Potential Drug-Drug Interactions among Patients prescriptions collected from Medicine Out-patient Setting.

PubMed

Farooqui, Riffat; Hoor, Talea; Karim, Nasim; Muneer, Mehtab

2018-01-01

To identify and evaluate the frequency, severity, mechanism and common pairs of drug-drug interactions (DDIs) in prescriptions by consultants in medicine outpatient department. This cross sectional descriptive study was done by Pharmacology department of Bahria University Medical & Dental College (BUMDC) in medicine outpatient department (OPD) of a private hospital in Karachi from December 2015 to January 2016. A total of 220 prescriptions written by consultants were collected. Medications given with patient's diagnosis were recorded. Drugs were analyzed for interactions by utilizing Medscape drug interaction checker, drugs.com checker and stockley`s drug interactions index. Two hundred eleven prescriptions were selected while remaining were excluded from the study because of unavailability of the prescribed drugs in the drug interaction checkers. In 211 prescriptions, two common diagnoses were diabetes mellitus (28.43%) and hypertension (27.96%). A total of 978 medications were given. Mean number of medications per prescription was 4.6. A total of 369 drug-drug interactions were identified in 211 prescriptions (175%). They were serious 4.33%, significant 66.12% and minor 29.53%. Pharmacokinetic and pharmacodynamic interactions were 37.94% and 51.21% respectively while 10.84% had unknown mechanism. Number wise common pairs of DDIs were Omeprazole-Losartan (S), Gabapentine- Acetaminophen (M), Losartan-Diclofenac (S). The frequency of DDIs is found to be too high in prescriptions of consultants from medicine OPD of a private hospital in Karachi. Significant drug-drug interactions were more and mostly caused by Pharmacodynamic mechanism. Number wise evaluation showed three common pairs of drugs involved in interactions.

Potential Drug-Drug Interactions among Patients prescriptions collected from Medicine Out-patient Setting

PubMed Central

Farooqui, Riffat; Hoor, Talea; Karim, Nasim; Muneer, Mehtab

2018-01-01

Objective: To identify and evaluate the frequency, severity, mechanism and common pairs of drug-drug interactions (DDIs) in prescriptions by consultants in medicine outpatient department. Methods: This cross sectional descriptive study was done by Pharmacology department of Bahria University Medical & Dental College (BUMDC) in medicine outpatient department (OPD) of a private hospital in Karachi from December 2015 to January 2016. A total of 220 prescriptions written by consultants were collected. Medications given with patient's diagnosis were recorded. Drugs were analyzed for interactions by utilizing Medscape drug interaction checker, drugs.com checker and stockley`s drug interactions index. Two hundred eleven prescriptions were selected while remaining were excluded from the study because of unavailability of the prescribed drugs in the drug interaction checkers. Results: In 211 prescriptions, two common diagnoses were diabetes mellitus (28.43%) and hypertension (27.96%). A total of 978 medications were given. Mean number of medications per prescription was 4.6. A total of 369 drug-drug interactions were identified in 211 prescriptions (175%). They were serious 4.33%, significant 66.12% and minor 29.53%. Pharmacokinetic and pharmacodynamic interactions were 37.94% and 51.21% respectively while 10.84% had unknown mechanism. Number wise common pairs of DDIs were Omeprazole-Losartan (S), Gabapentine- Acetaminophen (M), Losartan-Diclofenac (S). Conclusion: The frequency of DDIs is found to be too high in prescriptions of consultants from medicine OPD of a private hospital in Karachi. Significant drug-drug interactions were more and mostly caused by Pharmacodynamic mechanism. Number wise evaluation showed three common pairs of drugs involved in interactions. PMID:29643896

Food-Drug Interactions

PubMed Central

Bushra, Rabia; Aslam, Nousheen; Khan, Arshad Yar

2011-01-01

The effect of drug on a person may be different than expected because that drug interacts with another drug the person is taking (drug-drug interaction), food, beverages, dietary supplements the person is consuming (drug-nutrient/food interaction) or another disease the person has (drug-disease interaction). A drug interaction is a situation in which a substance affects the activity of a drug, i.e. the effects are increased or decreased, or they produce a new effect that neither produces on its own. These interactions may occur out of accidental misuse or due to lack of knowledge about the active ingredients involved in the relevant substances. Regarding food-drug interactions physicians and pharmacists recognize that some foods and drugs, when taken simultaneously, can alter the body's ability to utilize a particular food or drug, or cause serious side effects. Clinically significant drug interactions, which pose potential harm to the patient, may result from changes in pharmaceutical, pharmacokinetic, or pharmacodynamic properties. Some may be taken advantage of, to the benefit of patients, but more commonly drug interactions result in adverse drug events. Therefore it is advisable for patients to follow the physician and doctors instructions to obtain maximum benefits with least food-drug interactions. The literature survey was conducted by extracting data from different review and original articles on general or specific drug interactions with food. This review gives information about various interactions between different foods and drugs and will help physicians and pharmacists prescribe drugs cautiously with only suitable food supplement to get maximum benefit for the patient. PMID:22043389

Discovery and explanation of drug-drug interactions via text mining.

PubMed

Percha, Bethany; Garten, Yael; Altman, Russ B

2012-01-01

Drug-drug interactions (DDIs) can occur when two drugs interact with the same gene product. Most available information about gene-drug relationships is contained within the scientific literature, but is dispersed over a large number of publications, with thousands of new publications added each month. In this setting, automated text mining is an attractive solution for identifying gene-drug relationships and aggregating them to predict novel DDIs. In previous work, we have shown that gene-drug interactions can be extracted from Medline abstracts with high fidelity - we extract not only the genes and drugs, but also the type of relationship expressed in individual sentences (e.g. metabolize, inhibit, activate and many others). We normalize these relationships and map them to a standardized ontology. In this work, we hypothesize that we can combine these normalized gene-drug relationships, drawn from a very broad and diverse literature, to infer DDIs. Using a training set of established DDIs, we have trained a random forest classifier to score potential DDIs based on the features of the normalized assertions extracted from the literature that relate two drugs to a gene product. The classifier recognizes the combinations of relationships, drugs and genes that are most associated with the gold standard DDIs, correctly identifying 79.8% of assertions relating interacting drug pairs and 78.9% of assertions relating noninteracting drug pairs. Most significantly, because our text processing method captures the semantics of individual gene-drug relationships, we can construct mechanistic pharmacological explanations for the newly-proposed DDIs. We show how our classifier can be used to explain known DDIs and to uncover new DDIs that have not yet been reported.

Macrolide drug interactions: an update.

PubMed

Pai, M P; Graci, D M; Amsden, G W

2000-04-01

To describe the current drug interaction profiles for the commonly used macrolides in the US and Europe, and to comment on the clinical impact of these interactions. A MEDLINE search (1975-1998) was performed to identify all pertinent studies, review articles, and case reports. When appropriate information was not available in the literature, data were obtained from the product manufacturers. All available data were reviewed to provide an unbiased account of possible drug interactions. Data for some of the interactions were not available from the literature, but were available from abstracts or company-supplied materials. Although the data were not always explicit, the best attempt was made to deliver pertinent information that clinical practitioners would need to formulate practice opinions. When more in-depth information was supplied in the form of a review or study report, a thorough explanation of pertinent methodology was supplied. Several clinically significant drug interactions have been identified since the approval of erythromycin. These interactions usually were related to the inhibition of the cytochrome P450 enzyme systems, which are responsible for the metabolism of many drugs. The decreased metabolism by the macrolides has in some instances resulted in potentially severe adverse events. The development and marketing of newer macrolides are hoped to improve the drug interaction profile associated with this class. However, this has produced variable success. Some of the newer macrolides demonstrated an interaction profile similar to that of erythromycin; others have improved profiles. The most success in avoiding drug interactions related to the inhibition of cytochrome P450 has been through the development of the azalide subclass, of which azithromycin is the first and only to be marketed. Azithromycin has not been demonstrated to inhibit the cytochrome P450 system in studies using a human liver microsome model, and to date has produced none of the

Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks.

PubMed

Park, Kyunghyun; Kim, Docyong; Ha, Suhyun; Lee, Doheon

2015-01-01

As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.

Drug-nutrient interactions.

PubMed

Trovato, A; Nuhlicek, D N; Midtling, J E

1991-11-01

Drug-nutrient interactions are a commonly overlooked aspect of the prescribing practices of physicians. As more pharmaceutical agents become available, attention should be focused on interactions of drugs with foods and nutrients. Although drug-nutrient interactions are not as common as drug-drug interactions, they can have an impact on therapeutic outcome. Drugs can affect nutritional status by altering nutrient absorption, metabolism, utilization or excretion. Food, beverages and mineral or vitamin supplements can affect the absorption and effectiveness of drugs. Knowledge of drug-nutrient interactions can help reduce the incidence of these effects. Physicians should question patients about their dietary habits so that patients can be informed about possible interactions between a prescribed drug and foods and nutrients.

[Drug-drug interactions: interactions between xenobiotics].

PubMed

Haen, E

2014-04-01

Drug-drug interactions (DDI) are a major topic in programs for continuous medical education (CME). Many physicians are afraid of being trapped into charges of malpractice; however, DDI cannot be avoided in many cases. They belong to routine medical practice and it is often impossible to avoid them. Moreover, they do not just occur between drugs but between any kind of foreign substance (xenobiotica), such as food (e.g. grapefruit juice, broccoli, barbecue) as well as legal (e.g. tobacco smoke, caffeine and alcohol) and illegal drugs. Therefore, the medical challenge is not just to avoid any interaction. Instead the physician faces the question of how to proceed with drug treatment in the presence of such interactions. Based on the medical education a physician has to judge first of all whether there is a risk for interactions in the prescription being planned for an individual patient. The classification of interactions proposed in this article (PD1-PD4, PK1-PK3) might help as a sort of check list. For more detailed information the physician can then consult one of the many databases available on the internet, such as PSIAConline (http://www.psiac.de) and MediQ (http://www.mediq.ch). Pharmacokinetic interactions can be easily assessed, monitored and controlled by therapeutic drug monitoring (TDM). Besides these tools it is important to keep in mind that nobody knows everything; even physicians do not know everything. So take pride in asking someone who might help and for this purpose AGATE offers a drug information service AID (http://www.amuep-agate.de). Just good for nothing, without being based on any kind of medical approach are computer programs that judge prescriptions without taking into account a patient's individual peculiarities. In case these types of programs produce red exclamation marks or traffic lights to underline their judgment, they might even work in a contrapuntal way by just eliciting insecurity and fear.

High-throughput matrix screening identifies synergistic and antagonistic antimalarial drug combinations

PubMed Central

Mott, Bryan T.; Eastman, Richard T.; Guha, Rajarshi; Sherlach, Katy S.; Siriwardana, Amila; Shinn, Paul; McKnight, Crystal; Michael, Sam; Lacerda-Queiroz, Norinne; Patel, Paresma R.; Khine, Pwint; Sun, Hongmao; Kasbekar, Monica; Aghdam, Nima; Fontaine, Shaun D.; Liu, Dongbo; Mierzwa, Tim; Mathews-Griner, Lesley A.; Ferrer, Marc; Renslo, Adam R.; Inglese, James; Yuan, Jing; Roepe, Paul D.; Su, Xin-zhuan; Thomas, Craig J.

2015-01-01

Drug resistance in Plasmodium parasites is a constant threat. Novel therapeutics, especially new drug combinations, must be identified at a faster rate. In response to the urgent need for new antimalarial drug combinations we screened a large collection of approved and investigational drugs, tested 13,910 drug pairs, and identified many promising antimalarial drug combinations. The activity of known antimalarial drug regimens was confirmed and a myriad of new classes of positively interacting drug pairings were discovered. Network and clustering analyses reinforced established mechanistic relationships for known drug combinations and identified several novel mechanistic hypotheses. From eleven screens comprising >4,600 combinations per parasite strain (including duplicates) we further investigated interactions between approved antimalarials, calcium homeostasis modulators, and inhibitors of phosphatidylinositide 3-kinases (PI3K) and the mammalian target of rapamycin (mTOR). These studies highlight important targets and pathways and provide promising leads for clinically actionable antimalarial therapy. PMID:26403635

Deep-Learning-Based Drug-Target Interaction Prediction.

PubMed

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions.

PubMed

Vandervalk, Ben; McCarthy, E Luke; Cruz-Toledo, José; Klein, Artjom; Baker, Christopher J O; Dumontier, Michel; Wilkinson, Mark D

2013-04-05

The Web provides widespread access to vast quantities of health-related information that can improve quality-of-life through better understanding of personal symptoms, medical conditions, and available treatments. Unfortunately, identifying a credible and personally relevant subset of information can be a time-consuming and challenging task for users without a medical background. The objective of the Personal Health Lens system is to aid users when reading health-related webpages by providing warnings about personally relevant drug interactions. More broadly, we wish to present a prototype for a novel, generalizable approach to facilitating interactions between a patient, their practitioner(s), and the Web. We utilized a distributed, Semantic Web-based architecture for recognizing personally dangerous drugs consisting of: (1) a private, local triple store of personal health information, (2) Semantic Web services, following the Semantic Automated Discovery and Integration (SADI) design pattern, for text mining and identifying substance interactions, (3) a bookmarklet to trigger analysis of a webpage and annotate it with personalized warnings, and (4) a semantic query that acts as an abstract template of the analytical workflow to be enacted by the system. A prototype implementation of the system is provided in the form of a Java standalone executable JAR file. The JAR file bundles all components of the system: the personal health database, locally-running versions of the SADI services, and a javascript bookmarklet that triggers analysis of a webpage. In addition, the demonstration includes a hypothetical personal health profile, allowing the system to be used immediately without configuration. Usage instructions are provided. The main strength of the Personal Health Lens system is its ability to organize medical information and to present it to the user in a personalized and contextually relevant manner. While this prototype was limited to a single knowledge domain

The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions

PubMed Central

Vandervalk, Ben; McCarthy, E Luke; Cruz-Toledo, José; Klein, Artjom; Baker, Christopher J O; Dumontier, Michel

2013-01-01

Background The Web provides widespread access to vast quantities of health-related information that can improve quality-of-life through better understanding of personal symptoms, medical conditions, and available treatments. Unfortunately, identifying a credible and personally relevant subset of information can be a time-consuming and challenging task for users without a medical background. Objective The objective of the Personal Health Lens system is to aid users when reading health-related webpages by providing warnings about personally relevant drug interactions. More broadly, we wish to present a prototype for a novel, generalizable approach to facilitating interactions between a patient, their practitioner(s), and the Web. Methods We utilized a distributed, Semantic Web-based architecture for recognizing personally dangerous drugs consisting of: (1) a private, local triple store of personal health information, (2) Semantic Web services, following the Semantic Automated Discovery and Integration (SADI) design pattern, for text mining and identifying substance interactions, (3) a bookmarklet to trigger analysis of a webpage and annotate it with personalized warnings, and (4) a semantic query that acts as an abstract template of the analytical workflow to be enacted by the system. Results A prototype implementation of the system is provided in the form of a Java standalone executable JAR file. The JAR file bundles all components of the system: the personal health database, locally-running versions of the SADI services, and a javascript bookmarklet that triggers analysis of a webpage. In addition, the demonstration includes a hypothetical personal health profile, allowing the system to be used immediately without configuration. Usage instructions are provided. Conclusions The main strength of the Personal Health Lens system is its ability to organize medical information and to present it to the user in a personalized and contextually relevant manner. While this

Evaluation of drug interaction microcomputer software: Dambro's Drug Interactions.

PubMed

Poirier, T I; Giudici, R A

1990-01-01

Dambro's Drug Interactions was evaluated using general and specific criteria. The installation process, ease of learning and use were rated excellent. The user documentation and quality of the technical support were good. The scope of coverage, clinical documentation, frequency of updates, and overall clinical performance were fair. The primary advantages of the program are the quick searching and detection of drug interactions, and the attempt to provide useful interaction data, i.e., significance and reference. The disadvantages are the lack of current drug interaction information, outdated references, lack of evaluative drug interaction information, and the inability to save or print patient profiles. The program is not a good value for the pharmacist but has limited use as a quick screening tool.

Potential drug interactions in patients given antiretroviral therapy

PubMed Central

dos Santos, Wendel Mombaque; Secoli, Silvia Regina; Padoin, Stela Maris de Mello

2016-01-01

ABSTRACT Objective: to investigate potential drug-drug interactions (PDDI) in patients with HIV infection on antiretroviral therapy. Methods: a cross-sectional study was conducted on 161 adults with HIV infection. Clinical, socio demographic, and antiretroviral treatment data were collected. To analyze the potential drug interactions, we used the software Micromedex(r). Statistical analysis was performed by binary logistic regression, with a p-value of ≤0.05 considered statistically significant. Results: of the participants, 52.2% were exposed to potential drug-drug interactions. In total, there were 218 potential drug-drug interactions, of which 79.8% occurred between drugs used for antiretroviral therapy. There was an association between the use of five or more medications and potential drug-drug interactions (p = 0.000) and between the time period of antiretroviral therapy being over six years and potential drug-drug interactions (p < 0.00). The clinical impact was prevalent sedation and cardiotoxicity. Conclusions: the PDDI identified in this study of moderate and higher severity are events that not only affect the therapeutic response leading to toxicity in the central nervous and cardiovascular systems, but also can interfere in tests used for detection of HIV resistance to antiretroviral drugs. PMID:27878224

SFINX-a drug-drug interaction database designed for clinical decision support systems.

PubMed

Böttiger, Ylva; Laine, Kari; Andersson, Marine L; Korhonen, Tuomas; Molin, Björn; Ovesjö, Marie-Louise; Tirkkonen, Tuire; Rane, Anders; Gustafsson, Lars L; Eiermann, Birgit

2009-06-01

The aim was to develop a drug-drug interaction database (SFINX) to be integrated into decision support systems or to be used in website solutions for clinical evaluation of interactions. Key elements such as substance properties and names, drug formulations, text structures and references were defined before development of the database. Standard operating procedures for literature searches, text writing rules and a classification system for clinical relevance and documentation level were determined. ATC codes, CAS numbers and country-specific codes for substances were identified and quality assured to ensure safe integration of SFINX into other data systems. Much effort was put into giving short and practical advice regarding clinically relevant drug-drug interactions. SFINX includes over 8,000 interaction pairs and is integrated into Swedish and Finnish computerised decision support systems. Over 31,000 physicians and pharmacists are receiving interaction alerts through SFINX. User feedback is collected for continuous improvement of the content. SFINX is a potentially valuable tool delivering instant information on drug interactions during prescribing and dispensing.

Inferring protein domains associated with drug side effects based on drug-target interaction network.

PubMed

Iwata, Hiroaki; Mizutani, Sayaka; Tabei, Yasuo; Kotera, Masaaki; Goto, Susumu; Yamanishi, Yoshihiro

2013-01-01

Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, however, our knowledge about the molecular mechanism of the drug-target interactions is very limited. One of challenging issues in recent pharmaceutical science is to identify the underlying molecular features which govern drug-target interactions. In this paper, we make a systematic analysis of the correlation between drug side effects and protein domains, which we call "pharmacogenomic features," based on the drug-target interaction network. We detect drug side effects and protein domains that appear jointly in known drug-target interactions, which is made possible by using classifiers with sparse models. It is shown that the inferred pharmacogenomic features can be used for predicting potential drug-target interactions. We also discuss advantages and limitations of the pharmacogenomic features, compared with the chemogenomic features that are the associations between drug chemical substructures and protein domains. The inferred side effect-domain association network is expected to be useful for estimating common drug side effects for different protein families and characteristic drug side effects for specific protein domains.

Large-Scale Identification and Analysis of Suppressive Drug Interactions

PubMed Central

Cokol, Murat; Weinstein, Zohar B.; Yilancioglu, Kaan; Tasan, Murat; Doak, Allison; Cansever, Dilay; Mutlu, Beste; Li, Siyang; Rodriguez-Esteban, Raul; Akhmedov, Murodzhon; Guvenek, Aysegul; Cokol, Melike; Cetiner, Selim; Giaever, Guri; Iossifov, Ivan; Nislow, Corey; Shoichet, Brian; Roth, Frederick P.

2014-01-01

SUMMARY One drug may suppress the effects of another. Although knowledge of drug suppression is vital to avoid efficacy-reducing drug interactions or discover countermeasures for chemical toxins, drug-drug suppression relationships have not been systematically mapped. Here, we analyze the growth response of Saccharomyces cerevisiae to anti-fungal compound (“drug”) pairs. Among 440 ordered drug pairs, we identified 94 suppressive drug interactions. Using only pairs not selected on the basis of their suppression behavior, we provide an estimate of the prevalence of suppressive interactions between anti-fungal compounds as 17%. Analysis of the drug suppression network suggested that Bromopyruvate is a frequently suppressive drug and Staurosporine is a frequently suppressed drug. We investigated potential explanations for suppressive drug interactions, including chemogenomic analysis, coaggregation, and pH effects, allowing us to explain the interaction tendencies of Bromopyruvate. PMID:24704506

Drug-drug interactions between anti-retroviral therapies and drugs of abuse in HIV systems.

PubMed

Kumar, Santosh; Rao, P S S; Earla, Ravindra; Kumar, Anil

2015-03-01

Substance abuse is a common problem among HIV-infected individuals. Importantly, addictions as well as moderate use of alcohol, smoking, or other illicit drugs have been identified as major reasons for non-adherence to antiretroviral therapy (ART) among HIV patients. The literature also suggests a decrease in the response to ART among HIV patients who use these substances, leading to failure to achieve optimal virological response and increased disease progression. This review discusses the challenges with adherence to ART as well as observed drug interactions and known toxicities with major drugs of abuse, such as alcohol, smoking, methamphetamine, cocaine, marijuana, and opioids. The lack of adherence and drug interactions potentially lead to decreased efficacy of ART drugs and increased ART, and drugs of abuse-mediated toxicity. As CYP is the common pathway in metabolizing both ART and drugs of abuse, we discuss the possible involvement of CYP pathways in such drug interactions. We acknowledge that further studies focusing on common metabolic pathways involving CYP and advance research in this area would help to potentially develop novel/alternate interventions and drug dose/regimen adjustments to improve medication outcomes in HIV patients who consume drugs of abuse.

Predicting Drug-Target Interactions With Multi-Information Fusion.

PubMed

Peng, Lihong; Liao, Bo; Zhu, Wen; Li, Zejun; Li, Keqin

2017-03-01

Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.

Inferring protein domains associated with drug side effects based on drug-target interaction network

PubMed Central

2013-01-01

Background Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, however, our knowledge about the molecular mechanism of the drug-target interactions is very limited. One of challenging issues in recent pharmaceutical science is to identify the underlying molecular features which govern drug-target interactions. Results In this paper, we make a systematic analysis of the correlation between drug side effects and protein domains, which we call "pharmacogenomic features," based on the drug-target interaction network. We detect drug side effects and protein domains that appear jointly in known drug-target interactions, which is made possible by using classifiers with sparse models. It is shown that the inferred pharmacogenomic features can be used for predicting potential drug-target interactions. We also discuss advantages and limitations of the pharmacogenomic features, compared with the chemogenomic features that are the associations between drug chemical substructures and protein domains. Conclusion The inferred side effect-domain association network is expected to be useful for estimating common drug side effects for different protein families and characteristic drug side effects for specific protein domains. PMID:24565527

Macrolides versus azalides: a drug interaction update.

PubMed

Amsden, G W

1995-09-01

To describe the current drug interaction profiles for all approved and investigational macrolide and azalide antimicrobials, and to comment on the clinical impact of these interactions when appropriate. MEDLINE was searched to identify all pertinent studies, review articles, and case reports from 1975 to 1995. When appropriate information was not available in the literature, data were obtained from the product manufacturers. All available data were reviewed to give an unbiased account of possible drug interactions. Data for some of the interactions were not available from the literature, but were available from abstracts or from company-supplied materials. Although the data were not always entirely explicative, the best attempt was made to deliver the pertinent information that clinical practitioners would need to formulate practice opinions. When more in-depth information was supplied in the form of a review or study report, a thorough explanation of pertinent methodology was supplied. Since the introduction of erythromycin into clinical practice, there have been several clinically significant drug interactions identified throughout the literature associated with this drug. These interactions have been caused mostly by inhibition of the CYP3A subclass of hepatic enzymes, thereby decreasing the metabolism of any other agent given concurrently that is also cleared through this mechanism. With the development and marketing of several new macrolides, it was hoped that the drug interaction profile associated with this class would improve. This has been met with variable success. Although some of the extensions of the 14-membered ring macrolides have shown an incidence of interactions equal to that of erythromycin, others have shown improved profiles. In contrast, the 16-membered ring macrolides have demonstrated a much improved, though not absent, interaction profile. The most success in avoiding drug interactions through structure modification has been accomplished

Detecting signals of drug-drug interactions in a spontaneous reports database.

PubMed

Thakrar, Bharat T; Grundschober, Sabine Borel; Doessegger, Lucette

2007-10-01

The spontaneous reports database is widely used for detecting signals of ADRs. We have extended the methodology to include the detection of signals of ADRs that are associated with drug-drug interactions (DDI). In particular, we have investigated two different statistical assumptions for detecting signals of DDI. Using the FDA's spontaneous reports database, we investigated two models, a multiplicative and an additive model, to detect signals of DDI. We applied the models to four known DDIs (methotrexate-diclofenac and bone marrow depression, simvastatin-ciclosporin and myopathy, ketoconazole-terfenadine and torsades de pointes, and cisapride-erythromycin and torsades de pointes) and to four drug-event combinations where there is currently no evidence of a DDI (fexofenadine-ketoconazole and torsades de pointes, methotrexade-rofecoxib and bone marrow depression, fluvastatin-ciclosporin and myopathy, and cisapride-azithromycine and torsade de pointes) and estimated the measure of interaction on the two scales. The additive model correctly identified all four known DDIs by giving a statistically significant (P < 0.05) positive measure of interaction. The multiplicative model identified the first two of the known DDIs as having a statistically significant or borderline significant (P < 0.1) positive measure of interaction term, gave a nonsignificant positive trend for the third interaction (P = 0.27), and a negative trend for the last interaction. Both models correctly identified the four known non interactions by estimating a negative measure of interaction. The spontaneous reports database is a valuable resource for detecting signals of DDIs. In particular, the additive model is more sensitive in detecting such signals. The multiplicative model may further help qualify the strength of the signal detected by the additive model.

