathy and maculopathy at each visit, progression/regression of diabetic eye disease, and prevalence of blindness were obtained. Data analysis from DiaBase’s latest annual report (2014–2015) indicates that the prevalence of no diabetic retinopathy, nonproliferative diabetic retinopathy, and proliferative diabetic retinopathy is 78%, 18%, and 4%, respectively. The percentage of patients without diabetic maculopathy is 97%. The proportion of patients with regression of diabetic retinopathy (20%) is greater than the proportion of patients with progression of diabetic retinopathy (10%). Conclusion The collection of data from diabetic eye screening is still expanding in Denmark. Analysis of the data collected during the period 2014–2015 reveals an overall decrease of diabetic retinopathy compared to the previous year, although the number of patients newly diagnosed with diabetes has been increasing in Denmark. DiaBase is a useful tool to observe the quality of screening

  20. Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

    EPA Science Inventory

    The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

  1. Quantitative structure-activity relationship (QSAR) study of a series of benzimidazole derivatives as inhibitors of Saccharomyces cerevisiae.

    PubMed

    Podunavac-Kuzmanović, Sonja O; Cvetković, Dragoljub D; Jevrić, Lidija R; Uzelac, Natasa J

    2013-01-01

    A quantitative structure activity relationship (QSAR) has been carried out on a series of benzimidazole derivatives to identify the structural requirements for their inhibitory activity against yeast Saccharomyces cerevisiae. A multiple linear regression (MLR) procedure was used to model the relationships between various physicochemical, steric, electronic, and structural molecular descriptors and antifungal activity of benzimidazole derivatives. The QSAR expressions were generated using a training set of 16 compounds and the predictive ability of the resulting models was evaluated against a test set of 8 compounds. The best QSAR models were further validated by leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. Therefore, satisfactory relationships between antifungal activity and molecular descriptors were found. QSAR analysis reveals that lipophilicity descriptor (logP), dipole moment (DM) and surface area grid (SAG) govern the inhibitory activity of compounds studied against Saccharomyces cerevisiae.

  2. An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modeling

    EPA Science Inventory

    Increasing availability of large collections of chemical structures and associated experimental data provides an opportunity to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associat...

  3. Summary of a workshop on regulatory acceptance of (Q)SARs for human health and environmental endpoints.

    PubMed Central

    Jaworska, Joanna S; Comber, M; Auer, C; Van Leeuwen, C J

    2003-01-01

    The "Workshop on Regulatory Use of (Q)SARs for Human Health and Environmental Endpoints," organized by the European Chemical Industry Council and the International Council of Chemical Associations, gathered more than 60 human health and environmental experts from industry, academia, and regulatory agencies from around the world. They agreed, especially industry and regulatory authorities, that the workshop initiated great potential for the further development and use of predictive models, that is, quantitative structure-activity relationships [(Q)SARs], for chemicals management in a much broader scope than is currently the case. To increase confidence in (Q)SAR predictions and minimization of their misuse, the workshop aimed to develop proposals for guidance and acceptability criteria. The workshop also described the broad outline of a system that would apply that guidance and acceptability criteria to a (Q)SAR when used for chemical management purposes, including priority setting, risk assessment, and classification and labeling. PMID:12896859

  4. Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

    EPA Science Inventory

    The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

  5. Loser cows in Danish dairy herds: definition, prevalence and consequences.

    PubMed

    Thomsen, Peter T; Østergaard, Søren; Sørensen, Jan Tind; Houe, Hans

    2007-05-16

    During the last few years, many Danish dairy farmers have expressed increasing concerns regarding a group of cows, which we have chosen to term 'loser cows'. Until now, a loser cow has not been described scientifically. We defined a loser cow on the basis of a clinical examination of the cow. A total of 15,151 clinical examinations were made on 6,451 individual cows from 39 randomly selected, large Danish dairy herds with loose-housing systems using a clinical protocol. Scores for the clinical signs lameness, body condition, hock lesions, other cutaneous lesions, vaginal discharge, condition of hair coat and general condition were converted into a loser cow score. Cows with a loser cow score of 8 or more were classified as loser cows. The overall prevalence of loser cows was 2.15%, 4.50% and 2.98% during the first, second and third round of herd visits, respectively. The associations between the loser cow state and milk production, mortality, morbidity, culling and workload for the farmer were evaluated using data from herd visits and from the Danish Cattle Database and a number of different statistical techniques. It was concluded that the loser cow state has significant negative consequences for both the farmer and the cow. On average, loser cows yielded 0.61 to 2.24 kg energy corrected milk less per day than non-loser cows depending on parity. Hazard ratio for death or euthanasia was 5.69 for loser cows compared to non-loser cows. Incidence rate ratio for disease treatments was 0.69 for non-loser cows compared to loser cows. Loser cows were often culled in an 'unfavourable' way and generally caused extra workload for the farmer. A simplified version of the loser cow score was evaluated and is recommended for future research and use in practice.

  6. Glaucoma database.

    PubMed

    K, Rangachari; M, Dhivya; Pj, Eswari Pandaranayaka; N, Prasanthi; P, Sundaresan; Sr, Krishnadas; S, Krishnaswamy

    2011-02-07

    Glaucoma, a complex heterogenous disease, is the leading cause for optic nerve-related blindness worldwide. Primary open angle glaucoma (POAG) is the most common subset and by the year 2020 it is estimated that approximately 60 million people will be affected. MYOC, OPTN, CYP1B1 and WDR36 are the important candidate genes. Nearly 4% of the glaucoma patients have mutation in any one of these genes. Mutation in any of these genes causes disease either directly or indirectly and the severity of the disease varies according to position of the genes. We have compiled all the related mutations and SNPs in the above genes and developed a database, to help access statistical and clinical information of particular mutation. This database is available online at http:bicmku.in:8081/glaucoma The database, constructed using SQL, contains data pertaining to the SNPs and mutation information involved in the above genes and relevant study data. The database is available for free at http:bicmku.in:8081/glaucoma.

  7. 3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications.

    PubMed

    Ballante, Flavio; Ragno, Rino

    2012-06-25

    Since it first appeared in 1988 3-D QSAR has proved its potential in the field of drug design and activity prediction. Although thousands of citations now exist in 3-D QSAR, its development was rather slow with the majority of new 3-D QSAR applications just extensions of CoMFA. An alternative way to build 3-D QSAR models, based on an evolution of software, has been named 3-D QSAutogrid/R and has been developed to use only software freely available to academics. 3-D QSAutogrid/R covers all the main features of CoMFA and GRID/GOLPE with implementation by multiprobe/multiregion variable selection (MPGRS) that improves the simplification of interpretation of the 3-D QSAR map. The methodology is based on the integration of the molecular interaction fields as calculated by AutoGrid and the R statistical environment that can be easily coupled with many free graphical molecular interfaces such as UCSF-Chimera, AutoDock Tools, JMol, and others. The description of each R package is reported in detail, and, to assess its validity, 3-D QSAutogrid/R has been applied to three molecular data sets of which either CoMFA or GRID/GOLPE models were reported in order to compare the results. 3-D QSAutogrid/R has been used as the core engine to prepare more that 240 3-D QSAR models forming the very first 3-D QSAR server ( www.3d-qsar.com ) with its code freely available through R-Cran distribution.

  8. QSAR of anticancer compounds. Bis(11-oxo-11H-indeno[1,2-b]quinoline-6-carboxamides), bis(phenazine-1-carboxamides), and bis(naphthalimides).

    PubMed

    Mekapati, S B; Denny, W A; Kurup, A; Hansch, C

    2001-11-01

    QSAR have been developed for the anticancer activity (growth inhibition) of various tumor cells by bis(11-oxo-11H-indeno[1,2-b]quinoline-6-carboxamides), bis(phenazine-1-carboxamides), and bis(naphthalimides). Of the seven QSAR, positive hydrophobic interactions are found in only two examples: bis(naphthalimides) versus human colon cancer cells. This is consistent with other QSAR of anticancer compounds where hydrophobic interactions are found to be unimportant.

  9. QSAR modeling of the antimicrobial activity of peptides as a mathematical function of a sequence of amino acids.

    PubMed

    Toropova, Mariya A; Veselinović, Aleksandar M; Veselinović, Jovana B; Stojanović, Dušica B; Toropov, Andrey A

    2015-12-01

    Antimicrobial peptides have emerged as new therapeutic agents for fighting multi-drug-resistant bacteria. However, the process of optimizing peptide antimicrobial activity and specificity using large peptide libraries is both tedious and expensive. Therefore, computational techniques had to be applied for process optimization. In this work, the representation of the molecular structure of peptides (mastoparan analogs) by a sequence of amino acids has been used to establish quantitative structure-activity relationships (QSARs) for their antibacterial activity. The data for the studied peptides were split three times into the training, calibration and test sets. The Monte Carlo method was used as a computational technique for QSAR models calculation. The statistical quality of QSAR for the antibacterial activity of peptides for the external validation set was: n=7, r(2)=0.8067, s=0.248 (split 1); n=6, r(2)=0.8319, s=0.169 (split 2); and n=6, r(2)=0.6996, s=0.297 (split 3). The stated statistical parameters favor the presented QSAR models in comparison to 2D and 3D descriptor based ones. The Monte Carlo method gave a reasonably good prediction for the antibacterial activity of peptides. The statistical quality of the prediction is different for three random splits. However, the predictive potential is reasonably well for all cases. The presented QSAR modeling approach can be an attractive alternative of 3D QSAR at least for the described peptides. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids

    PubMed Central

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167

  11. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

    PubMed

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

  12. 2D-QSAR study of some 2,5-diaminobenzophenone farnesyltransferase inhibitors by different chemometric methods

    PubMed Central

    Ghanbarzadeh, Saeed; Ghasemi, Saeed; Shayanfar, Ali; Ebrahimi-Najafabadi, Heshmatollah

    2015-01-01

    Quantitative structure activity relationship (QSAR) models can be used to predict the activity of new drug candidates in early stages of drug discovery. In the present study, the information of the ninety two 2,5-diaminobenzophenone-containing farnesyltranaferase inhibitors (FTIs) were taken from the literature. Subsequently, the structures of the molecules were optimized using Hyperchem software and molecular descriptors were obtained using Dragon software. The most suitable descriptors were selected using genetic algorithms-partial least squares and stepwise regression, where exhibited that the volume, shape and polarity of the FTIs are important for their activities. The two-dimensional QSAR models (2D-QSAR) were obtained using both linear methods (multiple linear regression) and non-linear methods (artificial neural networks and support vector machines). The proposed QSAR models were validated using internal validation method. The results showed that the proposed 2D-QSAR models were valid and they can be used for prediction of the activities of the 2,5-diaminobenzophenone-containing FTIs. In conclusion, the 2D-QSAR models (both linear and non-linear) showed good prediction capability and the non-linear models were exhibited more accuracy than the linear models. PMID:26600747

  13. A review of QSAR studies to discover new drug-like compounds actives against leishmaniasis and trypanosomiasis.

    PubMed

    Castillo-Garit, Juan Alberto; Abad, Concepción; Rodríguez-Borges, J Enrique; Marrero-Ponce, Yovani; Torrens, Francisco

    2012-01-01

    The neglected tropical diseases (NTDs) affect more than one billion people (one-sixth of the world's population) and occur primarily in undeveloped countries in sub-Saharan Africa, Asia, and Latin America. Available drugs for these diseases are decades old and present an important number of limitations, especially high toxicity and, more recently, the emergence of drug resistance. In the last decade several Quantitative Structure-Activity Relationship (QSAR) studies have been developed in order to identify new organic compounds with activity against the parasites responsible for these diseases, which are reviewed in this paper. The topics summarized in this work are: 1) QSAR studies to identify new organic compounds actives against Chaga's disease; 2) Development of QSAR studies to discover new antileishmanial drusg; 3) Computational studies to identify new drug-like compounds against human African trypanosomiasis. Each topic include the general characteristics, epidemiology and chemotherapy of the disease as well as the main QSAR approaches to discovery/identification of new actives compounds for the corresponding neglected disease. The last section is devoted to a new approach know as multi-target QSAR models developed for antiparasitic drugs specifically those actives against trypanosomatid parasites. At present, as a result of these QSAR studies several promising compounds, active against these parasites, are been indentify. However, more efforts will be required in the future to develop more selective (specific) useful drugs.

  14. The Future of the Danish Army

    DTIC Science & Technology

    2013-03-01

    as a natural consequence of being a co-founder of the United Nations, focused on promoting peace and stability in the world, as a relatively large...Soviet invasion to a more expeditionary course of deploying forces to promote peace and stability around the globe. As a result, Danish defense policy...Danish government including the armed forces. As a consequence Defense Agreement 2010 – 2014 was replaced by Defense Agreement 2013 – 2017 including

  15. Effect of diuretics on fetal growth: A drug effect or confounding by indication? Pooled Danish and Scottish cohort data

    PubMed Central

    Olesen, Charlotte; de Vries, Corinne S; Thrane, Nana; MacDonald, Tom M; Larsen, Helle; Sørensen, Henrik Toft

    2001-01-01

    Aims The diabetogenic effect of diuretics, as well as the indication for prescribing them, may impact on fetal growth. We analysed whether the purchase of prescription drugs for diuretics during pregnancy was associated with measures of fetal growth. Methods During 1991–98 all women who purchased prescription drugs for diuretics during pregnancy were identified in the Northern Jutland Prescription Database (NJDP), Denmark, and in the Medicines Monitoring Unit's Database (MEMO), Scotland. Information on birth weight and gestational age was obtained from the Danish Birth Registry, the Danish Hospital Discharge Registry and the Scottish Tayside Neonatal Database. Information on diabetes, hypertension and prepregnancy weight were obtained by hospital record review in a sample of women in the Danish cohort. Women who did not purchase prescription diuretics during pregnancy were used as a reference group in both cohorts. Results Danish women who purchased prescription loop diuretics during pregnancy gave birth to infants with higher birth weights than women who did not use diuretics; mean difference 104.7 g (95% CI; 2.6, 206.9). However, the high prevalence of diabetes (10.3%) among Danish women who purchased prescription loop diuretics during pregnancy might explain this result. Both the Danish and the Scottish women who purchased prescription diuretics during their pregnancy were at increased risk of preterm delivery (< 37 completed weeks); ORs: 1.8 (CI; 1.2, 2.7)NJDP, 1.9 (CI; 0.9, 4.3)MEMO. The proportion of hypertension among women who purchased prescription thiazides was 15.8%, and the risk of having an infant with a birth weight (BW) < 2500 g was increased; ORs: 2.6 (CI; 1.4, 5.0)NJDP, 2.4 (CI; 0.8, 7.8)MEMO. Conclusions Prescribing diuretics during pregnancy was associated with differences in birth weight and incidence of preterm delivery. Confounding by indication may explain the findings. PMID:11259987

  16. Drinking Water Treatability Database (Database)

    EPA Science Inventory

    The drinking Water Treatability Database (TDB) will provide data taken from the literature on the control of contaminants in drinking water, and will be housed on an interactive, publicly-available USEPA web site. It can be used for identifying effective treatment processes, rec...

  17. Are Danish doctors comfortable teaching in English?

    PubMed

    Nilas, L; Løkkegaard, E C; Laursen, J B; Kling, J; Cortes, D

    2016-08-27

    From 2012-2015, the Departments of Obstetrics and Gynecology and of Pediatrics at the University of Copenhagen conducted a project, "Internationalization at Home ", offering clinical teaching in English. The project allowed international students to work with Danish speaking students in a clinical setting. Using semi-quantitative questionnaires to 89 clinicians about use of English and need for training, this paper considers if Danish clinical doctors are prepared to teach in English. The majority self-assessed their English proficiency between seven and eight on a 10 unit visual analogue scale, with 10 equivalent to working in Danish, while 15 % rated five or less. However, one-fourth found teaching and writing in English to be twice as difficult than in Danish, and 12 % rated all teaching tasks in English at four or less compared to Danish. The self-assessed need for additional English skills was perceived low. Teaching in English was rated as 30 % more difficult than in Danish, and a significant subgroup of doctors had difficulties in all forms of communication in English, resulting in challenges when introducing international students in non-native English speaking medical departments.

  18. A combined pharmacophore modeling, 3D-QSAR and molecular docking study of substituted bicyclo-[3.3.0]oct-2-enes as liver receptor homolog-1 (LRH-1) agonists

    NASA Astrophysics Data System (ADS)

    Lalit, Manisha; Gangwal, Rahul P.; Dhoke, Gaurao V.; Damre, Mangesh V.; Khandelwal, Kanchan; Sangamwar, Abhay T.

    2013-10-01

    A combined pharmacophore modelling, 3D-QSAR and molecular docking approach was employed to reveal structural and chemical features essential for the development of small molecules as LRH-1 agonists. The best HypoGen pharmacophore hypothesis (Hypo1) consists of one hydrogen-bond donor (HBD), two general hydrophobic (H), one hydrophobic aromatic (HYAr) and one hydrophobic aliphatic (HYA) feature. It has exhibited high correlation coefficient of 0.927, cost difference of 85.178 bit and low RMS value of 1.411. This pharmacophore hypothesis was cross-validated using test set, decoy set and Cat-Scramble methodology. Subsequently, validated pharmacophore hypothesis was used in the screening of small chemical databases. Further, 3D-QSAR models were developed based on the alignment obtained using substructure alignment. The best CoMFA and CoMSIA model has exhibited excellent rncv2 values of 0.991 and 0.987, and rcv2 values of 0.767 and 0.703, respectively. CoMFA predicted rpred2 of 0.87 and CoMSIA predicted rpred2 of 0.78 showed that the predicted values were in good agreement with the experimental values. Molecular docking analysis reveals that π-π interaction with His390 and hydrogen bond interaction with His390/Arg393 is essential for LRH-1 agonistic activity. The results from pharmacophore modelling, 3D-QSAR and molecular docking are complementary to each other and could serve as a powerful tool for the discovery of potent small molecules as LRH-1 agonists.