Identifying interactions between chemical entities in biomedical text.

PubMed

Lamurias, Andre; Ferreira, João D; Couto, Francisco M

2014-10-23

Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, \\"Identifying Interactions between Chemical Entities\\" (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to state-of-the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.

Identifying interactions between chemical entities in biomedical text.

PubMed

Lamurias, Andre; Ferreira, João D; Couto, Francisco M

2014-12-01

Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, "Identifying Interactions between Chemical Entities" (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to stateof- the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.

Content and Usability Evaluation of Patient Oriented Drug-Drug Interaction Websites.

PubMed

Adam, Terrence J; Vang, Joseph

Drug-Drug Interactions (DDI) are an important source of preventable adverse drug events and a common reason for hospitalization among patients on multiple drug therapy regimens. DDI information systems are important patient safety tools with the capacity to identify and warn health professionals of clinically significant DDI risk. While substantial research has been completed on DDI information systems in professional settings such as community, hospital, and independent pharmacies; there has been limited research on DDI systems offered through online websites directly for use by ambulatory patients. The focus of this project is to test patient oriented website capacity to correctly identify drug interactions among well established and clinically significant medication combinations and convey clinical risk data to patients. The patient education capability was assessed by evaluating website Information Capacity, Patient Usability and Readability. The study results indicate that the majority of websites identified which met the inclusion and exclusion criteria operated similarly, but vary in risk severity assessment and are not optimally patient oriented to effectively deliver risk information. The limited quality of information and complex medical term content complicate DDI risk data conveyance and the sites may not provide optimal information delivery to allow medication consumers to understand and manage their medication regimens.

Herb–drug interactions: an overview of systematic reviews

PubMed Central

Posadzki, Paul; Watson, Leala; Ernst, Edzard

2013-01-01

OBJECTIVES The aim of this overview of systematic reviews (SRs) is to evaluate critically the evidence regarding interactions between herbal medicinal products (HMPs) and synthetic drugs. METHODS Four electronic databases were searched to identify relevant SRs. RESULTS Forty‐six SRs of 46 different HMPs met our inclusion criteria. The vast majority of SRs were of poor methodological quality. The majority of these HMPs were not associated with severe herb–drug interactions. Serious herb–drug interactions were noted for Hypericum perforatum and Viscum album. The most severe interactions resulted in transplant rejection, delayed emergence from anaesthesia, cardiovascular collapse, renal and liver toxicity, cardiotoxicity, bradycardia, hypovolaemic shock, inflammatory reactions with organ fibrosis and death. Moderately severe interactions were noted for Ginkgo biloba, Panax ginseng, Piper methysticum, Serenoa repens and Camellia sinensis. The most commonly interacting drugs were antiplatelet agents and anticoagulants. CONCLUSION The majority of the HMPs evaluated in SRs were not associated with drug interactions with serious consequences. However, the poor quality and the scarcity of the primary data prevent firm conclusions. PMID:22670731

Genetic determinants of drug responsiveness and drug interactions.

PubMed

Caraco, Y

1998-10-01

Six cytochrome P450 enzymes mediate the oxidative metabolism of most drugs in common use: CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4. These enzymes have selective substrate specificity, and their activity is characterized by marked interindividual variation. Some of these systems (CYP2C19, CYP2D6) are polymorphically distributed; thus, a subset of the population may be genetically deficient in enzyme activity. Phenotyping procedures designed to identify subjects with impaired metabolism who may be at increased risk for drug toxicity have been developed and validated. This has been supplemented in recent years by the availability of genetic analysis and the identification of specific alleles that are associated with altered (i.e., reduced, deficient, or increased) enzyme activity. The potential of genotyping to predict pharmacodynamics holds great promise for the future because it does not involve the administration of exogenous compound and is not confounded by drug therapy. Drug interactions caused by the inhibition or induction of oxidative drug metabolism may be of great clinical importance because they may result in drug toxicity or therapeutic failure. Further understanding of cytochrome P450 complexity may allow, through a combined in vitro-in vivo approach, the reliable prediction and possible prevention of deleterious drug interactions.

Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

PubMed Central

Vinayagam, Arunachalam; Gibson, Travis E.; Lee, Ho-Joon; Yilmazel, Bahar; Roesel, Charles; Hu, Yanhui; Kwon, Young; Sharma, Amitabh; Liu, Yang-Yu; Perrimon, Norbert; Barabási, Albert-László

2016-01-01

The protein–protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as “indispensable,” “neutral,” or “dispensable,” which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets. PMID:27091990

Drug-nutrient interactions in elderly people.

PubMed

Akamine, Dirce; Filho, Michel K; Peres, Carmem M

2007-05-01

The presence of multiple diseases, polypharmacy, malnutrition, and impaired metabolism in elderly individuals increases the risks of adverse events related to drug-food interactions. Some considerations for elderly people influenced by drug-food interactions are reviewed. When investigating pharmacokinetic and pharmacodynamic modifications in the elderly, other factors have to be considered, such as anorexia, dementia, depression, intolerance, gastrointestinal-tract disorders, social and economic factors, reduced abilities (visual and manual) and difficulties in chewing or swallowing. Specific reference is made herein to the health status of the elderly Brazilian population based on the observations of our research group. In addition, the most common diseases (such as cancer, coronary heart disease, dementia, diabetes mellitus, hypertension and osteoporosis), the drugs usually prescribed to treat them, and the adverse nutritional reactions that occur in older patients are summarized. In order to develop a correct drug prescription plan and nutritional intervention to avoid any kind of undesirable drug-food interaction effect, it is necessary to adequately diagnose the disease and often re-evaluate the chosen treatment, identify disease stages and the necessary therapies to minimize the number of drugs administered, and select a reasonable nutritional assessment.

Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions.

PubMed

Kolchinsky, A; Lourenço, A; Li, L; Rocha, L M

2013-01-01

Drug-drug interaction (DDI) is a major cause of morbidity and mortality. DDI research includes the study of different aspects of drug interactions, from in vitro pharmacology, which deals with drug interaction mechanisms, to pharmaco-epidemiology, which investigates the effects of DDI on drug efficacy and adverse drug reactions. Biomedical literature mining can aid both kinds of approaches by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology. We implemented a set of classifiers for identifying published articles relevant to experimental pharmacokinetic DDI evidence. These documents are important for identifying causal mechanisms behind putative drug-drug interactions, an important step in the extraction of large numbers of potential DDIs. We evaluate performance of several linear classifiers on PubMed abstracts, under different feature transformation and dimensionality reduction methods. In addition, we investigate the performance benefits of including various publicly-available named entity recognition features, as well as a set of internally-developed pharmacokinetic dictionaries. We found that several classifiers performed well in distinguishing relevant and irrelevant abstracts. We found that the combination of unigram and bigram textual features gave better performance than unigram features alone, and also that normalization transforms that adjusted for feature frequency and document length improved classification. For some classifiers, such as linear discriminant analysis (LDA), proper

Clinically significant drug-drug interactions involving opioid analgesics used for pain treatment in patients with cancer: a systematic review.

PubMed

Kotlinska-Lemieszek, Aleksandra; Klepstad, Pål; Haugen, Dagny Faksvåg

2015-01-01

Opioids are the most frequently used drugs to treat pain in cancer patients. In some patients, however, opioids can cause adverse effects and drug-drug interactions. No advice concerning the combination of opioids and other drugs is given in the current European guidelines. To identify studies that report clinically significant drug-drug interactions involving opioids used for pain treatment in adult cancer patients. Systematic review with searches in Embase, MEDLINE, and Cochrane Central Register of Controlled Trials from the start of the databases (Embase from 1980) through January 2014. In addition, reference lists of relevant full-text papers were hand-searched. Of 901 retrieved papers, 112 were considered as potentially eligible. After full-text reading, 17 were included in the final analysis, together with 15 papers identified through hand-searching of reference lists. All of the 32 included publications were case reports or case series. Clinical manifestations of drug-drug interactions involving opioids were grouped as follows: 1) sedation and respiratory depression, 2) other central nervous system symptoms, 3) impairment of pain control and/or opioid withdrawal, and 4) other symptoms. The most common mechanisms eliciting drug-drug interactions were alteration of opioid metabolism by inhibiting the activity of cytochrome P450 3A4 and pharmacodynamic interactions due to the combined effect on opioid, dopaminergic, cholinergic, and serotonergic activity in the central nervous system. Evidence for drug-drug interactions associated with opioids used for pain treatment in cancer patients is very limited. Still, the cases identified in this systematic review give some important suggestions for clinical practice. Physicians prescribing opioids should recognize the risk of drug-drug interactions and if possible avoid polypharmacy.

Important drug-nutrient interactions.

PubMed

Mason, Pamela

2010-11-01

Drugs have the potential to interact with nutrients potentially leading to reduced therapeutic efficacy of the drug, nutritional risk or increased adverse effects of the drug. Despite significant interest in such interactions going back to over more than 40 years, the occurrence and clinical significance of many drug-nutrient interactions remains unclear. However, interactions involving drugs with a narrow therapeutic margin such as theophylline and digoxin and those that require careful blood monitoring such as warfarin are likely to be those of clinical significance. Drugs can affect nutrition as a result of changes in appetite and taste as well as having an influence on absorption or metabolism of nutrients. Moreover, foods and supplements can also interact with drugs, of which grapefruit juice and St John's wort are key examples. Significant numbers of people take both supplements and medication and are potentially at risk from interactions. Professionals, such as pharmacists, dietitians, nurses and doctors, responsible for the care of patients should therefore check whether supplements are being taken, while for researchers this is an area worthy of significant further study, particularly in the context of increasingly complex drug regimens and the plethora of new drugs.

In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions.

PubMed

Ivanov, Sergey; Semin, Maxim; Lagunin, Alexey; Filimonov, Dmitry; Poroikov, Vladimir

2017-07-01

Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

[Clinical relevance of drug interactions between nonsteroidal antiinflammatory drugs (NSAIDs) and antihypertensives].

PubMed

Villa, Juan; Cano, Alejandra; Franco, David; Monsalve, Mauricio; Hincapié, Jaime; Amariles, Pedro

2014-11-01

To establish the clinical relevance of drug interactions between nonsteroidal antiinflammatory drugs (NSAIDs) and antihypertensives, based on the interaction severity and probability of occurrence. Systematic review. A PubMed/Medline search was made using the MeSH terms: NSAIDs, Antihypertensive drugs, and Drug interactions. Articles between 2002 and 2012, human studies, in Spanish and English and full text access were included. Found articles were included and some of the references used in this works. Studies with in vitro methods, effects on ocular hypertension and those who do not consider the interaction NSAIDs, antihypertensives were excluded. For the selection of the papers included three independent reviewers were involved. We used a tool for data extraction and for assess of the interaction clinical relevance. Nineteen of 50 papers found were included. There were identified 21 interactions with pharmacodynamic mechanism, classified by their clinical relevance in level-2 high risk (76.2%) and level-3 medium risk (23.8%). In addition, evidence of 16 combinations of no interaction were found. Some NSAIDs may attenuate the effectiveness of antihypertensive drugs when used concurrently, especially with angiotensin converting enzyme inhibitors, diuretics, beta blockers and angiotensin receptorsii blockers. There was no evidence of effect modification of calcium channel antagonists, especially dihydropyridine, by concurrent use of NSAIDs. Copyright © 2013 Elsevier España, S.L.U. All rights reserved.

Drug interactions between common illicit drugs and prescription therapies.

PubMed

Lindsey, Wesley T; Stewart, David; Childress, Darrell

2012-07-01

The aim was to summarize the clinical literature on interactions between common illicit drugs and prescription therapies. Medline, Iowa Drug Information Service, International Pharmaceutical Abstracts, EBSCO Academic Search Premier, and Google Scholar were searched from date of origin of database to March 2011. Search terms were cocaine, marijuana, cannabis, methamphetamine, amphetamine, ecstasy, N-methyl-3,4-methylenedioxymethamphetamine, methylenedioxymethamphetamine, heroin, gamma-hydroxybutyrate, sodium oxybate, and combined with interactions, drug interactions, and drug-drug interactions. This review focuses on established clinical evidence. All applicable full-text English language articles and abstracts found were evaluated and included in the review as appropriate. The interactions of illicit drugs with prescription therapies have the ability to potentiate or attenuate the effects of both the illicit agent and/or the prescription therapeutic agent, which can lead to toxic effects or a reduction in the prescription agent's therapeutic activity. Most texts and databases focus on theoretical or probable interactions due to the kinetic properties of the drugs and do not fully explore the pharmacodynamic and clinical implications of these interactions. Clinical trials with coadministration of illicit drugs and prescription drugs are discussed along with case reports that demonstrate a potential interaction between agents. The illicit drugs discussed are cocaine, marijuana, amphetamines, methylenedioxymethamphetamine, heroin, and sodium oxybate. Although the use of illicit drugs is widespread, there are little experimental or clinical data regarding the effects of these agents on common prescription therapies. Potential drug interactions between illicit drugs and prescription drugs are described and evaluated on the Drug Interaction Probability Scale by Horn and Hansten.

Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews.

PubMed

Romagnoli, Katrina M; Nelson, Scott D; Hines, Lisa; Empey, Philip; Boyce, Richard D; Hochheiser, Harry

2017-02-22

Drug information compendia and drug-drug interaction information databases are critical resources for clinicians and pharmacists working to avoid adverse events due to exposure to potential drug-drug interactions (PDDIs). Our goal is to develop information models, annotated data, and search tools that will facilitate the interpretation of PDDI information. To better understand the information needs and work practices of specialists who search and synthesize PDDI evidence for drug information resources, we conducted an inquiry that combined a thematic analysis of published literature with unstructured interviews. Starting from an initial set of relevant articles, we developed search terms and conducted a literature search. Two reviewers conducted a thematic analysis of included articles. Unstructured interviews with drug information experts were conducted and similarly coded. Information needs, work processes, and indicators of potential strengths and weaknesses of information systems were identified. Review of 92 papers and 10 interviews identified 56 categories of information needs related to the interpretation of PDDI information including drug and interaction information; study design; evidence including clinical details, quality and content of reports, and consequences; and potential recommendations. We also identified strengths/weaknesses of PDDI information systems. We identified the kinds of information that might be most effective for summarizing PDDIs. The drug information experts we interviewed had differing goals, suggesting a need for detailed information models and flexible presentations. Several information needs not discussed in previous work were identified, including temporal overlaps in drug administration, biological plausibility of interactions, and assessment of the quality and content of reports. Richly structured depictions of PDDI information may help drug information experts more effectively interpret data and develop recommendations

Grapefruit and drug interactions.

PubMed

2012-12-01

Since the late 1980s, grapefruit juice has been known to affect the metabolism of certain drugs. Several serious adverse effects involving drug interactions with grapefruit juice have been published in detail. The components of grapefruit juice vary considerably depending on the variety, maturity and origin of the fruit, local climatic conditions, and the manufacturing process. No single component accounts for all observed interactions. Other grapefruit products are also occasionally implicated, including preserves, lyophylised grapefruit juice, powdered whole grapefruit, grapefruit seed extract, and zest. Clinical reports of drug interactions with grapefruit juice are supported by pharmacokinetic studies, each usually involving about 10 healthy volunteers, in which the probable clinical consequences were extrapolated from the observed plasma concentrations. Grapefruit juice inhibits CYP3A4, the cytochrome P450 isoenzyme most often involved in drug metabolism. This increases plasma concentrations of the drugs concerned, creating a risk of overdose and dose-dependent adverse effects. Grapefruit juice also inhibits several other cytochrome P450 isoenzymes, but they are less frequently implicated in interactions with clinical consequences. Drugs interacting with grapefruit and inducing serious clinical consequences (confirmed or very probable) include: immunosuppressants, some statins, benzodiazepines, most calcium channel blockers, indinavir and carbamazepine. There are large inter-individual differences in enzyme efficiency. Along with the variable composition of grapefruit juice, this makes it difficult to predict the magnitude and clinical consequences of drug interactions with grapefruit juice in a given patient. There is increasing evidence that transporter proteins such as organic anion transporters and P-glycoprotein are involved in interactions between drugs and grapefruit juice. In practice, numerous drugs interact with grapefruit juice. Although only a few

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

PubMed

Zhang, Wen; Chen, Yanlin; Li, Dingfang

2017-11-25

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

Pre-Clinical Drug Prioritization via Prognosis-Guided Genetic Interaction Networks

PubMed Central

Xiong, Jianghui; Liu, Juan; Rayner, Simon; Tian, Ze; Li, Yinghui; Chen, Shanguang

2010-01-01

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies. PMID:21085674

Potential drug-drug and drug-disease interactions in well-functioning community-dwelling older adults.

PubMed

Hanlon, J T; Perera, S; Newman, A B; Thorpe, J M; Donohue, J M; Simonsick, E M; Shorr, R I; Bauer, D C; Marcum, Z A

2017-04-01

There are few studies examining both drug-drug and drug-disease interactions in older adults. Therefore, the objective of this study was to describe the prevalence of potential drug-drug and drug-disease interactions and associated factors in community-dwelling older adults. This cross-sectional study included 3055 adults aged 70-79 without mobility limitations at their baseline visit in the Health Aging and Body Composition Study conducted in the communities of Pittsburgh PA and Memphis TN, USA. The outcome factors were potential drug-drug and drug-disease interactions as per the application of explicit criteria drawn from a number of sources to self-reported prescription and non-prescription medication use. Over one-third of participants had at least one type of interaction. Approximately one quarter (25·1%) had evidence of had one or more drug-drug interactions. Nearly 10·7% of the participants had a drug-drug interaction that involved a non-prescription medication. % The most common drug-drug interaction was non-steroidal anti-inflammatory drugs (NSAIDs) affecting antihypertensives. Additionally, 16·0% had a potential drug-disease interaction with 3·7% participants having one involving non-prescription medications. The most common drug-disease interaction was aspirin/NSAID use in those with history of peptic ulcer disease without gastroprotection. Over one-third (34·0%) had at least one type of drug interaction. Each prescription medication increased the odds of having at least one type of drug interaction by 35-40% [drug-drug interaction adjusted odds ratio (AOR) = 1·35, 95% confidence interval (CI) = 1·27-1·42; drug-disease interaction AOR = 1·30; CI = 1·21-1·40; and both AOR = 1·45; CI = 1·34-1·57]. A prior hospitalization increased the odds of having at least one type of drug interaction by 49-84% compared with those not hospitalized (drug-drug interaction AOR = 1·49, 95% CI = 1·11-2·01; drug-disease interaction AOR = 1·69, CI = 1·15-2�

Drug-food and drug-nutrient interactions.

PubMed

Roe, D A

1985-07-01

This article analyzes the modifying effects on absorption rates, disposition, and therapeutic effects when drugs interact with both nutrient and non-nutrient food and beverage components. A classification of drug-nutrient interactions is presented and a profile of risk factors is developed. Drug absorption can be affected by food components through changes in gastric emptying time, filling of the gastrointestinal tract, adsorption of drug onto food components, interaction of drug with a food substance, changes in splanchnic blood flow, and bile release. Drugs may be metabolized faster when patients are on high protein-low carbohydrate diets. Adverse drug reactions can be precipitated by intake with specific foods or alcoholic beverages. In addition, certain drugs can produce nutritional toxicity or deficiencies. For example, the vitamin B6 requirements of oral contraceptive (OC) users are increased over those of nonusers; however, the subclinical deficiencies of folacin, riboflavin, and vitamins B12 and C that were associated with pre-1974 OCs have been lessened by recent reductions in OC's estrogen content. The major risk factor for drug-nutrient and drug-alcohol incompatibilities is lack of awareness on the part of the patient of the circumstances in which such a reaction is likely to occur. Patients with diagnoses of depression, anxiety-depression, phobic anxiety, Hodgkin's disease, tuberculosis, bacterial enteritis, giadiasis, trichomonal vaginitis, dermatophytosis, and alcoholism are at greatest risk. High-risk groups for drug-induced nutritional deficiencies are the elderly, alcoholics, pregnant women, epileptics, and cancer patients.

Drug interactions with the dietary fiber Plantago ovata husk.

PubMed

Fernandez, Nelida; Lopez, Cristina; Díez, Raquel; Garcia, Juan J; Diez, Maria Jose; Sahagun, Ana; Sierra, Matilde

2012-11-01

Plantago ovata husk is a viscous water-soluble fiber obtained by milling the seed of Plantago ovata. The increased use of this compound for the treatment of diseases makes it necessary to know of its potential drug interactions. The present paper reviews the literature concerning interactions between drugs and the dietary fiber Plantago ovata husk. All publications which might describe interactions between the dietetic fiber Plantago ovata husk and other drugs were identified by searches using databases such as MEDLINE or EMBASE. Drug interactions have been the subject of numerous studies, but few of them have been carried out with dietary fiber and the results obtained have often been variable. The incidence and importance of interactions between fiber and drugs has increased due to a worldwide rise in the use of dietary fiber. Plantago ovata husk has the potential for producing both benefits and risks with both desirable and undesirable effects when coadministered with drugs. More clinical studies are justifiably needed to improve treatments and to better evaluate patient safety.

Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

PubMed

Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

2015-07-15

Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

A Drug Interactions Elective Course

PubMed Central

2009-01-01

Objectives To evaluate the impact of a drug interactions elective course on student knowledge and skills. Design A drug interactions elective which focused on assessment and application of drug interaction information and identification and management of commonly encountered drug interactions by therapeutic category was offered to third-year PharmD students. Students were expected to (1) determine whether a given interaction was clinically significant or required pharmacist intervention, and (2) make rational, scientifically sound, practical recommendations for management of drug interactions. Evaluation and Assessment Assessment included course evaluations, student self-assessments, and knowledge and skills assessments. Students who completed the course were more confident in their abilities relating to drug interactions than students who did not complete the course. Students who completed the course scored significantly better in all areas of the assessment compared to students who did not complete the course. Course evaluation results were also positive. Conclusion A course devoted to the identification and management of drug interactions improved PharmD students' knowledge and skills and could potentially improve the patient care they provide in the future. PMID:19657505

Emory University: MEDICI (Mining Essentiality Data to Identify Critical Interactions) for Cancer Drug Target Discovery and Development | Office of Cancer Genomics

Cancer.gov

The CTD2 Center at Emory University has developed a computational methodology to combine high-throughput knockdown data with known protein network topologies to infer the importance of protein-protein interactions (PPIs) for the survival of cancer cells.  Applying these data to the Achilles shRNA results, the CCLE cell line characterizations, and known and newly identified PPIs provides novel insights for potential new drug targets for cancer therapies and identifies important PPI hubs.

Predicting drug-target interactions using restricted Boltzmann machines.

PubMed

Wang, Yuhao; Zeng, Jianyang

2013-07-01

In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are

Herb-drug interactions: challenges and opportunities for improved predictions.

PubMed

Brantley, Scott J; Argikar, Aneesh A; Lin, Yvonne S; Nagar, Swati; Paine, Mary F

2014-03-01

Supported by a usage history that predates written records and the perception that "natural" ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb-drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb-drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb-drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb-drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens.

Transporter-mediated natural product-drug interactions for the treatment of cardiovascular diseases.

PubMed

Zha, Weibin

2018-04-01

The growing use of natural products in cardiovascular (CV) patients has been greatly raising the concerns about potential natural product-CV drug interactions. Some of these may lead to unexpected cardiovascular adverse effects and it is, therefore, essential to identify or predict potential natural product-CV drug interactions, and to understand the underlying mechanisms. Drug transporters are important determinants for the pharmacokinetics of drugs and alterations of drug transport has been recognized as one of the major causes of natural product-drug interactions. In last two decades, many CV drugs (e.g., angiotensin II receptor blockers, beta-blockers and statins) have been identified to be substrates and inhibitors of the solute carrier (SLC) transporters and the ATP-binding cassette (ABC) transporters, which are two major transporter superfamilies. Meanwhile, in vitro and in vivo studies indicate that a growing number of natural products showed cardioprotective effects (e.g., gingko biloba, danshen and their active ingredients) are also substrates and inhibitors of drug transporters. Thus, to understand transporter-mediated natural product-CV drug interactions is important and some transporter-mediated interactions have already shown to have clinical relevance. In this review, we review the current knowledge on the role of ABC and SLC transporters in CV therapy, as well as transporter modulation by natural products used in CV diseases and their induced natural product-CV drug interactions through alterations of drug transport. We hope our review will aid in a comprehensive summary of transporter-mediated natural product-CV drug interactions and help public and physicians understand these type of interactions. Copyright © 2017. Published by Elsevier B.V.

Nano-assembly of Surfactants with Interfacial Drug-Interactive Motifs as Tailor-Designed Drug Carriers

PubMed Central

Gao, Xiang; Huang, Yixian; Makhov, Alexander M.; Epperly, Michael; Lu, Jianqin; Grab, Sheila; Zhang, Peijun; Rohan, Lisa; Xie, Xiang-qun (Sean); Wipf, Peter; Greenberger, Joel; Li, Song

2012-01-01

PEGylated lipopeptide surfactants carrying drug-interactive motifs specific for a peptide-nitroxide antioxidant, JP4-039, were designed and constructed to facilitate the solubilization of this drug candidate as micelles and emulsion nanoparticles. A simple screening process based on the ability that prevents the formation of crystals of JP4-039 in aqueous solution was used to identify agents that have potential drug-interactive activities. Several protected lysine derivatives possessing this activity were identified, of which α-Fmoc-ε-tBoc lysine is the most potent, followed by α-Cbz- and α-iso-butyloxycarbonyl-ε-tBoc-lysine. Using polymer-supported liquid-phase synthesis approach, a series of synthetic lipopeptide surfactants with PEG head group, varied numbers and geometries of α-Fmoc or α-Cbz-lysyl groups located at interfacial region as the drug-interactive domains, and oleoyl chains as the hydrophobic tails were synthesized. All α-Fmoc-lysyl-containing lipopeptide surfactants were able to solubilize JP4-039 as micelles, with enhanced solubilizing activity for surfactants with increased numbers of α-Fmoc groups. The PEGylated lipopeptide surfactants with α-Fmoc-lysyl groups alone tend to form filamentous or worm-like micelles. The presence of JP4-039 transformed α-Fmoc-containing filamentous micelles into dots and bar-like mixed micelles with substantially reduced sizes. Fluorescence quenching and NMR studies revealed that the drug and surfactant molecules were in a close proximity in the complex. JP4-039-loaded emulsion carrying α-Cbz-containing surfactants demonstrated enhanced stability over drug loaded emulsion without lipopeptide surfactants. JP4-039-emulsion showed significant mitigation effect on mice exposed to a lethal dose of radiation. PEGylated lipopeptides with an interfacially located drug-interactive domain are therefore tailor-designed formulation materials potentially useful for drug development. PMID:23244299

Drug-Drug Interactions and Diagnostics for Drug Users With HIV and HIV/HCV Coinfections: Introduction.

PubMed

Khalsa, Jag H; Talal, Andrew H; Morse, Gene

2017-03-01

Substance use and pharmacologic treatment of co-occurring infections such as human immunodeficiency virus (HIV) and hepatitis C virus (HCV) are associated with many adverse consequences including pharmacokinetic and pharmacodynamic drug-drug interactions (DDIs). The National Institute on Drug Abuse sponsored a 2-day conference on DDIs at which clinicians/scientists from government, academia, and the pharmaceutical industry presented the most current research findings to formulate a comprehensive overview of DDIs. Specific topics discussed included drug metabolism; drug interactions between medications used in the treatment of HIV, HCV, and substance use disorders; intrahepatic concentrations and methods of assessment of drugs in liver disease of varying etiologies and degrees of impairment; and minimally invasive sampling techniques for the assessment of intrahepatic drug concentrations, viral replication, and changes in gene expression in response to treatment. Finally, the speakers identified research targets and priorities on DDIs. Areas of emphasis included development of diagnostic assays for drug concentration assessment in different organs, an enhanced understanding of factors responsible for alterations in drug metabolism and excretion, and establishment of clinical trials and work groups to study DDIs. Our long-term objective is to broaden investigation in the field of DDIs in substance users. © 2017, The American College of Clinical Pharmacology.

Consistency of psychotropic drug-drug interactions listed in drug monographs.

PubMed

Liu, Xinyue; Hatton, Randy C; Zhu, Yanmin; Hincapie-Castillo, Juan M; Bussing, Regina; Barnicoat, Marie; Winterstein, Almut G

With an increasing prevalence of psychotropic polypharmacy, clinicians depend on drug-drug interaction (DDI) references to ensure safe regimens, but the consistency of such information is frequently questioned. To evaluate the consistency of psychotropic DDIs documented in Clinical Pharmacology (CP), Micromedex (MM), and Lexicomp (LC) and summarize consistent psychotropic DDIs. In May 2016, we extracted severe or major psychotropic DDIs for 102 psychotropic drugs, including central nervous system (CNS) stimulants, antidepressants, an antimanic agent (lithium), antipsychotics, anticonvulsants, and anxiolytics-sedatives-hypnotics from CP, MM, and LC. We then summarized the psychotropic DDIs that were included in all 3 references and with evidence quality of "excellent" or "good" based on MM. We identified 1496, 938, and 1006 unique severe or major psychotropic DDIs from CP, MM, and LC, respectively. Common adverse effects related to psychotropic DDIs include increased or decreased effectiveness, CNS depression, neurotoxicity, QT prolongation, serotonin syndrome, and multiple adverse effects. Among these interactions, only 371 psychotropic DDIs were documented in all 3 references, 59 of which had "excellent" or "good" quality of evidence based on MM. The consistency of psychotropic DDI documentation across CP, MM, and LC is poor. DDI documentations need standards that would encourage consistency among drug information references. The list of the 59 DDIs may be useful in the assessment of psychotropic polypharmacy and highlighting DDI alerts in clinical practice. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Drug-nutrient interactions in transplant recipients.

PubMed

Chan, L N

2001-01-01

Drug-nutrient interaction refers to an alteration of kinetics or dynamics of a drug or a nutritional element, or a compromise in nutritional status as a result of the addition of a drug. The potentials for drug-nutrient interaction increase with the number of drugs taken by the patient. Organ transplant recipients are therefore at high risk for drug-nutrient interactions because multiple medications are used to manage graft rejection, opportunistic infections, and other associated complications. Unrecognized or unmanaged drug-nutrient interactions in this patient population can have an adverse impact on their outcomes. This paper reviews the importance of recognizing drug-nutrient interaction when using cyclosporine-based regimens.

A side-effect free method for identifying cancer drug targets.

PubMed

Ashraf, Md Izhar; Ong, Seng-Kai; Mujawar, Shama; Pawar, Shrikant; More, Pallavi; Paul, Somnath; Lahiri, Chandrajit

2018-04-27

Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.

Co-morbidity and clinically significant interactions between antiepileptic drugs and other drugs in elderly patients with newly diagnosed epilepsy.

PubMed

Bruun, Emmi; Virta, Lauri J; Kälviäinen, Reetta; Keränen, Tapani

2017-08-01

A study was conducted to investigate the frequency of potential pharmacokinetic drug-to-drug interactions in elderly patients with newly diagnosed epilepsy. We also investigated co-morbid conditions associated with epilepsy. From the register of Kuopio University Hospital (KUH) we identified community-dwelling patients aged 65 or above with newly diagnosed epilepsy and in whom use of the first individual antiepileptic drug (AED) began in 2000-2013 (n=529). Furthermore, register data of the Social Insurance Institution of Finland were used for assessing potential interactions in a nationwide cohort of elderly subjects with newly diagnosed epilepsy. We extracted all patients aged 65 or above who had received special reimbursement for the cost of AEDs prescribed on account of epilepsy in 2012 where their first AED was recorded in 2011-2012 as monotherapy (n=1081). Clinically relevant drug interactions (of class C or D) at the time of starting of the first AED, as assessed via the SFINX-PHARAO database, were analysed. Hypertension (67%), dyslipidemia (45%), and ischaemic stroke (32%) were the most common co-morbid conditions in the hospital cohort of patients. In these patients, excessive polypharmacy (more than 10 concomitant drugs) was identified in 27% of cases. Of the patients started on carbamazepine, 52 subjects (32%) had one class-C or class-D drug interaction and 51 (31%) had two or more C- or D-class interactions. Only 2% of the subjects started on valproate exhibited a class-C interaction. None of the subjects using oxcarbazepine displayed class-C or class-D interactions. Patients with 3-5 (OR 4.22; p=0.05) or over six (OR 8.86; p=0.003) other drugs were more likely to have C- or D-class interaction. The most common drugs with potential interactions with carbamazepine were dihydropyridine calcium-blockers, statins, warfarin, and psychotropic drugs. Elderly patients with newly diagnosed epilepsy are at high risk of clinically relevant pharmacokinetic

Drug–drug interactions between anti-retroviral therapies and drugs of abuse in HIV systems

PubMed Central

Rao, PSS; Earla, Ravindra; Kumar, Anil

2015-01-01

Introduction Substance abuse is a common problem among HIV-infected individuals. Importantly, addictions as well as moderate use of alcohol, smoking, or other illicit drugs have been identified as major reasons for non-adherence to antiretroviral therapy (ART) among HIV patients. The literature also suggests a decrease in the response to ART among HIV patients who use these substances, leading to failure to achieve optimal virological response and increased disease progression. Areas covered This review discusses the challenges with adherence to ART as well as observed drug interactions and known toxicities with major drugs of abuse, such as alcohol, smoking, methamphetamine, cocaine, marijuana, and opioids. The lack of adherence and drug interactions potentially lead to decreased efficacy of ART drugs and increased ART, and drugs of abuse-mediated toxicity. As CYP is the common pathway in metabolizing both ART and drugs of abuse, we discuss the possible involvement of CYP pathways in such drug interactions. Expert opinion We acknowledge that further studies focusing on common metabolic pathways involving CYP and advance research in this area would help to potentially develop novel/alternate interventions and drug dose/regimen adjustments to improve medication outcomes in HIV patients who consume drugs of abuse. PMID:25539046

Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

PubMed Central

Brantley, Scott J.; Argikar, Aneesh A.; Lin, Yvonne S.; Nagar, Swati

2014-01-01

Supported by a usage history that predates written records and the perception that “natural” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb–drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb–drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb–drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb–drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens. PMID:24335390

Making Transporter Models for Drug-Drug Interaction Prediction Mobile.

PubMed

Ekins, Sean; Clark, Alex M; Wright, Stephen H

2015-10-01

The past decade has seen increased numbers of studies publishing ligand-based computational models for drug transporters. Although they generally use small experimental data sets, these models can provide insights into structure-activity relationships for the transporter. In addition, such models have helped to identify new compounds as substrates or inhibitors of transporters of interest. We recently proposed that many transporters are promiscuous and may require profiling of new chemical entities against multiple substrates for a specific transporter. Furthermore, it should be noted that virtually all of the published ligand-based transporter models are only accessible to those involved in creating them and, consequently, are rarely shared effectively. One way to surmount this is to make models shareable or more accessible. The development of mobile apps that can access such models is highlighted here. These apps can be used to predict ligand interactions with transporters using Bayesian algorithms. We used recently published transporter data sets (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) to build preliminary models in a commercial tool and in open software that can deliver the model in a mobile app. In addition, several transporter data sets extracted from the ChEMBL database were used to illustrate how such public data and models can be shared. Predicting drug-drug interactions for various transporters using computational models is potentially within reach of anyone with an iPhone or iPad. Such tools could help prioritize which substrates should be used for in vivo drug-drug interaction testing and enable open sharing of models. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

Drug Interactions and Antiretroviral Drug Monitoring

PubMed Central

Foy, Matthew; Sperati, C. John; Lucas, Gregory M.

2014-01-01

Due to the improved longevity afforded by combination antiretroviral therapy (cART), HIV-infected individuals are developing several non-AIDS related comorbid conditions. Consequently, medical management of the HIV-infected population is increasingly complex, with a growing list of potential drug-drug interactions (DDIs). This article reviews some of the most relevant and emerging potential interactions between antiretroviral medications and other agents. The most common DDIs are those involving protease inhibitors or non-nucleoside reverse transcriptase inhibitors which alter the cytochrome P450 enzyme system and/or drug transporters such as p-glycoprotein. Of note are the new agents for the treatment of chronic hepatitis C virus infection. These new classes of drugs and others drugs which are increasingly used in this patient population represent a significant challenge with regard to achieving the goals of effective HIV suppression and minimization of drug-related toxicities. Awareness of DDIs and a multidisciplinary approach are imperative in reaching these goals. PMID:24950731

Organic Ion Transporters and Statin Drug Interactions.

PubMed

Kellick, Kenneth

2017-11-25

Statin drug-drug interactions (DDIs) are both troublesome to patients as well as costly to medical resources. The ability to predict and avoid these events could lead to improved outcomes as well as patient satisfaction. This review will explore efforts to better understand and predict these interactions specifically related to one drug transport system, the organic anion-transporting polypeptides (OATPs) specifically OATP1B1 and OATP1B3. Since the publication of the discovery of OATPs, there have been various pharmacokinetic models that have been proposed to explain the variation in pharmacokinetic and clinical effects related to the OATPs. The effects in transport activity appear to be partially related to the individual polymorphisms studied. Drug-drug interactions can occur when other drugs compete for the metabolic site on the OATPs. Various medications are identified as substrates and/or inhibitors of the OATPs, thereby complicating the ability to fully predict the impact on levels and effects. All of the models reviewed claim successes but show limited clinical utility. There are specific populations that have been identified, predominately various Asian descendants that require lower doses of statins to avoid adverse events. The concept of attributing these actions to the OATPs has been explored, but current models cannot accurately predict statin blood levels or elimination constants. The current research only points to the differences in the human genome and the single-nucleotide polymorphisms that exist between us. Based upon the currently available studies, there is beginning to be a glimmer in the understanding how different populations respond to statin transport and elimination. Additionally and unfortunately, there are other enzymes to be studied to better predict patient differences. Clearly, there has been much work completed, yet many more questions require answering to better understand these transport proteins.

Pharmacogenetics of drug-drug interaction and drug-drug-gene interaction: a systematic review on CYP2C9, CYP2C19 and CYP2D6.

PubMed

Bahar, Muh Akbar; Setiawan, Didik; Hak, Eelko; Wilffert, Bob

2017-05-01

Currently, most guidelines on drug-drug interaction (DDI) neither consider the potential effect of genetic polymorphism in the strength of the interaction nor do they account for the complex interaction caused by the combination of DDI and drug-gene interaction (DGI) where there are multiple biotransformation pathways, which is referred to as drug-drug-gene interaction (DDGI). In this systematic review, we report the impact of pharmacogenetics on DDI and DDGI in which three major drug-metabolizing enzymes - CYP2C9, CYP2C19 and CYP2D6 - are central. We observed that several DDI and DDGI are highly gene-dependent, leading to a different magnitude of interaction. Precision drug therapy should take pharmacogenetics into account when drug interactions in clinical practice are expected.

Drug-Food Interactions

MedlinePlus

... article was contributed by: familydoctor.org editorial staff Categories: Drugs, Procedures & Devices, Prescription Medicines, Your Health ResourcesTags: adverse reactions, Food-Drug Interactions, patient education, patient information September 1, ...

Drug-nutrient interactions: a review.

PubMed

Maka, D A; Murphy, L K

2000-11-01

Concurrent administration of medications and nutrients can lead to interactions that change the absorption or metabolism of the medication or nutrient. Some of these interactions have little or no impact on the patient while others may be fatal. The objective of this article is to review the mechanisms of various drug-nutrient interactions. Topics to be discussed include specific populations at risk of interactions, nutrients that have a positive and negative effect on drug absorption, nutrients that result in alterations of drug metabolism, and a variety of pharmacologic interactions of medications with nutrients. It is vital that healthcare providers are familiar with drug-nutrient interactions and continue to educate themselves and their patients to optimize the effectiveness and minimize the toxicities of medications.

Initial Drug Dissolution from Amorphous Solid Dispersions Controlled by Polymer Dissolution and Drug-Polymer Interaction.

PubMed

Chen, Yuejie; Wang, Shujing; Wang, Shan; Liu, Chengyu; Su, Ching; Hageman, Michael; Hussain, Munir; Haskell, Roy; Stefanski, Kevin; Qian, Feng

2016-10-01

To identify the key formulation factors controlling the initial drug and polymer dissolution rates from an amorphous solid dispersion (ASD). Ketoconazole (KTZ) ASDs using PVP, PVP-VA, HMPC, or HPMC-AS as polymeric matrix were prepared. For each drug-polymer system, two types of formulations with the same composition were prepared: 1. Spray dried dispersion (SDD) that is homogenous at molecular level, 2. Physical blend of SDD (80% drug loading) and pure polymer (SDD-PB) that is homogenous only at powder level. Flory-Huggins interaction parameters (χ) between KTZ and the four polymers were obtained by Flory-Huggins model fitting. Solution (13)C NMR and FT-IR were conducted to investigate the specific drug-polymer interaction in the solution and solid state, respectively. Intrinsic dissolution of both the drug and the polymer from ASDs were studied using a Higuchi style intrinsic dissolution apparatus. PXRD and confocal Raman microscopy were used to confirm the absence of drug crystallinity on the tablet surface before and after dissolution study. In solid state, KTZ is completely miscible with PVP, PVP-VA, or HPMC-AS, demonstrated by the negative χ values of -0.36, -0.46, -1.68, respectively; while is poorly miscible with HPMC shown by a positive χ value of 0.23. According to solution (13)C NMR and FT-IR studies, KTZ interacts with HPMC-AS strongly through H-bonding and dipole induced interaction; with PVPs and PVP-VA moderately through dipole-induced interactions; and with HPMC weakly without detectable attractive interaction. Furthermore, the "apparent" strength of drug-polymer interaction, measured by the extent of peak shift on NMR or FT-IR spectra, increases with the increasing number of interacting drug-polymer pairs. For ASDs with the presence of considerable drug-polymer interactions, such as KTZ/PVPs, KTZ/PVP-VA, or KTZ /HPMC-AS systems, drug released at the same rate as the polymer when intimate drug-polymer mixing was ensured (i.e., the SDD systems

Drug-food interaction counseling programs in teaching hospitals.

PubMed

Wix, A R; Doering, P L; Hatton, R C

1992-04-01

The results of a survey to characterize drug-food interaction counseling programs in teaching hospitals and solicit opinions on these programs from pharmacists and dietitians are reported. A questionnaire was mailed to the pharmacy director and the director of dietary services at teaching hospitals nationwide. The questionnaire contained 33 questions relating to hospital characteristics, drug-food interaction counseling programs, and the standard calling for such programs issued by the Joint Commission on Accreditation of Healthcare Organizations. Of 792 questionnaires mailed, 425 were returned (response rate, 53.7). A majority of the pharmacists and dietitians (51.2%) did not consider their drug-food interaction counseling program to be formal; some had no program. The pharmacy department was involved more in program development than in the daily operation of such programs. The most frequent methods of identifying patients for counseling were using lists of patients' drugs and using physicians' orders. A mean of only five drugs were targeted per program. Slightly over half the respondents rated the Joint Commission standard less effective than other standards in its ability to improve patient care. A majority of teaching hospitals did not have formal drug-food interaction counseling programs. Pharmacists and dietitians did not view these programs as greatly beneficial and did not believe that the Joint Commission has clearly delineated the requirements for meeting its standard.

Assessment of Drug-Drug Interaction in Ayder Comprehensive Specialized Hospital, Mekelle, Northern Ethiopia: A Retrospective Study

PubMed Central

Gebretsadik, Zeru; Gebrehans, Micheale; Getnet, Desalegn; Gebrie, Desye; Alema, Tsgab

2017-01-01

Introduction Adverse drug interaction is a major cause of morbidity and mortality. Its occurrence is influenced by a multitude of factors. The influences of drug-drug interactions (DDIs) can be minimized through creation of awareness to health care professionals. Objective The objective of this study was to assess DDIs in Ayder Comprehensive Specialized Hospital (ACSH). Methodology A retrospective study design was employed on patient prescriptions available in the outpatient department of pharmacy and filled from September 2016 to February 2017 in ACSH. Result From the 600 prescription records assessed, the average number of drugs on single prescription was 2.73. Regarding the interaction observed 34 (9.63%) prescriptions with major drug-drug interaction, 210 (59.5%) moderate, 87 (24.65%) minor, and 22 (6.22%) unknown were identified. Age category showed significant association to affect the occurrence of DDIs and polypharmacy had statistically significant association with DDIs in bivariate analysis which was lost in adjusted OR. Conclusion From the current study it can be concluded that nearly half of the prescription ordered in ACSH contained DDIs and from the prescription with interacting medications majority of them had moderate DDIs. PMID:29250554

Drug-disease interactions: narrative review of aspirin in gastric ulcer.

PubMed

Nwose, Ezekiel Uba; Yee, Kwang Choon

2016-09-01

Drug-disease interactions include the impact of a drug and a particular disease condition on each other. However, the current practice in addressing drug-disease interaction is unbalanced and mostly limited to how the drug worsens the disease or health condition. Aspirin and gastric ulcer interaction are used as an example to illustrate this concept, especially the narration of how disease affects drug efficacy. The number of molecules that make up 100 mg of aspirin is identified with a view to discuss the pharmacokinetics, especially in terms of absorption and distribution. Using hypothetical scenarios, the pharmacodynamics in co-morbidities that could involve gastric ulcer and aspirin are also discussed. There seems to be oversight in definition and description of drug-disease interaction, which is often limited to 'how drug exacerbates disease'. The implication of this limited definition is that the discussions, research and teaching of the topic either lacks information, or are not clear on 'how disease affects drug efficacy'. For example, gastric ulcer has the potential to enhance absorption, bioavailability and therapeutic effects of aspirin, but this is rarely discussed in preference to the probability of gastro-intestinal bleeding side-effect.

Drug-nutrient interactions: a broad view with implications for practice.

PubMed

Boullata, Joseph I; Hudson, Lauren M

2012-04-01

The relevance of drug?nutrient interactions in daily practice continues to grow with the widespread use of medication. Interactions can involve a single nutrient, multiple nutrients, food in general, or nutrition status. Mechanistically, drug?nutrient interactions occur because of altered intestinal transport and metabolism, or systemic distribution, metabolism and excretion, as well as additive or antagonistic effects. Optimal patient care includes identifying, evaluating, and managing these interactions. This task can be supported by a systematic approach for categorizing interactions and rating their clinical significance. This review provides such a broad framework using recent examples, as well as some classic drug?nutrient interactions. Pertinent definitions are presented, as is a suggested approach for clinicians. This important and expanding subject will benefit tremendously from further clinician involvement. Copyright © 2012 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Drug-drug interactions involving lysosomes: mechanisms and potential clinical implications.

PubMed

Logan, Randall; Funk, Ryan S; Axcell, Erick; Krise, Jeffrey P

2012-08-01

Many commercially available, weakly basic drugs have been shown to be lysosomotropic, meaning they are subject to extensive sequestration in lysosomes through an ion trapping-type mechanism. The extent of lysosomal trapping of a drug is an important therapeutic consideration because it can influence both activity and pharmacokinetic disposition. The administration of certain drugs can alter lysosomes such that their accumulation capacity for co-administered and/or secondarily administered drugs is altered. In this review the authors explore what is known regarding the mechanistic basis for drug-drug interactions involving lysosomes. Specifically, the authors address the influence of drugs on lysosomal pH, volume and lipid processing. Many drugs are known to extensively accumulate in lysosomes and significantly alter their structure and function; however, the therapeutic and toxicological implications of this remain controversial. The authors propose that drug-drug interactions involving lysosomes represent an important potential source of variability in drug activity and pharmacokinetics. Most evaluations of drug-drug interactions involving lysosomes have been performed in cultured cells and isolated tissues. More comprehensive in vivo evaluations are needed to fully explore the impact of this drug-drug interaction pathway on therapeutic outcomes.

An approach to evaluating drug-nutrient interactions.

PubMed

Santos, Cristina A; Boullata, Joseph I

2005-12-01

Although the significance of interactions between drugs is widely appreciated, little attention has been given to interactions between drugs and nutrients. Pharmacists are challenged to remember documented interactions involving available drugs, and they face the possibility that each newly approved therapeutic agent may be involved not only in unrecognized drug-drug interactions but in drug-nutrient interactions as well. A more consistent approach to evaluating drug-nutrient interactions is needed. The approach must be systematic in order to assess the influence of nutritional status, food, or specific nutrients on a drug's pharmacokinetics and pharmacodynamics, as well as the influence of a drug on overall nutritional status or on the status of a specific nutrient. We provide such a process, using several recently approved drugs as working examples. Risk factors and clinical relevance are described, with distinctions made between documented and potential interactions. Application of this process by the pharmacist to any drug will help increase their expertise. Furthermore, full consideration by pharmacists of all possible interactions of the drug regimens used in practice can allow for improved patient care.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing.

PubMed

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-09-07

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

PubMed Central

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-01-01

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications. PMID:27599720

Data-driven prediction of adverse drug reactions induced by drug drug interactions

DTIC Science & Technology

2017-06-08

currently on the market and for which drug-protein interaction information is available . These predictions are publicly accessible at http://avoid...associated with these ADRs via DDIs. We made the predictions publicly available via internet access. Keywords: Drug-drug interactions, Adverse drug reactions...ˆDeceased Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research

Interactions between drugs and drug-nutrient in enteral nutrition: a review based on evidences.

PubMed

Ferreira Silva, Renata; Rita Carvalho Garbi Novaes, Maria

2014-09-01

Enteral nutrition (EN) provides calories, macronutrients and micronutrients in adequate quantity and quality to meet the patient's needs. Some drugs when crushed and diluted may have their properties altered, including the reduction of bioavailability causing the reduction of the serum concentration of the drug; tube obstruction; drug-drug interaction or drug-nutrient interaction. The study was conducted through review of submitted articles in the databases of the Virtual Health Library (VHL): MEDLINE (National Library of Medicine, USA), Lilacs (Latin American and Caribbean Literature on Health Sciences) PUBMED - NCBI (National Center for Biotechnology Information) and COCHRANE. For this survey, 42 articles were identified during database searching. After applying the inclusion and exclusion criteria, 08 articles were selected, obtained from the MEDLINE and Lilacs. Some interactions were found such as the aluminium hydroxide and lactulose with the enteral nutrition, which may result in a precipitation and reduction of drug bioavailability. Mineral oil will alter the absorption of fat-soluble vitamins and reduces the tube light. Others results were found as phenytoin, warfarin, captopril and furosemide with enteral nutrition may reduce the maximum serum concentration. Drug interactions are more common in day-to-day activities than health professionals may suppose. Knowledge on the matter may also assist in reducing cases of obstruction of tubes, through which enteral nutrition and medications are administered. Thus, the multidisciplinary team, acting together, may have more beneficial effects to the patient. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Food and drug interaction: consequences for the nutrition/health status.

PubMed

Genser, Dieter

2008-01-01

Food-drug interactions are defined as alterations of pharmacokinetics or pharmacodynamics of a drug or nutritional element or a compromise in nutritional status as a result of the addition of a drug. Elderly patients are particularly at risk because more than 30% of all the prescription drugs are taken by this population. Failure to identify and properly manage drug-nutrient interactions can lead to serious consequences. For instance, drug-nutrient interactions can result in reduced absorption of certain oral antibiotics and may lead to suboptimal antibiotic concentrations at the site of infection. This predisposes the patient to treatment failure. Induction or inhibition of enzymes in the gut by nutrients may lead to a significant change in oral bioavailability of drugs or vice versa. For example, grapefruit juice is a selective intestinal CYP3A4 inhibitor. The overall exposure of some drugs can be increased by more than fivefold when taken with grapefruit juice and increase the risk of adverse effects. The use of certain drugs may affect GI tract function and may lead to a loss of bodily electrolytes and fluid. Limiting drug prescriptions to essential medications for as short a period as possible and periodic re-evaluations of the treatment chosen are essential to minimize adverse drug-nutrient interactions. Copyright 2008 S. Karger AG, Basel.

Drug Interactions: What You Should Know

MedlinePlus

... driving a car or operating machinery dangerous. Drug-food/beverage interactions result from drugs reacting with foods or ... it with other drugs? Should I avoid certain foods, beverages or other products? What are possible drug interaction ...