  19. Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models

    NASA Astrophysics Data System (ADS)

    Sizochenko, Natalia; Gajewicz, Agnieszka; Leszczynski, Jerzy; Puzyn, Tomasz

    2016-03-01

    In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal analysis of multivariate data. Methods of causal discovery have been applied for broader physical insight into mechanisms of action and interpretation of the developed nano-QSAR models. Previously developed nano-QSAR models for toxicity of 17 nano-sized metal oxides towards E. coli bacteria have been validated by means of the causality criteria. Using the descriptors confirmed by the causal technique, we have developed new models consistent with the straightforward causal-reasoning account. It was proven that causal inference methods are able to provide a more robust mechanistic interpretation of the developed nano-QSAR models.In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal

  20. Assessment of the probability of introducing Mycobacterium tuberculosis into Danish cattle herds.

    PubMed

    Foddai, Alessandro; Nielsen, Liza Rosenbaum; Krogh, Kaspar; Alban, Lis

    2015-11-01

    Tuberculosis is a zoonosis caused by Mycobacterium spp. International trade in cattle is regulated with respect to Mycobacterium bovis (M. bovis) but not Mycobacterium tuberculosis (M. tuberculosis), despite that cattle can become infected with both species. In this study we estimated the annual probability (PIntro) of introducing M. tuberculosis into the Danish cattle population, by the import of cattle and/or by immigrants working in Danish cattle herds. Data from 2013 with date, number, and origin of imported live cattle were obtained from the Danish cattle database. Information on immigrants working in Danish cattle herds was obtained through a questionnaire sent to Danish cattle farmers. The gained inputs were fed into three stochastic scenario trees to assess the PIntro for the current and alternative test-and-manage strategies, such as testing of imported animals and/or testing immigrant workers with the tuberculin skin test. We considered the population of Danish farmers and practitioners free of tuberculosis, because in Denmark, the incidence of the disease in humans is low and primarily related to immigrants and socially disadvantaged people. The median annual probability of introducing M. tuberculosis into the Danish cattle population due to imported live cattle was 0.008% (90% P.I.: 0.0007%; 0.03%), while the probability due to immigrant workers was 4.1% (90% P.I.: 0.8%; 12.1%). The median combined probability (PIntro) due to imported cattle plus workers was 4.1% (90% P.I.: 0.8%; 12.6%). Hence, on average at least one introduction each 24 (90% P.I.: 8; 125) years could be expected. Imported live cattle appeared to play a marginal role on the overall annual PIntro, because they represented only approximately 0.2% of the median annual probability. By testing immigrant workers the overall annual PIntro could be reduced to 0.2% (90% P.I.: 0.04%; 0.7%). Thus, testing of immigrant workers could be considered as a risk mitigation strategy to markedly reduce

  1. The Danish National Registry for Biological Therapy in Inflammatory Bowel Disease

    PubMed Central

    Larsen, Lone; Jensen, Michael Dam; Larsen, Michael Due; Nielsen, Rasmus Gaardskær; Thorsgaard, Niels; Vind, Ida; Wildt, Signe; Kjeldsen, Jens

    2016-01-01

    Aim The aims of The Danish National Registry for Biological Therapy in Inflammatory Bowel Disease are to ensure that biological therapy and the clinical management of patients with inflammatory bowel disease (IBD) receiving biological treatment are in accordance with the national clinical guidelines and, second, the database allows register-based clinical epidemiological research. Study population The study population comprises all Danish patients with IBD (both children and adults) with ulcerative colitis, Crohn’s disease, and IBD unclassified who receive biological therapy. Patients will be enrolled consecutively when biological treatment is initiated. Main variables The variables in the database are: diagnosis, time of diagnosis, disease manifestation, indication for biological therapy, previous biological and nonbiological therapy, date of visit, clinical indices, physician’s global assessment, pregnancy and breastfeeding (women), height (children), weight, dosage (current biological agent), adverse events, surgery, endoscopic procedures, and radiology. Descriptive data Eleven clinical indicators have been selected to monitor the quality of biological treatment. For each indicator, a standard has been defined based on the available evidence. National results will be published in an annual report and local results on a quarterly basis. The indicators will be reported as department-specific proportions with 95% confidence intervals, and the national average will be provided for comparison. An estimated 1,200–1,300 new biological therapies are initiated each year in Danish patients with IBD. Conclusion The database will be available for research during 2016. Data will be made available by The Danish Clinical Registries (www.rkkp.dk). PMID:27822107

  2. Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening

    NASA Astrophysics Data System (ADS)

    Hsieh, Jui-Hua; Wang, Xiang S.; Teotico, Denise; Golbraikh, Alexander; Tropsha, Alexander

    2008-09-01

    The use of inaccurate scoring functions in docking algorithms may result in the selection of compounds with high predicted binding affinity that nevertheless are known experimentally not to bind to the target receptor. Such falsely predicted binders have been termed `binding decoys'. We posed a question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors using approaches commonly used in ligand based drug design. We have applied the k-Nearest Neighbor ( kNN) classification QSAR approach to a dataset of compounds characterized as binders or binding decoys of AmpC beta-lactamase. Models were subjected to rigorous internal and external validation as part of our standard workflow and a special QSAR modeling scheme was employed that took into account the imbalanced ratio of inhibitors to non-binders (1:4) in this dataset. 342 predictive models were obtained with correct classification rate (CCR) for both training and test sets as high as 0.90 or higher. The prediction accuracy was as high as 100% (CCR = 1.00) for the external validation set composed of 10 compounds (5 true binders and 5 decoys) selected randomly from the original dataset. For an additional external set of 50 known non-binders, we have achieved the CCR of 0.87 using very conservative model applicability domain threshold. The validated binary kNN QSAR models were further employed for mining the NCGC AmpC screening dataset (69653 compounds). The consensus prediction of 64 compounds identified as screening hits in the AmpC PubChem assay disagreed with their annotation in PubChem but was in agreement with the results of secondary assays. At the same time, 15 compounds were identified as potential binders contrary to their annotation in PubChem. Five of them were tested experimentally and showed inhibitory activities in millimolar range with the highest binding constant Ki of 135 μM. Our studies suggest that validated QSAR models could complement

  3. QSAR analysis for ADA upon interaction with a series of adenine derivatives as inhibitors.

    PubMed

    Moosavi-Movahedi, A A; Safarian, S; Hakimelahi, G H; Ataei, G; Ajloo, D; Panjehpour, S; Riahi, S; Mousavi, M F; Mardanyan, S; Soltani, N; Khalafi-Nezhad, A; Sharghi, H; Moghadamnia, H; Saboury, A A

    2004-01-01

    The kinetic parameters of adenosine deaminase such as Km and Ki were determined in the absence and presence of adenine derivatives (R1-R24) in sodium phosphate buffer (50 mM; pH 7.5) solution at 27 degrees C. These kinetic parameters were used for QSAR analysis. As such, we found some theoretical descriptors to which the binding affinity of adenosine deaminase (ADA) towards several adenine nucleosides as inhibitors is correlated. QSAR analysis has revealed that binding affinity of the adenine nucleosides upon interaction with ADA depends on the molecular volume, dipole moment of the molecule, electric charge around the N1 atom, and the highest of positive charge for the related molecules.

  4. The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models?

    PubMed

    Toropov, Andrey A; Toropova, Alla P

    2017-07-01

    The index of ideality of correlation (IIC) is a new criterion of the predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs). This IIC is calculated with using of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The mutagenicity is well-known important characteristic of substances from ecological point of view. Consequently, the estimation of the IIC for mutagenicity is well motivated. It is confirmed that the utilization of this criterion significantly improves the predictive potential of QSAR models of mutagenicity. The new criterion can be used for other endpoints. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Three-dimensional QSAR analysis and design of new 1,2,4-oxadiazole antibacterials.

    PubMed

    Leemans, Erika; Mahasenan, Kiran V; Kumarasiri, Malika; Spink, Edward; Ding, Derong; O'Daniel, Peter I; Boudreau, Marc A; Lastochkin, Elena; Testero, Sebastian A; Yamaguchi, Takao; Lee, Mijoon; Hesek, Dusan; Fisher, Jed F; Chang, Mayland; Mobashery, Shahriar

    2016-02-01

    The oxadiazole antibacterials, a class of newly discovered compounds that are active against Gram-positive bacteria, target bacterial cell-wall biosynthesis by inhibition of a family of essential enzymes, the penicillin-binding proteins. Ligand-based 3D-QSAR analyses by comparative molecular field analysis (CoMFA), comparative molecular shape indices analysis (CoMSIA) and Field-Based 3D-QSAR evaluated a series of 102 members of this class. This series included inactive compounds as well as compounds that were moderately to strongly antibacterial against Staphylococcus aureus. Multiple models were constructed using different types of energy minimization and charge calculations. CoMFA derived contour maps successfully defined favored and disfavored regions of the molecules in terms of steric and electrostatic properties for substitution.

  6. Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility.

    PubMed

    Obrezanova, Olga; Gola, Joelle M R; Champness, Edmund J; Segall, Matthew D

    2008-01-01

    In this article, we present an automatic model generation process for building QSAR models using Gaussian Processes, a powerful machine learning modeling method. We describe the stages of the process that ensure models are built and validated within a rigorous framework: descriptor calculation, splitting data into training, validation and test sets, descriptor filtering, application of modeling techniques and selection of the best model. We apply this automatic process to data sets of blood-brain barrier penetration and aqueous solubility and compare the resulting automatically generated models with 'manually' built models using external test sets. The results demonstrate the effectiveness of the automatic model generation process for two types of data sets commonly encountered in building ADME QSAR models, a small set of in vivo data and a large set of physico-chemical data.

  7. Lacosamide derivatives with anticonvulsant activity as carbonic anhydrase inhibitors. Molecular modeling, docking and QSAR analysis.

    PubMed

    Garro Martinez, Juan C; Vega-Hissi, Esteban G; Andrada, Matías F; Duchowicz, Pablo R; Torrens, Francisco; Estrada, Mario R

    2014-01-01

    Lacosamide is an anticonvulsant drug which presents carbonic anhydrase inhibition. In this paper, we analyzed the apparent relationship between both activities performing a molecular modeling, docking and QSAR studies on 18 lacosamide derivatives with known anticonvulsant activity. Docking results suggested the zinc-binding site of carbonic anhydrase is a possible target of lacosamide and lacosamide derivatives making favorable Van der Waals interactions with Asn67, Gln92, Phe131 and Thr200. The mathematical models revealed a poor relationship between the anticonvulsant activity and molecular descriptors obtained from DFT and docking calculations. However, a QSAR model was developed using Dragon software descriptors. The statistic parameters of the model are: correlation coefficient, R=0.957 and standard deviation, S=0.162. Our results provide new valuable information regarding the relationship between both activities and contribute important insights into the essential molecular requirements for the anticonvulsant activity.

  8. Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity.

    PubMed

    Gramatica, P; Pilutti, P; Papa, E

    2007-01-01

    Nitrated Polycyclic Aromatic Hydrocarbons (nitro-PAHs), ubiquitous environmental pollutants, are recognized mutagens and carcinogens. A set of mutagenicity data (TA100) for 48 nitro-PAHs was modeled by the Quantitative Structure-Activity Relationships (QSAR) regression method, and OECD principles for QSAR model validation were applied. The proposed Multiple Linear Regression (MLR) models are based on two topological molecular descriptors. The models were validated for predictivity by both internal and external validation. For the external validation, three different splitting approaches, D-optimal Experimental Design, Self Organizing Maps (SOM) and Random Selection by activity sampling, were applied to the original data set in order to compare these methodologies and to select the best descriptors able to model each prediction set chemicals independently of the splitting method applied. The applicability domain was verified by the leverage approach.

  9. QSAR and QM/MM approaches applied to drug metabolism prediction.

    PubMed

    Braga, R C; Andrade, C H

    2012-06-01

    In modern drug discovery process, ADME/Tox properties should be determined as early as possible in the test cascade to allow a timely assessment of their property profiles. To help medicinal chemists in designing new compounds with improved pharmacokinetics, the knowledge of the soft spot position or the site of metabolism (SOM) is needed. In recent years, large number of in silico approaches for metabolism prediction have been developed and reported. Among these methods, QSAR models and combined quantum mechanics/molecular mechanics (QM/MM) methods for predicting drug metabolism have undergone significant advances. This review provides a perspective of the utility of QSAR and QM/MM approaches on drug metabolism prediction, highlighting the present challenges, limitations, and future perspectives in medicinal chemistry.

  10. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: hints from nano-QSAR studies.

    PubMed

    Gajewicz, Agnieszka; Schaeublin, Nicole; Rasulev, Bakhtiyor; Hussain, Saber; Leszczynska, Danuta; Puzyn, Tomasz; Leszczynski, Jerzy

    2015-05-01

    The production of nanomaterials increases every year exponentially and therefore the probability these novel materials that they could cause adverse outcomes for human health and the environment also expands rapidly. We proposed two types of mechanisms of toxic action that are collectively applied in a nano-QSAR model, which provides governance over the toxicity of metal oxide nanoparticles to the human keratinocyte cell line (HaCaT). The combined experimental-theoretical studies allowed the development of an interpretative nano-QSAR model describing the toxicity of 18 nano-metal oxides to the HaCaT cell line, which is a common in vitro model for keratinocyte response during toxic dermal exposure. The comparison of the toxicity of metal oxide nanoparticles to bacteria Escherichia coli (prokaryotic system) and a human keratinocyte cell line (eukaryotic system), resulted in the hypothesis that different modes of toxic action occur between prokaryotic and eukaryotic systems.

  11. Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery.

    PubMed

    Cruz-Monteagudo, Maykel; Schürer, Stephan; Tejera, Eduardo; Pérez-Castillo, Yunierkis; Medina-Franco, José L; Sánchez-Rodríguez, Aminael; Borges, Fernanda

    2017-07-01

    Current advances in systems biology suggest a new change of paradigm reinforcing the holistic nature of the drug discovery process. According to the principles of systems biology, a simple drug perturbing a network of targets can trigger complex reactions. Therefore, it is possible to connect initial events with final outcomes and consequently prioritize those events, leading to a desired effect. Here, we introduce a new concept, 'Systemic Chemogenomics/Quantitative Structure-Activity Relationship (QSAR)'. To elaborate on the concept, relevant information surrounding it is addressed. The concept is challenged by implementing a systemic QSAR approach for phenotypic virtual screening (VS) of candidate ligands acting as neuroprotective agents in Parkinson's disease (PD). The results support the suitability of the approach for the phenotypic prioritization of drug candidates. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Discovery of DPP IV inhibitors by pharmacophore modeling and QSAR analysis followed by in silico screening.

    PubMed

    Al-Masri, Ihab M; Mohammad, Mohammad K; Taha, Mutasem O

    2008-11-01

    Dipeptidyl peptidase IV (DPP IV) deactivates the natural hypoglycemic incretin hormones. Inhibition of this enzyme should restore glucose homeostasis in diabetic patients making it an attractive target for the development of new antidiabetic drugs. With this in mind, the pharmacophoric space of DPP IV was explored using a set of 358 known inhibitors. Thereafter, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors that yield selfconsistent and predictive quantitative structure-activity relationships (QSAR) (r(2) (287)=0.74, F-statistic=44.5, r(2) (BS)=0.74, r(2) (LOO)=0.69, r(2) (PRESS) against 71 external testing inhibitors=0.51). Two orthogonal pharmacophores (of cross-correlation r(2)=0.23) emerged in the QSAR equation suggesting the existence of at least two distinct binding modes accessible to ligands within the DPP IV binding pocket. Docking experiments supported the binding modes suggested by QSAR/pharmacophore analyses. The validity of the QSAR equation and the associated pharmacophore models were established by the identification of new low-micromolar anti-DPP IV leads retrieved by in silico screening. One of our interesting potent anti-DPP IV hits is the fluoroquinolone gemifloxacin (IC(50)=1.12 muM). The fact that gemifloxacin was recently reported to potently inhibit the prodiabetic target glycogen synthase kinase 3beta (GSK-3beta) suggests that gemifloxacin is an excellent lead for the development of novel dual antidiabetic inhibitors against DPP IV and GSK-3beta.

  13. Synthesis and QSAR study of novel anti-inflammatory active mesalazine-metronidazole conjugates.

    PubMed

    Naumov, Roman N; Panda, Siva S; Girgis, Adel S; George, Riham F; Farhat, Michel; Katritzky, Alan R

    2015-06-01

    Novel, mesalazine, metronidazole conjugates 6a-e with amino acid linkers were synthesized utilizing benzotriazole chemistry. Biological data acquired for all the novel bis-conjugates showed (a) some bis-conjugates exhibit comparable anti-inflammatory activity with parent drugs and (b) the potent bis-conjugates show no visible stomach lesions. 3D-pharmacophore and 2D-QSAR modeling support the observed bio-properties. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Molecular modeling of histamine H3 receptor and QSAR studies on arylbenzofuran derived H3 antagonists.

    PubMed

    Dastmalchi, Siavoush; Hamzeh-Mivehroud, Maryam; Ghafourian, Taravat; Hamzeiy, Hossain

    2008-01-01

    Histamine H3 receptors are presynaptic autoreceptors found in both central and peripheral nervous systems of many species. The central effects of these receptors suggest a potential therapeutic role for their antagonists in treatment of several neurological disorders such as epilepsy, schizophrenia, Alzheimer's and Parkinson's diseases. The purpose of this study was to identify the structural requirements for H3 antagonistic activity via quantitative structure-activity relationship (QSAR) studies and receptor modeling/docking techniques. A combination of partial least squares (PLS) and genetic algorithm (GA) was used in the QSAR approach to select the structural descriptors relevant to the receptor binding affinity of a series of 58 H3 antagonists. The descriptors were selected out of a pool of >1000 descriptors calculated by DRAGON, Hyperchem and ACD labs suite of programs. The resulting QSAR models for rat and human H3 binding affinities were validated using different strategies. QSAR models generated in the current work suggested the role of charge transfer interactions in the ligand-receptor interaction verified using the molecular modeling of the receptor and docking two antagonists to the binding site. The 3D model of human H3 receptor was built based on bovine rhodopsin structure and evaluated by molecular dynamics (MD) simulation in a mixed water-vacuum-water environment. The results were indicative of the stability of the model relating the observed structural changes during the MD simulation to the suggested ligand-receptor interactions. The results of this investigation are expected to be useful in the process of design and development of new potent H3 receptor antagonists.

  15. QSAR models for the removal of organic micropollutants in four different river water matrices.

    PubMed

    Sudhakaran, Sairam; Calvin, James; Amy, Gary L

    2012-04-01

    Ozonation is an advanced water treatment process used to remove organic micropollutants (OMPs) such as pharmaceuticals and personal care products (PPCPs). In this study, Quantitative Structure Activity Relationship (QSAR) models, for ozonation and advanced oxidation process (AOP), were developed with percent-removal of OMPs by ozonation as the criterion variable. The models focused on PPCPs and pesticides elimination in bench-scale studies done within natural water matrices: Colorado River, Passaic River, Ohio River and Suwannee synthetic water. The OMPs removal for the different water matrices varied depending on the water quality conditions such as pH, DOC, alkalinity. The molecular descriptors used to define the OMPs physico-chemical properties range from one-dimensional (atom counts) to three-dimensional (quantum-chemical). Based on a statistical modeling approach using more than 40 molecular descriptors as predictors, descriptors influencing ozonation/AOP were chosen for inclusion in the QSAR models. The modeling approach was based on multiple linear regression (MLR). Also, a global model based on neural networks was created, compiling OMPs from all the four river water matrices. The chemically relevant molecular descriptors involved in the QSAR models were: energy difference between lowest unoccupied and highest occupied molecular orbital (E(LUMO)-E(HOMO)), electron-affinity (EA), number of halogen atoms (#X), number of ring atoms (#ring atoms), weakly polar component of the solvent accessible surface area (WPSA) and oxygen to carbon ratio (O/C). All the QSAR models resulted in a goodness-of-fit, R(2), greater than 0.8. Internal and external validations were performed on the models.

  16. Does rational selection of training and test sets improve the outcome of QSAR modeling?

    PubMed

    Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander

    2012-10-22

    Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

  17. 3D-QSAR and molecular fragment replacement study on diaminopyrimidine and pyrrolotriazine ALK inhibitors

    NASA Astrophysics Data System (ADS)

    Ke, Zhipeng; Lu, Tao; Liu, Haichun; Yuan, Haoliang; Ran, Ting; Zhang, Yanmin; Yao, Sihui; Xiong, Xiao; Xu, Jinxing; Xu, Anyang; Chen, Yadong

    2014-06-01

    Over expression of anaplastic lymphoma kinase (ALK) has been found in many types of cancer, and ALK is a promising therapeutic target for the treatment of cancer. To obtain new potent inhibitors of ALK, we conducted lead optimization using 3D-QSAR modeling and molecular docking investigation of 2,4-diaminopyrimidines and 2,7-disubstituted-pyrrolo[2,1-f][1,2,4]triazine-based compounds. Three favorable 3D-QSAR models (CoMFA with q2, 0.555; r2, 0.939; CoMSIA with q2, 0.625; r2, 0.974; Topomer CoMFA with q2, 0.557; r2 0.756) have been developed to predict the biological activity of novel compounds. Topomer Search was utilized for virtual screening to obtain suitable fragments. The novel compounds generated by molecular fragment replacement (MFR) were evaluated by Topomer CoMFA prediction, Glide (docking) and further evaluated with CoMFA and CoMSIA prediction. 25 novel 2,7-disubstituted-pyrrolo[2,1-f][1,2,4]triazine derivatives as potential ALK inhibitors were finally obtained. In this paper, a combination of CoMFA, CoMSIA and Topomer CoMFA could obtain favorable 3D-QSAR models and suitable fragments for ALK inhibitors optimization. The work flow which comprised 3D-QSAR modeling, Topomer Search, MFR, molecular docking and evaluating criteria could be applied to de novo drug design and the resulted compounds initiate us to further optimize and design new potential ALK inhibitors.

  18. The current status of antimalarial drug research with special reference to application of QSAR models.

    PubMed

    Ojha, Probir Kumar; Roy, Kunal

    2015-01-01

    Malaria, the most virulent parasitic disease, has become a devastating health problem in tropical and subtropical regions, especially in Africa, due to favorable temperature and rainfall conditions for the development of the causative vector. Due to the spread of multidrug resistance to the marketed antimalarial drugs including the "magic bullet" artemisinin, discovery and development of new antimalarial drugs is one of the utmost challenges. Different government and non-government chemical regulatory authorities have recommended the application of non-animal, alternative techniques and in particular, in silico, methods in order to provide information about the basic physicochemical properties as well as the ecological and human health effects of chemicals before they reach into the market for public use. In this aspect, application of chemometric methods along with structure-based approaches may be useful for the design and discovery of new antimalarial compounds. The quantitative structureactivity relationship (QSAR) along with molecular docking and pharmacophore modeling techniques play a crucial role in the field of drug design. QSAR focuses on the chemical attributes influencing the activity and thereby allows synthesis of selective potential candidate molecules. In this communication, we have reviewed the QSAR reports along with some pharmacophore modeling and docking studies of antimalarial agents published during the year 2011 to 2014 and attempted to focus on the importance of physicochemical properties and structural features required for antimalarial activity of different chemical classes of compounds. Note that this is not an exhaustive review and all the given examples should be considered as the representative ones. The reader will gain an insight of the current status of QSAR and related in silico models developed for different classes of antimalarial compounds. This review suggests that combination of both ligand and structure-based drug designing

  19. Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation.

    PubMed

    Baumann, Désirée; Baumann, Knut

    2014-01-01

    Generally, QSAR modelling requires both model selection and validation since there is no a priori knowledge about the optimal QSAR model. Prediction errors (PE) are frequently used to select and to assess the models under study. Reliable estimation of prediction errors is challenging - especially under model uncertainty - and requires independent test objects. These test objects must not be involved in model building nor in model selection. Double cross-validation, sometimes also termed nested cross-validation, offers an attractive possibility to generate test data and to select QSAR models since it uses the data very efficiently. Nevertheless, there is a controversy in the literature with respect to the reliability of double cross-validation under model uncertainty. Moreover, systematic studies investigating the adequate parameterization of double cross-validation are still missing. Here, the cross-validation design in the inner loop and the influence of the test set size in the outer loop is systematically studied for regression models in combination with variable selection. Simulated and real data are analysed with double cross-validation to identify important factors for the resulting model quality. For the simulated data, a bias-variance decomposition is provided. The prediction errors of QSAR/QSPR regression models in combination with variable selection depend to a large degree on the parameterization of double cross-validation. While the parameters for the inner loop of double cross-validation mainly influence bias and variance of the resulting models, the parameters for the outer loop mainly influence the variability of the resulting prediction error estimate. Double cross-validation reliably and unbiasedly estimates prediction errors under model uncertainty for regression models. As compared to a single test set, double cross-validation provided a more realistic picture of model quality and should be preferred over a single test set.

  20. Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction

    PubMed Central

    Frimayanti, Neni; Yam, Mun Li; Lee, Hong Boon; Othman, Rozana; Zain, Sharifuddin M.; Rahman, Noorsaadah Abd.

    2011-01-01

    Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r2 value, r2 (CV) value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 μM to 7.04 μM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set. PMID:22272096

  1. Atomic Databases

    NASA Astrophysics Data System (ADS)

    Mendoza, Claudio

    2000-10-01

    Atomic and molecular data are required in a variety of fields ranging from the traditional astronomy, atmospherics and fusion research to fast growing technologies such as lasers, lighting, low-temperature plasmas, plasma assisted etching and radiotherapy. In this context, there are some research groups, both theoretical and experimental, scattered round the world that attend to most of this data demand, but the implementation of atomic databases has grown independently out of sheer necessity. In some cases the latter has been associated with the data production process or with data centers involved in data collection and evaluation; but sometimes it has been the result of individual initiatives that have been quite successful. In any case, the development and maintenance of atomic databases call for a number of skills and an entrepreneurial spirit that are not usually associated with most physics researchers. In the present report we present some of the highlights in this area in the past five years and discuss what we think are some of the main issues that have to be addressed.

  2. 3D-QSAR and molecular docking studies on HIV protease inhibitors

    NASA Astrophysics Data System (ADS)

    Tong, Jianbo; Wu, Yingji; Bai, Min; Zhan, Pei

    2017-02-01

    In order to well understand the chemical-biological interactions governing their activities toward HIV protease activity, QSAR models of 34 cyclic-urea derivatives with inhibitory HIV were developed. The quantitative structure activity relationship (QSAR) model was built by using comparative molecular similarity indices analysis (CoMSIA) technique. And the best CoMSIA model has rcv2, rncv2 values of 0.586 and 0.931 for cross-validated and non-cross-validated. The predictive ability of CoMSIA model was further validated by a test set of 7 compounds, giving rpred2 value of 0.973. Docking studies were used to find the actual conformations of chemicals in active site of HIV protease, as well as the binding mode pattern to the binding site in protease enzyme. The information provided by 3D-QSAR model and molecular docking may lead to a better understanding of the structural requirements of 34 cyclic-urea derivatives and help to design potential anti-HIV protease molecules.

  3. QSAR model as a random event: A case of rat toxicity.

    PubMed

    Toropova, Alla P; Toropov, Andrey A; Benfenati, Emilio; Leszczynska, Danuta; Leszczynski, Jerzy

    2015-03-15

    Quantitative structure-property/activity relationships (QSPRs/QSARs) can be used to predict physicochemical and/or biochemical behavior of substances which were not studied experimentally. Typically predicted values for chemicals in the training set are accurate since they were used to build the model. QSPR/QSAR models must be validated before they are used in practice. Unfortunately, the majority of the suggested approaches of the validation of QSPR/QSAR models are based on consideration of geometrical features of clusters of data points in the plot of experimental versus calculated values of an endpoint. We believe these geometrical criteria can be more useful if they are analyzed for several splits into the training and test sets. In this way, one can estimate the reproducibility of the model with various splits and better evaluate model reliability. The probability of the correct prediction of an endpoint for external validation set (in the series of the above-mentioned splits) can provide an useful way to evaluate the domain of applicability of the model. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. QSAR analysis of nitroaromatics' toxicity in Tetrahymena pyriformis: structural factors and possible modes of action

    PubMed Central

    Artemenko, A.G.; Muratov, E. N.; Kuz’min, V.E.; Muratov, N.N.; Varlamova, E.V.; Kuz'mina, A.V.; Gorb, L. G.; Golius, A.; Hill, F.C.; Leszczynski, J.; Tropsha, A.

    2012-01-01

    The Hierarchical Technology for Quantitative Structure - Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC50) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. Then, Partial Least Squares (PLS) statistical approach was used for the development of 2D QSAR models. Validated PLS models were explored to (i) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (ii) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; (iii) analyze the role of various physical-chemical factors responsible for compounds’ toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (R2ext=0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modeling and 76% for external set). PMID:21714735

  5. Application of quantitative structure activity relationship (QSAR) models to predict ozone toxicity in the lung.

    PubMed

    Kafoury, Ramzi M; Huang, Ming-Ju

    2005-08-01

    The sequence of events leading to ozone-induced airway inflammation is not well known. To elucidate the molecular and cellular events underlying ozone toxicity in the lung, we hypothesized that lipid ozonation products (LOPs) generated by the reaction of ozone with unsaturated fatty acids in the epithelial lining fluid and cell membranes play a key role in mediating ozone-induced airway inflammation. To test our hypothesis, we ozonized 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC) and generated LOPs. Confluent human bronchial epithelial cells were exposed to the derivatives of ozonized POPC-9-oxononanoyl, 9-hydroxy-9-hydroperoxynonanoyl, and 8-(5-octyl-1,2,4-trioxolan-3-yl-)octanoyl-at a concentration of 10 muM, and the activity of phospholipases A2 (PLA2), C (PLC), and D (PLD) was measured (1, 0.5, and 1 h, respectively). Quantitative structure-activity relationship (QSAR) models were utilized to predict the biological activity of LOPs in airway epithelial cells. The QSAR results showed a strong correlation between experimental and computed activity (r = 0.97, 0.98, 0.99, for PLA2, PLC, and PLD, respectively). The results indicate that QSAR models can be utilized to predict the biological activity of the various ozone-derived LOP species in the lung.

  6. Fragment-based strategy for structural optimization in combination with 3D-QSAR.

    PubMed

    Yuan, Haoliang; Tai, Wenting; Hu, Shihe; Liu, Haichun; Zhang, Yanmin; Yao, Sihui; Ran, Ting; Lu, Shuai; Ke, Zhipeng; Xiong, Xiao; Xu, Jinxing; Chen, Yadong; Lu, Tao

    2013-10-01

    Fragment-based drug design has emerged as an important methodology for lead discovery and drug design. Different with other studies focused on fragment library design and active fragment identification, a fragment-based strategy was developed in combination with three-dimensional quantitative structure-activity relationship (3D-QSAR) for structural optimization in this study. Based on a validated scaffold or fragment hit, a series of structural optimization was conducted to convert it to lead compounds, including 3D-QSAR modelling, active site analysis, fragment-based structural optimization and evaluation of new molecules. 3D-QSAR models and active site analysis provided sufficient information for confirming the SAR and pharmacophoric features for fragments. This strategy was evaluated through the structural optimization on a c-Met inhibitor scaffold 5H-benzo[4,5]cyclohepta[1,2-b]pyridin-5-one, which resulted in an c-Met inhibitor with high inhibitory activity. Our study suggested the effectiveness of this fragment-based strategy and the druggability of our newly explored active region. The reliability of this strategy indicated it could also be applied to facilitate lead optimization of other targets.

  7. QSAR modeling of a set of pyrazinoate esters as antituberculosis prodrugs.

    PubMed

    Fernandes, João P S; Pasqualoto, Kerly F M; Felli, Veni M A; Ferreira, Elizabeth I; Brandt, Carlos A

    2010-02-01

    Tuberculosis is an infection caused mainly by Mycobacterium tuberculosis. A first-line antimycobacterial drug is pyrazinamide (PZA), which acts partially as a prodrug activated by a pyrazinamidase releasing the active agent, pyrazinoic acid (POA). As pyrazinoic acid presents some difficulty to cross the mycobacterial cell wall, and also the pyrazinamide-resistant strains do not express the pyrazinamidase, a set of pyrazinoic acid esters have been evaluated as antimycobacterial agents. In this work, a QSAR approach was applied to a set of forty-three pyrazinoates against M. tuberculosis ATCC 27294, using genetic algorithm function and partial least squares regression (WOLF 5.5 program). The independent variables selected were the Balaban index (J), calculated n-octanol/water partition coefficient (ClogP), van-der-Waals surface area, dipole moment, and stretching-energy contribution. The final QSAR model (N = 32, r(2) = 0.68, q(2) = 0.59, LOF = 0.25, and LSE = 0.19) was fully validated employing leave-N-out cross-validation and y-scrambling techniques. The test set (N = 11) presented an external prediction power of 73%. In conclusion, the QSAR model generated can be used as a valuable tool to optimize the activity of future pyrazinoic acid esters in the designing of new antituberculosis agents.

  8. A QSAR-modeling perspective on cationic transfection lipids. 1. Predicting efficiency and understanding mechanisms.

    PubMed

    Horobin, Richard W; Weissig, Volkmar

    2005-08-01

    As gene therapy using viral vectors involves clinical risks, limited DNA-carrying capacity, and manufacturing problems, non-viral vectors, including cationic lipids, have been investigated. Unfortunately, these agents have significantly lower transfectional ability and, due to the complexity of the transfectional pathway, no general schemes exist for correlating cationic lipid chemistry with transfectional efficacy. Quantitative structure-activity relationship (QSAR) analyses were carried out on sets of routinely used, experimental, and unsuccessful cationic lipid vectors taken from the literature. This approach described the amphipathic character, basicity, headgroup size, lipophilicity and shape of cationic lipids using numerical parameters. Compounds were plotted onto various parameter diagrams, and correlations were sought between numerical parameters and transfectional efficiency. Transfectionally effective cationic lipids fell into restricted zones in various parameter spaces, indicating that amphipathic character, lipid shape and lipophilicity were generally significant factors, whilst basicity and headgroup size were only important for certain compounds. The data supported the general significance of membrane mixing followed by induction of membrane curvature, and the more limited role of osmotic shock, as mechanisms of membrane disruption. QSAR descriptions of effective lipids permitted detailed chemical guidelines for optimizing cationic lipid structure to be given. Limitations of the approach and models are discussed. QSAR modeling indicated that induction of membrane curvature and osmotic shock are important mechanisms for membrane disruption by cationic lipids. The models also allowed specification of chemically detailed guidelines for selection or design of optimal cationic lipids. Copyright (c) 2005 John Wiley & Sons, Ltd.

  9. Blood-brain barrier permeability mechanisms in view of quantitative structure-activity relationships (QSAR).

    PubMed

    Bujak, Renata; Struck-Lewicka, Wiktoria; Kaliszan, Michał; Kaliszan, Roman; Markuszewski, Michał J

    2015-04-10

    The goal of the present paper was to develop a quantitative structure-activity relationship (QSAR) method using a simple statistical approach, such as multiple linear regression (MLR) for predicting the blood-brain barrier (BBB) permeability of chemical compounds. The "best" MLR models, comprised logP and either molecular mass (M) or isolated atomic energy (E(isol)), tested on a structurally diverse set of 66 compounds, is characterized the by correlation coefficients (R) around 0.8. The obtained models were validated using leave-one-out (LOO) cross-validation technique and the correlation coefficient of leave-one-out- R(LOO)(2) (Q(2)) was at least 0.6. Analysis of a case from legal medicine demonstrated informative value of our QSAR model. To best authors' knowledge the present study is a first application of the developed QSAR models of BBB permeability to case from the legal medicine. Our data indicate that molecular energy-related descriptors, in combination with the well-known descriptors of lipophilicity may have a supportive value in predicting blood-brain distribution, which is of utmost importance in drug development and toxicological studies.

  10. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants.

    PubMed

    Kovarich, Simona; Papa, Ester; Gramatica, Paola

    2011-06-15

    The identification of potential endocrine disrupting (ED) chemicals is an important task for the scientific community due to their diffusion in the environment; the production and use of such compounds will be strictly regulated through the authorization process of the REACH regulation. To overcome the problem of insufficient experimental data, the quantitative structure-activity relationship (QSAR) approach is applied to predict the ED activity of new chemicals. In the present study QSAR classification models are developed, according to the OECD principles, to predict the ED potency for a class of emerging ubiquitary pollutants, viz. brominated flame retardants (BFRs). Different endpoints related to ED activity (i.e. aryl hydrocarbon receptor agonism and antagonism, estrogen receptor agonism and antagonism, androgen and progesterone receptor antagonism, T4-TTR competition, E2SULT inhibition) are modeled using the k-NN classification method. The best models are selected by maximizing the sensitivity and external predictive ability. We propose simple QSARs (based on few descriptors) characterized by internal stability, good predictive power and with a verified applicability domain. These models are simple tools that are applicable to screen BFRs in relation to their ED activity, and also to design safer alternatives, in agreement with the requirements of REACH regulation at the authorization step. Copyright © 2011 Elsevier B.V. All rights reserved.