Herb-drug interactions.

PubMed

Fugh-Berman, A

2000-01-08

Concurrent use of herbs may mimic, magnify, or oppose the effect of drugs. Plausible cases of herb-drug interactions include: bleeding when warfarin is combined with ginkgo (Ginkgo biloba), garlic (Allium sativum), dong quai (Angelica sinensis), or danshen (Salvia miltiorrhiza); mild serotonin syndrome in patients who mix St John's wort (Hypericum perforatum) with serotonin-reuptake inhibitors; decreased bioavailability of digoxin, theophylline, cyclosporin, and phenprocoumon when these drugs are combined with St John's wort; induction of mania in depressed patients who mix antidepressants and Panax ginseng; exacerbation of extrapyramidal effects with neuroleptic drugs and betel nut (Areca catechu); increased risk of hypertension when tricyclic antidepressants are combined with yohimbine (Pausinystalia yohimbe); potentiation of oral and topical corticosteroids by liquorice (Glycyrrhiza glabra); decreased blood concentrations of prednisolone when taken with the Chinese herbal product xaio chai hu tang (sho-salko-to); and decreased concentrations of phenytoin when combined with the Ayurvedic syrup shankhapushpi. Anthranoid-containing plants (including senna [Cassia senna] and cascara [Rhamnus purshiana]) and soluble fibres (including guar gum and psyllium) can decrease the absorption of drugs. Many reports of herb-drug interactions are sketchy and lack laboratory analysis of suspect preparations. Health-care practitioners should caution patients against mixing herbs and pharmaceutical drugs.

Clinical nutrition and drug interactions

PubMed Central

Ekincioğlu, Aygin Bayraktar; Demirkan, Kutay

2013-01-01

A drug’s plasma level, pharmacological effects or side effects, elimination, physicochemical properties or stability could be changed by interactions of drug-drug or drug-nutrition products in patients who receive enteral or parenteral nutritional support. As a result, patients might experience ineffective outcomes or unexpected effects of therapy (such as drug toxicity, embolism). Stability or incompatibility problems between parenteral nutrition admixtures and drugs might lead to alterations in expected therapeutic responses from drug and/or parenteral nutrition, occlusion in venous catheter or symptoms or mortality due to infusion of composed particles. Compatibilities between parenteral nutrition and drugs are not always guaranteed in clinical practice. Although the list of compatibility or incompatibilities of drugs are published for the use of clinicians in their practices, factors such as composition of parenteral nutrition admixture, drug concentration, contact time in catheter, temperature of the environment and exposure to light could change the status of compatibilities between drugs and nutrition admixtures. There could be substantial clinical changes occurring in the patient’s nutritional status and pharmacological effects of drugs due to interactions between enteral nutrition and drugs. Drug toxicity and ineffective nutritional support might occur as a result of those predictable interactions. Although administration of drugs via feeding tube is a complex and problematic route for drug usage, it is possible to minimise the risk of tube occlusion, decreased effects of drug and drug toxicity by using an appropriate technique. Therefore, it is important to consider pharmacological dosage forms of drugs while administering drugs via a feeding tube. In conclusion, since the pharmacists are well-experienced and more knowledgeable professionals in drugs and drug usage compared to other healthcare providers, it is suggested that provision of information

Prevalence of the prescription of potentially interacting drugs.

PubMed

Tragni, Elena; Casula, Manuela; Pieri, Vasco; Favato, Giampiero; Marcobelli, Alberico; Trotta, Maria Giovanna; Catapano, Alberico Luigi

2013-01-01

The use of multiple medications is becoming more common, with a correspondingly increased risk of untoward effects and drug-related morbidity and mortality. We aimed at estimating the prevalence of prescription of relevant potentially interacting drugs and at evaluating possible predictors of potentially interacting drug exposure. We retrospectively analyzed data on prescriptions dispensed from January 2004 to August 2005 to individuals of two Italian regions with a population of almost 2.1 million individuals. We identified 27 pairs of potentially interacting drugs by examining clinical relevance, documentation, and volume of use in Italy. Subjects who received at least one prescription of both drugs were selected. Co-prescribing denotes "two prescriptions in the same day", and concomitant medication "the prescription of two drugs with overlapping coverage". A logistic regression analysis was conducted to examine the predictors of potential Drug-Drug Interaction (pDDIs). 957,553 subjects (45.3% of study population) were exposed to at least one of the drugs/classes of the 27 pairs. Overall, pDDIs occurred 2,465,819 times. The highest rates of concomitant prescription and of co-prescription were for ACE inhibitors+NSAIDs (6,253 and 4,621/100,000 plan participants). Considering concomitance, the male/female ratio was <1 in 17/27 pairs (from 0.31 for NSAIDs-ASA+SSRI to 0.74 for omeprazole+clopidogrel). The mean age was lowest for methotrexate pairs (+omeprazole, 59.9 years; +NSAIDs-ASA, 59.1 years) and highest for digoxin+verapamil (75.4 years). In 13/27 pairs, the mean ages were ≥70 years. On average, subjects involved in pDDIs received ≥10 drugs. The odds of exposure were more frequently higher for age ≥65 years, males, and those taking a large number of drugs. A substantial number of clinically important pDDIs were observed, particularly among warfarin users. Awareness of the most prevalent pDDIs could help practitioners in preventing concomitant use

DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction

PubMed Central

Jiang, Jinjian; Wang, Nian; Zhang, Jun

2017-01-01

Background Drug-target interaction is key in drug discovery, especially in the design of new lead compound. However, the work to find a new lead compound for a specific target is complicated and hard, and it always leads to many mistakes. Therefore computational techniques are commonly adopted in drug design, which can save time and costs to a significant extent. Results To address the issue, a new prediction system is proposed in this work to identify drug-target interaction. First, drug-target pairs are encoded with a fragment technique and the software “PaDEL-Descriptor.” The fragment technique is for encoding target proteins, which divides each protein sequence into several fragments in order and encodes each fragment with several physiochemical properties of amino acids. The software “PaDEL-Descriptor” creates encoding vectors for drug molecules. Second, the dataset of drug-target pairs is resampled and several overlapped subsets are obtained, which are then input into kNN (k-Nearest Neighbor) classifier to build an ensemble system. Conclusion Experimental results on the drug-target dataset showed that our method performs better and runs faster than the state-of-the-art predictors. PMID:28744468

Frequency and severity of potential drug interactions in a cohort of HIV-infected patients Identified through a Multidisciplinary team.

PubMed

Molas, E; Luque, S; Retamero, A; Echeverría-Esnal, D; Guelar, A; Montero, M; Guerri, R; Sorli, L; Lerma, E; Villar, J; Knobel, H

2018-02-01

Interactions between antiretroviral treatment (ART) and comedications are a concern in HIV-infected patients. This study aimed to determine the frequency and severity of potential drug-drug interactions (PDDIs) with ART in our setting. Observational study by a multidisciplinary team in 1259 consecutive HIV patients (March 2015-September 2016). Data on demographics, toxic habits, comorbidities, and current ART were collected. A structured questionnaire recorded concomitant medications (including occasional and over-the-counter drugs). PDDIs were classified into four categories: (1) no interactions, (2) mild (clinically non-significant), (3) moderate (requiring close monitoring or drug modification/dose adjustment), and (4) severe (contraindicated). chi-square test, logistic regression analysis. In total, 881 (70%) patients took comedication, and 563 (44.7%) had ≥ PDDI. Forty-one comedicated patients (4.6%) had severe and 522 (59.2%) moderate PDDIs. Moderate PDDIs mainly involved cardiovascular (53.8%) and central nervous system (40.2%) drugs. Independent risk factors for PDDIs were ART containing a boosted protease inhibitor (odds ratio [OR]=9.11, 95% confidence interval [CI] 5.15-16.11; p = 0.0001) and/or non-nucleoside reverse transcriptase (NNRTI) (OR = 4.34, 95%CI 2.49-7.55; p = 0.0001), HCV co-infection (OR = 3.26, 95%CI 2.15-4.93; p = 0.0001), and use of two or more comedications (OR = 3.36, 95%CI 2.27-4.97; p = 0.0001). Adherence and effectiveness of ART were similar in patients with and without PDDIs. The team made 133 recommendations related to comedications (drug change or dose adjustment) or ART (drug switch or change in administration schedule). Systematic evaluation detected a significant percentage of PDDIs requiring an intervention in HIV patients on ART. Monitoring and advice about drug-drug interactions should be part of routine practice.

Exploring drug-target interaction networks of illicit drugs.

PubMed

Atreya, Ravi V; Sun, Jingchun; Zhao, Zhongming

2013-01-01

Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care. In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies. This study presents the first systematic review of the network

Exploring drug-target interaction networks of illicit drugs

PubMed Central

2013-01-01

Background Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care. Results In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies. Conclusions This study presents the first systematic

Prevalence of Potential and Clinically Relevant Statin-Drug Interactions in Frail and Robust Older Inpatients.

PubMed

Thai, Michele; Hilmer, Sarah; Pearson, Sallie-Anne; Reeve, Emily; Gnjidic, Danijela

2015-10-01

A significant proportion of older people are prescribed statins and are also exposed to polypharmacy, placing them at increased risk of statin-drug interactions. To describe the prevalence rates of potential and clinically relevant statin-drug interactions in older inpatients according to frailty status. A cross-sectional study of patients aged ≥65 years who were prescribed a statin and were admitted to a teaching hospital between 30 July and 10 October 2014 in Sydney, Australia, was conducted. Data on socio-demographics, comorbidities and medications were collected using a standardized questionnaire. Potential statin-drug interactions were defined if listed in the Australian Medicines Handbook and three international drug information sources: the British National Formulary, Drug Interaction Facts and Drug-Reax(®). Clinically relevant statin-drug interactions were defined as interactions with the highest severity rating in at least two of the three international drug information sources. Frailty was assessed using the Reported Edmonton Frail Scale. A total of 180 participants were recruited (median age 78 years, interquartile range 14), 35.0% frail and 65.0% robust. Potential statin-drug interactions were identified in 10% of participants, 12.7% of frail participants and 8.5% of robust participants. Clinically relevant statin-drug interactions were identified in 7.8% of participants, 9.5% of frail participants and 6.8% of robust participants. Depending on the drug information source used, the prevalence rates of potential and clinically relevant statin-drug interactions ranged between 14.4 and 35.6% and between 14.4 and 20.6%, respectively. In our study of frail and robust older inpatients taking statins, the overall prevalence of potential statin-drug interactions was low and varied significantly according to the drug information source used.

Interactions between antihypertensive drugs and food.

PubMed

Jáuregui-Garrido, B; Jáuregui-Lobera, I

2012-01-01

A drug interaction is defined as any alteration, pharmacokinetics and/or pharmacodynamics, produced by different substances, other drug treatments, dietary factors and habits such as drinking and smoking. These interactions can affect the antihypertensive drugs, altering their therapeutic efficacy and causing toxic effects. The aim of this study was to conduct a review of available data about interactions between antihypertensive agents and food. The purpose of this review was to report an update of main findings with respect to the interactions between food and antihypertensive drugs by way of a search conducted in PubMed, which yielded a total of 236 articles initially. After excluding different articles, which were not focusing on the specific objective, the main results refer to interactions between antihypertensive drugs and food (in general) as well as between antihypertensive agents and grapefruit juice. Food may affect the bioavailability of antihypertensive drugs and this should be carefully considered. Advising patients to remove the grapefruit juice from their diet when treatment with these drugs seems to be the best recommendation. Given these interactions and the associated potential adverse effects the anamnesis must include detailed information about the specific eating habits of the patients.

Drug interactions between hormonal contraceptives and psychotropic drugs: a systematic review.

PubMed

Berry-Bibee, Erin N; Kim, Myong-Jin; Simmons, Katharine B; Tepper, Naomi K; Riley, Halley E M; Pagano, H Pamela; Curtis, Kathryn M

2016-12-01

To examine whether the co-administration of hormonal contraceptives (HC) and psychotropic drugs commonly used to treat anxiety and/or depression results in safety or efficacy concerns for either drug. We searched PubMed and Cochrane libraries for clinical or pharmacokinetic (PK) studies that examined co-administration of any HC with psychotropic drugs [selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), tricyclic antidepressants (TCAs), oral benzodiazepines, bupropion, mirtazapine, trazadone, buspirone, hydroxyzine, monoamine oxidase inhibitors (MAOIs), or atypical antipsychotics] in reproductive aged women. Of 555 articles identified, 22 articles (18 studies) met inclusion criteria. We identified 5 studies on SSRIs, four on TCAs, one on bupropion, three on atypical antipsychotics and five on oral benzodiazepines. No articles met inclusion criteria for SNRIs, mirtazapine, trazadone, buspirone, hydroxyzine or MAOIs. Overall, clinical studies did not demonstrate differences in unintended pregnancy rates when HCs were administered with and without psychotropic drugs or in psychotropic drug treatment outcomes when psychotropic drugs were administered with and without HCs. PK studies did not demonstrate changes in drug exposure related to contraceptive safety, contraceptive effectiveness or psychotropic drug effectiveness for most classes of psychotropic drugs. However, limited PK data raise concern for HCs increasing systemic exposure of amitriptyline and imipramine (both TCAs), theoretically posing safety concerns. Limited quality and quantity evidence on use of psychotropic drugs and HCs suggests low concern for clinically significant interactions, though no data exist specifically for non-oral formulations of HC. Given the high frequency of use for both HCs and psychotropic drugs among reproductive-age women in the US, this review highlights a need for further research in this area. Copyright © 2016 Elsevier Inc

Participatory design for drug-drug interaction alerts.

PubMed

Luna, Daniel; Otero, Carlos; Almerares, Alfredo; Stanziola, Enrique; Risk, Marcelo; González Bernaldo de Quirós, Fernán

2015-01-01

The utilization of decision support systems, in the point of care, to alert drug-drug interactions has been shown to improve quality of care. Still, the use of these systems has not been as expected, it is believed, because of the difficulties in their knowledge databases; errors in the generation of the alerts and the lack of a suitable design. This study expands on the development of alerts using participatory design techniques based on user centered design process. This work was undertaken in three stages (inquiry, participatory design and usability testing) it showed that the use of these techniques improves satisfaction, effectiveness and efficiency in an alert system for drug-drug interactions, a fact that was evident in specific situations such as the decrease of errors to meet the specified task, the time, the workload optimization and users overall satisfaction in the system.

A physarum-inspired prize-collecting steiner tree approach to identify subnetworks for drug repositioning.

PubMed

Sun, Yahui; Hameed, Pathima Nusrath; Verspoor, Karin; Halgamuge, Saman

2016-12-05

Drug repositioning can reduce the time, costs and risks of drug development by identifying new therapeutic effects for known drugs. It is challenging to reposition drugs as pharmacological data is large and complex. Subnetwork identification has already been used to simplify the visualization and interpretation of biological data, but it has not been applied to drug repositioning so far. In this paper, we fill this gap by proposing a new Physarum-inspired Prize-Collecting Steiner Tree algorithm to identify subnetworks for drug repositioning. Drug Similarity Networks (DSN) are generated using the chemical, therapeutic, protein, and phenotype features of drugs. In DSNs, vertex prizes and edge costs represent the similarities and dissimilarities between drugs respectively, and terminals represent drugs in the cardiovascular class, as defined in the Anatomical Therapeutic Chemical classification system. A new Physarum-inspired Prize-Collecting Steiner Tree algorithm is proposed in this paper to identify subnetworks. We apply both the proposed algorithm and the widely-used GW algorithm to identify subnetworks in our 18 generated DSNs. In these DSNs, our proposed algorithm identifies subnetworks with an average Rand Index of 81.1%, while the GW algorithm can only identify subnetworks with an average Rand Index of 64.1%. We select 9 subnetworks with high Rand Index to find drug repositioning opportunities. 10 frequently occurring drugs in these subnetworks are identified as candidates to be repositioned for cardiovascular diseases. We find evidence to support previous discoveries that nitroglycerin, theophylline and acarbose may be able to be repositioned for cardiovascular diseases. Moreover, we identify seven previously unknown drug candidates that also may interact with the biological cardiovascular system. These discoveries show our proposed Prize-Collecting Steiner Tree approach as a promising strategy for drug repositioning.

Drug-nutrient interaction in clinical nutrition.

PubMed

Chan, Lingtak-Neander

2002-05-01

Drug-nutrient interactions have been recognized for decades. It is known that improper management of some of these interactions may lead to therapeutic failure or cause serious adverse effects to the patients. While most of the known drug-nutrient interactions involve changes in oral bioavailabilities and absorption of the offending compounds, recent investigations suggest that different mechanisms also exist. A mechanism-derived classification system for drug-nutrient interactions has only recently been developed. This system should facilitate the future research and development of practice guidelines in the identification and management of important interactions.

Important drug-nutrient interactions in the elderly.

PubMed

Thomas, J A; Burns, R A

1998-09-01

Several drug-nutrient interactions can occur, but their prevalence may be accentuated in the elderly. Geriatric patients may experience age-related changes in the pharmacokinetics of a drug-absorption, distribution, metabolism and excretion. When drug-nutrient interactions occur, they usually affect absorptive processes more frequently. Specific transporter systems facilitate the absorption of many drugs. Little is known about how these transporter systems are affected by aging. Co-existing disease states in the elderly may exaggerate the action of a drug and represent a confounding factor in drug-nutrient interactions. While several different drug-nutrient interactions are important in the elderly, those affecting the cardiovascular system warrant special attention.

Biophysical interactions with model lipid membranes: applications in drug discovery and drug delivery

PubMed Central

Peetla, Chiranjeevi; Stine, Andrew; Labhasetwar, Vinod

2009-01-01

The transport of drugs or drug delivery systems across the cell membrane is a complex biological process, often difficult to understand because of its dynamic nature. In this regard, model lipid membranes, which mimic many aspects of cell-membrane lipids, have been very useful in helping investigators to discern the roles of lipids in cellular interactions. One can use drug-lipid interactions to predict pharmacokinetic properties of drugs, such as their transport, biodistribution, accumulation, and hence efficacy. These interactions can also be used to study the mechanisms of transport, based on the structure and hydrophilicity/hydrophobicity of drug molecules. In recent years, model lipid membranes have also been explored to understand their mechanisms of interactions with peptides, polymers, and nanocarriers. These interaction studies can be used to design and develop efficient drug delivery systems. Changes in the lipid composition of cells and tissue in certain disease conditions may alter biophysical interactions, which could be explored to develop target-specific drugs and drug delivery systems. In this review, we discuss different model membranes, drug-lipid interactions and their significance, studies of model membrane interactions with nanocarriers, and how biophysical interaction studies with lipid model membranes could play an important role in drug discovery and drug delivery. PMID:19432455

Drug-drug Interactions of Statins Potentially Leading to Muscle-Related Side Effects in Hospitalized Patients.

PubMed

Bucsa, Camelia; Farcas, Andreea; Leucuta, D; Mogosan, Cristina; Bojita, M; Dumitrascu, D L

2015-01-01

The associations of drugs that may interact with the statins resulting in elevated serum concentration of the statins are an important risk factor for statin induced muscle disorders. We aimed to determine the prevalence of these associations in all hospitalized patients that had been prescribed statins before/during hospitalization and to find out how often they are associated with muscle-related side effects. This prospective, non-interventional study performed in two internal medicine departments included patients with statin therapy before/during hospitalization. Data on each patient demographic characteristics, co-morbidities and treatment was collected from medical charts and interviews. We evaluated patients' therapy for the targeted associations using Thomson Micromedex Drug Interactions checker and we ranked the identified drug-drug interactions (DDIs) accordingly. Each patient with statin treatment before admission was additionally interviewed in order to identify muscular symptoms. In 109 patients on statin treatment we found 35 potential (p) DDIs of statins in 30 (27.5%) patients, most of which were in the therapy before admission (27 pDDIs). The pDDIs were moderate (20 pDDIs) and major (15 pDDIs). Of the total number of pDDIs, 24 were targeting the muscular system. The drugs most frequently involved in the statins' pDDIs were amiodarone and fenofibrate. Two of the patients with pDDIs reported muscle pain, both having additional risk factors for statin induced muscular effects. The prevalence of statins' pDDIs was high in our study, mostly in the therapy before admission, with only a small number of pDDIs resulting in clinical outcome.

HIV Treatment: What is a Drug Interaction?

MedlinePlus

... more) drugs or between a drug and a food or beverage. Taking a drug while having certain medical conditions ... interaction : A reaction between a drug and a food or beverage. Drug-condition interaction : A reaction that occurs when ...

Interactions between antiarrhythmic drugs and food.

PubMed

Jáuregui-Garrido, B; Jáuregui-Lobera, I

2012-01-01

A drug interaction is defined as any alteration, pharmacokinetics and/or pharmacodynamics, produced by different substances, other drug treatments, dietary factors and habits such as drinking and smoking. These interactions can affect the antiarrhythmic drugs, altering their therapeutic efficacy and adverse effects. The aim of this study was to conduct a review of available data about interactions between antiarrhythmic drugs and food. The purpose of this review was to report an update of the existing literature data on the main findings with respect to food and antiarrhythmic drugs interactions by means of a search conducted in PubMed, which yielded a total of 250 articles initially. After excluding different articles which were not focusing on the specific objective, the main results refer to interactions among antiarrhythmic drugs and food in general, grapefruit juice, and others like fibre or medicinal plants. Food may affect the bioavailability of antiarrhythmic drugs and in some specific cases (dairy products, rich-in-protein diets, grapefruit juice), this should be carefully considered. The best recommendation seems to advise patients to remove the grapefruit juice from their diet when treatment with these drugs. Fibre should be separated from taking these drugs and regarding medicinal plants and given their increased use, the anamnesis must include information about its use, the reason for that use and what types of plants are used, all in order to give the corresponding recommendations.

Assessment of healthcare professionals' knowledge about warfarin-vitamin K drug-nutrient interactions.

PubMed

Couris, R R; Tataronis, G R; Dallal, G E; Blumberg, J B; Dwyer, J T

2000-08-01

Dietary vitamin K can interact with oral anticoagulant drugs and interfere with their therapeutic safety and efficacy. Therefore, knowledge about drug-nutrient interactions involving vitamin K possessed by physicians, pharmacists, dietitians and nurses practicing anticoagulant therapy was assessed. Healthcare practitioners were surveyed using a 30-question, 98-item questionnaire on the most common and/or important food interactions with warfarin, drug interactions with warfarin and general drug-nutrient interactions involving vitamin K. The study sample included 160 randomly selected healthcare providers (40 physicians, pharmacists, dietitians and nurses) from 10 hospitals with 200 to 1000 beds from six Massachusetts regions. Random selection was conducted from a pool of selected healthcare providers practicing anticoagulant therapy who counsel patients receiving warfarin. All surveys were completed within three months of the start of the study, and all participants provided usable data for statistical analysis. The mean scores (+/- SD) on the overall test were 72.5+/-9.0 for pharmacists, 62.51+/-10.6 for physicians, 56.9+/-8.8 for dietitians and 50.2+/-9.3 for nurses, with 100 being a perfect score. Pharmacists scored significantly higher in the area of drug interactions (75.9+/-11.3, p<0.05). Dietitians scored higher in the area of food interactions (73.0+/-10.3). No significant differences between physicians and pharmacists were evident on general drug-nutrient interactions. While over 87% of the healthcare professionals correctly identified some common foods containing large amounts of vitamin K, such as broccoli and spinach, fewer than 25% were able to identify others such as pea soup, coleslaw and dill pickles. Although the healthcare professionals surveyed in this study appear to have demonstrated some proficiency in their respective areas of expertise, they exhibited less knowledge in others. Therefore, additional training and integration of knowledge and

Pharmacodynamics and common drug-drug interactions of the third-generation antiepileptic drugs.

PubMed

Stefanović, Srđan; Janković, Slobodan M; Novaković, Milan; Milosavljević, Marko; Folić, Marko

2018-02-01

Anticonvulsants that belong to the third generation are considered as 'newer' antiepileptic drugs, including: eslicarbazepine acetate, lacosamide, perampanel, brivaracetam, rufinamide and stiripentol. Areas covered: This article reviews pharmacodynamics (i.e. mechanisms of action) and clinically relevant drug-drug interactions of the third-generation antiepileptic drugs. Expert opinion: Newer antiepileptic drugs have mechanisms of action which are not shared with the first and the second generation anticonvulsants, like inhibition of neurotransmitters release, blocking receptors for excitatory amino acids and new ways of sodium channel inactivation. New mechanisms of action increase chances of controlling forms of epilepsy resistant to older anticonvulsants. Important advantage of the third-generation anticonvulsants could be their little propensity for interactions with both antiepileptic and other drugs observed until now, making prescribing much easier and safer. However, this may change with new studies specifically designed to discover drug-drug interactions. Although the third-generation antiepileptic drugs enlarged therapeutic palette against epilepsy, 20-30% of patients with epilepsy is still treatment-resistant and need new pharmacological approach. There is great need to explore all molecular targets that may directly or indirectly be involved in generation of seizures, so a number of candidate compounds for even newer anticonvulsants could be generated.

Common drug-drug interactions in antifungal treatments for superficial fungal infections.

PubMed

Gupta, Aditya K; Versteeg, Sarah G; Shear, Neil H

2018-04-01

Antifungal agents can be co-administered alongside several other medications for a variety of reasons such as the presence of comorbidities. Pharmacodynamic interactions such as synergistic and antagonistic interactions could be the result of co-administered medications. Pharmacokinetic interactions could also transpire through the inhibition of metabolizing enzymes and drug transport systems, altering the absorption, metabolism and excretion of co-administered medications. Both pharmacodynamic and pharmacokinetic interactions can result in hospitalization due to serious adverse effects associated with antifungal agents, lower therapeutic doses required to achieve desired antifungal activity, and prevent antifungal resistance. Areas covered: The objective of this review is to summarize pharmacodynamic and pharmacokinetic interactions associated with common antifungal agents used to treat superficial fungal infections. Pharmacodynamic and pharmacokinetic interactions that impact the therapeutic effects of antifungal agents and drugs that are influenced by the presence of antifungal agents was the context to which these antifungal agents were addressed. Expert opinion: The potential for drug-drug interactions is minimal for topical antifungals as opposed to oral antifungals as they have minimal exposure to other co-administered medications. Developing non-lipophilic antifungals that have unique metabolizing pathways and are topical applied are suggested properties that could help limit drug-drug interactions associated with future treatments.