  11. QSAR modeling and prediction of the endocrine-disrupting potencies of brominated flame retardants.

    PubMed

    Papa, Ester; Kovarich, Simona; Gramatica, Paola

    2010-05-17

    In the European Union REACH regulation, the chemicals with particularly harmful behaviors, such as endocrine disruptors (EDs), are subject to authorization, and the identification of safer alternatives to these chemicals is required. In this context, the use of quantitative structure-activity relationships (QSAR) becomes particularly useful to fill the data gap due to the very small number of experimental data available to characterize the environmental and toxicological profiles of new and emerging pollutants with ED behavior such as brominated flame retardants (BFRs). In this study, different QSAR models were developed on different responses of endocrine disruption measured for several BFRs. The multiple linear regression approach was applied to a variety of theoretical molecular descriptors, and the best models, which were identified from all of the possible combinations of the structural variables, were internally validated for their performance using the leave-one-out (Q(LOO)(2) = 73-91%) procedure and scrambling of the responses. External validation was provided, when possible, by splitting the data sets in training and test sets (range of Q(EXT)(2) = 76-90%), which confirmed the predictive ability of the proposed equations. These models, which were developed according to the principles defined by the Organization for Economic Co-operation and Development to improve the regulatory acceptance of QSARs, represent a simple tool for the screening and characterization of BFRs.

  12. QSAR prediction of the competitive interaction of emerging halogenated pollutants with human transthyretin.

    PubMed

    Papa, E; Kovarich, S; Gramatica, P

    2013-01-01

    The determination of the potential endocrine disruption (ED) activity of chemicals such as poly/perfluorinated compounds (PFCs) and brominated flame retardants (BFRs) is still hindered by a limited availability of experimental data. Quantitative structure-activity relationship (QSAR) strategies can be applied to fill this data gap, help in the characterization of the ED potential, and screen PFCs and BFRs with a hazardous toxicological profile. This paper proposes the modelling of T4-TTR (thyroxin-transthyretin) competing potency and relative binding potency toward T4 (logT4-REP) of PFCs and BFRs by regression and classification QSAR models. This study is a follow up of a former work, which analysed separately the interaction of BFRs and PFCs with the carrier TTR. The new results demonstrate the possibility of developing robust and predictive QSARs, which include both BFRs and PFCs in the training set, obtaining larger applicability domains than the existing models developed separately for BFRs and PFCs. The selection of modelling molecular descriptors confirms the importance of structural features, such as the aromatic OH or the molecular length, to increase the binding of the studied chemicals to TTR. Additionally, the need of experimental tests for some chemicals, and in particular for some of the BFRs, is highlighted.

  13. Is conformation a fundamental descriptor in QSAR? A case for halogenated anesthetics

    PubMed Central

    Guimarães, Maria C; Duarte, Mariene H; Silla, Josué M

    2016-01-01

    Summary An intriguing question in 3D-QSAR lies on which conformation(s) to use when generating molecular descriptors (MD) for correlation with bioactivity values. This is not a simple task because the bioactive conformation in molecule data sets is usually unknown and, therefore, optimized structures in a receptor-free environment are often used to generate the MD´s. In this case, a wrong conformational choice can cause misinterpretation of the QSAR model. The present computational work reports the conformational analysis of the volatile anesthetic isoflurane (2-chloro-2-(difluoromethoxy)-1,1,1-trifluoroethane) in the gas phase and also in polar and nonpolar implicit and explicit solvents to show that stable minima (ruled by intramolecular interactions) do not necessarily coincide with the bioconformation (ruled by enzyme induced fit). Consequently, a QSAR model based on two-dimensional chemical structures was built and exhibited satisfactory modeling/prediction capability and interpretability, then suggesting that these 2D MD´s can be advantageous over some three-dimensional descriptors. PMID:27340468

  14. QSAR studies, synthesis and antibacterial assessment of new inhibitors against multidrug-resistant Mycobacterium tuberculosis.

    PubMed

    Kovalishyn, Vasyl; Brovarets, Volodymyr; Blagodatnyi, Volodymyr; Kopernyk, Iryna; Hodyna, Diana; Chumachenko, Svitlana; Shablykin, Oleg; Kozachenko, Oleksandr; Vovk, Myhailo; Barus, Marianna; Bratenko, Myhailo; Metelytsia, Larysa

    2016-11-08

    This paper describes Quantitative Structure-Activity Relationships (QSAR) studies using Artificial Neural Networks (ANN), synthesis and in vitro antitubercular activity of several potent compounds against H37Rv and resistant Mycobacterium tuberculosis (Mtb) strains. Eight QSAR models were built using various types of descriptors with four publicly available structurally diverse datasets, including recent data from PubChem and ChEMBL. The predictive power of the obtained QSAR models was evaluated with a cross-validation procedure, giving a q2=0.74-0.78 for regression models and overall accuracy 78.9-94.4% for classification models. The external test sets were predicted with accuracies in the range of 84.1-95.0% (for the active/inactive classifications) and q2=0.80-0.83 for regressions. The 15 synthesized compounds showed inhibitory activity against H37Rv strain whereas the compounds 1-7 were also active against resistant Mtb strain (resistant to isoniazid and rifampicin). The results indicated that compounds 1-7 could serve as promising leads for further optimization as novel antibacterial inhibitors, in particular, for the treatment of drug resistance of Mtb forms.

  15. Latest QSAR study of adenosine A[Formula: see text] receptor affinity of xanthines and deazaxanthines.

    PubMed

    Pérez-Garrido, Alfonso; Rivero-Buceta, Virginia; Cano, Gaspar; Kumar, Sanjay; Pérez-Sánchez, Horacio; Bautista, Marta Teijeira

    2015-11-01

    Adenosine, a widespread and endogenous nucleoside that acts as a powerful neuromodulator in the nervous system, is a promising therapeutic target in a wide range of conditions. The structural similarity between xanthine derivatives and neurotransmitter adenosine has led to the derivatives of the heterocyclic ring being among the most abundant chemical classes of ligand antagonists of adenosine receptor subtypes. Small changes in the xanthine scaffold have resulted in a wide array of adenosine receptor antagonists. In this work, we developed a QSAR model for the [Formula: see text] subtype, which is, as yet, not well characterized, with two purposes in mind: to predict adenosine [Formula: see text] antagonist activity and to offer a substructural interpretation of this group of xanthines. The QSAR model provided good classifications of both the test and external sets. In addition, most of the contributions to adenosine [Formula: see text] receptor affinity derived by subfragmentation of the molecules in the training set agree with the relationships observed in the literature. These two factors mean that this QSAR ensemble could be used as a model to predict future adenosine [Formula: see text] antagonist candidates.

  16. Understanding hERG inhibition with QSAR models based on a one-dimensional molecular representation

    NASA Astrophysics Data System (ADS)

    Diller, David J.; Hobbs, Doug W.

    2007-07-01

    Blockage of the potassium channel encoded by the human ether-a-go-go related gene (hERG) is well understood to be the root cause of the cardio-toxicity of numerous approved and investigational drugs. As such, a cascade of in vitro and in vivo assays have been developed to filter compounds with hERG inhibitory activity. Quantitative structure activity relationship (QSAR) models are used at the very earliest part of this cascade to eliminate compounds that are likely to have this undesirable activity prior to synthesis. Here a new QSAR technique based on the one-dimensional representation is described in the context of the development of a model to predict hERG inhibition. The model is shown to perform close to the limits of the quality of the data used for model building. In order to make optimal use of the available data, a general robust mathematical scheme was developed and is described to simultaneously incorporate quantitative data, such as IC50 = 50 nM, and qualitative data, such as inactive or IC50 > 30 μM into QSAR models without discarding any experimental information.

  17. QSAR: an in silico approach for predicting the partitioning of pesticides into breast milk.

    PubMed

    Agatonovic-Kustrin, Suezana; Morton, David W; Celebic, D

    2013-03-01

    The aim of this study was to develop an in silico Quantitative Structure Activity Relationship (QSAR) model capable of predicting partitioning of pesticides into breast milk from their respective chemical structures. A large data set of 190 diverse compounds, including drugs and their active metabolites (87%), and pesticides (13%) with experimentally derived milk/plasma (M/P) ratios taken from the literature, was used to train, test and validate a predictive model. Each compound was encoded with 65 calculated chemical structure descriptors. Sensitivity analysis was then used to select a subset of the descriptors that best describe the transfer of pesticides into breast milk and Artificial neural networks modeling was applied to correlate selected descriptors (inputs) with the M/P ratio (output) in order to develop a predictive QSAR. The developed QSAR model included 26 molecular descriptors related to the molecular size, polarity and hydrogen binding capacity. Together with aromatic rings, these descriptors account for molecule's size and hydrophobic interaction capabilities. The average correlation for the final model (incorporating training, testing, and validation) was 0.85. The developed model provides a useful method for predicting the M/P ratios of pesticides from just a sketch of their respective molecular structures. However, these predictions should only be used to assist in the evaluation of risk in conjunction with an assessment of the infant's response to a given drug/pesticide.

  18. QSAR, docking, ADMET, and system pharmacology studies on tormentic acid derivatives for anticancer activity.

    PubMed

    Alam, Sarfaraz; Khan, Feroz

    2017-08-02

    To explore the anticancer compounds from tormentic acid derivatives, a quantitative structure-activity relationship (QSAR) model was developed by the multiple linear regression methods. The developed QSAR model yielded a high activity-descriptors relationship accuracy of 94% referred by regression coefficient (r(2) = .94) and a high activity prediction accuracy of 91%. The QSAR study indicates that chemical descriptors, chiV5, T_T_Cl_7, T_2_T_4, SsCH3count, and Epsilon3 are significantly correlated with anticancer activity. This validated model was further been used for virtual screening and thus identification of new potential breast cancer inhibitors. Lipinski's rule of five, ADMET risk and synthetic accessibility are used to filter false positive hits. Filtered compounds were then docked to identify the possible target binding pocket, to obtain a set of aligned ligand poses and to prioritize the predicted active compounds. The scrutinized compounds, as well as their metabolites, were predicted and analyzed for different pharmacokinetics parameters such as absorption, distribution, metabolism, excretion, and toxicity. Finally, the top-ranked compound NB-12 was evaluated by system pharmacology approach. Later studied the metabolic networks, disease biomarker networks, pathway maps, drug-target networks and generate significant gene networks. The strategy applied in this research work may act as a framework for rational design of potential anticancer drugs.

  19. BCL::EMAS — Enantioselective Molecular Asymmetry Descriptor for 3D-QSAR

    PubMed Central

    Sliwoski, Gregory; Lowe, Edward W.; Butkiewicz, Mariusz; Meiler, Jens

    2013-01-01

    Stereochemistry is an important determinant of a molecule's biological activity. Stereoisomers can have different degrees of efficacy or even opposing effects when interacting with a target protein. Stereochemistry is a molecular property difficult to represent in 2D-QSAR as it is an inherently three-dimensional phenomenon. A major drawback of most proposed descriptors for 3D-QSAR that encode stereochemistry is that they require a heuristic for defining all stereocenters and rank-ordering its substituents. Here we propose a novel 3D-QSAR descriptor termed Enantioselective Molecular ASymmetry (EMAS) that is capable of distinguishing between enantiomers in the absence of such heuristics. The descriptor aims to measure the deviation from an overall symmetric shape of the molecule. A radial-distribution function (RDF) determines a signed volume of tetrahedrons of all triplets of atoms and the molecule center. The descriptor can be enriched with atom-centric properties such as partial charge. This descriptor showed good predictability when tested with a dataset of thirty-one steroids commonly used to benchmark stereochemistry descriptors (r2 = 0.89, q2 = 0.78). Additionally, EMAS improved enrichment of 4.38 versus 3.94 without EMAS in a simulated virtual high-throughput screening (vHTS) for inhibitors and substrates of cytochrome P450 (PUBCHEM AID891). PMID:22907158

  20. Development of biologically active compounds by combining 3D QSAR and structure-based design methods

    NASA Astrophysics Data System (ADS)

    Sippl, Wolfgang

    2002-11-01

    One of the major challenges in computational approaches to drug design is the accurate prediction of the binding affinity of novel biomolecules. In the present study an automated procedure which combines docking and 3D-QSAR methods was applied to several drug targets. The developed receptor-based 3D-QSAR methodology was tested on several sets of ligands for which the three-dimensional structure of the target protein has been solved - namely estrogen receptor, acetylcholine esterase and protein-tyrosine-phosphatase 1B. The molecular alignments of the studied ligands were determined using the docking program AutoDock and were compared with the X-ray structures of the corresponding protein-ligand complexes. The automatically generated protein-based ligand alignment obtained was subsequently taken as basis for a comparative field analysis applying the GRID/GOLPE approach. Using GRID interaction fields and applying variable selection procedures, highly predictive models were obtained. It is expected that concepts from receptor-based 3D QSAR will be valuable tools for the analysis of high-throughput screening as well as virtual screening data

  1. Theoretical studies on QSAR and mechanism of 2-indolinone derivatives as tubulin inhibitors

    NASA Astrophysics Data System (ADS)

    Liao, Si Yan; Qian, Li; Miao, Ti Fang; Lu, Hai Liang; Zheng, Kang Cheng

    The theoretical studies on three-dimensional quantitative structure activity relationship (3D-QSAR) and action mechanism of a series of 2-indolinone derivatives as tubulin inhibitors against human breast cancer cell line MDA-MB-231 have been carried out. The established 3D-QSAR model from the comparative molecular field analysis (CoMFA) shows not only significant statistical quality but also predictive ability, with high correlation coefficient (R2 = 0.986) and cross-validation coefficient (q2 = 0.683). In particular, the appropriate binding orientations and conformations of these 2-indolinone derivatives interacting with tubulin are located by docking study, and it is very interesting to find that the plot of the energy scores of these compounds in DOCK versus the corresponding experimental pIC50 values exhibits a considerable linear correlation. Therefore, the inhibition mechanism that 2-indolinone derivatives were regarded as tubulin inhibitors can be theoretically confirmed. Based on such an inhibition mechanism along with 3D-QSAR results, some important factors improving the activities of these compounds were discussed in detail. These factors can be summarized as follows: the H atom adopted as substituent R1, the substituent R2 with higher electropositivity and smaller bulk, the substituents R4-R6 (on the phenyl ring) with higher electropositivity and larger bulk, and so on. These results can offer useful theoretical references for understanding the action mechanism, designing more potent inhibitors, and predicting their activities prior to synthesis.

  2. Fragment-based strategy for structural optimization in combination with 3D-QSAR

    NASA Astrophysics Data System (ADS)

    Yuan, Haoliang; Tai, Wenting; Hu, Shihe; Liu, Haichun; Zhang, Yanmin; Yao, Sihui; Ran, Ting; Lu, Shuai; Ke, Zhipeng; Xiong, Xiao; Xu, Jinxing; Chen, Yadong; Lu, Tao

    2013-10-01

    Fragment-based drug design has emerged as an important methodology for lead discovery and drug design. Different with other studies focused on fragment library design and active fragment identification, a fragment-based strategy was developed in combination with three-dimensional quantitative structure-activity relationship (3D-QSAR) for structural optimization in this study. Based on a validated scaffold or fragment hit, a series of structural optimization was conducted to convert it to lead compounds, including 3D-QSAR modelling, active site analysis, fragment-based structural optimization and evaluation of new molecules. 3D-QSAR models and active site analysis provided sufficient information for confirming the SAR and pharmacophoric features for fragments. This strategy was evaluated through the structural optimization on a c-Met inhibitor scaffold 5H-benzo[4,5]cyclohepta[1,2-b]pyridin-5-one, which resulted in an c-Met inhibitor with high inhibitory activity. Our study suggested the effectiveness of this fragment-based strategy and the druggability of our newly explored active region. The reliability of this strategy indicated it could also be applied to facilitate lead optimization of other targets.

  3. The Danish Centre for Strategic Research in Type 2 Diabetes (DD2): organization of diabetes care in Denmark and supplementary data sources for data collection among DD2 study participants.

    PubMed

    Thomsen, Reimar Wernich; Friborg, Søren; Nielsen, Jens Steen; Schroll, Henrik; Johnsen, Søren Paaske

    2012-01-01

    This paper provides a short overview of the Danish health care system and the organization of care for type 2 diabetes patients in Denmark. It also describes the supplementary data sources that are used for collection of baseline data in the nationwide Danish Centre for Strategic Research in Type 2 Diabetes (DD2) Project. The Danish National Health Service provides tax-funded medical care for all 5.6 million Danish residents. The health care system is characterized by extensive individual-level registration of data used for planning, administration, quality improvement, and research. It is estimated that there are currently at least 250,000 individuals with known diabetes in Denmark (approximately 4.5% of the Danish population), of which an estimated 80% are followed and treated by their general practitioners and approximately 20% are followed at hospital specialist outpatient clinics. These health care providers form the basis for recruiting diabetes patients in the DD2 project, and the data sources that these providers use in clinical practice give access to important supplementary patient data. The DD2's patient-enrollment system is designed to be fast and simple, and thus only collects primary interview data that cannot be extracted from already existing data sources. Thus, in addition to an online DD2 questionnaire filled out by general practitioners and hospital physicians at the time of patient enrollment, supplementary data are obtained from the Danish Diabetes Database for Adults, a nationwide clinical quality improvement registry. Both hospital physicians and a growing number of general practitioners routinely report data to this database. For general practitioners, the Danish General Practice Database acts as an important feeder database for the Danish Diabetes Database for Adults and thereby also for the DD2 project.

  4. Evaluation and comparison of benchmark QSAR models to predict a relevant REACH endpoint: The bioconcentration factor (BCF)

    SciTech Connect

    Gissi, Andrea; Lombardo, Anna; Roncaglioni, Alessandra; Gadaleta, Domenico; Mangiatordi, Giuseppe Felice; Nicolotti, Orazio; Benfenati, Emilio

    2015-02-15

    The bioconcentration factor (BCF) is an important bioaccumulation hazard assessment metric in many regulatory contexts. Its assessment is required by the REACH regulation (Registration, Evaluation, Authorization and Restriction of Chemicals) and by CLP (Classification, Labeling and Packaging). We challenged nine well-known and widely used BCF QSAR models against 851 compounds stored in an ad-hoc created database. The goodness of the regression analysis was assessed by considering the determination coefficient (R{sup 2}) and the Root Mean Square Error (RMSE); Cooper's statistics and Matthew's Correlation Coefficient (MCC) were calculated for all the thresholds relevant for regulatory purposes (i.e. 100 L/kg for Chemical Safety Assessment; 500 L/kg for Classification and Labeling; 2000 and 5000 L/kg for Persistent, Bioaccumulative and Toxic (PBT) and very Persistent, very Bioaccumulative (vPvB) assessment) to assess the classification, with particular attention to the models' ability to control the occurrence of false negatives. As a first step, statistical analysis was performed for the predictions of the entire dataset; R{sup 2}>0.70 was obtained using CORAL, T.E.S.T. and EPISuite Arnot–Gobas models. As classifiers, ACD and log P-based equations were the best in terms of sensitivity, ranging from 0.75 to 0.94. External compound predictions were carried out for the models that had their own training sets. CORAL model returned the best performance (R{sup 2}{sub ext}=0.59), followed by the EPISuite Meylan model (R{sup 2}{sub ext}=0.58). The latter gave also the highest sensitivity on external compounds with values from 0.55 to 0.85, depending on the thresholds. Statistics were also compiled for compounds falling into the models Applicability Domain (AD), giving better performances. In this respect, VEGA CAESAR was the best model in terms of regression (R{sup 2}=0.94) and classification (average sensitivity>0.80). This model also showed the best regression (R{sup 2

  5. Mogens Jansen: An Interview with a Danish Reading Educator.

    ERIC Educational Resources Information Center

    Engberg, Eva

    1985-01-01

    The president of the Danish Association of Reading Teachers discusses the positive effects of international cooperation on reading education, the influence of society's demands on curriculum, and the instinctive features and benefits of Danish Reading instruction. (FL)

  6. Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

    PubMed Central

    Nikolic, Katarina; Mavridis, Lazaros; Djikic, Teodora; Vucicevic, Jelica; Agbaba, Danica; Yelekci, Kemal; Mitchell, John B. O.

    2016-01-01

    HIGHLIGHTS Many CNS targets are being explored for multi-target drug designNew databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compoundsQSAR, virtual screening and docking methods increase the potential of rational drug design The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent

  7. QSAR-assisted virtual screening of lead-like molecules from marine and microbial natural sources for antitumor and antibiotic drug discovery.

    PubMed

    Pereira, Florbela; Latino, Diogo A R S; Gaudêncio, Susana P

    2015-03-17

    A Quantitative Structure-Activity Relationship (QSAR) approach for classification was used for the prediction of compounds as active/inactive relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1746 compounds from PubChem with empirical CDK descriptors and semi-empirical quantum-chemical descriptors. A data set of 183 active pharmaceutical ingredients was additionally used for the external validation of the best models. The best classification models for antibiotic and antitumor activities were used to screen a data set of marine and microbial natural products from the AntiMarin database-25 and four lead compounds for antibiotic and antitumor drug design were proposed, respectively. The present work enables the presentation of a new set of possible lead like bioactive compounds and corroborates the results of our previous investigations. By other side it is shown the usefulness of quantum-chemical descriptors in the discrimination of biologically active and inactive compounds. None of the compounds suggested by our approach have assigned non-antibiotic and non-antitumor activities in the AntiMarin database and almost all were lately reported as being active in the literature.

  8. Stackfile Database

    NASA Technical Reports Server (NTRS)

    deVarvalho, Robert; Desai, Shailen D.; Haines, Bruce J.; Kruizinga, Gerhard L.; Gilmer, Christopher

    2013-01-01

    This software provides storage retrieval and analysis functionality for managing satellite altimetry data. It improves the efficiency and analysis capabilities of existing database software with improved flexibility and documentation. It offers flexibility in the type of data that can be stored. There is efficient retrieval either across the spatial domain or the time domain. Built-in analysis tools are provided for frequently performed altimetry tasks. This software package is used for storing and manipulating satellite measurement data. It was developed with a focus on handling the requirements of repeat-track altimetry missions such as Topex and Jason. It was, however, designed to work with a wide variety of satellite measurement data [e.g., Gravity Recovery And Climate Experiment -- GRACE). The software consists of several command-line tools for importing, retrieving, and analyzing satellite measurement data.

  9. QSAR modeling and chemical space analysis of antimalarial compounds

    NASA Astrophysics Data System (ADS)

    Sidorov, Pavel; Viira, Birgit; Davioud-Charvet, Elisabeth; Maran, Uko; Marcou, Gilles; Horvath, Dragos; Varnek, Alexandre

    2017-05-01

    Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including 3000 molecules tested in one or several of 17 anti- Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.

  10. QSAR modeling and chemical space analysis of antimalarial compounds.

    PubMed

    Sidorov, Pavel; Viira, Birgit; Davioud-Charvet, Elisabeth; Maran, Uko; Marcou, Gilles; Horvath, Dragos; Varnek, Alexandre

    2017-04-03

    Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.

  11. 2D and 3D-QSAR studies on antiproliferative thiazolidine analogs

    NASA Astrophysics Data System (ADS)

    Liao, Si Yan; Qian, Li; Chen, Jin Can; Lu, Hai Liang; Zheng, Kang Cheng

    Two-dimensional (2D) and three-dimensional (3D) quantitative structure-activity relationships (QSARs) of 22 thiazolidine analogs with antiproliferative activity expressed as pIC50, which is defined as the negative value of the logarithm of necessary molar concentration of these compounds to cause 50% growth inhibition against melanoma cell lines WM-164, have been studied by using a combined method of the DFT, MM2 and statistics for 2D, as well as the comparative molecular field analysis (CoMFA) method for 3D. The established 2D-QSAR model in training set comprised of random 18 compounds shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient (R2A = 0.832) and the square of the cross-validation coefficient (q2 = 0.803). The same model was further applied to predict pIC50 values of the four compounds in the test set, and the resulting R2pred reaching 0.784, further confirms that this 2D-QSAR model has high predictive ability. The 3D-QSAR model also shows good correlative and predictive capabilities in terms of R2 (0.956) and q2 (0.615) obtained from CoMFA model. Further, the robustness of the CoMFA model was verified by bootstrapping analysis (100 runs) with R2bs (0.979) and SDbs (0.056). It is very interesting to find that the results from 2D- and 3D-QSAR analyses accord with each other, and they all show that the steric interaction plays a crucial role in determining the cytotoxicities of the compounds, and that selecting a moderate-size or appropriate-hydrophobicity substituent R as well as increasing the negative charges of C4 on phenyl ring at the same time are advantageous to improving the cytotoxicity. Such results can offer some useful theoretical references for directing the molecular design and understanding the action mechanism of this kind of compound with antiproliferative activity.

  12. Integrated QSAR study for inhibitors of hedgehog signal pathway against multiple cell lines:a collaborative filtering method

    PubMed Central

    2012-01-01

    Background The Hedgehog Signaling Pathway is one of signaling pathways that are very important to embryonic development. The participation of inhibitors in the Hedgehog Signal Pathway can control cell growth and death, and searching novel inhibitors to the functioning of the pathway are in a great demand. As the matter of fact, effective inhibitors could provide efficient therapies for a wide range of malignancies, and targeting such pathway in cells represents a promising new paradigm for cell growth and death control. Current research mainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bind specifically to the Smo protein, and can be used for cancer therapy. While quantitatively structure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction of activity values of new compounds. In this study, we proposed a novel collaborative QSAR model for inhibitors of the Hedgehog Signaling Pathway by integration the information from multiple cell lines. Such a model is expected to substantially improve the QSAR ability from single cell lines, and provide useful clues in developing clinically effective inhibitors and modifications of parent lead compounds for target on the Hedgehog Signaling Pathway. Results In this study, we have presented: (1) a collaborative QSAR model, which is used to integrate information among multiple cell lines to boost the QSAR results, rather than only a single cell line QSAR modeling. Our experiments have shown that the performance of our model is significantly better than single cell line QSAR methods; and (2) an efficient feature selection strategy under such collaborative environment, which can derive the commonly important features related to the entire given cell lines, while simultaneously showing their specific contributions to a specific cell-line. Based on feature selection results, we have

  13. The Danish Communicative Developmental Inventories: Validity and Main Developmental Trends

    ERIC Educational Resources Information Center

    Bleses, Dorthe; Vach, Werner; Slott, Malene; Wehberg, Sonja; Thomsen, Pia; Madsen, Thomas O.; Basboll, Hans

    2008-01-01

    This paper presents a large-scale cross-sectional study of Danish children's early language acquisition based on the Danish adaptation of the "MacArthur-Bates Communicative Development Inventories" (CDI). Measures of validity and reliability imply that the Danish adaptation of the American CDI has been adjusted linguistically and culturally in…

  14. Monitoring of equine health in Denmark: the importance, purpose, research areas and content of a future database.

    PubMed

    Hartig, Wendy; Houe, Hans; Andersen, Pia Haubro

    2013-04-01

    The plentiful data on Danish horses are currently neither organized nor easily accessible, impeding register-based epidemiological studies on Danish horses. A common database could be beneficial. In principle, databases can contain a wealth of information, but no single database can serve every purpose. Hence the establishment of a Danish equine health database should be preceded by careful consideration of its purpose and content, and stakeholder attitudes should be investigated. The objectives of the present study were to identify stakeholder attitudes to the importance, purpose, research areas and content of a health database for horses in Denmark. A cross-sectional study was conducted with 13 horse-related stakeholder groups in Denmark. The groups surveyed included equine veterinarians, researchers, veterinary students, representatives from animal welfare organizations, horse owners, trainers, farriers, authority representatives, ordinary citizens, and representatives from laboratories, insurance companies, medical equipment companies and pharmaceutical companies. Supplementary attitudes were inferred from qualitative responses. The overall response rate for all stakeholder groups was 45%. Stakeholder group-specific response rates were 27-80%. Sixty-eight percent of questionnaire respondents thought a national equine health database was important. Most respondents wanted the database to contribute to improved horse health and welfare, to be used for research into durability and disease heritability, and to serve as a basis for health declarations for individual horses. The generally preferred purpose of the database was thus that it should focus on horse health and welfare rather than on performance or food safety, and that it should be able to function both at a population and an individual horse level. In conclusion, there is a positive attitude to the establishment of a health database for Danish horses. These results could enrich further reflection on the

  15. A study of the relationship between cornea permeability and eye irritation using membrane-interaction QSAR analysis.

    PubMed

    Li, Yi; Liu, Jianzhong; Pan, Dahua; Hopfinger, A J

    2005-12-01

    A methodology termed membrane-interaction QSAR (MI-QSAR) analysis has been used to develop QSAR models to predict drug permeability coefficients across cornea and its component layers (epithelium, stroma, and endothelium). From a training set of 25 structurally diverse drugs, significant QSAR models are constructed and compared for the permeability of the cornea, epithelium, and stroma plus endothelium. Cornea permeability is found to depend on the measured distribution coefficient of the drug, the cohesive energy of the drug, the total potential energy of the drug-membrane "complex," and three other energy refinement descriptor terms. The endothelium may be a more important barrier in cornea permeation than the stroma. Moreover, an investigation of the correlation between cornea permeation and eye irritation is presented as an example of a cross study on different ADMET properties using MI-QSAR analysis. Thirteen structurally diverse drugs, whose molar-adjusted eye irritation scores (MES) have been measured using the Draize rabbit-eye test, were chosen as an eye irritation comparison set. A poor correlation (R(2) = 0.0232) between the MES measures and the predicted cornea permeability coefficients for the drugs in the eye irritation set suggests there is no significant relationship between eye irritation potency and the cornea permeability.

  16. Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models.

    PubMed

    Sizochenko, Natalia; Gajewicz, Agnieszka; Leszczynski, Jerzy; Puzyn, Tomasz

    2016-04-07

    In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase "correlation does not imply causation" reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal analysis of multivariate data. Methods of causal discovery have been applied for broader physical insight into mechanisms of action and interpretation of the developed nano-QSAR models. Previously developed nano-QSAR models for toxicity of 17 nano-sized metal oxides towards E. coli bacteria have been validated by means of the causality criteria. Using the descriptors confirmed by the causal technique, we have developed new models consistent with the straightforward causal-reasoning account. It was proven that causal inference methods are able to provide a more robust mechanistic interpretation of the developed nano-QSAR models.

  17. Adherence to treatment: practice, education and research in Danish community pharmacy

    PubMed Central

    Haugbølle, Lotte S.; Herborg, Hanne

    2009-01-01

    Objective: To describe the practice, education and research concerning medication adherence in Danish community pharmacy. Methods: The authors supplemented their expertise in the area of medication adherence through their contacts with other educators and researchers as well as by conducting searches in the Danish Pharmacy Practice Evidence Database, which provides annually updated literature reviews on intervention research in Danish pharmacy practice. Results: Practice: Medication adherence is the focus of and/or is supported by a large number of services and initiatives used in pharmacy practice such as governmental funding, IT-supported medicine administration systems, dose-dispensing systems, theme years in pharmacies on adherence and concordance, standards for counselling at the counter, pharmacist counselling, medication reviews and inhaler technique assessment. Education: In Denmark, pharmacy and pharmaconomist students are extensively trained in the theory and practice of adherence to therapy. Pharmacy staff can choose from a variety of continuing education and post-graduate programmes which address patient adherence. Research: Nine ongoing and recently completed studies are described. Early research in Denmark comprised primarily smaller, qualitative studies centred on user perspectives, whereas later research has shifted the focus towards larger, quantitative, controlled studies and action-oriented studies focusing on patient groups with chronic diseases (such as diabetes, asthma, coronary vascular diseases). Conclusions: Our analysis has documented that Danish pharmaceutical education and research has focused strongly on adherence to treatment for more than three decades. Adherence initiatives in Danish community pharmacies have developed substantially in the past 5-10 years, and, as pharmacies have prioritised their role in health care and patient safety, this development can be expected to continue in future years. PMID:25136393

  18. Danish retinoblastoma patients 1943-2013 - genetic testing and clinical implications.

    PubMed

    Gregersen, Pernille A; Urbak, Steen F; Funding, Mikkel; Overgaard, Jens; Jensen, Uffe B; Alsner, Jan

    2016-01-01

    In heritable retinoblastoma there is a 50% risk of transmitting the RB1 mutation, and offspring carriers have more than 90% risk of developing retinoblastoma. Today, all newly diagnosed retinoblastoma patients in Denmark are screened for mutations in RB1, as opposed to only a minority of patients diagnosed before DNA testing was offered. Knowledge of heredity increases the chance of early diagnosis in offspring, leading to improved prognosis. We present data from the Danish retinoblastoma patients that emphasize the need for genetic counseling and RB1 screening in all untested retinoblastoma survivors. Data are extracted from The Danish Ocular Oncology Group Database, a national population database containing data on all Danish retinoblastoma patients since 1943. In total 323 retinoblastoma patients have been diagnosed between 1943 and 2013. Since 1963, the rate has been stable around 1 per 14 000 live births with 95% of the patients surviving their retinoblastoma. Stratifying data on the time of diagnosis and status of genetic testing, the number of screened patients gradually increased from 5% in the beginning of the period to 96% in the last five-year period. A cohort of 181 retinoblastoma survivors with sporadic disease (15% heritable) did not receive genetic testing. Since the introduction of routine testing, one of 14 sporadic unilateral patients tested (7%) has been identified with a germline mutation. Before routine testing, five additional sporadic unilateral patients have been identified as heritable. Only a minority of Danish retinoblastoma patients diagnosed before routine genetic testing was offered have been RB1 screened. To counsel the remaining untested patients and their families sufficiently regarding the risk to offspring and elevated risk of second primary cancers, we recommend information and access to genetic counseling and RB1 screening. This has ethical, psychological and possible economic consequences, and should be handled with caution.

  19. Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters.

    PubMed

    Rácz, A; Bajusz, D; Héberger, K

    2015-01-01

    Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.

  20. Aldose reductase inhibitors for diabetic complications: Receptor induced atom-based 3D-QSAR analysis, synthesis and biological evaluation.

    PubMed

    Vyas, Bhawna; Singh, Manjinder; Kaur, Maninder; Bahia, Malkeet Singh; Jaggi, Amteshwar Singh; Silakari, Om; Singh, Baldev

    2015-06-01

    Herein, atom-based 3D-QSAR analysis was performed using receptor-guided alignment of 46 flavonoid inhibitors of aldose reductase (ALR2) enzyme. 3D-QSAR models were generated in PHASE programme, and the best model corresponding to PLS factor four (QSAR4), was selected based on different statistical parameters (i.e., Rtrain(2), 0.96; Qtest(2) 0.81; SD, 0.26). The contour plots of different structural properties generated from the selected model were utilized for the designing of five new congener molecules. These designed molecules were duly synthesized, and evaluated for their in vitro ALR2 inhibitory activity that resulted in the micromolar (IC50<22μM) activity of all molecules. Thus, the newly designed molecules having ALR inhibitory potential could be employed for the management of diabetic complications.

  1. Determination of receptor-bound drug conformations by QSAR using flexible fitting to derive a molecular similarity index

    NASA Astrophysics Data System (ADS)

    Montanari, C. A.; Tute, M. S.; Beezer, A. E.; Mitchell, J. C.

    1996-02-01

    Results are presented for a QSAR analysis of bisamidines, using a similarity index as descriptor. The method allows for differences in conformation of bisamidines at the receptor site to be taken into consideration. In particular, it has been suggested by others that pentamidine binds in the minor groove of DNA in a so-called isohelical conformation, and our QSAR supports this suggestion. The molecular similarity index for comparison of molecules can be used as a parameter for correlating and hence rationalising the activity as well as suggesting the design of bioactive molecules. The studied compounds had been evaluated for potency against Leishmania mexicana amazonensis, and this potency was used as a dependent variable in a series of QSAR analyses. For the calculation of similarity indexes, each analogue was in turn superimposed on a chosen lead compound in a reference conformation, either extended or isohelical, maximising overlap and hence similarity by flexible fitting.