A Population-Based Assessment of the Drug Interaction Between Levothyroxine and Warfarin

PubMed Central

Pincus, D; Gomes, T; Hellings, C; Zheng, H; Paterson, JM; Mamdani, MM; Juurlink, DN

2013-01-01

Most drug interaction resources suggest that levothyroxine can dramatically potentiate the effect of warfarin. However, the mechanistic basis of the interaction is speculative, and little evidence supports a meaningful drug interaction. We conducted a population-based nested case–control study to examine the risk of hospitalization for hemorrhage following the initiation of levothyroxine in a cohort of 260,076 older patients receiving warfarin. In this group, we identified 10,532 case subjects hospitalized for hemorrhage and 40,595 controls. In the primary analysis, we found no association between hospitalization for hemorrhage during warfarin therapy and initiation of levothyroxine in the preceding 30 days (adjusted odds ratio 1.11, 95% confidence interval 0.67–1.86). Secondary analyses using more remote initiation of levothyroxine also found no association. These findings suggest that concerns about a clinically meaningful levothyroxine–warfarin drug interaction are not justified. Drug interaction resources that presently characterize this interaction as important should reevaluate this classification. PMID:23093318

Drug Interactions in Childhood Cancer

PubMed Central

Haidar, Cyrine; Jeha, Sima

2016-01-01

Children with cancer are increasingly benefiting from novel therapeutic strategies and advances in supportive care, as reflected in improvements in both their survival and quality of life. However, the continuous emergence of new oncology drugs and supportive care agents has also increased the possibility of deleterious drug interactions and healthcare providers need to practice extreme caution when combining medications. In this review, we discuss the most common interactions of chemotherapeutic agents with supportive care drugs such as anticonvulsants, antiemetics, uric acid–lowering agents, acid suppressants, antimicrobials, and pain management medications in pediatric oncology patients. As chemotherapy agents interact not only with medications but also with foods and herbal supplements that patients receive during the course of their treatment, we also briefly review such interactions and provide recommendations to avoid unwanted and potentially fatal interactions in children with cancer. PMID:20869315

Identification of drug interactions in hospitals--computerized screening vs. bedside recording.

PubMed

Blix, H S; Viktil, K K; Moger, T A; Reikvam, A

2008-04-01

Managing drug interactions in hospitalized patients is important and challenging. The objective of the study was to compare two methods for identification of drug interactions (DDIs)--computerized screening and prospective bedside recording--with regard to capability of identifying DDIs. Patient characteristics were recorded for patients admitted to five hospitals. By bedside evaluation drug-related problems, including DDIs, were prospectively recorded by pharmacists and discussed in multidisciplinary teams. A computer screening programme was used to identify DDIs retrospectively--dividing DDIs into four classes: A, avoid; B, avoid/take precautions; C, take precautions; D, no action needed. Among 827 patients, computer screening identified DDIs in 544 patients (66%); 351 had DDIs introduced in hospital. The 1513 computer-identified DDIs had the following distribution: type A 78; type B 915; type C 38; type D 482. By bedside evaluation, 99 DDIs were identified in 73 patients (9%). The proportions of computer recorded DDIs which were also identified at the bedside were: 5%, 8%, 8%, 2% DDIs of types A, B, C and D respectively. In 10 patients, DDIs not registered by computer screening were identified by bedside evaluation. The drugs most frequently involved in DDIs, identified by computerized screening were acetylsalicylic acid, warfarin, furosemide and digitoxin compared with warfarin, simvastatin, theophylline and carbamazepine, by bedside evaluation. Despite an active prospective bedside search for DDIs, this approach identified less than one in 10 of the DDIs recorded by computer screening, including those regarded as hazardous. However, computer screening overestimates considerably when the objective is to identify clinically relevant DDIs.

Assessing and managing drug-nutrient interactions.

PubMed

Anderson, Karl E; Greenblatt, David J

2002-01-01

Drug-nutrient interactions can occur through many mechanisms. The amount of protein in the diet and the presence of micronutrients, such as polycyclic aromatic hydrocarbons and indoles, can affect drug metabolism. Although furanocoumarins in grapefruit juice can interact with certain oral medications, noninteracting medications generally can be substituted. Pharmacists need to provide patients with accurate information about drug-nutrient interactions and help to clarify common misconceptions about these effects.

CLINICALLY SIGNIFICANT PSYCHOTROPIC DRUG-DRUG INTERACTIONS IN THE PRIMARY CARE SETTING

PubMed Central

English, Brett A.; Dortch, Marcus; Ereshefsky, Larry; Jhee, Stanford

2014-01-01

In recent years, the growing numbers of patients seeking care for a wide range of psychiatric illnesses in the primary care setting has resulted in an increase in the number of psychotropic medications prescribed. Along with the increased utilization of psychotropic medications, considerable variability is noted in the prescribing patterns of primary care providers and psychiatrists. Because psychiatric patients also suffer from a number of additional medical comorbidities, the increased utilization of psychotropic medications presents an elevated risk of clinically significant drug interactions in these patients. While life-threatening drug interactions are rare, clinically significant drug interactions impacting drug response or appearance of serious adverse drug reactions have been documented and can impact long-term outcomes. Additionally, the impact of genetic variability on the psychotropic drug’s pharmacodynamics and/or pharmacokinetics may further complicate drug therapy. Increased awareness of clinically relevant psychotropic drug interactions can aid clinicians to achieve optimal therapeutic outcomes in patients in the primary care setting. PMID:22707017

Antiretroviral Drug Interactions: Overview of Interactions Involving New and Investigational Agents and the Role of Therapeutic Drug Monitoring for Management

PubMed Central

Rathbun, R. Chris; Liedtke, Michelle D.

2011-01-01

Antiretrovirals are prone to drug-drug and drug-food interactions that can result in subtherapeutic or supratherapeutic concentrations. Interactions between antiretrovirals and medications for other diseases are common due to shared metabolism through cytochrome P450 (CYP450) and uridine diphosphate glucuronosyltransferase (UGT) enzymes and transport by membrane proteins (e.g., p-glycoprotein, organic anion-transporting polypeptide). The clinical significance of antiretroviral drug interactions is reviewed, with a focus on new and investigational agents. An overview of the mechanistic basis for drug interactions and the effect of individual antiretrovirals on CYP450 and UGT isoforms are provided. Interactions between antiretrovirals and medications for other co-morbidities are summarized. The role of therapeutic drug monitoring in the detection and management of antiretroviral drug interactions is also briefly discussed. PMID:24309307

Protein interactions in 3D: from interface evolution to drug discovery.

PubMed

Winter, Christof; Henschel, Andreas; Tuukkanen, Anne; Schroeder, Michael

2012-09-01

Over the past 10years, much research has been dedicated to the understanding of protein interactions. Large-scale experiments to elucidate the global structure of protein interaction networks have been complemented by detailed studies of protein interaction interfaces. Understanding the evolution of interfaces allows one to identify convergently evolved interfaces which are evolutionary unrelated but share a few key residues and hence have common binding partners. Understanding interaction interfaces and their evolution is an important basis for pharmaceutical applications in drug discovery. Here, we review the algorithms and databases on 3D protein interactions and discuss in detail applications in interface evolution, drug discovery, and interface prediction. Copyright © 2012 Elsevier Inc. All rights reserved.

Combinatorial Drug Screening Identifies Ewing Sarcoma-specific Sensitivities.

PubMed

Radic-Sarikas, Branka; Tsafou, Kalliopi P; Emdal, Kristina B; Papamarkou, Theodore; Huber, Kilian V M; Mutz, Cornelia; Toretsky, Jeffrey A; Bennett, Keiryn L; Olsen, Jesper V; Brunak, Søren; Kovar, Heinrich; Superti-Furga, Giulio

2017-01-01

Improvements in survival for Ewing sarcoma pediatric and adolescent patients have been modest over the past 20 years. Combinations of anticancer agents endure as an option to overcome resistance to single treatments caused by compensatory pathways. Moreover, combinations are thought to lessen any associated adverse side effects through reduced dosing, which is particularly important in childhood tumors. Using a parallel phenotypic combinatorial screening approach of cells derived from three pediatric tumor types, we identified Ewing sarcoma-specific interactions of a diverse set of targeted agents including approved drugs. We were able to retrieve highly synergistic drug combinations specific for Ewing sarcoma and identified signaling processes important for Ewing sarcoma cell proliferation determined by EWS-FLI1 We generated a molecular target profile of PKC412, a multikinase inhibitor with strong synergistic propensity in Ewing sarcoma, revealing its targets in critical Ewing sarcoma signaling routes. Using a multilevel experimental approach including quantitative phosphoproteomics, we analyzed the molecular rationale behind the disease-specific synergistic effect of simultaneous application of PKC412 and IGF1R inhibitors. The mechanism of the drug synergy between these inhibitors is different from the sum of the mechanisms of the single agents. The combination effectively inhibited pathway crosstalk and averted feedback loop repression, in EWS-FLI1-dependent manner. Mol Cancer Ther; 16(1); 88-101. ©2016 AACR. ©2016 American Association for Cancer Research.

Similarity-based modeling in large-scale prediction of drug-drug interactions.

PubMed

Vilar, Santiago; Uriarte, Eugenio; Santana, Lourdes; Lorberbaum, Tal; Hripcsak, George; Friedman, Carol; Tatonetti, Nicholas P

2014-09-01

Drug-drug interactions (DDIs) are a major cause of adverse drug effects and a public health concern, as they increase hospital care expenses and reduce patients' quality of life. DDI detection is, therefore, an important objective in patient safety, one whose pursuit affects drug development and pharmacovigilance. In this article, we describe a protocol applicable on a large scale to predict novel DDIs based on similarity of drug interaction candidates to drugs involved in established DDIs. The method integrates a reference standard database of known DDIs with drug similarity information extracted from different sources, such as 2D and 3D molecular structure, interaction profile, target and side-effect similarities. The method is interpretable in that it generates drug interaction candidates that are traceable to pharmacological or clinical effects. We describe a protocol with applications in patient safety and preclinical toxicity screening. The time frame to implement this protocol is 5-7 h, with additional time potentially necessary, depending on the complexity of the reference standard DDI database and the similarity measures implemented.

Free software to analyse the clinical relevance of drug interactions with antiretroviral agents (SIMARV®) in patients with HIV/AIDS.

PubMed

Giraldo, N A; Amariles, P; Monsalve, M; Faus, M J

Highly active antiretroviral therapy has extended the expected lifespan of patients with HIV/AIDS. However, the therapeutic benefits of some drugs used simultaneously with highly active antiretroviral therapy may be adversely affected by drug interactions. The goal was to design and develop a free software to facilitate analysis, assessment, and clinical decision making according to the clinical relevance of drug interactions in patients with HIV/AIDS. A comprehensive Medline/PubMed database search of drug interactions was performed. Articles that recognized any drug interactions in HIV disease were selected. The publications accessed were limited to human studies in English or Spanish, with full texts retrieved. Drug interactions were analyzed, assessed, and grouped into four levels of clinical relevance according to gravity and probability. Software to systematize the information regarding drug interactions and their clinical relevance was designed and developed. Overall, 952 different references were retrieved and 446 selected; in addition, 67 articles were selected from the citation lists of identified articles. A total of 2119 pairs of drug interactions were identified; of this group, 2006 (94.7%) were drug-drug interactions, 1982 (93.5%) had an identified pharmacokinetic mechanism, and 1409 (66.5%) were mediated by enzyme inhibition. In terms of clinical relevance, 1285 (60.6%) drug interactions were clinically significant in patients with HIV (levels 1 and 2). With this information, a software program that facilitates identification and assessment of the clinical relevance of antiretroviral drug interactions (SIMARV ® ) was developed. A free software package with information on 2119 pairs of antiretroviral drug interactions was designed and developed that could facilitate analysis, assessment, and clinical decision making according to the clinical relevance of drug interactions in patients with HIV/AIDS. Copyright © 2016 Elsevier Inc. All rights reserved.

6-mercaptopurine and daunorubicin double drug liposomes-preparation, drug-drug interaction and characterization.

PubMed

Agrawal, Vineet; Paul, Manash K; Mukhopadhyay, Anup K

2005-01-01

This article addresses and investigates the dual incorporation of daunorubicin (DR) and 6-mercaptopurine (6-MP) in liposomes for better chemotherapy. These drugs are potential candidates for interaction due to the quinone (H acceptor) and hydroxyl (H donor) groups on DR and 6-MP, respectively. Interactions between the two drugs in solution were monitored by UV/Vis and fluorescence spectroscopy. Interaction between the two drugs inside the liposomes was evaluated by HPLC (for 6-MP) and by fluorescence spectroscopy (for daunorubicin) after phospholipase-mediated liposome lysis. Our results provide evidence for the lack of interaction between the two drugs in solution and in liposomes. The entrapment efficiencies of 6-MP in the neutral Phosphatidyl choline (PC):Cholesterol (Chol):: 2:1 and anionic PC:Chol:Cardiolipin (CL) :: 4:5:1 single and double drug liposomes were found to be 0.4% and 1.5% (on average), respectively. The entrapment efficiencies of DR in the neutral and anionic double drug liposomes were found to be 55% and 31%, respectively. The corresponding entrapment of daunorubicin in the single drug liposomes was found to be 62% on average. Our thin layer chromatography (TLC) and transmission electron microscopy (TEM) results suggest stability of lipid and liposomes, thus pointing plausible existence of double drug liposomes. Cytotoxicity experiments were performed by using both single drug and double drug liposomes. By comparing the results of phase contrast and fluorescence microscopy, it was observed that the double drug liposomes were internalized in the jurkat and Hut78 (highly resistant cell line) leukemia cells as viewed by the fluorescence of daunorubicin. The cytotoxicity was dose dependent and had shown a synergistic effect when double drug liposome was used.

Evaluation of knowledge of Health care professionals on warfarin interactions with drug and herb medicinal in Central Saudi Arabia

PubMed Central

Al-Arifi, Mohamed N.; Wajid, Syed; Al-Manie, Nawaf K.; Al-Saker, Faisal M.; Babelgaith, Salmeen D.; Asiri, Yousif A.; Sales, Ibrahim

2016-01-01

Objectives: To evaluate health care professionals’ knowledge on warfarin interactions with drugs and herbs. Methods: A self-administered questionnaire was developed to assess health care professionals’ knowledge on warfarin interactions with drug and herb. Respondents were asked to classify 15 drugs that may effect on warfarin action as “enhance”, “inhibit “, “no effect”. The study sample involved health care professionals (physicians, pharmacists and nurses) from king Salman hospital, Saudi Arabia. Results: About 92.2% of health care professionals identified warfarin interactions with aspirin, 4.4% for warfarin and fluoxetine. Warfarin and cardiac agents (atenolol) was correctly identified by 11.1% of respondents. In warfarin –herb interactions section, the majority of respondents (66.7%) identified the interaction between green tea and warfarin. Approximately one-third of respondents (n=33) correctly classified warfarin interactions with cardamom. No significant difference was found between the health care professionals (p=0.49) for warfarin-drug interactions knowledge score and p= 0.52 for warfarin- herb interactions knowledge score. Conclusion: This study suggests that health care professionals’ knowledge of warfarin- drug-herb interactions was inadequate. Therefore, health care professionals should receive more education programs about drug-drug/herb interactions to provide appropriate patient counseling and optimal therapeutic outcomes. PMID:27022381

Microbiota-drug interactions: Impact on metabolism and efficacy of therapeutics.

PubMed

Wilkinson, Ellen M; Ilhan, Zehra Esra; Herbst-Kralovetz, Melissa M

2018-06-01

The microbiome not only represents a vital modifier of health and disease, but is a clinically important drug target. Therefore, study of the impact of the human microbiome on drug metabolism, toxicity and efficacy is urgently needed. This review focuses on gut and vaginal microbiomes, and the effect of those microbiomes or components thereof on the pharmacokinetics of specific chemotherapeutic agents, immunotherapies, anti-inflammatory and antimicrobial drugs. In some cases, the presence of specific bacterial species within the microbiome can alter the metabolism of certain drugs, such as chemotherapeutic agents and antiviral drugs. These microbiota-drug interactions are identified mostly through studies using germ-free or microbiome-depleted animal models, or by the administration of specific bacterial isolates. The biotransformation of drugs can cause drug-related toxicities; however, biotransformation also provides a mechanism by which drug developers could exploit host microbiota to create more site-specific drugs. Within this review we consider the importance of the route of drug administration and interactions with microbiota at various mucosal sites. Notably, we discuss the potential utility of bacterial therapeutics in altering the microbiome to enhance therapeutic efficacy and clinical outcomes in a personalized fashion. Based on the data to date, there is a clinically important relationship between microbiota and drug metabolism throughout the lifespan; therefore, profiling of the human microbiome will be essential in order to understand the mechanisms by which these microbiota-drug interactions occur and the degree to which this complex interplay affects drug efficacy. Copyright © 2018 Elsevier B.V. All rights reserved.

Radiopharmaceuticals drug interactions: a critical review.

PubMed

Santos-Oliveira, Ralph; Smith, Sheila W; Carneiro-Leão, Ana Maria A

2008-12-01

Radiopharmaceuticals play a critical role in modern medicine primarily for diagnostic purposes, but also for monitoring disease progression and response to treatment. As the use of image has been increased, so has the use of prescription medications. These trends increase the risk of interactions between medications and radiopharmaceuticals. These interactions which have an impact on image by competing with the radiopharmaceutical for binding sites for example can lead to false negative results. Drugs that accelerate the metabolism of the radiopharmaceutical can have a positive impact (i.e. speeding its clearance) or, if repeating image is needed, a negative impact. In some cases, for example in cardiac image among patients taking doxirubacin, these interactions may have a therapeutic benefit. The incidence of drug-radiopharmaceuticals adverse reactions is unknown, since they may not be reported or even recognized. Here,we compiled the medical literature, using the criteria of a systematic review established by the Cochrane Collaboration, on pharmaceutical-drug interactions to provide a summary of documented interactions by organ system and radiopharmaceuticals. The purpose is to provide a reference on drug interactions that could inform the nuclear medicine staff in their daily routine. Efforts to increase adverse event reporting, and ideally consolidate reports worldwide, can provide a critically needed resource for prevention of drug-radiopharmaceuticals interactions.

RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information.

PubMed

Wang, Lei; You, Zhu-Hong; Chen, Xing; Yan, Xin; Liu, Gang; Zhang, Wei

2018-01-01

Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drugtarget interactions (DTI). In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments. Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-theart Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods. Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

[Psychiatric polypharmacy: hazard through drug-drug-interaction and possibilities for prevention].

PubMed

Hahn, M; Braus, D F

2012-09-01

Psychiatric diseases and comorbidity have increased over the past years. Commonly used psychotropic drugs contain a high risk of drug interactions and adverse drug events (ADE). With a frequency of 10-12% psychotropic drugs are, among all pharmaceuticals, the most common cause of hospitalisation due to ADE. During a hospital stay the application of psychotropic drugs can also lead to adverse drug events--sometimes due to drug interactions. Currently, ADEs and drug interactions are the most frequent cause of death for in-patients (18% of all causes of death) with an overall mortality of 0.95%. As studies have shown, hospitals as well as insurers could save a considerable amount of resources by implementing a system with on-ward pharmacists, hereby reducing ADE and re-hospitalisation rates. In recent studies a large amount of current ADEs were rated as preventable. Patient impairment due to ADE is leading to an increase in liability cases with an expected 5% increase of compensation payments in 2011. To evaluate these ADE-related cases, a pharmaceutical assessment should be included in the expert trials, especially since a lack of awareness of medication errors is prevalent. When aiming towards a successful drug therapy, physicians must also consider that cheaper substances may often have an unfavourable drug interaction profile.

Energetics of drug-DNA interactions.

PubMed

Chaires, J B

1997-01-01

Understanding the thermodynamics of drug binding to DNA is of both practical and fundamental interest. The practical interest lies in the contribution that thermodynamics can make to the rational design process for the development of new DNA targeted drugs. Thermodynamics offer key insights into the molecular forces that drive complex formation that cannot be obtained by structural or computational studies alone. The fundamental interest in these interactions lies in what they can reveal about the general problems of parsing and predicting ligand binding free energies. For these problems, drug-DNA interactions offer several distinct advantages, among them being that the structures of many drug-DNA complexes are known at high resolution and that such structures reveal that in many cases the drug acts as a rigid body, with little conformational change upon binding. Complete thermodynamic profiles (delta G, delta H, delta S, delta Cp) for numerous drug-DNA interactions have been obtained, with the help of high-sensitivity microcalorimetry. The purpose of this article is to offer a perspective on the interpretation of these thermodynamics parameters, and in particular how they might be correlated with known structural features. Obligatory conformational changes in the DNA to accommodate intercalators and the loss of translational and rotational freedom upon complex formation both present unfavorable free energy barriers for binding. Such barriers must be overcome by favorable free energy contributions from the hydrophobic transfer of ligand from solution into the binding site, polyelectrolyte contributions from coupled ion release, and molecular interactions (hydrogen and ionic bonds, van der Waals interactions) that form within the binding site. Theoretical and semiempirical tools that allow estimates of these contributions to be made will be discussed, and their use in dissecting experimental data illustrated. This process, even at the current level of approximation

Prediction of the cause, effects, and prevention of drug-nutrient interactions using attributes and attribute values.

PubMed

Roe, D A

1985-01-01

Drug-nutrient interactions and their adverse outcomes have previously been identified by observation, investigation, and literature reports. Knowing the attributes of the drugs, availability of knowledge base management systems for microcomputer use can facilitate prediction of the mechanism and the effects of drug-nutrient interactions. Examples used to illustrate this approach are prediction of lactose intolerance in drug-induced malabsorption, and prediction of the mechanism responsible for drug-induced flush reactions. In the future we see that there may be many opportunities to use this system further in the investigation of complex drug-nutrient interactions.

Construction of drug-polymer thermodynamic phase diagrams using Flory-Huggins interaction theory: identifying the relevance of temperature and drug weight fraction to phase separation within solid dispersions.

PubMed

Tian, Yiwei; Booth, Jonathan; Meehan, Elizabeth; Jones, David S; Li, Shu; Andrews, Gavin P

2013-01-07

Amorphous drug-polymer solid dispersions have the potential to enhance the dissolution performance and thus bioavailability of BCS class II drug compounds. The principle drawback of this approach is the limited physical stability of amorphous drug within the dispersion. Accurate determination of the solubility and miscibility of drug in the polymer matrix is the key to the successful design and development of such systems. In this paper, we propose a novel method, based on Flory-Huggins theory, to predict and compare the solubility and miscibility of drug in polymeric systems. The systems chosen for this study are (1) hydroxypropyl methylcellulose acetate succinate HF grade (HPMCAS-HF)-felodipine (FD) and (2) Soluplus (a graft copolymer of polyvinyl caprolactam-polyvinyl acetate-polyethylene glycol)-FD. Samples containing different drug compositions were mixed, ball milled, and then analyzed by differential scanning calorimetry (DSC). The value of the drug-polymer interaction parameter χ was calculated from the crystalline drug melting depression data and extrapolated to lower temperatures. The interaction parameter χ was also calculated at 25 °C for both systems using the van Krevelen solubility parameter method. The rank order of interaction parameters of the two systems obtained at this temperature was comparable. Diagrams of drug-polymer temperature-composition and free energy of mixing (ΔG(mix)) were constructed for both systems. The maximum crystalline drug solubility and amorphous drug miscibility may be predicted based on the phase diagrams. Hyper-DSC was used to assess the validity of constructed phase diagrams by annealing solid dispersions at specific drug loadings. Three different samples for each polymer were selected to represent different regions within the phase diagram.

Potential risks resulting from fruit/vegetable-drug interactions: effects on drug-metabolizing enzymes and drug transporters.

PubMed

Rodríguez-Fragoso, Lourdes; Martínez-Arismendi, José Luis; Orozco-Bustos, Danae; Reyes-Esparza, Jorge; Torres, Eliseo; Burchiel, Scott W

2011-05-01

It has been well established that complex mixtures of phytochemicals in fruits and vegetables can be beneficial for human health. Moreover, it is becoming increasingly apparent that phytochemicals can influence the pharmacological activity of drugs by modifying their absorption characteristics through interactions with drug transporters as well as drug-metabolizing enzyme systems. Such effects are more likely to occur in the intestine and liver, where high concentrations of phytochemicals may occur. Alterations in cytochrome P450 and other enzyme activities may influence the fate of drugs subject to extensive first-pass metabolism. Although numerous studies of nutrient-drug interactions have been published and systematic reviews and meta-analyses of these studies are available, no generalizations on the effect of nutrient-drug interactions on drug bioavailability are currently available. Several publications have highlighted the unintended consequences of the combined use of nutrients and drugs. Many phytochemicals have been shown to have pharmacokinetic interactions with drugs. The present review is limited to commonly consumed fruits and vegetables with significant beneficial effects as nutrients and components in folk medicine. Here, we discuss the phytochemistry and pharmacokinetic interactions of the following fruit and vegetables: grapefruit, orange, tangerine, grapes, cranberry, pomegranate, mango, guava, black raspberry, black mulberry, apple, broccoli, cauliflower, watercress, spinach, tomato, carrot, and avocado. We conclude that our knowledge of the potential risk of nutrient-drug interactions is still limited. Therefore, efforts to elucidate potential risks resulting from food-drug interactions should be intensified in order to prevent undesired and harmful clinical consequences. © 2011 Institute of Food Technologists®

Acute drug prescribing to children on chronic antiepilepsy therapy and the potential for adverse drug interactions in primary care

PubMed Central

Novak, Philipp H; Ekins-Daukes, Suzie; Simpson, Colin R; Milne, Robert M; Helms, Peter; McLay, James S

2005-01-01

Aims To investigate the extent of acute coprescribing in primary care to children on chronic antiepileptic therapy, which could give rise to potentially harmful drug–drug interactions. Design Acute coprescribing to children on chronic antiepileptic drug therapy in primary care was assessed in 178 324 children aged 0–17 years for the year 1 November 1999 to 31 October 2000. Computerized prescribing data were retrieved from 161 representative general practices in Scotland. Setting One hundred and sixty-one general practices throughout Scotland. Results During the study year 723 (0.41%) children chronically prescribed antiepileptic therapy were identified. Fourteen antiepileptic agents were prescribed, with carbamazepine, sodium valproate and lamotrigine accounting for 80% of the total. During the year children on chronic antiepileptic therapy were prescribed 4895 acute coprescriptions for 269 different medicines. The average number of acute coprescriptions for non-epileptic drug therapy were eight, 11, six, and six for the 0–1, 2–4, 5–11, and 12–17-year-olds, respectively. Of these acute coprescriptions 72 (1.5%) prescribed to 22 (3.0%) children were identified as a potential source of clinically serious interactions. The age-adjusted prevalence rates for potentially serious coprescribing were 86, 26, 22, and 33/1000 children chronically prescribed antiepileptic therapy in the 0–1, 2–4, 5–11, and 12–17-year-old age groups, respectively. The drugs most commonly coprescribed which could give rise to such interactions were antacids, erythromycin, ciprofloxacin, theophylline and the low-dose oral contraceptive. For 10 (45.5%0 of the 20 children identified at risk of a potentially clinically serious adverse drug interaction, the acute coprescription was prescribed off label because of age or specific contraindication/warning. Conclusions In primary care, 3.0% of children on chronic antiepileptic therapy are coprescribed therapeutic agents, which could

Clinical Confirmation that the Selective JAK1 Inhibitor Filgotinib (GLPG0634) has a Low Liability for Drug-drug Interactions.

PubMed

Namour, Florence; Desrivot, Julie; Van der Aa, Annegret; Harrison, Pille; Tasset, Chantal; van't Klooster, Gerben

2016-01-01

The selective Janus kinase 1 inhibitor filgotinib (GLPG0634), which is currently in clinical development for the treatment of rheumatoid arthritis (RA) and Crohn's disease, demonstrated encouraging safety and efficacy profiles in RA patients after 4 weeks of daily dosing. As RA patients might be treated with multiple medications simultaneously, possible drug-drug interactions of filgotinib with cytochrome P450 enzymes and with key drug transporters were evaluated in vitro and in clinical studies. The enzymes involved in filgotinib's metabolism and the potential interactions of the parent and its active major metabolite with drug-metabolizing enzymes and drug transporters, were identified using recombinant enzymes, human microsomes, and cell systems. Furthermore, filgotinib's interaction potential with CYP3A4 was examined in an open-label study in healthy volunteers, which evaluated the impact of filgotinib co-administration on the CYP3A4-sensitive substrate midazolam. The potential interaction with the common RA drug methotrexate was investigated in a clinical study in RA patients. In vitro, filgotinib and its active metabolite at clinically relevant concentrations did not interact with cytochrome P450 enzymes and uridine 5'-diphospho-glucuronosyltransferases, and did not inhibit key drug transporters. In the clinic, a lack of relevant pharmacokinetic drug interactions by filgotinib and its active metabolite with substrates of CYP3A4, as well as with organic anion transporters involved in methotrexate elimination were found. the collective in vivo and in vitro data on drug-metabolizing enzymes and on key drug transporters, support co-administration of filgotinib with commonly used RA drugs to patients without the need for dose adjustments.

[Prevalence of potential drug interactions with azithromycin in Colombia, 2012-2013].

PubMed

Machado-Alba, Jorge E; Martínez-Pulgarín, Dayron F; Gómez-Suta, Daniela

2015-05-01

Objective To determine the prevalence of potential drug interactions between azithromycin and different IA and III antiarrhythmic groups in a national database of drug prescriptions in 2012-2013. Methods Retrospective study based on a population database of medicine dispensation. Data from patients who received azithromycin between January 1, 2012 and June 30, 2013 were extracted along with data from patients who received azithromycin in combination with other medications shown to cause heart arrhythmias when used concomitantly. Frequencies and proportions were established. Results 13 859 patients receiving azithromycin alone or in combination with other drugs were identified. The average time of use was 4.5 ± 0.9 days. A total of 702 patients (5.1 %) received azithromycin plus 19 other potentially risky drugs. The most frequently associated were loratadine (77.1 %), diphenhydramine (16.5 %) and amitriptyline (8.1 %). Combinations with a single drug were the most frequent (n=533, 75.9 %), predominantly azithromycin+loratadine. The maximum number of combined drugs was six (n=2, 0.3 %). Conclusions Identification of drug prescriptions through population databases is an effective way to find potential drug interactions. The frequency of potential interactions between azithromycin and other drugs is common in Colombian patients. Future research should assess the risk of occurrence of adverse cardiac events.

Mining association patterns of drug-interactions using post marketing FDA's spontaneous reporting data.

PubMed

Ibrahim, Heba; Saad, Amr; Abdo, Amany; Sharaf Eldin, A

2016-04-01

Pharmacovigilance (PhV) is an important clinical activity with strong implications for population health and clinical research. The main goal of PhV is the timely detection of adverse drug events (ADEs) that are novel in their clinical nature, severity and/or frequency. Drug interactions (DI) pose an important problem in the development of new drugs and post marketing PhV that contribute to 6-30% of all unexpected ADEs. Therefore, the early detection of DI is vital. Spontaneous reporting systems (SRS) have served as the core data collection system for post marketing PhV since the 1960s. The main objective of our study was to particularly identify signals of DI from SRS. In addition, we are presenting an optimized tailored mining algorithm called "hybrid Apriori". The proposed algorithm is based on an optimized and modified association rule mining (ARM) approach. A hybrid Apriori algorithm has been applied to the SRS of the United States Food and Drug Administration's (U.S. FDA) adverse events reporting system (FAERS) in order to extract significant association patterns of drug interaction-adverse event (DIAE). We have assessed the resulting DIAEs qualitatively and quantitatively using two different triage features: a three-element taxonomy and three performance metrics. These features were applied on two random samples of 100 interacting and 100 non-interacting DIAE patterns. Additionally, we have employed logistic regression (LR) statistic method to quantify the magnitude and direction of interactions in order to test for confounding by co-medication in unknown interacting DIAE patterns. Hybrid Apriori extracted 2933 interacting DIAE patterns (including 1256 serious ones) and 530 non-interacting DIAE patterns. Referring to the current knowledge using four different reliable resources of DI, the results showed that the proposed method can extract signals of serious interacting DIAEs. Various association patterns could be identified based on the relationships among

Drug-micronutrient interactions: food for thought and thought for action.

PubMed

Karadima, Vasiliki; Kraniotou, Christina; Bellos, George; Tsangaris, George Th

2016-01-01

Micronutrients are indispensable for a variety of vital functions. Micronutrient deficiencies are a global problem concerning two billion people. In most cases, deficiencies are treatable with supplementation of the elements in lack. Drug-nutrient interactions can also lead to micronutrient reduce or depletion by various pathways. Supplementation of the elements and long-term fortification programs for populations at risk can prevent and restore the related deficiencies. Within the context of Predictive, Preventive, and Personalized Medicine, a multi-professional network should be developed in order to identify, manage, and prevent drug-micronutrient interactions that can potentially result to micronutrient deficiencies.

Pharmacokinetic Drug Interactions with Panax ginseng.

PubMed

Ramanathan, Meenakshi R; Penzak, Scott R

2017-08-01

Panax ginseng is widely used as an adaptogen throughout the world. The major active constituents of P. ginseng are ginsenosides. Most naturally occurring ginsenosides are deglycosylated by colonic bacteria to intestinal metabolites. Ginsenosides along with these metabolites are widely accepted as being responsible for the pharmacologic activity and drug interaction potential of ginseng. Numerous preclinical studies have assessed the influence of various ginseng components on cytochrome P450 (CYP), glucuronidation, and drug transport activity. Results from these investigations have been largely inconclusive due to the use of different ginseng products and variations in methodology between studies. Drug interaction studies in humans have been conflicting and have largely yielded negative results or results that suggest only a weak interaction. One study using a midazolam probe found weak CYP3A induction and another using a fexofenadine probe found weak P-gp inhibition. Despite several case reports indicating a drug interaction between warfarin and P. ginseng, pharmacokinetic studies involving these agents in combination have failed to find significant pharmacokinetic or pharmacodynamic interactions. To this end, drug interactions involving P. ginseng appear to be rare; however, close clinical monitoring is still suggested for patients taking warfarin or CYP3A or P-gp substrates with narrow therapeutic indices.

Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile.

PubMed

van Laarhoven, Twan; Marchiori, Elena

2013-01-01

In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction. Drug-target interaction data are available for many classes of pharmaceutically useful target proteins including enzymes, ion channels, GPCRs and nuclear receptors. However, current drug-target interaction databases contain a small number of drug-target pairs which are experimentally validated interactions. In particular, for some drug compounds (or targets) there is no available interaction. This motivates the need for developing methods that predict interacting pairs with high accuracy also for these 'new' drug compounds (or targets). We show that a simple weighted nearest neighbor procedure is highly effective for this task. We integrate this procedure into a recent machine learning method for drug-target interaction we developed in previous work. Results of experiments indicate that the resulting method predicts true interactions with high accuracy also for new drug compounds and achieves results comparable or better than those of recent state-of-the-art algorithms. Software is publicly available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2013/.

Potential intravenous drug interactions in intensive care.

PubMed

Moreira, Maiara Benevides; Mesquita, Maria Gefé da Rosa; Stipp, Marluci Andrade Conceição; Paes, Graciele Oroski

2017-07-20

To analyze potential intravenous drug interactions, and their level of severity associated with the administration of these drugs based on the prescriptions of an intensive care unit. Quantitative study, with aretrospective exploratory design, and descriptive statistical analysis of the ICU prescriptions of a teaching hospital from March to June 2014. The sample consisted of 319 prescriptions and subsamples of 50 prescriptions. The mean number of drugs per patient was 9.3 records, and a higher probability of drug interaction inherent to polypharmacy was evidenced. The study identified severe drug interactions, such as concomitant administration of Tramadol with selective serotonin reuptake inhibitor drugs (e.g., Metoclopramide and Fluconazole), increasing the risk of seizures due to their epileptogenic actions, as well as the simultaneous use of Ranitidine-Fentanyl®, which can lead to respiratory depression. A previous mapping of prescriptions enables the characterization of the drug therapy, contributing to prevent potential drug interactions and their clinical consequences. Analisar as potenciais interações medicamentosas intravenosas e seu grau de severidade associadas à administração desses medicamentos a partir das prescrições do Centro de Terapia Intensiva. Estudo quantitativo, tipologia retrospectiva exploratória, com análise estatística descritiva das prescrições medicamentosas do Centro de Terapia Intensiva de um Hospital Universitário, no período de março-junho/2014. A amostra foi composta de 319 prescrições e subamostras de 50 prescrições. Constatou-se que a média de medicamentos por paciente foi de 9,3 registros, e evidenciou-se maior probabilidade para ocorrência de interação medicamentosa inerente à polifarmácia. O estudo identificou interações medicamentosas graves, como a administração concomitante de Tramadol com medicamentos inibidores seletivos da recaptação da serotonina, (exemplo: Metoclopramida e Fluconazol

Food-drug interactions: grapefruit juice.

PubMed

Diaconu, Camelia Harapu; Cuciureanu, Magdalena; Vlase, L; Cuciureanu, Rodica

2011-01-01

Food-drug interactions are increasingly recognized as important clinical events which may change significantly the bioavailability of oral administrated drugs. Grapefruit juice (GFJ) demonstrated multiple interactions with drugs leading to loss of the therapeutic effects or increased side-effects. GFJ decreases pre-systemic metabolism through a) competitive or mechanism-based inhibition of gut wall CYP3A4 isoenzymes and b) P-glycoprotein (P-gp), c) multidrug resistance protein-2 (MRP2) or d) organic anion-transporting polypeptide (OATP) inhibition. Although, GFJ presents high amounts of flavonoids (e.g. naringin, naringenin), furanocoumarins (e.g. 6',7'-dihydroxybergamottin, bergamottin) are the main chemicals involved in the pharmacokinetic interactions. As compounds of GFJ show additive or synergistic effects, all the major furanocoumarins are necessary for the maximal inhibitory effect. Also, related citrus fruits (sweeties, pummelo and sour orange) or various plants containing furanocoumarins may present pharmacological interactions, yet to be discovered.

Cognitive enhancers (nootropics). Part 2: drugs interacting with enzymes. Update 2014.

PubMed

Froestl, Wolfgang; Muhs, Andreas; Pfeifer, Andrea

2014-01-01

Scientists working in the field of Alzheimer's disease and, in particular, cognitive enhancers are very productive. The review on Drugs interacting with Enzymes was accepted in August 2012. However, this field is very dynamic. New potential targets for the treatment of Alzheimer's disease were identified. This update describes drugs interacting with 60 enzymes versus 43 enzymes in the first paper. Some compounds progressed in their development, while many others were discontinued. The present review covers the evolution of research in this field through April 2014.

Cognitive enhancers (Nootropics). Part 1: drugs interacting with receptors. Update 2014.

PubMed

Froestl, Wolfgang; Muhs, Andreas; Pfeifer, Andrea

2014-01-01

Scientists working in the fields of Alzheimer's disease and, in particular, cognitive enhancers are very productive. The review "Cognitive enhancers (nootropics): drugs interacting with receptors" was accepted for publication in July 2012. Since then, new targets for the potential treatment of Alzheimer's disease were identified. This update describes drugs interacting with 42 receptors versus 32 receptors in the first paper. Some compounds progressed in their development, while many others were discontinued. The present review covers the evolution of research in this field through March 2014.

Surface mediated cooperative interactions of drugs enhance mechanical forces for antibiotic action

NASA Astrophysics Data System (ADS)

Ndieyira, Joseph W.; Bailey, Joe; Patil, Samadhan B.; Vögtli, Manuel; Cooper, Matthew A.; Abell, Chris; McKendry, Rachel A.; Aeppli, Gabriel

2017-02-01

The alarming increase of pathogenic bacteria that are resistant to multiple antibiotics is now recognized as a major health issue fuelling demand for new drugs. Bacterial resistance is often caused by molecular changes at the bacterial surface, which alter the nature of specific drug-target interactions. Here, we identify a novel mechanism by which drug-target interactions in resistant bacteria can be enhanced. We examined the surface forces generated by four antibiotics; vancomycin, ristomycin, chloroeremomycin and oritavancin against drug-susceptible and drug-resistant targets on a cantilever and demonstrated significant differences in mechanical response when drug-resistant targets are challenged with different antibiotics although no significant differences were observed when using susceptible targets. Remarkably, the binding affinity for oritavancin against drug-resistant targets (70 nM) was found to be 11,000 times stronger than for vancomycin (800 μM), a powerful antibiotic used as the last resort treatment for streptococcal and staphylococcal bacteria including methicillin-resistant Staphylococcus aureus (MRSA). Using an exactly solvable model, which takes into account the solvent and membrane effects, we demonstrate that drug-target interactions are strengthened by pronounced polyvalent interactions catalyzed by the surface itself. These findings further enhance our understanding of antibiotic mode of action and will enable development of more effective therapies.

Interactions of dendrimers with biological drug targets: reality or mystery - a gap in drug delivery and development research.

PubMed

Ahmed, Shaimaa; Vepuri, Suresh B; Kalhapure, Rahul S; Govender, Thirumala

2016-07-21

Dendrimers have emerged as novel and efficient materials that can be used as therapeutic agents/drugs or as drug delivery carriers to enhance therapeutic outcomes. Molecular dendrimer interactions are central to their applications and realising their potential. The molecular interactions of dendrimers with drugs or other materials in drug delivery systems or drug conjugates have been extensively reported in the literature. However, despite the growing application of dendrimers as biologically active materials, research focusing on the mechanistic analysis of dendrimer interactions with therapeutic biological targets is currently lacking in the literature. This comprehensive review on dendrimers over the last 15 years therefore attempts to identify the reasons behind the apparent lack of dendrimer-receptor research and proposes approaches to address this issue. The structure, hierarchy and applications of dendrimers are briefly highlighted, followed by a review of their various applications, specifically as biologically active materials, with a focus on their interactions at the target site. It concludes with a technical guide to assist researchers on how to employ various molecular modelling and computational approaches for research on dendrimer interactions with biological targets at a molecular level. This review highlights the impact of a mechanistic analysis of dendrimer interactions on a molecular level, serves to guide and optimise their discovery as medicinal agents, and hopes to stimulate multidisciplinary research between scientific, experimental and molecular modelling research teams.

DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.

PubMed

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-06-15

Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. http://datamining-iip.fudan.edu.cn/service/DrugE-Rank zhusf@fudan.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Anticoagulant Medicine: Potential for Drug-Food Interactions

MedlinePlus

... Medications Anticoagulants and Drug-Food Interactions Anticoagulants and Drug-Food Interactions Make an Appointment Ask a Question ... care provider before making the change. Anticoagulants and Medicine There are many medicines that can interact with ...

Detection of First-Line Drug Resistance Mutations and Drug-Protein Interaction Dynamics from Tuberculosis Patients in South India.

PubMed

Nachappa, Somanna Ajjamada; Neelambike, Sumana M; Amruthavalli, Chokkanna; Ramachandra, Nallur B

2018-05-01

Diagnosis of drug-resistant tuberculosis predominantly relies on culture-based drug susceptibility testing, which take weeks to produce a result and a more time-efficient alternative method is multiplex allele-specific PCR (MAS-PCR). Also, understanding the role of mutations in causing resistance helps better drug designing. To evaluate the ability of MAS-PCR in the detection of drug resistance and to understand the mechanism of interaction of drugs with mutant proteins in Mycobacterium tuberculosis. Detection of drug-resistant mutations using MAS-PCR and validation through DNA sequencing. MAS-PCR targeted five loci on three genes, katG 315 and inhA -15 for the drug isoniazid (INH), and rpoB 516, 526, and 531 for rifampicin (RIF). Furthermore, the sequence data were analyzed to study the effect on interaction of the anti-TB drug molecule with the target protein using in silico docking. We identified drug-resistant mutations in 8 out of 114 isolates with 2 of them as multidrug-resistant TB using MAS-PCR. DNA sequencing confirmed only six of these, recording a sensitivity of 85.7% and specificity of 99.3% for MAS-PCR. Molecular docking showed estimated free energy of binding (ΔG) being higher for RIF binding with RpoB S531L mutant. Codon 315 in KatG does not directly interact with INH but blocks the drug access to active site. We propose DNA sequencing-based drug resistance detection for TB, which is more accurate than MAS-PCR. Understanding the action of resistant mutations in disrupting the normal drug-protein interaction aids in designing effective drug alternatives.

The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective.

PubMed

Li, Ying Hong; Wang, Pan Pan; Li, Xiao Xu; Yu, Chun Yan; Yang, Hong; Zhou, Jin; Xue, Wei Wei; Tan, Jun; Zhu, Feng

2016-01-01

The human kinome is one of the most productive classes of drug target, and there is emerging necessity for treating complex diseases by means of polypharmacology (multi-target drugs and combination products). However, the advantages of the multi-target drugs and the combination products are still under debate. A comparative analysis between FDA approved multi-target drugs and combination products, targeting the human kinome, was conducted by mapping targets onto the phylogenetic tree of the human kinome. The approach of network medicine illustrating the drug-target interactions was applied to identify popular targets of multi-target drugs and combination products. As identified, the multi-target drugs tended to inhibit target pairs in the human kinome, especially the receptor tyrosine kinase family, while the combination products were able to against targets of distant homology relationship. This finding asked for choosing the combination products as a better solution for designing drugs aiming at targets of distant homology relationship. Moreover, sub-networks of drug-target interactions in specific disease were generated, and mechanisms shared by multi-target drugs and combination products were identified. In conclusion, this study performed an analysis between approved multi-target drugs and combination products against the human kinome, which could assist the discovery of next generation polypharmacology.

Interaction between DNA and Drugs Having Protonable Basic Groups: Characterization through Affinity Constants, Drug Release Kinetics, and Conformational Changes

PubMed Central

Alarcón, Liliana P.; Baena, Yolima; Manzo, Rubén H.

2017-01-01

This paper reports the in vitro characterization of the interaction between the phosphate groups of DNA and the protonated species of drugs with basic groups through the determination of the affinity constants, the reversibility of the interaction, and the effect on the secondary structure of the macromolecule. Affinity constants of the counterionic condensation DNA–drug were in the order of 106. The negative electrokinetic potential of DNA decreased with the increase of the proportion of loading drugs. The drugs were slowly released from the DNA–drug complexes and had release kinetics consistent with the high degree of counterionic condensation. The circular dichroism profile of DNA was not modified by complexation with atenolol, lidocaine, or timolol, but was significantly altered by the more lipophilic drugs benzydamine and propranolol, revealing modifications in the secondary structure of the DNA. The in vitro characterization of such interactions provides a physicochemical basis that would contribute to identify the effects of this kind of drugs in cellular cultures, as well as side effects observed under their clinical use. Moreover, this methodology could also be projected to the fields of intracellular DNA transfection and the use of DNA as a carrier of active drugs. PMID:28054999

Clinical relevance of cimetidine drug interactions.

PubMed

Shinn, A F

1992-01-01

The excellent efficacy and tolerability profiles of H2-antagonists have established these agents as the leading class of antiulcer drugs. Attention has been focused on drug interactions with H2-antagonists as a means of product differentiation and because many patients are receiving multiple drug therapy. The main mechanism of most drug interactions involving cimetidine appears to be inhibition of the hepatic microsomal enzyme cytochrome P450, an effect which may be related to the different structures of H2-antagonists. Ranitidine appears to have less affinity than cimetidine for this system. There have been many published case reports and studies of drug interactions with cimetidine, but many of these have provided pharmacokinetic data only, with little information concerning the clinical significance of these findings. Nevertheless, the coadministration of cimetidine with drugs that have a narrow therapeutic margin (such as theophylline) may potentially result in clinically significant adverse effects. The monitoring of serum concentrations of drugs coadministered with cimetidine may reduce the risk of adverse events but does not abolish the problem. However, for most patients, concomitant administration of cimetidine with drugs possessing a wide therapeutic margin is unlikely to pose a significant problem.

Identification and evaluation of drug-supplement interactions in Hungarian hospital patients.

PubMed

Végh, Anna; Lankó, Erzsébet; Fittler, András; Vida, Róbert György; Miseta, Ildikó; Takács, Gábor; Botz, Lajos

2014-04-01

The increasing number of patients taking supplementary products together with prescribed medicines has become a new challenge for health care systems. These products may influence therapy outcomes by inducing unwanted effects. Particularly concerning is the potential for harmful interactions between prescribed medicines and supplementary products. The aims of the study were to evaluate supplement use, to identify and analyse potential interactions, and to assess the efficiency of computerised interaction screening. Participants of the study were inpatients and outpatients of a Hungarian university hospital. A cross-sectional point-of-care survey of 200 patients was carried out. Data was collected through personal interviews and a review of the medical records. Drug-drug, drug-supplement and supplement-supplement interactions were analysed with three interaction databases (Lexi-Interact Online, Medscape Drug Interaction Checker and Mediris). Prevalence of supplementary product use, number of medicines and supplementary products per patient, procurement sources of products, number of potentially severe interactions. There was a marked difference between data obtained from patient interviews and the medical records. 85.5 % of the surveyed patients took supplementary products during the 2 weeks prior to the interview. The average number of prescribed medicines and supplementary products were 7.8 and 2.5, respectively. Women were more likely to take supplements than men. There was no significant difference in supplement use between patients under or over 60 years, between inpatients and outpatients and among patients in various wards. 39.4 % of supplementary products were purchased outside a regulated pharmacy environment. Potentially severe drug-supplement interactions were detected with 45.2 % of supplement users; however the majority of interactions were not included in one or the other of the three databases. In addition to that the risk ratings of the same

Is the clinical relevance of drug-food and drug-herb interactions limited to grapefruit juice and Saint-John's Wort?

PubMed

Mouly, Stéphane; Lloret-Linares, Célia; Sellier, Pierre-Olivier; Sene, Damien; Bergmann, J-F

2017-04-01

An interaction of drug with food, herbs, and dietary supplements is usually the consequence of a physical, chemical or physiologic relationship between a drug and a product consumed as food, nutritional supplement or over-the-counter medicinal plant. The current educational review aims at reminding to the prescribing physicians that the most clinically relevant drug-food interactions may not be strictly limited to those with grapefruit juice and with the Saint John's Wort herbal extract and may be responsible for changes in drug plasma concentrations, which in turn decrease efficacy or led to sometimes life-threatening toxicity. Common situations handled in clinical practice such as aging, concomitant medications, transplant recipients, patients with cancer, malnutrition, HIV infection and those receiving enteral or parenteral feeding may be at increased risk of drug-food or drug-herb interactions. Medications with narrow therapeutic index or potential life-threatening toxicity, e.g., the non-steroidal anti-inflammatory drugs, opioid analgesics, cardiovascular medications, warfarin, anticancer drugs and immunosuppressants may be at risk of significant drug-food interactions to occur. Despite the fact that considerable effort has been achieved to increase patient' and doctor's information and ability to anticipate their occurrence and consequences in clinical practice, a thorough and detailed health history and dietary recall are essential for identifying potential problems in order to optimize patient prescriptions and drug dosing on an individual basis as well as to increase the treatment risk/benefit ratio. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Antineoplastic oral agents and drug-nutrient interactions: a sistematic review].