  2. A quantitative structure-activity relationship (QSAR) study of some diaryl urea derivatives of B-RAF inhibitors

    PubMed Central

    Sadeghian-Rizi, Sedighe; Sakhteman, Amirhossein; Hassanzadeh, Farshid

    2016-01-01

    In the current study, both ligand-based molecular docking and receptor-based quantitative structure activity relationships (QSAR) modeling were performed on 35 diaryl urea derivative inhibitors of V600EB-RAF. In this QSAR study, a linear (multiple linear regressions) and a nonlinear (partial least squares least squares support vector machine (PLS-LS-SVM)) were used and compared. The predictive quality of the QSAR models was tested for an external set of 31 compounds, randomly chosen out of 35 compounds. The results revealed the more predictive ability of PLS-LS-SVM in analysis of compounds with urea structure. The selected descriptors indicated that size, degree of branching, aromaticity, and polarizability affected the inhibition activity of these inhibitors. Furthermore, molecular docking was carried out to study the binding mode of the compounds. Docking analysis indicated some essential H-bonding and orientations of the molecules in the active site. PMID:28003837

  3. Synthesis and 3D-QSAR study of 1,4-dihydropyridine derivatives as MDR cancer reverters.

    PubMed

    Radadiya, Ashish; Khedkar, Vijay; Bavishi, Abhay; Vala, Hardevsinh; Thakrar, Shailesh; Bhavsar, Dhairya; Shah, Anamik; Coutinho, Evans

    2014-03-03

    A series of symmetrical and unsymmetrical 1,4-dihydropyridines were synthesized by a rapid, single pot microwave irradiation (MWI) based protocol along with conventional approach and characterized by NMR, IR and mass spectroscopic techniques. The compounds were evaluated for their tumor cell cytotoxicity in HL-60 tumor cells. A 3D-QSAR study using CoMFA and CoMSIA was carried out to decipher the factors governing MDR reversing ability in cancer. The resulting contour maps derived by the best 3D-QSAR models provide a good insight into the molecular features relevant to the biological activity in this series of analogs. 3D contour maps as a result of 3D-QSAR were utilized to identify some novel features that can be incorporated into the 1,4-dihydropyridine framework to enhance the activity.

  4. A Danish Survey of Antihistamine Use and Poisoning Patterns.

    PubMed

    Jensen, Louise Line; Rømsing, Janne; Dalhoff, Kim

    2017-01-01

    The first-generation antihistamine, promethazine, became a prescription-only drug in Denmark as of December 2014. First-generation antihistamines are known to have a higher toxic potential than second-generation antihistamines. The aim of this study was to provide a nationwide description of the antihistamine use and poisoning pattern from 2007 to 2013 in Denmark based on two independent databases. There were 1049 antihistamine exposures in the national, advisory telephone service specialized in poisonings, the Danish Poison and Information Centre (DPIC), and 456 exposures in the three registers used within the State Serum Institute of Denmark (SSI), a department under the Danish Ministry of Health dealing with research-based health surveillance in Denmark. First-generation antihistamines constitute 61% and 73% of antihistamine registrations in DPIC and SSI, respectively. Antihistamine exposures increased by 7 exposures/10,000 enquiries per year in DPIC and six admissions per year in SSI - this increase is not significant due to a sudden decrease in 2012. Intentional exposures constituted 65% in DPIC of which 82% was due to suicide attempts, and 78% of the involved antihistamines were first-generation antihistamines. Accidental exposures constituted 33% of which 61% were due to play and 29% involved first-generation antihistamines. Single antihistamine exposures constituted 65% of DPIC exposures of which 98% involved only one brand of antihistamine. Multidrug exposures constituted 35% of DPIC exposures with equally distributed coingestants. Hospitalization was recommended in 78% of DPIC exposures. Admissions required only 1-day of treatment in 91% of the SSI exposures. One of the 14 identified deaths in the SSI study population was directly related to antihistamine poisoning. Results support the limited disclosure of promethazine in Denmark and illustrate a generation-specific pattern indicating a suspected replacement of promethazine with other first

  5. Trend Analyses of Nitrate in Danish Groundwater

    NASA Astrophysics Data System (ADS)

    Hansen, B.; Thorling, L.; Dalgaard, T.; Erlandsen, M.

    2012-04-01

    This presentation assesses the long-term development in the oxic groundwater nitrate concentration and nitrogen (N) loss due to intensive farming in Denmark. Firstly, up to 20-year time-series from the national groundwater monitoring network enable a statistically systematic analysis of distribution, trends and trend reversals in the groundwater nitrate concentration. Secondly, knowledge about the N surplus in Danish agriculture since 1950 is used as an indicator of the potential loss of N. Thirdly, groundwater recharge CFC (Chlorofluorocarbon) age determination allows linking of the first two dataset. The development in the nitrate concentration of oxic groundwater clearly mirrors the development in the national agricultural N surplus, and a corresponding trend reversal is found in groundwater. Regulation and technical improvements in the intensive farming in Denmark have succeeded in decreasing the N surplus by 40% since the mid 1980s while at the same time maintaining crop yields and increasing the animal production of especially pigs. Trend analyses prove that the youngest (0-15 years old) oxic groundwater shows more pronounced significant downward nitrate trends (44%) than the oldest (25-50 years old) oxic groundwater (9%). This amounts to clear evidence of the effect of reduced nitrate leaching on groundwater nitrate concentrations in Denmark. Are the Danish groundwater monitoring strategy obtimal for detection of nitrate trends? Will the nitrate concentrations in Danish groundwater continue to decrease or are the Danish nitrate concentration levels now appropriate according to the Water Framework Directive?

  6. Care and Education in the Danish Creche

    ERIC Educational Resources Information Center

    Brostrom, Stig; Hansen, Ole Henrik

    2010-01-01

    This article seeks to identify the relation between policy and lived life, for the small child in the Danish creche. To accomplish this, the article integrates demography, traditions, national curriculum and psychological, educational, and recent developments in research. It is an attempt to reveal knowledge and consequences, by conducting the…

  7. Increasing Staff Mobility--A Danish Initiative.

    ERIC Educational Resources Information Center

    Sorensen, Henning

    1985-01-01

    Recent Danish government proposals to increase the national and international mobility of scientists are reviewed, including a formalized sabbatical system in the universities, new rules for obtaining leaves of absence with or without salary, and plans for increased mobility between public and private sectors. (MSE)

  8. The Danish Free School Tradition under Pressure

    ERIC Educational Resources Information Center

    Olsen, Tore Vincents

    2015-01-01

    The Danish free school tradition has entailed a large degree of associational freedom for non-governmental schools, religious as well as non-religious. Until the late 1990s, the non-governmental schools were under no strict ideological or pedagogical limitations; they could recruit teachers and students according to their own value base, and were…

  9. The Danish Free School Tradition under Pressure

    ERIC Educational Resources Information Center

    Olsen, Tore Vincents

    2015-01-01

    The Danish free school tradition has entailed a large degree of associational freedom for non-governmental schools, religious as well as non-religious. Until the late 1990s, the non-governmental schools were under no strict ideological or pedagogical limitations; they could recruit teachers and students according to their own value base, and were…

  10. Flow network QSAR for the prediction of physicochemical properties by mapping an electrical resistance network onto a chemical reaction poset.

    PubMed

    Ivanciuc, Ovidiu; Ivanciuc, Teodora; Klein, Douglas J

    2013-06-01

    Usual quantitative structure-activity relationship (QSAR) models are computed from unstructured input data, by using a vector of molecular descriptors for each chemical in the dataset. Another alternative is to consider the structural relationships between the chemical structures, such as molecular similarity, presence of certain substructures, or chemical transformations between compounds. We defined a class of network-QSAR models based on molecular networks induced by a sequence of substitution reactions on a chemical structure that generates a partially ordered set (or poset) oriented graph that may be used to predict various molecular properties with quantitative superstructure-activity relationships (QSSAR). The network-QSAR interpolation models defined on poset graphs, namely average poset, cluster expansion, and spline poset, were tested with success for the prediction of several physicochemical properties for diverse chemicals. We introduce the flow network QSAR, a new poset regression model in which the dataset of chemicals, represented as a reaction poset, is transformed into an oriented network of electrical resistances in which the current flow results in a potential at each node. The molecular property considered in the QSSAR model is represented as the electrical potential, and the value of this potential at a particular node is determined by the electrical resistances assigned to each edge and by a system of batteries. Each node with a known value for the molecular property is attached to a battery that sets the potential on that node to the value of the respective molecular property, and no external battery is attached to nodes from the prediction set, representing chemicals for which the values of the molecular property are not known or are intended to be predicted. The flow network QSAR algorithm determines the values of the molecular property for the prediction set of molecules by applying Ohm's law and Kirchhoff's current law to the poset

  11. Comparison of Different 2D and 3D-QSAR Methods on Activity Prediction of Histamine H3 Receptor Antagonists.

    PubMed

    Dastmalchi, Siavoush; Hamzeh-Mivehroud, Maryam; Asadpour-Zeynali, Karim

    2012-01-01

    Histamine H3 receptor subtype has been the target of several recent drug development programs. Quantitative structure-activity relationship (QSAR) methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. The aim of this study was to compare the predictive powers of three different QSAR techniques, namely, multiple linear regression (MLR), artificial neural network (ANN), and HASL as a 3D QSAR method, in predicting the receptor binding affinities of arylbenzofuran histamine H3 receptor antagonists. Genetic algorithm coupled partial least square as well as stepwise multiple regression methods were used to select a number of calculated molecular descriptors to be used in MLR and ANN-based QSAR studies. Using the leave-group-out cross-validation technique, the performances of the MLR and ANN methods were evaluated. The calculated values for the mean absolute percentage error (MAPE), ranging from 2.9 to 3.6, and standard deviation of error of prediction (SDEP), ranging from 0.31 to 0.36, for both MLR and ANN methods were statistically comparable, indicating that both methods perform equally well in predicting the binding affinities of the studied compounds toward the H3 receptors. On the other hand, the results from 3D-QSAR studies using HASL method were not as good as those obtained by 2D methods. It can be concluded that simple traditional approaches such as MLR method can be as reliable as those of more advanced and sophisticated methods like ANN and 3D-QSAR analyses.

  12. Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets.

    PubMed

    Martínez-Santiago, O; Marrero-Ponce, Y; Vivas-Reyes, R; Rivera-Borroto, O M; Hurtado, E; Treto-Suarez, M A; Ramos, Y; Vergara-Murillo, F; Orozco-Ugarriza, M E; Martínez-López, Y

    2017-05-01

    Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively

  13. Comprehension of drug toxicity: software and databases.

    PubMed

    Toropov, Andrey A; Toropova, Alla P; Raska, Ivan; Leszczynska, Danuta; Leszczynski, Jerzy

    2014-02-01

    Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool (in silico) to rapidly predict various endpoints in general, and drug toxicity in particular. However, this dynamic evolution of experimental data (expansion of existing experimental data on drugs toxicity) leads to the problem of critical estimation of the data. The carcinogenicity, mutagenicity, liver effects and cardiac toxicity should be evaluated as the most important aspects of the drug toxicity. The toxicity is a multidimensional phenomenon. It is apparent that the main reasons for the increase in applications of in silico prediction of toxicity include the following: (i) the need to reduce animal testing; (ii) computational models provide reliable toxicity prediction; (iii) development of legislation that is related to use of new substances; (iv) filling data gaps; (v) reduction of cost and time; (vi) designing of new compounds; (vii) advancement of understanding of biology and chemistry. This mini-review provides analysis of existing databases and software which are necessary for use of robust computational assessments and robust prediction of potential drug toxicities by means of in silico methods. © 2013 Published by Elsevier Ltd.

  14. The air quality in Danish urban areas.

    PubMed Central

    Jensen, F P; Fenger, J

    1994-01-01

    The Danish air pollution abatement is based by and large on emission control. Since the ratification of the international sulfur protocol of 1985, there has been a continuous tightening of the permissible sulfur content in fuels and of the maximum emissions from power plants. As a consequence, the total annual emission of sulfur dioxide (SO2) has been reduced from 450,000 tons in the seventies to 180,000 tons in 1990. This has had a pronounced effect on the SO2 levels in Danish urban areas. Thus, in Copenhagen, the yearly averages have fallen to about 25%. For nitrogen oxides emitted from the power plants, similar regulations are in force. With this legislation, the most important and crucial source of air pollution in Danish urban areas is road traffic. The contribution of nitrogen oxides from national traffic accounts for nearly half the total Danish emission and is increasing steadily; this is consistent with an observed increase of nitrogen oxides in ambient air. The permissible levels of lead in petrol has been reduced drastically. After an introduction of reduced tax on lead-free petrol, it now accounts for more than two-thirds of the total consumption. As a result, the concentration of lead in urban ambient air has been reduced to less than one-sixth. The introduction of 3-way catalytic converters from October 1990 will result in reductions in the emission of a series of pollutants, e.g., lead, volatile organic compounds, carbon monoxide, and nitrogen oxides. In 1980, a Danish air quality monitoring program was established as a cooperative effort between the authorities, the Government, the countries, the municipalities, and the Greater Copenhagen Council.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:7821296

  15. 3D QSAR studies, pharmacophore modeling, and virtual screening of diarylpyrazole-benzenesulfonamide derivatives as a template to obtain new inhibitors, using human carbonic anhydrase II as a model protein.

    PubMed

    Entezari Heravi, Yeganeh; Sereshti, Hassan; Saboury, Ali Akbar; Ghasemi, Jahan; Amirmostofian, Marzieh; Supuran, Claudiu T

    2017-12-01

    A 3D-QSAR modeling was performed on a series of diarylpyrazole-benzenesulfonamide derivatives acting as inhibitors of the metalloenzyme carbonic anhydrase (CA, EC 4.2.1.1). The compounds were collected from two datasets with the same scaffold, and utilized as a template for a new pharmacophore model to screen the ZINC database of commercially available derivatives. The datasets were divided into training, test, and validation sets. As the first step, comparative molecular field analysis (CoMFA), CoMFA region focusing and comparative molecular similarity indices analysis (CoMSIA) in parallel with docking studies were applied to a set of 41 human (h) CA II inhibitors. The validity and the prediction capacity of the resulting models were evaluated by leave-one-out (LOO) cross-validation approach. The reliability of the model for the prediction of possibly new CA inhibitors was also tested.

  16. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes.

    PubMed

    Toropov, Andrey A; Toropova, Alla P

    2015-04-01

    Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set). Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. QSAR and Docking Studies on Capsazepine Derivatives for Immunomodulatory and Anti-Inflammatory Activity

    PubMed Central

    Shukla, Aparna; Sharma, Pooja; Prakash, Om; Singh, Monika; Kalani, Komal; Khan, Feroz; Bawankule, Dnyaneshwar Umrao; Luqman, Suaib; Srivastava, Santosh Kumar

    2014-01-01

    Capsazepine, an antagonist of capsaicin, is discovered by the structure and activity relationship. In previous studies it has been found that capsazepine has potency for immunomodulation and anti-inflammatory activity and emerging as a favourable target in quest for efficacious and safe anti-inflammatory drug. Thus, a 2D quantitative structural activity relationship (QSAR) model against target tumor necrosis factor-α (TNF-α) was developed using multiple linear regression method (MLR) with good internal prediction (r2 = 0.8779) and external prediction (r2pred = 0.5865) using Discovery Studio v3.5 (Accelrys, USA). The predicted activity was further validated by in vitro experiment. Capsazepine was tested in lipopolysaccharide (LPS) induced inflammation in peritoneal mouse macrophages. Anti-inflammatory profile of capsazepine was assessed by its potency to inhibit the production of inflammatory mediator TNF-α. The in vitro experiment indicated that capsazepine is an efficient anti-inflammatory agent. Since, the developed QSAR model showed significant correlations between chemical structure and anti-inflammatory activity, it was successfully applied in the screening of forty-four virtual derivatives of capsazepine, which finally afforded six potent derivatives, CPZ-29, CPZ-30, CPZ-33, CPZ-34, CPZ-35 and CPZ-36. To gain more insights into the molecular mechanism of action of capsazepine and its derivatives, molecular docking and in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) studies were performed. The results of QSAR, molecular docking, in silico ADMET screening and in vitro experimental studies provide guideline and mechanistic scope for the identification of more potent anti-inflammatory & immunomodulatory drug. PMID:25003344

  18. A quantitative structure activity relationships (QSAR) analysis of triarylmethane dye tracers

    NASA Astrophysics Data System (ADS)

    Mon, Jarai; Flury, Markus; Harsh, James B.

    2006-01-01

    Dyes are important hydrological tracers. Many different dyes have been proposed as optimal tracers, but none of these dyes can be considered an ideal water tracer. Some dyes are toxic and most sorb to subsurface materials. The objective of this study was to find the molecular structure of an optimal water tracer. We used QSAR to screen a large number of hypothetical molecules, belonging to the class of triarylmethane dyes, in regard to their sorption characteristics to a sandy soil. The QSAR model was based on experimental sorption data obtained from four triarylmethane dyes: C.I. Food Blue 2 (C.I. 42090; Brilliant Blue FCF), C.I. Food Green 3 (C.I. 42053; FD&C Green No. 3), C.I. Acid Blue 7 (C.I. 42080; ORCOacid Blue A 150%), and C.I. Acid Green 9 (C.I. 42100; ORCOacid Fast Green B). Sorption characteristics of the dyes to the sandy soil were expressed with the Langmuir isotherm. Our premise was that dye sorption can be reduced by attachment of sulfonic acid (SO 3) groups to the triarylmethane template. About 70 hypothetical dyes were created and QSAR were used to estimate sorption characteristics. The results indicated that both the position and the number of SO 3 groups affected dye sorption. Sorption decreased with increasing number of SO 3 groups attached to the molecule. Increasing the number of sulfonic acid groups also decreases the toxicity of the compounds. An optimal triarylmethane water tracer contains 4 to 6 SO 3 groups.

  19. Quantitative structure-activity relationships (QSARs) for estrogen binding to the estrogen receptor: predictions across species.