PubMed

Jiménez Torres, N V; Romero Crespo, I; Ballester Solaz, M; Albert Marí, A; Jiménez Arenas, V

2009-01-01

Studies on bioavailability are part of the clinical development of drugs for oral use in order to identify potential drug-food interactions. For oral antitumor drugs, their clinical importance is currently recognized although regrettably the information available presents variability concerning the scientific evidence. To review the available scientific evidence about oral anti-tumor medications and establish the recommendations for their administration with foods. We carried out a bibliographic search in Medline and The Cochrane Library for the period January of 1966 to March of 2008, focused on identifying those publications about drug-food interactions with oral antitumor medications. The bibliographical analysis was made in two steps. During the first phase, we excluded those articles in which the title or their content did not correspond with the objective settled; during the second phase, we deleted all the references duplicated in both databases. The inclusion criteria to select the articles were: design (systematic reviews, meta-analysis, Phase I and Phase II randomized clinical trials), population (adult patients; >19 years of age), intervention evaluated (administration of oral anti-tumor drugs under fasting conditions or with food) and measurement of the iFA results (calculation of the 90% CI of the odds ratio between the geometric mean of the values under the curve of the plasma concentrations (ABC) or the maximal plasma concentration (Cmax) with and without foods). We excluded those publications that did not make reference to the bioequivalence dictamen established by the Food and Drugs Administration (FDA) in their outcomes measurement. A critical appraisal of the selected articles was done according to the recommendations that the FDA established to be met by these studies. At the initial search we obtained 850 references (98.5% Medline + and 1.4% Cochrane). During the first phase, we excluded 87.7% (746) of the articles, 100% of them corresponding

Interactions between recreational drugs and antiretroviral agents.

PubMed

Antoniou, Tony; Tseng, Alice Lin-In

2002-10-01

To summarize existing data regarding potential interactions between recreational drugs and drugs commonly used in the management of HIV-positive patients. Information was obtained via a MEDLINE search (1966-August 2002) using the MeSH headings human immunodeficiency virus, drug interactions, cytochrome P450, medication names commonly prescribed for the management of HIV and related opportunistic infections, and names of commonly used recreational drugs. Abstracts of national and international conferences, review articles, textbooks, and references of all articles were also reviewed. Literature on pharmacokinetic interactions was considered for inclusion. Pertinent information was selected and summarized for discussion. In the absence of specific data, prediction of potential clinically significant interactions was based on pharmacokinetic and pharmacodynamic properties. All protease inhibitors (PIs) and nonnucleoside reverse transcriptase inhibitors are substrates and potent inhibitors or inducers of the cytochrome P450 system. Many classes of recreational drugs, including benzodiazepines, amphetamines, and opioids, are also metabolized by the liver and can potentially interact with antiretrovirals. Controlled interaction studies are often not available, but clinically significant interactions have been observed in a number of case reports. Overdoses secondary to interactions between the "rave" drugs methylenedioxymethamphetamine (MDMA) or gamma-hydroxybutyrate (GHB) and PIs have been reported. PIs, particularly ritonavir, may also inhibit metabolism of amphetamines, ketamine, lysergic acid diethylmide (LSD), and phencyclidine (PCP). Case series and pharmacokinetic studies suggest that nevirapine and efavirenz induce methadone metabolism, which may lead to symptoms of opiate withdrawal. A similar interaction may exist between methadone and the PIs ritonavir and nelfinavir, although the data are less consistent. Opiate metabolism can be inhibited or induced by

Evaluation of a New Molecular Entity as a Victim of Metabolic Drug-Drug Interactions-an Industry Perspective.

PubMed

Bohnert, Tonika; Patel, Aarti; Templeton, Ian; Chen, Yuan; Lu, Chuang; Lai, George; Leung, Louis; Tse, Susanna; Einolf, Heidi J; Wang, Ying-Hong; Sinz, Michael; Stearns, Ralph; Walsky, Robert; Geng, Wanping; Sudsakorn, Sirimas; Moore, David; He, Ling; Wahlstrom, Jan; Keirns, Jim; Narayanan, Rangaraj; Lang, Dieter; Yang, Xiaoqing

2016-08-01

Under the guidance of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ), scientists from 20 pharmaceutical companies formed a Victim Drug-Drug Interactions Working Group. This working group has conducted a review of the literature and the practices of each company on the approaches to clearance pathway identification (fCL), estimation of fractional contribution of metabolizing enzyme toward metabolism (fm), along with modeling and simulation-aided strategy in predicting the victim drug-drug interaction (DDI) liability due to modulation of drug metabolizing enzymes. Presented in this perspective are the recommendations from this working group on: 1) strategic and experimental approaches to identify fCL and fm, 2) whether those assessments may be quantitative for certain enzymes (e.g., cytochrome P450, P450, and limited uridine diphosphoglucuronosyltransferase, UGT enzymes) or qualitative (for most of other drug metabolism enzymes), and the impact due to the lack of quantitative information on the latter. Multiple decision trees are presented with stepwise approaches to identify specific enzymes that are involved in the metabolism of a given drug and to aid the prediction and risk assessment of drug as a victim in DDI. Modeling and simulation approaches are also discussed to better predict DDI risk in humans. Variability and parameter sensitivity analysis were emphasized when applying modeling and simulation to capture the differences within the population used and to characterize the parameters that have the most influence on the prediction outcome. Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.

[Pharmacokinetic interactions of telaprevir with other drugs].

PubMed

Berenguer Berenguer, Juan; González-García, Juan

2013-07-01

Telaprevir is a new direct-acting antiviral drug for the treatment of hepatitis C virus (HCV) infection and is both a substrate and an inhibitor of cytochrome P450 (CYP450) isoenzymes. With the introduction of this new drug, assessment of drug-drug interactions has become a key factor in the evaluation of patients under treatment for HCV infection. During the treatment of this infection, many patients require other drugs to mitigate the adverse effects of anti-HCV drugs and to control other comorbidities. Moreover, most patients coinfected with HIV and HCV require antiretroviral therapy during treatment for HCV. Physicians should therefore be familiar with the pharmacokinetic properties of direct-acting antivirals for HCV treatment and their potential drug-drug interactions. The present article reviews the available information to date on the interactions of telaprevir with other drugs and provides recommendations for daily clinical practice. Copyright © 2013 Elsevier España, S.L. All rights reserved.

Drug–drug interactions with imatinib

PubMed Central

Récoché, Isabelle; Rousseau, Vanessa; Bourrel, Robert; Lapeyre-Mestre, Maryse; Chebane, Leila; Despas, Fabien; Montastruc, Jean-Louis; Bondon-Guitton, Emmanuelle

2016-01-01

Abstract Many patients treated with imatinib, used in cancer treatment, are using several other drugs that could interact with imatinib. Our aim was to study all the drug–drug interactions (DDIs) observed in patients treated with imatinib. We performed 2 observational studies, between the 1st January 2012 and the 31st August 2015 in the Midi-Pyrénées area (South Western France), using the French health insurance reimbursement database and then the French Pharmacovigilance Database (FPVD). A total of 544 patients received at least 1 reimbursement for imatinib. Among them, 486 (89.3%) had at least 1 drug that could potentially interact with imatinib. Paracetamol was the most frequent drug involved (77.4%). Proton pump inhibitors, dexamethasone and levothyroxine, were found in >10% of patients. In the FPVD, among a total of 25 reports of ADRs with imatinib recorded in the Midi-Pyrénées area, 10 (40%) had potential DDIs with imatinib. Imatinib was most frequently prescribed by hospital physicians and drugs interacting with imatinib, by general practitioners. Our study showed that at least 40% of the patients treated with imatinib were at risk of DDIs and that all prescribers must be cautious with DDIs in patients treated with imatinib. During imatinib treatment, we particularly recommend to limit the dose of paracetamol at 1300 mg per day, to avoid the use of dexamethasone, and to double the dose of levothyroxine. PMID:27749579

High-Throughput Cytochrome P450 Cocktail Inhibition Assay for Assessing Drug-Drug and Drug-Botanical Interactions

PubMed Central

Li, Guannan; Huang, Ke; Nikolic, Dejan

2015-01-01

Detection of drug-drug interactions is essential during the early stages of drug discovery and development, and the understanding of drug-botanical interactions is important for the safe use of botanical dietary supplements. Among the different forms of drug interactions that are known, inhibition of cytochrome P450 (P450) enzymes is the most common cause of drug-drug or drug-botanical interactions. Therefore, a rapid and comprehensive mass spectrometry–based in vitro high-throughput P450 cocktail inhibition assay was developed that uses 10 substrates simultaneously against nine CYP isoforms. Including probe substrates for CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and two probes targeting different binding sites of CYP3A4/5, this cocktail simultaneously assesses at least as many P450 enzymes as previous assays while remaining among the fastest due to short incubation times and rapid analysis using ultrahigh pressure liquid chromatography–tandem mass spectrometry. The method was validated using known inhibitors of each P450 enzyme and then shown to be useful not only for single-compound testing but also for the evaluation of potential drug-botanical interactions using the botanical dietary supplement licorice (Glycyrrhiza glabra) as an example. PMID:26285764

Interactions between drugs and polymers influencing hot melt extrusion.

PubMed

Li, Yongcheng; Pang, Huishi; Guo, Zhefei; Lin, Ling; Dong, Yixuan; Li, Ge; Lu, Ming; Wu, Chuangbin

2014-02-01

Hot melt extrusion (HME) as a technique for producing amorphous solid dispersion (ASD) has been widely used in pharmaceutical research. The biggest challenge for the application of HME is the thermal degradation of drug, poor physical stability of ASD and precipitation of drug during dissolution. Interactions between drugs and polymers may play an important role in overcoming these barriers. In this review, influence of drug-polymer interactions on HME and the methods for characterizing the drug-polymer interactions were reviewed. Strong drug-polymer interactions, especially ionic interactions and hydrogen bonds, are helpful to improving the thermal stability of drug during HME, enhancing the physical stability of ASD during storage and maintaining supersaturated solution after dissolution in gastrointestinal tract. The interactions can be quantitatively and qualitatively characterized by many analysing methods. As many factors collectively determine the properties of HME products, drug-polymer interactions play an extremely important role. However, the action mechanisms of drug-polymer interactions need intensive investigation to provide more useful information for optimizing the formulation and the process parameters of HME. © 2013 Royal Pharmaceutical Society.

ABC multidrug transporters: target for modulation of drug pharmacokinetics and drug-drug interactions.

PubMed

Marquez, Béatrice; Van Bambeke, Françoise

2011-05-01

Nine proteins of the ABC superfamily (P-glycoprotein, 7 MRPs and BCRP) are involved in multidrug transport. Being localised at the surface of endothelial or epithelial cells, they expel drugs back to the external medium (if located at the apical side [P-glycoprotein, BCRP, MRP2, MRP4 in the kidney]) or to the blood (if located at the basolateral side [MRP1, MRP3, MRP4, MRP5]), modulating thereby their absorption, distribution, and elimination. In the CNS, most transporters are oriented to expel drugs to the blood. Transporters also cooperate with Phase I/Phase II metabolism enzymes by eliminating drug metabolites. Their major features are (i) their capacity to recognize drugs belonging to unrelated pharmacological classes, and (ii) their redundancy, a single molecule being possibly substrate for different transporters. This ensures an efficient protection of the body against invasion by xenobiotics. Competition for transport is now characterized as a mechanism of interaction between co-administered drugs, one molecule limiting the transport of the other, potentially affecting bioavailability, distribution, and/or elimination. Again, this mechanism reinforces drug interactions mediated by cytochrome P450 inhibition, as many substrates of P-glycoprotein and CYP3A4 are common. Induction of the expression of genes coding for MDR transporters is another mechanism of drug interaction, which could affect all drug substrates of the up-regulated transporter. Overexpression of MDR transporters confers resistance to anticancer agents and other therapies. All together, these data justify why studying drug active transport should be part of the evaluation of new drugs, as recently recommended by the FDA.

Public health relevance of drug-nutrition interactions.

PubMed

Péter, Szabolcs; Navis, Gerjan; de Borst, Martin H; von Schacky, Clemens; van Orten-Luiten, Anne Claire B; Zhernakova, Alexandra; Witkamp, Renger F; Janse, André; Weber, Peter; Bakker, Stephan J L; Eggersdorfer, Manfred

2017-08-01

The public health relevance of drug-nutrition interactions is currently highly undervalued and overlooked. This is particularly the case for elderly persons where multi-morbidity and consequently polypharmacy is very common. Vitamins and other micronutrients have central functions in metabolism, and their interactions with drugs may result in clinically relevant physiological impairments but possibly also in positive effects. On 12 April 2016, the University Medical Center Groningen (The Netherlands), as part of its Healthy Ageing program, organized a workshop on the public health relevance of drug-nutrient interactions. In this meeting, experts in the field presented results from recent studies on interactions between pharmaceuticals and nutrients, and discussed the role of nutrition for elderly, focusing on those persons receiving pharmaceutical treatment. This paper summarizes the proceedings of the symposium and provides an outlook for future research needs and public health measures. Since food, pharma and health are closely interconnected domains, awareness is needed in the medical community about the potential relevance of drug-nutrition interactions. Experts and stakeholders should advocate for the integration of drug-nutrition evaluations in the drug development process. Strategies for the individual patients should be developed, by installing drug review protocols, screening for malnutrition and integrating this topic into the general medical advice.

Comprehensive prediction of drug-protein interactions and side effects for the human proteome

PubMed Central

Zhou, Hongyi; Gao, Mu; Skolnick, Jeffrey

2015-01-01

Identifying unexpected drug-protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug-protein interaction prediction, FINDSITEcomb, whose average precision is ~30% and recall ~27%, is employed. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24%. Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38), while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre-filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome, and DISeasome) webserver at http://cssb.biology.gatech.edu/dr.prodis/. DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds. PMID:26057345

Mechanisms Underlying Food-Drug Interactions: Inhibition of Intestinal Metabolism and Transport

PubMed Central

Won, Christina S.; Oberlies, Nicholas H.; Paine, Mary F.

2012-01-01

Food-drug interaction studies are critical to evaluate appropriate dosing, timing, and formulation of new drug candidates. These interactions often reflect prandial-associated changes in the extent and/or rate of systemic drug exposure. Physiologic and physicochemical mechanisms underlying food effects on drug disposition are well-characterized. However, biochemical mechanisms involving drug metabolizing enzymes and transport proteins remain underexplored. Several plant-derived beverages have been shown to modulate enzymes and transporters in the intestine, leading to altered pharmacokinetic (PK) and potentially negative pharmacodynamic (PD) outcomes. Commonly consumed fruit juices, teas, and alcoholic drinks contain phytochemicals that inhibit intestinal cytochrome P450 and phase II conjugation enzymes, as well as uptake and efflux transport proteins. Whereas myriad phytochemicals have been shown to inhibit these processes in vitro, translation to the clinic has been deemed insignificant or undetermined. An overlooked prerequisite for elucidating food effects on drug PK is thorough knowledge of causative bioactive ingredients. Substantial variability in bioactive ingredient composition and activity of a given dietary substance poses a challenge in conducting robust food-drug interaction studies. This confounding factor can be addressed by identifying and characterizing specific components, which could be used as marker compounds to improve clinical trial design and quantitatively predict food effects. Interpretation and integration of data from in vitro, in vivo, and in silico studies require collaborative expertise from multiple disciplines, from botany to clinical pharmacology (i.e., plant to patient). Development of more systematic methods and guidelines is needed to address the general lack of information on examining drug-dietary substance interactions prospectively. PMID:22884524

Possible drug-drug interaction in dogs and cats resulted from alteration in drug metabolism: A mini review.

PubMed

Sasaki, Kazuaki; Shimoda, Minoru

2015-05-01

Pharmacokinetic drug-drug interactions (in particular at metabolism) may result in fatal adverse effects in some cases. This basic information, therefore, is needed for drug therapy even in veterinary medicine, as multidrug therapy is not rare in canines and felines. The aim of this review was focused on possible drug-drug interactions in dogs and cats. The interaction includes enzyme induction by phenobarbital, enzyme inhibition by ketoconazole and fluoroquinolones, and down-regulation of enzymes by dexamethasone. A final conclusion based upon the available literatures and author's experience is given at the end of the review.

PXR as a mediator of herb-drug interaction.

PubMed

Hogle, Brett C; Guan, Xiudong; Folan, M Maggie; Xie, Wen

2018-04-01

Medicinal herbs have been a part of human medicine for thousands of years. The herb-drug interaction is an extension of drug-drug interaction, in which the consumptions of herbs cause alterations in the metabolism of drugs the patients happen to take at the same time. The pregnane X receptor (PXR) has been established as one of the most important transcriptional factors that regulate the expression of phase I enzymes, phase II enzymes, and drug transporters in the xenobiotic responses. Since its initial discovery, PXR has been implicated in multiple herb-drug interactions that can lead to alterations of the drug's pharmacokinetic properties and cause fluctuating therapeutic efficacies, possibly leading to complications. Regions of the world that heavily incorporate herbalism into their primary health care and people turning to alternative medicines as a personal choice could be at risk for adverse reactions or unintended results from these interactions. This article is intended to highlight our understanding of the PXR-mediated herb-drug interactions. Copyright © 2017. Published by Elsevier B.V.

Hazards and Benefits of Drug Interaction

ERIC Educational Resources Information Center

Labianca, Dominick A.

1978-01-01

Most cases of drug toxicity are direct consequences of drug misuse--either intentional or inadvertent. Discusses two types of drug interaction--synergistic and antagonistic. The former produces a combined effect greater than the sum of the effects of the individual drugs concerned; the latter is produced when the desired action of one drug is…

[Predictive factors of clinically significant drug-drug interactions among regimens based on protease inhibitors, non-nucleoside reverse transcriptase inhibitors and raltegravir].

PubMed

Cervero, Miguel; Torres, Rafael; Jusdado, Juan José; Pastor, Susana; Agud, Jose Luis

2016-04-15

To determine the prevalence and types of clinically significant drug-drug interactions (CSDI) in the drug regimens of HIV-infected patients receiving antiretroviral treatment. retrospective review of database. Centre: Hospital Universitario Severo Ochoa, Infectious Unit. one hundred and forty-two participants followed by one of the authors were selected from January 1985 to December 2014. from their outpatient medical records we reviewed information from the last available visit of the participants, in relation to HIV infection, comorbidities, demographics and the drugs that they were receiving; both antiretroviral drugs and drugs not related to HIV infection. We defined CSDI from the information sheet and/or database on antiretroviral drug interactions of the University of Liverpool (http://www.hiv-druginteractions.org) and we developed a diagnostic tool to predict the possibility of CSDI. By multivariate logistic regression analysis and by estimating the diagnostic performance curve obtained, we identified a quick tool to predict the existence of drug interactions. Of 142 patients, 39 (29.11%) had some type of CSDI and in 11.2% 2 or more interactions were detected. In only one patient the combination of drugs was contraindicated (this patient was receiving darunavir/r and quetiapine). In multivariate analyses, predictors of CSDI were regimen type (PI or NNRTI) and the use of 3 or more non-antiretroviral drugs (AUC 0.886, 95% CI 0.828 to 0.944; P=.0001). The risk was 18.55 times in those receiving NNRTI and 27,95 times in those receiving IP compared to those taking raltegravir. Drug interactions, including those defined as clinically significant, are common in HIV-infected patients treated with antiretroviral drugs, and the risk is greater in IP-based regimens. Raltegravir-based prescribing, especially in patients who receive at least 3 non-HIV drugs could avoid interactions. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

[Prevalence of Avoidable Potential Interactions Between Antidepressants and Other Drugs in Colombian Patients].

PubMed

Machado-Alba, Jorge E; Morales-Plaza, Cristhian David

2013-06-01

To determine the possible drugs interactions with antidepressive agents in data bases of patients in the Health Insurance System of Colombia. From data bases of about 4 million users in Colombia, a systematic review of drugs dispensation statistics was made to identify drug interactions between antidepressive agents, cholinergic antagonists and tramadol in 2010. We identified 114,465 monthly users of antidepressive agents. Of these, 5776 (5.0%) received two, and 178 (0.2%) received three antidepressive agents simultaneously. The most frequent combination was fluoxetine+trazodone (n=3235; 56.9% of cases). About 1127 (1.0%) patients were prescribed a cholinergic antagonist simultaneously; 2523 (2.1%) users were dispensed tramadol at the same time, while raising the risk of serotonin syndrome. Drug interactions represent a potential risk that is often underestimated by physicians. Pharmacovigilance is a useful tool to optimize resources and prevent negative outcomes associated with medication. It is recommended that systematic search is made to enhance surveillance programs for the rational use of medicines in this country. Copyright © 2013 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

Detecting drug-drug interactions using a database for spontaneous adverse drug reactions: an example with diuretics and non-steroidal anti-inflammatory drugs.

PubMed

van Puijenbroek, E P; Egberts, A C; Heerdink, E R; Leufkens, H G

2000-12-01

Drug-drug interactions are relatively rarely reported to spontaneous reporting systems (SRSs) for adverse drug reactions. For this reason, the traditional approach for analysing SRS has major limitations for the detection of drug-drug interactions. We developed a method that may enable signalling of these possible interactions, which are often not explicitly reported, utilising reports of adverse drug reactions in data sets of SRS. As an example, the influence of concomitant use of diuretics and non-steroidal anti-inflammatory drugs (NSAIDs) on symptoms indicating a decreased efficacy of diuretics was examined using reports received by the Netherlands Pharmacovigilance Foundation Lareb. Reports received between 1 January 1990 and 1 January 1999 of patients older than 50 years were included in the study. Cases were defined as reports with symptoms indicating a decreased efficacy of diuretics, non-cases as all other reports. Exposure categories were the use of NSAIDs or diuretics versus the use of neither of these drugs. The influence of the combined use of both drugs was examined using logistic regression analysis. The odds ratio of the statistical interaction term of the combined use of both drugs was increased [adjusted odds ratio 2.0, 95% confidence interval (CI) 1.1-3.7], which may indicate an enhanced effect of concomitant drug use. The findings illustrate that spontaneous reporting systems have a potential for signal detection and the analysis of possible drug-drug interactions. The method described may enable a more active approach in the detection of drug-drug interactions after marketing.

Characterization of the mechanism of drug-drug interactions from PubMed using MeSH terms.

PubMed

Lu, Yin; Figler, Bryan; Huang, Hong; Tu, Yi-Cheng; Wang, Ju; Cheng, Feng

2017-01-01

Identifying drug-drug interaction (DDI) is an important topic for the development of safe pharmaceutical drugs and for the optimization of multidrug regimens for complex diseases such as cancer and HIV. There have been about 150,000 publications on DDIs in PubMed, which is a great resource for DDI studies. In this paper, we introduced an automatic computational method for the systematic analysis of the mechanism of DDIs using MeSH (Medical Subject Headings) terms from PubMed literature. MeSH term is a controlled vocabulary thesaurus developed by the National Library of Medicine for indexing and annotating articles. Our method can effectively identify DDI-relevant MeSH terms such as drugs, proteins and phenomena with high accuracy. The connections among these MeSH terms were investigated by using co-occurrence heatmaps and social network analysis. Our approach can be used to visualize relationships of DDI terms, which has the potential to help users better understand DDIs. As the volume of PubMed records increases, our method for automatic analysis of DDIs from the PubMed database will become more accurate.

Indolealkylamines: biotransformations and potential drug-drug interactions.

PubMed

Yu, Ai-Ming

2008-06-01

Indolealkylamine (IAA) drugs are 5-hydroxytryptamine (5-HT or serotonin) analogs that mainly act on the serotonin system. Some IAAs are clinically utilized for antimigraine therapy, whereas other substances are notable as drugs of abuse. In the clinical evaluation of antimigraine triptan drugs, studies on their biotransformations and pharmacokinetics would facilitate the understanding and prevention of unwanted drug-drug interactions (DDIs). A stable, principal metabolite of an IAA drug of abuse could serve as a useful biomarker in assessing intoxication of the IAA substance. Studies on the metabolism of IAA drugs of abuse including lysergic acid amides, tryptamine derivatives and beta-carbolines are therefore emerging. An important role for polymorphic cytochrome P450 2D6 (CYP2D6) in the metabolism of IAA drugs of abuse has been revealed by recent studies, suggesting that variations in IAA metabolism, pharmaco- or toxicokinetics and dynamics can arise from distinct CYP2D6 status, and CYP2D6 polymorphism may represent an additional risk factor in the use of these IAA drugs. Furthermore, DDIs with IAA agents could occur additively at the pharmaco/toxicokinetic and dynamic levels, leading to severe or even fatal serotonin toxicity. In this review, the metabolism and potential DDIs of these therapeutic and abused IAA drugs are described.

Drug-vitamin D interactions: A systematic review of the literature

PubMed Central

Oppeneer, Sarah J.; Kelly, Julia A.; Hamilton-Reeves, Jill M.

2017-01-01

Extensive media coverage of the potential health benefits of vitamin D supplementation has translated into substantial increases in supplement sales over recent years. Yet, the potential for drug-vitamin D interactions is rarely considered. This systematic review of the literature was conducted to evaluate the extent to which drugs affect vitamin D status or supplementation alters drug effectiveness or toxicity in humans. Electronic databases were used to identify eligible peer-reviewed studies published through September 1, 2010. Study characteristics and findings were abstracted, and quality was assessed for each study. A total of 109 unique reports met the inclusion criteria. The majority of eligible studies were classified as Class C (non-randomized trials, case-control studies, or time series) or D (cross-sectional, trend, case report/series, or before-and-after studies). Only two Class C and three Class D studies were of positive quality. Insufficient evidence was available to determine whether lipase inhibitors, antimicrobial agents, antiepileptic drugs, highly active antiretroviral agents or H2 receptor antagonists alter serum 25(OH)D concentrations. Atorvastatin appears to increase 25(OH)D concentrations, while concurrent vitamin D supplementation decreases concentrations of atorvastatin. Use of thiazide diuretics in combination with calcium and vitamin D supplements may cause hypercalcemia in the elderly, or those with compromised renal function or hyperparathyroidism. Larger studies with stronger study designs are needed to clarify potential drug-vitamin D interactions, especially for drugs metabolized by cytochrome P450 3A4 (CYP3A4). Health care providers should be aware of the potential for drug-vitamin D interactions. PMID:23307906

Detecting signals of drug–drug interactions in a spontaneous reports database

PubMed Central

Thakrar, Bharat T; Grundschober, Sabine Borel; Doessegger, Lucette

2007-01-01

Aims The spontaneous reports database is widely used for detecting signals of ADRs. We have extended the methodology to include the detection of signals of ADRs that are associated with drug–drug interactions (DDI). In particular, we have investigated two different statistical assumptions for detecting signals of DDI. Methods Using the FDA's spontaneous reports database, we investigated two models, a multiplicative and an additive model, to detect signals of DDI. We applied the models to four known DDIs (methotrexate-diclofenac and bone marrow depression, simvastatin-ciclosporin and myopathy, ketoconazole-terfenadine and torsades de pointes, and cisapride-erythromycin and torsades de pointes) and to four drug-event combinations where there is currently no evidence of a DDI (fexofenadine-ketoconazole and torsades de pointes, methotrexade-rofecoxib and bone marrow depression, fluvastatin-ciclosporin and myopathy, and cisapride-azithromycine and torsade de pointes) and estimated the measure of interaction on the two scales. Results The additive model correctly identified all four known DDIs by giving a statistically significant (P< 0.05) positive measure of interaction. The multiplicative model identified the first two of the known DDIs as having a statistically significant or borderline significant (P< 0.1) positive measure of interaction term, gave a nonsignificant positive trend for the third interaction (P= 0.27), and a negative trend for the last interaction. Both models correctly identified the four known non interactions by estimating a negative measure of interaction. Conclusions The spontaneous reports database is a valuable resource for detecting signals of DDIs. In particular, the additive model is more sensitive in detecting such signals. The multiplicative model may further help qualify the strength of the signal detected by the additive model. PMID:17506784

Drug-drug and food-drug pharmacokinetic interactions with new insulinotropic agents repaglinide and nateglinide.