    PubMed Central

    Tong, W; Perkins, R; Strelitz, R; Collantes, E R; Keenan, S; Welsh, W J; Branham, W S; Sheehan, D M

    1997-01-01

    The recognition of adverse effects due to environmental endocrine disruptors in humans and wildlife has focused attention on the need for predictive tools to select the most likely estrogenic chemicals from a very large number of chemicals for subsequent screening and/or testing for potential environmental toxicity. A three-dimensional quantitative structure-activity relationship (QSAR) model using comparative molecular field analysis (CoMFA) was constructed based on relative binding affinity (RBA) data from an estrogen receptor (ER) binding assay using calf uterine cytosol. The model demonstrated significant correlation of the calculated steric and electrostatic fields with RBA and yielded predictions that agreed well with experimental values over the entire range of RBA values. Analysis of the CoMFA three-dimensional contour plots revealed a consistent picture of the structural features that are largely responsible for the observed variations in RBA. Importantly, we established a correlation between the predicted RBA values for calf ER and their actual RBA values for human ER. These findings suggest a means to begin to construct a more comprehensive estrogen knowledge base by combining RBA assay data from multiple species in 3D-QSAR based predictive models, which could then be used to screen untested chemicals for their potential to bind to the ER. Another QSAR model was developed based on classical physicochemical descriptors generated using the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program. The predictive ability of the CoMFA model was superior to the corresponding CODESSA model. Images Figure 2. Figure 3. Figure 4. Figure 5. PMID:9353176

  20. Novel 1,4-naphthoquinone-based sulfonamides: Synthesis, QSAR, anticancer and antimalarial studies.

    PubMed

    Pingaew, Ratchanok; Prachayasittikul, Veda; Worachartcheewan, Apilak; Nantasenamat, Chanin; Prachayasittikul, Supaluk; Ruchirawat, Somsak; Prachayasittikul, Virapong

    2015-10-20

    A novel series of 1,4-naphthoquinones (33-44) tethered by open and closed chain sulfonamide moieties were designed, synthesized and evaluated for their cytotoxic and antimalarial activities. All quinone-sulfonamide derivatives displayed a broad spectrum of cytotoxic activities against all of the tested cancer cell lines including HuCCA-1, HepG2, A549 and MOLT-3. Most quinones (33-36 and 38-43) exerted higher anticancer activity against HepG2 cell than that of the etoposide. The open chain analogs 36 and 42 were shown to be the most potent compounds. Notably, the restricted sulfonamide analog 38 with 6,7-dimethoxy groups exhibited the most potent antimalarial activity (IC₅₀ = 2.8 μM). Quantitative structure-activity relationships (QSAR) study was performed to reveal important chemical features governing the biological activities. Five constructed QSAR models provided acceptable predictive performance (Rcv 0.5647-0.9317 and RMSEcv 0.1231-0.2825). Four additional sets of structurally modified compounds were generated in silico (34a-34d, 36a-36k, 40a-40d and 42a-42k) in which their activities were predicted using the constructed QSAR models. A comprehensive discussion of the structure-activity relationships was made and a set of promising compounds (i.e., 33, 36, 38, 42, 36d, 36f, 42e, 42g and 42f) was suggested for further development as anticancer and antimalarial agents. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  1. QSAR models for prediction of chromatographic behavior of homologous Fab variants.

    PubMed

    Robinson, Julie R; Karkov, Hanne S; Woo, James A; Krogh, Berit O; Cramer, Steven M

    2016-12-12

    While quantitative structure activity relationship (QSAR) models have been employed successfully for the prediction of small model protein chromatographic behavior, there have been few reports to date on the use of this methodology for larger, more complex proteins. Recently our group generated focused libraries of antibody Fab fragment variants with different combinations of surface hydrophobicities and electrostatic potentials, and demonstrated that the unique selectivities of multimodal resins can be exploited to separate these Fab variants. In this work, results from linear salt gradient experiments with these Fabs were employed to develop QSAR models for six chromatographic systems, including multimodal (Capto MMC, Nuvia cPrime, and two novel ligand prototypes), hydrophobic interaction chromatography (HIC; Capto Phenyl), and cation exchange (CEX; CM Sepharose FF) resins. The models utilized newly developed "local descriptors" to quantify changes around point mutations in the Fab libraries as well as novel cluster descriptors recently introduced by our group. Subsequent rounds of feature selection and linearized machine learning algorithms were used to generate robust, well-validated models with high training set correlations (R(2)  > 0.70) that were well suited for predicting elution salt concentrations in the various systems. The developed models then were used to predict the retention of a deamidated Fab and isotype variants, with varying success. The results represent the first successful utilization of QSAR for the prediction of chromatographic behavior of complex proteins such as Fab fragments in multimodal chromatographic systems. The framework presented here can be employed to facilitate process development for the purification of biological products from product-related impurities by in silico screening of resin alternatives. Biotechnol. Bioeng. 2016;9999: 1-10. © 2016 Wiley Periodicals, Inc.

  2. 3D QSAR models built on structure-based alignments of Abl tyrosine kinase inhibitors.

    PubMed

    Falchi, Federico; Manetti, Fabrizio; Carraro, Fabio; Naldini, Antonella; Maga, Giovanni; Crespan, Emmanuele; Schenone, Silvia; Bruno, Olga; Brullo, Chiara; Botta, Maurizio

    2009-06-01

    Quality QSAR: A combination of docking calculations and a statistical approach toward Abl inhibitors resulted in a 3D QSAR model, the analysis of which led to the identification of ligand portions important for affinity. New compounds designed on the basis of the model were found to have very good affinity for the target, providing further validation of the model itself.The X-ray crystallographic coordinates of the Abl tyrosine kinase domain in its active, inactive, and Src-like inactive conformations were used as targets to simulate the binding mode of a large series of pyrazolo[3,4-d]pyrimidines (known Abl inhibitors) by means of GOLD software. Receptor-based alignments provided by molecular docking calculations were submitted to a GRID-GOLPE protocol to generate 3D QSAR models. Analysis of the results showed that the models based on the inactive and Src-like inactive conformations had very poor statistical parameters, whereas the sole model based on the active conformation of Abl was characterized by significant internal and external predictive ability. Subsequent analysis of GOLPE PLS pseudo-coefficient contour plots of this model gave us a better understanding of the relationships between structure and affinity, providing suggestions for the next optimization process. On the basis of these results, new compounds were designed according to the hydrophobic and hydrogen bond donor and acceptor contours, and were found to have improved enzymatic and cellular activity with respect to parent compounds. Additional biological assays confirmed the important role of the selected compounds as inhibitors of cell proliferation in leukemia cells.

  3. Novel Peptide-specific QSAR Analysis Applied to Collagen IV Peptides with Antiangiogenic Activity

    PubMed Central

    Rivera, Corban G.; Rosca, Elena V.; Pandey, Niranjan B.; Koskimaki, Jacob E.; Bader, Joel S.; Popel, Aleksander S.

    2011-01-01

    Angiogenesis is the growth of new blood vessels from existing vasculature. Excessive vascularization is associated with a number of diseases including cancer. Anti-angiogenic therapies have the potential to stunt cancer progression. Peptides derived from type IV collagen are potent inhibitors of angiogenesis. We wanted to gain a better understanding of collagen IV structure-activity relationships using a ligand-based approach. We developed novel peptide-specific QSAR models to study the activity of the peptides in endothelial cell proliferation, migration, and adhesion inhibition assays. We found that the models produced quantitatively accurate predictions of activity and provided insight into collagen IV derived peptide structure-activity relationships. PMID:21866962

  4. Efficient dynamic molecular simulation using QSAR model to know inhibition activity in breast cancer medicine

    NASA Astrophysics Data System (ADS)

    Zharifah, A.; Kusumowardani, E.; Saputro, A.; Sarwinda, D.

    2017-07-01

    According to data from GLOBOCAN (IARC) at 2012, breast cancer was the highest rated of new cancer case by 43.3 % (after controlled by age), with mortality rated as high as 12.9 %. Oncology is a major field which focusing on improving the development of drug and therapeutics cancer in pharmaceutical and biotechnology companies. Nowadays, many researchers lead to computational chemistry and bioinformatic for pharmacophore generation. A pharmacophore describes as a group of atoms in the molecule which is considered to be responsible for a pharmacological action. Prediction of biological function from chemical structure in silico modeling reduces the use of chemical reagents so the risk of environmental pollution decreased. In this research, we proposed QSAR model to analyze the composition of cancer drugs which assumed to be homogenous in character and treatment. Atomic interactions which analyzed are learned through parameters such as log p as descriptors hydrophobic, n_poinas descriptor contour strength and molecular structure, and also various concentrations inhibitor (micromolar and nanomolar) from NCBI drugs bank. The differences inhibitor activity was observed by the presence of IC 50 residues value from inhibitor substances at various concentration. Then, we got a general overview of the state of safety for drug stability seen from its IC 50 value. In our study, we also compared between micromolar and nanomolar inhibitor effect from QSAR model results. The QSAR model analysis shows that the drug concentration with nanomolar is better than micromolar, related with the content of inhibitor substances concentration. This QSAR model got the equation: Log 1/IC50 = (0.284) (±0.195) logP + (0.02) (±0.012) n_poin + (-0.005) (±0.083) Inhibition10.2nanoM + (0.1) (±0.079) Inhibition30.5nanoM + (-0.016) (±0.045) Inhibition91.5nanoM + (-2.572) (±1.570) (n = 13; r = 0.813; r2 = 0.660; s = 0.764; F = 2.720; q2 = 0.660).

  5. Design, synthesis and 3D-QSAR study of cytotoxic flavonoid derivatives.

    PubMed

    Ou, Lili; Han, Shuang; Ding, Wenbo; Chen, Zhe; Ye, Ziqi; Yang, Hongyu; Zhang, Goulin; Lou, Yijia; Chen, Jian-Zhong; Yu, Yongping

    2011-08-01

    Three series of flavonoid derivatives were designed and synthesized. All synthesized compounds were evaluated for cytotoxic activities against five human cancer cell lines, including K562, PC-3, MCF-7, A549, and HO8910. Among the compounds tested, compound 9 d exhibited the most potent cytotoxic activity with IC(50) values of 2.76-6.98 μM. Further comparative molecular field analysis was performed to conduct a 3D quantitative structure-activity relationship study. The generated 3D-QSAR model could be used for further rational design of novel flavonoid analogs as highly potent cytotoxic agents.

  6. Toxicity on the luminescent bacterium Vibrio fischeri (Beijerinck). I: QSAR equation for narcotics and polar narcotics.

    PubMed

    Vighi, Marco; Migliorati, Sonia; Monti, Gianna Serafina

    2009-01-01

    Toxicity data on chemicals, supposed to have a narcotic or polar narcotic toxicological mode of action, have been produced on the luminescent bacterium Vibrio fischeri using the Microtox test procedure. Advanced statistical methods have been used to calculate statistically sound values for ecotoxicological endpoints. Simple quantitative structure activity relationship (QSAR) equations were developed for narcotics and polar narcotics. These equations were compared with those proposed by the European Technical Guidance Document on Risk Assessment for other aquatic organisms (algae, Daphnia, and fish). Similarities and differences are discussed. The need for including the bacterial component in the ecotoxicological risk assessment for aquatic ecosystems is highlighted.

  7. Predicting mitochondrial targeting by small molecule xenobiotics within living cells using QSAR models.

    PubMed

    Horobin, Richard W

    2015-01-01

    Whether small molecule xenobiotics (biocides, drugs, probes, toxins) will target mitochondria in living cells can be predicted using an algorithm derived from QSAR modeling. Application of the algorithm requires the chemical structures of all ionic species of the xenobiotic compound in question to be defined, and for certain numerical structure parameters (AI, CBN, log P, pKa, and Z) to be obtained for all such species. How the chemical structures are specified, how the structure parameters are obtained or estimated, and how the algorithm is used are described in an explicit protocol.

  8. Finding new scaffolds of JAK3 inhibitors in public database: 3D-QSAR models & shape-based screening.

    PubMed

    Gadhe, Changdev G; Lee, Eunhee; Kim, Mi-Hyun

    2015-11-01

    The STAT/JAK3 pathway is a well-known therapeutic target in various diseases (ex. rheumatoid arthritis and psoriasis). The therapeutic advantage of JAK3 inhibition motivated to find new scaffolds with desired DMPK. For the purpose, in silico high-throughput sieves method is developed consisting of a receptor-guided three-dimensional quantitative structure-activity relationship study and shape-based virtual screening. We developed robust and predictive comparative molecular field analysis (q (2) = 0.760, r (2) = 0.915) and comparative molecular similarity index analysis (q (2) = 0.817, r (2) = 0.981) models and validated these using a test set, which produced satisfactory predictions of 0.925 and 0.838, respectively.

  9. Development of multiple QSAR models for consensus predictions and unified mechanistic interpretations of the free-radical scavenging activities of chromone derivatives.

    PubMed

    Mitra, Indrani; Saha, Achintya; Roy, Kunal

    2012-05-01

    Antioxidants are important defenders of the human body against nocive free radicals, which are the causative agents of most life-threatening diseases. The immense biomedicinal utility of antioxidants necessitates the development and design of new synthetic antioxidant molecules. The present report deals with the modeling of a series of chromone derivatives, which was done to provide detailed insight into the main structural fragments that impart antioxidant activity to these molecules. Four different quantitative structure-property relationship (QSAR) techniques, namely 3D pharmacophore mapping, comparative molecular similarity indices analysis (CoMSIA 3D-QSAR), hologram QSAR (HQSAR), and group-based QSAR (G-QSAR) techniques, were employed to obtain statistically significant models with encouraging external predictive potentials. Moreover, the visual contribution maps obtained for the different models signify the importance of different structural features in specific regions of the chromone nucleus. Additionally, the G-QSAR models determine the composite influence of pairs of substituent fragments on the overall antioxidant activity profiles of the molecules. Multiple models with different strategies for assessing structure-activity relationships were applied to reach a unified conclusion regarding the antioxidant mechanism and to provide consensus predictions, which are more reliable than values derived from a single model. The structural information obtained from the various QSAR models developed in the present work can thus be effectively utilized to design and predict the activities of new molecules belonging to the class of chromone derivatives.

  10. Existing data sources for clinical epidemiology: the Danish National Pathology Registry and Data Bank

    PubMed Central

    Erichsen, Rune; Lash, Timothy L; Hamilton-Dutoit, Stephen J; Bjerregaard, Beth; Vyberg, Mogens; Pedersen, Lars

    2010-01-01

    Diagnostic histological and cytological specimens are routinely stored in pathology department archives. These biobanks are a valuable research resource for many diseases, particularly if they can be linked to high quality population-based health registries, allowing large retrospective epidemiological studies to be carried out. Such studies are of significant importance, for example in the search for novel prognostic and predictive biomarkers in the era of personalized medicine. Denmark has a wealth of highly-regarded population-based registries that are ideally suited to conduct this type of epidemiological research. We describe two recent additions to these databases: the Danish National Pathology Registry (DNPR) and its underlying national online registration database, the Danish Pathology Data Bank (DPDB). The DNPR and the DPDB contain detailed nationwide records of all pathology specimens analyzed in Denmark since 1997, and an incomplete but nonetheless valuable record of specimens from some pathology departments dating back to the 1970s. The data are of high quality and completeness and are sufficient to allow precise and efficient localization of the specimens. We describe the relatively uncomplicated procedures required to use these pathology databases in clinical research and to gain access to the archived specimens. PMID:20865103

  11. FDA toxicity databases and real-time data entry

    SciTech Connect

    Arvidson, Kirk B.

    2008-11-15

    Structure-searchable electronic databases are valuable new tools that are assisting the FDA in its mission to promptly and efficiently review incoming submissions for regulatory approval of new food additives and food contact substances. The Center for Food Safety and Applied Nutrition's Office of Food Additive Safety (CFSAN/OFAS), in collaboration with Leadscope, Inc., is consolidating genetic toxicity data submitted in food additive petitions from the 1960s to the present day. The Center for Drug Evaluation and Research, Office of Pharmaceutical Science's Informatics and Computational Safety Analysis Staff (CDER/OPS/ICSAS) is separately gathering similar information from their submissions. Presently, these data are distributed in various locations such as paper files, microfiche, and non-standardized toxicology memoranda. The organization of the data into a consistent, searchable format will reduce paperwork, expedite the toxicology review process, and provide valuable information to industry that is currently available only to the FDA. Furthermore, by combining chemical structures with genetic toxicity information, biologically active moieties can be identified and used to develop quantitative structure-activity relationship (QSAR) modeling and testing guidelines. Additionally, chemicals devoid of toxicity data can be compared to known structures, allowing for improved safety review through the identification and analysis of structural analogs. Four database frameworks have been created: bacterial mutagenesis, in vitro chromosome aberration, in vitro mammalian mutagenesis, and in vivo micronucleus. Controlled vocabularies for these databases have been established. The four separate genetic toxicity databases are compiled into a single, structurally-searchable database for easy accessibility of the toxicity information. Beyond the genetic toxicity databases described here, additional databases for subchronic, chronic, and teratogenicity studies have been prepared.

  12. The Pharmacoepidemiologic Prescription Database of North Jutland - a valid tool in pharmacoepidemiological research.

    PubMed

    Nielsen, G L; Sørensen, H T; Zhou, W; Steffensen, F H; Olsen, J

    1997-01-01

    In this paper we describe the options of a pharmacoepidemiologic database comprising data on all reimbursed prescriptions taken up at pharmacies in the County of Northern Jutland since 1991 including precise identification of the individual person for whom the medicin was prescribed. The County comprises 487,000 inhabitants equal to approximately 10% of the total Danish population.

  13. Overlap in Bibliographic Databases.

    ERIC Educational Resources Information Center

    Hood, William W.; Wilson, Concepcion S.

    2003-01-01

    Examines the topic of Fuzzy Set Theory to determine the overlap of coverage in bibliographic databases. Highlights include examples of comparisons of database coverage; frequency distribution of the degree of overlap; records with maximum overlap; records unique to one database; intra-database duplicates; and overlap in the top ten databases.…

  14. Estimation of the chemical-induced eye injury using a Weight-of-Evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part II: corrosion potential.

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    This is part II of an in silico investigation of chemical-induced eye injury that was conducted at FDA's CFSAN. Serious eye damage caused by chemical (eye corrosion) is assessed using the rabbit Draize test, and this endpoint is an essential part of hazard identification and labeling of industrial and consumer products to ensure occupational and consumer safety. There is an urgent need to develop an alternative to the Draize test because EU's 7th amendment to the Cosmetic Directive (EC, 2003; 76/768/EEC) and recast Regulation now bans animal testing on all cosmetic product ingredients and EU's REACH Program limits animal testing for chemicals in commerce. Although in silico methods have been reported for eye irritation (reversible damage), QSARs specific for eye corrosion (irreversible damage) have not been published. This report describes the development of 21 ANN c-QSAR models (QSAR-21) for assessing eye corrosion potential of chemicals using a large and diverse CFSAN data set of 504 chemicals, ADMET Predictor's three sensitivity analyses and ANNE classification functionalities with 20% test set selection from seven different methods. QSAR-21 models were internally and externally validated and exhibited high predictive performance: average statistics for the training, verification, and external test sets of these models were 96/96/94% sensitivity and 91/91/90% specificity.

  15. The Automatic Light Curves Generated by Danish 1.54m Telescope

    NASA Astrophysics Data System (ADS)

    Skoda, Petr

    2015-12-01

    We present the Ondřejov Southern Photometry Survey, being conducted at the Danish 1.54m telescope in remote observing mode by several groups of Czech stellar astronomers. The automatic astrometry and photometry pipelines run on every CCD frame combined with sophisticated parallelized cross-matching and clustering algorithms result in an on-the-fly generation of light curves of every single object in the field. To allow powerful querying and visualization of current database of more than half billion of measurements, the technology of Virtual Observatory is used, combining IVOA protocols and powerful visualization tools as Aladin, TOPCAT and SPLAT-VO.

  16. JICST Factual Database JICST DNA Database

    NASA Astrophysics Data System (ADS)

    Shirokizawa, Yoshiko; Abe, Atsushi

    Japan Information Center of Science and Technology (JICST) has started the on-line service of DNA database in October 1988. This database is composed of EMBL Nucleotide Sequence Library and Genetic Sequence Data Bank. The authors outline the database system, data items and search commands. Examples of retrieval session are presented.

  17. MOAtox: A comprehensive mode of action and acute aquatic toxicity database for predictive model development.

    PubMed

    Barron, M G; Lilavois, C R; Martin, T M

    2015-04-01

    The mode of toxic action (MOA) has been recognized as a key determinant of chemical toxicity and as an alternative to chemical class-based predictive toxicity modeling. However, the development of quantitative structure activity relationship (QSAR) and other models has been limited by the availability of comprehensive high quality MOA and toxicity databases. The current study developed a dataset of MOA assignments for 1213 chemicals that included a diversity of metals, pesticides, and other organic compounds that encompassed six broad and 31 specific MOAs. MOA assignments were made using a combination of high confidence approaches that included international consensus classifications, QSAR predictions, and weight of evidence professional judgment based on an assessment of structure and literature information. A toxicity database of 674 acute values linked to chemical MOA was developed for fish and invertebrates. Additionally, species-specific measured or high confidence estimated acute values were developed for the four aquatic species with the most reported toxicity values: rainbow trout (Oncorhynchus mykiss), fathead minnow (Pimephales promelas), bluegill (Lepomis macrochirus), and the cladoceran (Daphnia magna). Measured acute toxicity values met strict standardization and quality assurance requirements. Toxicity values for chemicals with missing species-specific data were estimated using established interspecies correlation models and procedures (Web-ICE; http://epa.gov/ceampubl/fchain/webice/), with the highest confidence values selected. The resulting dataset of MOA assignments and paired toxicity values are provided in spreadsheet format as a comprehensive standardized dataset available for predictive aquatic toxicology model development.

  18. Dietary Supplement Ingredient Database

    MedlinePlus

    ... and US Department of Agriculture Dietary Supplement Ingredient Database Toggle navigation Menu Home About DSID Mission Current ... values can be saved to build a small database or add to an existing database for national, ...

  19. Pharmacophore modeling, 3D-QSAR and docking study of 2-phenylpyrimidine analogues as selective PDE4B inhibitors.

    PubMed

    Tripuraneni, Naga Srinivas; Azam, Mohammed Afzal

    2016-04-07

    Pharmacophore modeling, molecular docking, and molecular dynamics (MD) simulation studies have been performed, to explore the putative binding modes of 2-phenylpyrimidine series as PDE4B selective inhibitors. A five point pharmacophore model was developed using 87 molecules having pIC50 ranging from 8.52 to 5.07. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a high correlation coefficient (R(2)=0.918), cross validation coefficient (Q(2)=0.852), and F value (175) at 4 component PLS factor. The external validation indicated that our QSAR model possessed high predictive power (R(2)=0.70). The generated model was further validated by enrichment studies using the decoy test. To evaluate the effectiveness of docking protocol in flexible docking, we have selected crystallographic bound compound to validate our docking procedure as evident from root mean square deviation. A 10ns molecular dynamics simulation confirmed the docking results of both stability of the 1XMU-ligand complex and the presumed active conformation. Further, similar orientation was observed between the superposition of the conformations of 85 after MD simulation and best XP-docking pose; MD simulation and 3D-QSAR pose; best XP-docking and 3D-QSAR poses. Outcomes of the present study provide insight in designing novel molecules with better PDE4B selective inhibitory activity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. In silico evaluation, molecular docking and QSAR analysis of quinazoline-based EGFR-T790M inhibitors.

    PubMed

    Asadollahi-Baboli, M

    2016-08-01

    Mutated epidermal growth factor receptor (EGFR-T790M) inhibitors hold promise as new agents against cancer. Molecular docking and QSAR analysis were performed based on a series of fifty-three quinazoline derivatives to elucidate key structural and physicochemical properties affecting inhibitory activity. Molecular docking analysis identified the true conformations of ligands in the receptor's active pocket. The structural features of the ligands, expressed as molecular descriptors, were derived from the obtained docked conformations. Non-linear and spline QSAR models were developed through novel genetic algorithm and artificial neural network (GA-ANN) and multivariate adaptive regression spline techniques, respectively. The former technique was employed to consider non-linear relation between molecular descriptors and inhibitory activity of quinazoline derivatives. The later technique was also used to describe the non-linearity using basis functions and sub-region equations for each descriptor. Our QSAR model gave a high predictive performance [Formula: see text] and [Formula: see text]) using diverse validation techniques. Eight new compounds were designed using our QSAR model as potent EGFR-T790M inhibitors. Overall, the proposed in silico strategy based on docked derived descriptor and non-linear descriptor subset selection may help design novel quinazoline derivatives with improved EGFR-T790M inhibitory activity.

  1. Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology.

    PubMed

    Puzyn, Tomasz; Jeliazkova, Nina; Sarimveis, Haralambos; Marchese Robinson, Richard L; Lobaskin, Vladimir; Rallo, Robert; Richarz, Andrea-N; Gajewicz, Agnieszka; Papadopulos, Manthos G; Hastings, Janna; Cronin, Mark T D; Benfenati, Emilio; Fernández, Alberto

    2017-09-21

    Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles. Copyright © 2017. Published by Elsevier Ltd.

  2. Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1.

    PubMed

    Fatima, Sabiha; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

    2012-08-01

    Poly (ADP-ribose) polymerase-1 (PARP-1) operates in a DNA damage signaling network. Molecular docking and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on human PARP-1 inhibitors. Docked conformation obtained for each molecule was used as such for 3D-QSAR analysis. Molecules were divided into a training set and a test set randomly in four different ways, partial least square analysis was performed to obtain QSAR models using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Derived models showed good statistical reliability that is evident from their r², q²(loo) and r²(pred) values. To obtain a consensus for predictive ability from all the models, average regression coefficient r²(avg) was calculated. CoMFA and CoMSIA models showed a value of 0.930 and 0.936, respectively. Information obtained from the best 3D-QSAR model was applied for optimization of lead molecule and design of novel potential inhibitors.

  3. Quantitative studies on structure-ORAC relationships of anthocyanins from eggplant and radish using 3D-QSAR.

    PubMed

    Jing, Pu; Zhao, Shujuan; Ruan, Siyu; Sui, Zhongquan; Chen, Lihong; Jiang, Linlei; Qian, Bingjun

    2014-02-15

    The 3-dimensional quantitative structure activity relationship (3D-QSAR) models were established from 21 anthocyanins based on their oxygen radical absorbing capacity (ORAC) and were applied to predict anthocyanins in eggplant and radish for their ORAC values. The cross-validated q(2)=0.857/0.729, non-cross-validated r(2) = 0.958/0.856, standard error of estimate = 0.153/0.134, and F = 73.267/19.247 were for the best QSAR (CoMFA/CoMSIA) models, where the correlation coefficient r(2)pred = 0.998/0.997 (>0.6) indicated a high predictive ability for each. Additionally, the contour map results suggested that structural characteristics of anthocyanins favourable for the high ORAC. Four anthocyanins from eggplant and radish have been screened based on the QSAR models. Pelargonidin-3-[(6''-p-coumaroyl)-glucosyl(2 → 1)glucoside]-5-(6''-malonyl)-glucoside, delphinidin-3-rutinoside-5-glucoside, and delphinidin-3-[(4''-p-coumaroyl)-rhamnosyl(1 → 6)glucoside]-5-glucoside potential with high ORAC based the QSAR models were isolated and also confirmed for their relative high antioxidant ability, which might attribute to the bulky and/or electron-donating substituent at the 3-position in the C ring or/and hydrogen bond donor group/electron donating group on the R1 position in the B ring.

  4. QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles.

    PubMed

    Liu, Huanxiang; Papa, Ester; Gramatica, Paola

    2006-11-01

    A large number of environmental chemicals, known as endocrine-disrupting chemicals, are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones, and such chemicals may pose a serious threat to the health of humans and wildlife. They are thought to act through a variety of mechanisms, mainly estrogen-receptor-mediated mechanisms of toxicity. However, it is practically impossible to perform thorough toxicological tests on all potential xenoestrogens, and thus, the quantitative structure--activity relationship (QSAR) provides a promising method for the estimation of a compound's estrogenic activity. Here, QSAR models of the estrogen receptor binding affinity of a large data set of heterogeneous chemicals have been built using theoretical molecular descriptors, giving full consideration to the new OECD principles in regulation for QSAR acceptability, during model construction and assessment. An unambiguous multiple linear regression (MLR) algorithm was used to build the models, and model predictive ability was validated by both internal and external validation. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validation paths indicate that the proposed QSAR model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.

  5. An examination of data quality on QSAR Modeling in regards to the environmental sciences (UNC-CH talk)

    EPA Science Inventory

    The development of QSAR models is critically dependent on the quality of available data. As part of our efforts to develop public platforms to provide access to predictive models, we have attempted to discriminate the influence of the quality versus quantity of data available to...

  6. Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity-activity modelling.

    PubMed

    Cassani, Stefano; Kovarich, Simona; Papa, Ester; Roy, Partha Pratim; van der Wal, Leon; Gramatica, Paola

    2013-08-15

    Due to their chemical properties synthetic triazoles and benzo-triazoles ((B)TAZs) are mainly distributed to the water compartments in the environment, and because of their wide use the potential effects on aquatic organisms are cause of concern. Non testing approaches like those based on quantitative structure-activity relationships (QSARs) are valuable tools to maximize the information contained in existing experimental data and predict missing information while minimizing animal testing. In the present study, externally validated QSAR models for the prediction of acute (B)TAZs toxicity in Daphnia magna and Oncorhynchus mykiss have been developed according to the principles for the validation of QSARs and their acceptability for regulatory purposes, proposed by the Organization for Economic Co-operation and Development (OECD). These models are based on theoretical molecular descriptors, and are statistically robust, externally predictive and characterized by a verifiable structural applicability domain. They have been applied to predict acute toxicity for over 300 (B)TAZs without experimental data, many of which are in the pre-registration list of the REACH regulation. Additionally, a model based on quantitative activity-activity relationships (QAAR) has been developed, which allows for interspecies extrapolation from daphnids to fish. The importance of QSAR/QAAR, especially when dealing with specific chemical classes like (B)TAZs, for screening and prioritization of pollutants under REACH, has been highlighted.

  7. The influence of data curation on QSAR Modeling - examining issues of quality versus quantity of data (American Chemical Society)

    EPA Science Inventory

    This presentation will examine the impact of data quality on the construction of QSAR models being developed within the EPA‘s National Center for Computational Toxicology. We have developed a public-facing platform to provide access to predictive models. As part of the work we ha...

  8. The influence of data curation on QSAR Modeling – examining issues of quality versus quantity of data (SOT)

    EPA Science Inventory

    The construction of QSAR models is critically dependent on the quality of available data. As part of our efforts to develop public platforms to provide access to predictive models, we have attempted to discriminate the influence of the quality versus quantity of data available ...

  9. DEVELOPMENT OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSARS) TO PREDICT TOXICITY FOR A VARIETY OF HUMAN AND ECOLOGICAL ENDPOINTS

    EPA Science Inventory

    In general, the accuracy of a predicted toxicity value increases with increase in similarity between the query chemical and the chemicals used to develop a QSAR model. A toxicity estimation methodology employing this finding has been developed. A hierarchical based clustering t...

  10. A DFT-based toxicity QSAR study of aromatic hydrocarbons to Vibrio fischeri: Consideration of aqueous freely dissolved concentration.

    PubMed

    Wang, Ying; Yang, Xianhai; Wang, Juying; Cong, Yi; Mu, Jingli; Jin, Fei

    2016-05-05

    In the present study, quantitative structure-activity relationship (QSAR) techniques based on toxicity mechanism and density functional theory (DFT) descriptors were adopted to develop predictive models for the toxicity of alkylated and parent aromatic hydrocarbons to Vibrio fischeri. The acute toxicity data of 17 aromatic hydrocarbons from both literature and our experimental results were used to construct QSAR models by partial least squares (PLS) analysis. With consideration of the toxicity process, the partition of aromatic hydrocarbons between water phase and lipid phase and their interaction with the target biomolecule, the optimal QSAR model was obtained by introducing aqueous freely dissolved concentration. The high statistical values of R(2) (0.956) and Q(CUM)(2) (0.942) indicated that the model has good goodness-of-fit, robustness and internal predictive power. The average molecular polarizability (α) and several selected thermodynamic parameters reflecting the intermolecular interactions played important roles in the partition of aromatic hydrocarbons between the water phase and biomembrane. Energy of the highest occupied molecular orbital (E(HOMO)) was the most influential descriptor which dominated the toxicity of aromatic hydrocarbons through the electron-transfer reaction with biomolecules. The results demonstrated that the adoption of freely dissolved concentration instead of nominal concentration was a beneficial attempt for toxicity QSAR modeling of hydrophobic organic chemicals.

  11. The influence of data curation on QSAR Modeling - examining issues of quality versus quantity of data (American Chemical Society)

    EPA Science Inventory

    This presentation will examine the impact of data quality on the construction of QSAR models being developed within the EPA‘s National Center for Computational Toxicology. We have developed a public-facing platform to provide access to predictive models. As part of the work we ha...

  12. The influence of data curation on QSAR Modeling – examining issues of quality versus quantity of data (SOT)

    EPA Science Inventory

    The construction of QSAR models is critically dependent on the quality of available data. As part of our efforts to develop public platforms to provide access to predictive models, we have attempted to discriminate the influence of the quality versus quantity of data available ...

  13. Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase

    NASA Astrophysics Data System (ADS)

    Andersson, C. David; Hillgren, J. Mikael; Lindgren, Cecilia; Qian, Weixing; Akfur, Christine; Berg, Lotta; Ekström, Fredrik; Linusson, Anna

    2015-03-01

    Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

  14. Quantitative structure-activity relationship (QSAR) methodology in forensic toxicology: modeling postmortem redistribution of structurally diverse drugs using multivariate statistics.

    PubMed

    Giaginis, Costas; Tsantili-Kakoulidou, Anna; Theocharis, Stamatios

    2009-09-10

    Postmortem redistribution (PMR) constitutes a multifaceted process, which renders the analytical results of drug concentrations inaccurate to be interpreted by forensic toxicologists. The aim of the present study was to evaluate whether quantitative structure-activity relationship (QSAR) methodology could serve as an effective tool to estimate the ability of drugs to redistribute across tissue barriers during postmortem period on the basis of their molecular, physicochemical and structural properties. In this aspect, multivariate data analysis (MVDA) was applied to a set of 77 structurally diverse drugs. PMR data expressed by the central:peripheral concentration ratio (C:P ratio) was taken from the literature. An adequate and robust QSAR model (R(2)=0.65, Q(2)=0.56, RMSEE=0.34) was established for 59 (77%) out of 77 drugs. Although the derived QSAR model presented limited applicability, it provided an informative illustration of the contributing molecular, physicochemical and structural properties in PMR process. Drugs with strong basic properties and enhanced molecular size, flexibility, lipophilicity and number of halogens were found to be susceptible to increased PMR. Due to the high complexity of PMR process, further QSAR studies need to focus on structurally related drugs to develop more specific models, which could serve as alternative tools to evaluate PMR for different chemical classes.

  15. Pharmacophore modeling, molecular docking, QSAR, and in silico ADMET studies of gallic acid derivatives for immunomodulatory activity.

    PubMed

    Yadav, Dharmendra Kumar; Khan, Feroz; Negi, Arvind Singh

    2012-06-01

    Immunomodulation refers to an alteration in the immune response due to the intrusion of foreign molecules into the body. In the present communication, QSAR and docking studies of gallic acid derivatives were performed in relation to their immunomodulatory activities. Screening through the use of a QSAR model suggested that the compounds G-4, G-7, G-9, G-10, G-12, and G-13 possess immunomodulatory activity. Activity was predicted using a statistical model developed by the forward stepwise multiple linear regression method. The correlation coefficient (r(2)) and the prediction accuracy (rCV(2)) of the QSAR model were 0.99 and 0.96, respectively. The QSAR study indicated that chemical descriptors-dipole moment, steric energy, amide group count, λ(max) (UV-visible) and molar refractivity-are well correlated with activity, while decreases in the dipole moment, steric energy, and molar refractivity were negatively correlated. A molecular docking study showed that the compounds had high binding affinities for the INFα-2, IL-6, and IL-4 receptors. Binding site residues formed H-bonds with the designed gallic acid derivatives G-3, G-4, G-5, G-6, G-7, and G-10. Moreover, based on screening for oral bioavailability, in silico ADME, and toxicity risk assessment, we concluded that compound G-7 exhibits marked immunomodulatory activity, comparable to levamisole.

  16. DEVELOPMENT OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSARS) TO PREDICT TOXICITY FOR A VARIETY OF HUMAN AND ECOLOGICAL ENDPOINTS

    EPA Science Inventory

    In general, the accuracy of a predicted toxicity value increases with increase in similarity between the query chemical and the chemicals used to develop a QSAR model. A toxicity estimation methodology employing this finding has been developed. A hierarchical based clustering t...

  17. Local indices for similarity analysis (LISA)-a 3D-QSAR formalism based on local molecular similarity.

    PubMed

    Verma, Jitender; Malde, Alpeshkumar; Khedkar, Santosh; Iyer, Radhakrishnan; Coutinho, Evans

    2009-12-01

    A simple quantitative structure activity relationship (QSAR) approach termed local indices for similarity analysis (LISA) has been developed. In this technique, the global molecular similarity is broken up as local similarity at each grid point surrounding the molecules and is used as a QSAR descriptor. In this way, a view of the molecular sites permitting favorable and rational changes to enhance activity is obtained. The local similarity index, calculated on the basis of Petke's formula, segregates the regions into "equivalent", "favored similar", and "disfavored similar" (alternatively "favored dissimilar") potentials with respect to a reference molecule in the data set. The method has been tested on three large and diverse data sets-thrombin, glycogen phosphorylase b, and thermolysin inhibitors. The QSAR models derived using genetic algorithm incorporated partial least square analysis statistics are found to be comparable to the ones obtained by the standard three-dimensional (3D)-QSAR methods, such as comparative molecular field analysis and comparative molecular similarity indices analysis. The graphical interpretation of the LISA models is straightforward, and the outcome of the models corroborates well with literature data. The LISA models give insight into the binding mechanisms of the ligand with the enzyme and allow fine-tuning of the molecules at the local level to improve their activity.