PubMed

Scheen, André J

2007-01-01

This review describes the current knowledge on drug-drug and food-drug interactions with repaglinide and nateglinide. These two meglitinide derivatives, commonly called glinides, have been developed for improving insulin secretion of patients with type 2 diabetes mellitus. They are increasingly used either in monotherapy or in combination with other oral antihyperglycaemic agents for the treatment of type 2 diabetes. Compared with sulfonylureas, glinides have been shown to (i) provide a better control of postprandial hyperglycaemia, (ii) overcome some adverse effects, such as hypoglycaemia, and (iii) have a more favourable safety profile, especially in patients with renal failure. The meal-related timing of administration of glinides and the potential influence of food and meal composition on their bioavailability may be important. In addition, some food components (e.g. grapefruit juice) may cause pharmacokinetic interactions. Because glinides are metabolised via cytochrome P450 (CYP) 3A4 isoenzyme, they are indeed exposed to pharmacokinetic interactions. In addition to CYP3A4, repaglinide is metabolised via CYP2C8, while nateglinide metabolism also involves CYP2C9. Furthermore, both compounds and their metabolites may undergo specialised transport/uptake in the intestine, another source of pharmacokinetic interactions. Clinically relevant drug-drug interactions are those that occur when glinides are administered together with other glucose-lowering agents or compounds widely coadministered to diabetic patients (e.g. lipid-lowering agents), with drugs that are known to induce (risk of lower glinide plasma levels and thus of deterioration of glucose control) or inhibit (risk of higher glinide plasma levels leading to hypoglycaemia) CYP isoenzymes concerned in their metabolism, or with drugs that have a narrow efficacy : toxicity ratio. Pharmacokinetic interactions reported in the literature appear to be more frequent and more important with repaglinide than with

Predicting Drug-Target Interactions Based on Small Positive Samples.

PubMed

Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

2018-01-01

A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance

Genomes2Drugs: Identifies Target Proteins and Lead Drugs from Proteome Data

PubMed Central

Toomey, David; Hoppe, Heinrich C.; Brennan, Marian P.; Nolan, Kevin B.; Chubb, Anthony J.

2009-01-01

Background Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials. Methodology/Principal Findings To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins. Conclusions/Significance Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under ‘change-of-application’ patents. PMID:19593435

Drug membrane interaction and the importance for drug transport, distribution, accumulation, efficacy and resistance.

PubMed

Seydel, J K; Coats, E A; Cordes, H P; Wiese, M

1994-10-01

Some aspects of drug membrane interaction and its influence on drug transport, accumulation, efficacy and resistance have been discussed. The interactions manifest themselves macroscopically in changes in the physical and thermodynamic properties of "pure membranes" or bilayers. As various amounts of foreign molecules enter the membrane, in particular the main gel to liquid crystalline phase transition can be dramatically changed. This may change permeability, cell-fusion, cell resistance and may also lead to changes in conformation of the embedded receptor proteins. Furthermore, specific interactions with lipids may lead to drug accumulation in membranes and thus to much larger concentrations at the active site than present in the surrounding water phase. The lipid environment may also lead to changes in the preferred conformation of drug molecules. These events are directly related to drug efficacy. The determination of essential molecular criteria for the interaction could be used to design new and more selective therapeutics. This excursion in some aspects of drug membrane interaction underlines the importance of lipids and their interaction with drug molecules for our understanding of drug action, but this is not really a new thought but has been formulated in 1884 by THUDICUM: "Phospholipids are the centre, life and chemical soul of all bioplasm whatsoever, that of plants as well as of animals".

Drug interactions evaluation: An integrated part of risk assessment of therapeutics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Lei; Reynolds, Kellie S.; Zhao, Ping

2010-03-01

Pharmacokinetic drug interactions can lead to serious adverse events or decreased drug efficacy. The evaluation of a new molecular entity's (NME's) drug-drug interaction potential is an integral part of risk assessment during drug development and regulatory review. Alteration of activities of enzymes or transporters involved in the absorption, distribution, metabolism, or excretion of a new molecular entity by concomitant drugs may alter drug exposure, which can impact response (safety or efficacy). The recent Food and Drug Administration (FDA) draft drug interaction guidance ( (http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm072101.pdf)) highlights the methodologies and criteria that may be used to guide drug interaction evaluation by industrymore » and regulatory agencies and to construct informative labeling for health practitioner and patients. In addition, the Food and Drug Administration established a 'Drug Development and Drug Interactions' website to provide up-to-date information regarding evaluation of drug interactions ( (http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/DrugInteractionsLabeling/ucm080499.htm)). This review summarizes key elements in the FDA drug interaction guidance and new scientific developments that can guide the evaluation of drug-drug interactions during the drug development process.« less

Position-aware deep multi-task learning for drug-drug interaction extraction.

PubMed

Zhou, Deyu; Miao, Lei; He, Yulan

2018-05-01

A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed. In this paper, we propose a novel position-aware deep multi-task learning approach for extracting DDIs from biomedical texts. In particular, sentences are represented as a sequence of word embeddings and position embeddings. An attention-based bidirectional long short-term memory (BiLSTM) network is used to encode each sentence. The relative position information of words with the target drugs in text is combined with the hidden states of BiLSTM to generate the position-aware attention weights. Moreover, the tasks of predicting whether or not two drugs interact with each other and further distinguishing the types of interactions are learned jointly in multi-task learning framework. The proposed approach has been evaluated on the DDIExtraction challenge 2013 corpus and the results show that with the position-aware attention only, our proposed approach outperforms the state-of-the-art method by 0.99% for binary DDI classification, and with both position-aware attention and multi-task learning, our approach achieves a micro F-score of 72.99% on interaction type identification, outperforming the state-of-the-art approach by 1.51%, which demonstrates the effectiveness of the proposed approach. Copyright © 2018 Elsevier B.V. All rights reserved.

Food-drug interactions precipitated by fruit juices other than grapefruit juice: An update review.

PubMed

Chen, Meng; Zhou, Shu-Yi; Fabriaga, Erlinda; Zhang, Pian-Hong; Zhou, Quan

2018-04-01

This review addressed drug interactions precipitated by fruit juices other than grapefruit juice based on randomized controlled trials (RCTs). Literature was identified by searching PubMed, Cochrane Library, Scopus and Web of Science till December 30 2017. Among 46 finally included RCTs, six RCTs simply addressed pharmacodynamic interactions and 33 RCTs studied pharmacokinetic interactions, whereas seven RCTs investigated both pharmacokinetic and pharmacodynamic interactions. Twenty-two juice-drug combinations showed potential clinical relevance. The beneficial combinations included orange juice-ferrous fumarate, lemon juice- 99m Tc-tetrofosmin, pomegranate juice-intravenous iron during hemodialysis, cranberry juice-triple therapy medications for H. pylori, blueberry juice-etanercept, lime juice-antimalarials, and wheat grass juice-chemotherapy. The potential adverse interactions included decreased drug bioavailability (apple juice-fexofenadine, atenolol, aliskiren; orange juice-aliskiren, atenolol, celiprolol, montelukast, fluoroquinolones, alendronate; pomelo juice-sildenafil; grape juice-cyclosporine), increased bioavailability (Seville orange juice-felodipine, pomelo juice-cyclosporine, orange-aluminum containing antacids). Unlike furanocoumarin-rich grapefruit juice which could primarily precipitate drug interactions by strong inhibition of cytochrome P450 3A4 isoenzyme and P-glycoprotein and thus cause deadly outcomes due to co-ingestion with some medications, other fruit juices did not precipitate severely detrimental food-drug interaction despite of sporadic case reports. The extent of a juice-drug interaction may be associated with volume of drinking juice, fruit varieties, type of fruit, time between juice drinking and drug intake, genetic polymorphism in the enzymes or transporters and anthropometric variables. Pharmacists and health professionals should properly screen for and educate patients about potential adverse juice-drug interactions and help

Interaction Between Low-Dose Methotrexate and Nonsteroidal Anti-inflammatory Drugs, Penicillins, and Proton Pump Inhibitors.

PubMed

Hall, Jill J; Bolina, Monika; Chatterley, Trish; Jamali, Fakhreddin

2017-02-01

To review the potential drug interactions between low-dose methotrexate (LD-MTX) and nonsteroidal anti-inflammatory drugs (NSAIDs), penicillins, and proton-pump inhibitors (PPIs) given the disparity between interactions reported for high-dose and low-dose MTX to help guide clinicians. A literature search was performed in MEDLINE (1946 to September 2016), EMBASE (1974 to September 2016), and International Pharmaceutical Abstracts (1970 to January 2015) to identify reports describing potential drug interactions between LD-MTX and NSAIDS, penicillins, or PPIs. Reference lists of included articles were reviewed to find additional eligible articles. All English-language observational, randomized, and pharmacokinetic (PK) studies assessing LD-MTX interactions in humans were analyzed to determine clinical relevance in making recommendations to clinicians. Clinical case reports were assigned a Drug Interaction Probability Scale score. A total of 32 articles were included (28 with NSAIDs, 3 with penicillins, and 2 with PPIs [1 including both PPI and NSAID]). Although there are some PK data to describe increased LD-MTX concentrations when NSAIDs are used concomitantly, the clinical relevance remains unclear. Based on the limited data on LD-MTX with penicillins and PPIs, no clinically meaningful interaction was identified. Given the available evidence, the clinical importance of the interaction between LD-MTX and NSAIDs, penicillins, and PPIs cannot be substantiated. Health care providers should assess the benefit and risk of LD-MTX regardless of concomitant drug use, including factors known to predispose patients to MTX toxicity, and continue to monitor clinical and laboratory parameters per guideline recommendations.

The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.

PubMed

Vilar, Santiago; Hripcsak, George

2017-07-01

Explosion of the availability of big data sources along with the development in computational methods provides a useful framework to study drugs' actions, such as interactions with pharmacological targets and off-targets. Databases related to protein interactions, adverse effects and genomic profiles are available to be used for the construction of computational models. In this article, we focus on the description of biological profiles for drugs that can be used as a system to compare similarity and create methods to predict and analyze drugs' actions. We highlight profiles constructed with different biological data, such as target-protein interactions, gene expression measurements, adverse effects and disease profiles. We focus on the discovery of new targets or pathways for drugs already in the pharmaceutical market, also called drug repurposing, in the interaction with off-targets responsible for adverse reactions and in drug-drug interaction analysis. The current and future applications, strengths and challenges facing all these methods are also discussed. Biological profiles or signatures are an important source of data generation to deeply analyze biological actions with important implications in drug-related studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Drug Hypersensitivity: How Drugs Stimulate T Cells via Pharmacological Interaction with Immune Receptors.

PubMed

Pichler, Werner J; Adam, Jacqueline; Watkins, Stephen; Wuillemin, Natascha; Yun, James; Yerly, Daniel

2015-01-01

Small chemicals like drugs tend to bind to proteins via noncovalent bonds, e.g. hydrogen bonds, salt bridges or electrostatic interactions. Some chemicals interact with other molecules than the actual target ligand, representing so-called 'off-target' activities of drugs. Such interactions are a main cause of adverse side effects to drugs and are normally classified as predictable type A reactions. Detailed analysis of drug-induced immune reactions revealed that off-target activities also affect immune receptors, such as highly polymorphic human leukocyte antigens (HLA) or T cell receptors (TCR). Such drug interactions with immune receptors may lead to T cell stimulation, resulting in clinical symptoms of delayed-type hypersensitivity. They are assigned the 'pharmacological interaction with immune receptors' (p-i) concept. Analysis of p-i has revealed that drugs bind preferentially or exclusively to distinct HLA molecules (p-i HLA) or to distinct TCR (p-i TCR). P-i reactions differ from 'conventional' off-target drug reactions as the outcome is not due to the effect on the drug-modified cells themselves, but is the consequence of reactive T cells. Hence, the complex and diverse clinical manifestations of delayed-type hypersensitivity are caused by the functional heterogeneity of T cells. In the abacavir model of p-i HLA, the drug binding to HLA may result in alteration of the presenting peptides. More importantly, the drug binding to HLA generates a drug-modified HLA, which stimulates T cells directly, like an allo-HLA. In the sulfamethoxazole model of p-i TCR, responsive T cells likely require costimulation for full T cell activation. These findings may explain the similarity of delayed-type hypersensitivity reactions to graft-versus-host disease, and how systemic viral infections increase the risk of delayed-type hypersensitivity reactions. © 2015 The Author(s) Published by S. Karger AG, Basel.

Co-Prescription Trends in a Large Cohort of Subjects Predict Substantial Drug-Drug Interactions

PubMed Central

Sutherland, Jeffrey J.; Daly, Thomas M.; Liu, Xiong; Goldstein, Keith; Johnston, Joseph A.; Ryan, Timothy P.

2015-01-01

Pharmaceutical prescribing and drug-drug interaction data underlie recommendations on drug combinations that should be avoided or closely monitored by prescribers. Because the number of patients taking multiple medications is increasing, a comprehensive view of prescribing patterns in patients is important to better assess real world pharmaceutical response and evaluate the potential for multi-drug interactions. We obtained self-reported prescription data from NHANES surveys between 1999 and 2010, and confirm the previously reported finding of increasing drug use in the elderly. We studied co-prescription drug trends by focusing on the 2009-2010 survey, which contains prescription data on 690 drugs used by 10,537 subjects. We found that medication profiles were unique for individuals aged 65 years or more, with ≥98 unique drug regimens encountered per 100 subjects taking 3 or more medications. When drugs were viewed by therapeutic class, it was found that the most commonly prescribed drugs were not the most commonly co-prescribed drugs for any of the 16 drug classes investigated. We cross-referenced these medication lists with drug interaction data from Drugs.com to evaluate the potential for drug interactions. The number of drug alerts rose proportionally with the number of co-prescribed medications, rising from 3.3 alerts for individuals prescribed 5 medications to 11.7 alerts for individuals prescribed 10 medications. We found 22% of elderly subjects taking both a substrate and inhibitor of a given cytochrome P450 enzyme, and 4% taking multiple inhibitors of the same enzyme simultaneously. By examining drug pairs prescribed in 0.1% of the population or more, we found low agreement between co-prescription rate and co-discussion in the literature. These data show that prescribing trends in treatment could drive a large extent of individual variability in drug response, and that current pairwise approaches to assessing drug-drug interactions may be inadequate for

Potential drug interactions and chemotoxicity in older patients with cancer receiving chemotherapy.

PubMed

Popa, Mihaela A; Wallace, Kristie J; Brunello, Antonella; Extermann, Martine; Balducci, Lodovico

2014-07-01

Increased risk of drug interactions due to polypharmacy and aging-related changes in physiology among older patients with cancer is further augmented during chemotherapy. No previous studies examined potential drug interactions (PDIs) from polypharmacy and their association with chemotherapy tolerance in older patients with cancer. This study is a retrospective medical chart review of 244 patients aged 70+ years who received chemotherapy for solid or hematological malignancies. PDI among all drugs, supplements, and herbals taken with the first chemotherapy cycle were screened for using the Drug Interaction Facts software, which classifies PDIs into five levels of clinical significance with level 1 being the highest. Descriptive and correlative statistics were used to describe rates of PDI. The association between PDI and severe chemotoxicity was tested with logistic regressions adjusted for baseline covariates. A total of 769 PDIs were identified in 75.4% patients. Of the 82 level 1 PDIs identified among these, 32 PDIs involved chemotherapeutics. A large proportion of the identified PDIs were of minor clinical significance. The risk of severe non-hematological toxicity almost doubled with each level 1 PDI (OR=1.94, 95% CI: 1.22-3.09), and tripled with each level 1 PDI involving chemotherapeutics (OR=3.08, 95% CI: 1.33-7.12). No association between PDI and hematological toxicity was found. In this convenience sample of older patients with cancer receiving chemotherapy we found notable rates of PDI and a substantial adjusted impact of PDI on risk of non-hematological toxicity. These findings warrant further research to optimize chemotherapy outcomes. Copyright © 2014 Elsevier Inc. All rights reserved.

Recommendations for Selecting Drug-Drug Interactions for Clinical Decision Support

PubMed Central

Tilson, Hugh; Hines, Lisa E.; McEvoy, Gerald; Weinstein, David M.; Hansten, Philip D.; Matuszewski, Karl; le Comte, Marianne; Higby-Baker, Stefanie; Hanlon, Joseph T.; Pezzullo, Lynn; Vieson, Kathleen; Helwig, Amy L.; Huang, Shiew-Mei; Perre, Anthony; Bates, David W.; Poikonen, John; Wittie, Michael A.; Grizzle, Amy J.; Brown, Mary; Malone, Daniel C.

2016-01-01

Purpose To recommend principles for including drug-drug interactions (DDIs) in clinical decision support. Methods A conference series was conducted to improve clinical decision support (CDS) for DDIs. The Content Workgroup met monthly by webinar from January 2013 to February 2014, with two in-person meetings to reach consensus. The workgroup consisted of 20 experts in pharmacology, drug information, and CDS from academia, government agencies, health information (IT) vendors, and healthcare organizations. Workgroup members addressed four key questions: (1) What process should be used to develop and maintain a standard set of DDIs?; (2) What information should be included in a knowledgebase of standard DDIs?; (3) Can/should a list of contraindicated drug pairs be established?; and (4) How can DDI alerts be more intelligently filtered? Results To develop and maintain a standard set of DDIs for CDS in the United States, we recommend a transparent, systematic, and evidence-driven process with graded recommendations by a consensus panel of experts and oversight by a national organization. We outline key DDI information needed to help guide clinician decision-making. We recommend judicious classification of DDIs as contraindicated, as only a small set of drug combinations are truly contraindicated. Finally, we recommend more research to identify methods to safely reduce repetitive and less relevant alerts. Conclusion A systematic ongoing process is necessary to select DDIs for alerting clinicians. We anticipate that our recommendations can lead to consistent and clinically relevant content for interruptive DDIs, and thus reduce alert fatigue and improve patient safety. PMID:27045070

A current review of cytochrome P450 interactions of psychotropic drugs.

PubMed

Madhusoodanan, Subramoniam; Velama, Umamaheswararao; Parmar, Jeniel; Goia, Diana; Brenner, Ronald

2014-05-01

The number of psychotropic drugs has expanded tremendously over the past few decades with a proportional increase in drug-drug interactions. The majority of psychotropic agents are biotransformed by hepatic enzymes, which can lead to significant drug-drug interactions. Most drug-drug interactions of psychotropics occur at metabolic level involving the hepatic cytochrome P450 enzyme system. We searched the National Library of Medicine, PsycINFO, and Cochrane reviews from 1981 to 2012 for original studies including clinical trials, double-blind, placebo-controlled studies, and randomized controlled trials. In addition, case reports, books, review articles, and hand-selected journals were utilized to supplement this review. Based on the clinical intensity of outcome, cytochrome interactions can be classified as severe, moderate, and mild. Severe interactions include effects that might be acutely life threatening. They are mainly inhibitory interactions with cardiovascular drugs. Moderate interactions include efficacy issues. Mild interactions include nonserious side effects, such as somnolence. Psychotropic drugs may interact with other prescribed medications used to treat concomitant medical illnesses. A thorough understanding of the most prescribed medications and patient education will help reduce the likelihood of potentially fatal drug-drug interactions.

Drugs and Diseases Interacting with Cigarette Smoking in US Prescription Drug Labelling.

PubMed

Li, Haibo; Shi, Qiang

2015-05-01

The US Food and Drug Administration (FDA) draft guidance for industry on drug interaction studies recommends, but does not mandate, that both cigarette smokers and non-smokers can be used to study drug metabolism in clinical trials, and that important results related to smoking should be included in drug labelling to guide medication usage. This study aimed to provide a comprehensive review of drugs or diseases interacting with smoking, as presented in all US drug labelling. The 62,857 drug labels deposited in the FDA Online Label Repository were searched using the keywords 'smoke', 'smoker(s)', 'smoking', 'tobacco' and 'cigarette(s)' on 19 June 2014. The resultant records were refined to include only human prescription drug labelling, for manual examination. For 188 single-active-ingredient drugs and 36 multiple-active-ingredient drugs, the labelling was found to contain smoking-related information. The pharmacokinetics of 29 and 21 single-active-ingredient drugs were affected and unaffected, respectively, by smoking. For the remaining drugs, the provided information related to smoking affecting efficacy, safety or diseases but not pharmacokinetics. Depending on the nature of specific drugs, the perturbation in pharmacokinetic parameters in smokers ranged from 13 to 500%, in comparison with non-smokers. Dosage modifications in smokers are necessary for four drugs and may be necessary for six drugs, but are unnecessary for seven drugs although the pharmacokinetic parameters of four of them are affected by smoking. Cigarette smoking is a risk factor for 16 types of diseases or adverse drug reactions. For one single-active-ingredient contraceptive drug and 10 multiple-active-ingredient contraceptive drugs, a black box warning (the FDA's strongest safety warning) is included in the labelling for increased risks of heart attacks and strokes in female smokers, and "women are strongly advised not to smoke" when using these drugs. This study presents the first

Understanding drug interactions with St John's wort (Hypericum perforatum L.): impact of hyperforin content.

PubMed

Chrubasik-Hausmann, Sigrun; Vlachojannis, Julia; McLachlan, Andrew J

2018-02-07

The aim of this study was to review herb-drug interaction studies with St John's wort (Hypericum perforatum L.) with a focus on the hyperforin content of the extracts used in these studies. PUBMED was systematically searched to identify studies describing pharmacokinetic interactions involving St John's wort. Data on study design and the St John's wort extract or product were gathered to extract hyperforin content and daily dose used in interaction studies. This analysis demonstrates that significant herb-drug interactions (resulting in a substantial change in systemic exposure) with St John's wort products were associated with hyperforin daily dosage. Products that had a daily dose of <1 mg hyperforin were less likely to be associated with major interaction for drugs that were CYP3A4 or p-glycoprotein substrates. Although a risk of interactions cannot be excluded even for low-dose hyperforin St. John's wort extracts, the use of products that result in a dose of not more than 1 mg hyperforin per day is recommended to minimise the risk of interactions. This review highlights that the significance of herb-drug interactions with St John's wort is influenced by the nature of the herbal medicines product, particularly the hyperforin content. © 2018 Royal Pharmaceutical Society.

Managing Drug-Drug Interaction Between Ombitasvir, Paritaprevir/Ritonavir, Dasabuvir, and Mycophenolate Mofetil.

PubMed

Lemaitre, Florian; Ben Ali, Zeineb; Tron, Camille; Jezequel, Caroline; Boglione-Kerrien, Christelle; Verdier, Marie-Clémence; Guyader, Dominique; Bellissant, Eric

2017-08-01

No drug-drug interaction study has been conducted to date for the combination of ombitasvir, paritaprevir/ritonavir, dasabuvir (3D), and mycophenolic acid (MPA). We here report the case of a hepatitis C virus-infected patient treated with 3D and MPA for vasculitis. In light of the threat of drug-drug interaction, the concentration of MPA was measured before, during, and 15 days after the end of the 3D treatment. Similar values were found at all 3 time points, thus indicating that there is probably no need to adapt MPA dosage to 3D.

Physiologically-Based Pharmacokinetic Modeling of Macitentan: Prediction of Drug-Drug Interactions.

PubMed

de Kanter, Ruben; Sidharta, Patricia N; Delahaye, Stéphane; Gnerre, Carmela; Segrestaa, Jerome; Buchmann, Stephan; Kohl, Christopher; Treiber, Alexander

2016-03-01

Macitentan is a novel dual endothelin receptor antagonist for the treatment of pulmonary arterial hypertension (PAH). It is metabolized by cytochrome P450 (CYP) enzymes, mainly CYP3A4, to its active metabolite ACT-132577. A physiological-based pharmacokinetic (PBPK) model was developed by combining observations from clinical studies and physicochemical parameters as well as absorption, distribution, metabolism and excretion parameters determined in vitro. The model predicted the observed pharmacokinetics of macitentan and its active metabolite ACT-132577 after single and multiple dosing. It performed well in recovering the observed effect of the CYP3A4 inhibitors ketoconazole and cyclosporine, and the CYP3A4 inducer rifampicin, as well as in predicting interactions with S-warfarin and sildenafil. The model was robust enough to allow prospective predictions of macitentan-drug combinations not studied, including an alternative dosing regimen of ketoconazole and nine other CYP3A4-interacting drugs. Among these were the HIV drugs ritonavir and saquinavir, which were included because HIV infection is a known risk factor for the development of PAH. This example of the application of PBPK modeling to predict drug-drug interactions was used to support the labeling of macitentan (Opsumit).

Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization.

PubMed

Yu, Hui; Mao, Kui-Tao; Shi, Jian-Yu; Huang, Hua; Chen, Zhi; Dong, Kai; Yiu, Siu-Ming

2018-04-11

Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription. In this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs. Our approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution; (2) the drugs having large degrees tend to have a larger difference between positive degree

Adverse events caused by potential drug-drug interactions in an intensive care unit of a teaching hospital

PubMed Central

Alvim, Mariana Macedo; da Silva, Lidiane Ayres; Leite, Isabel Cristina Gonçalves; Silvério, Marcelo Silva

2015-01-01

Objective To evaluate the incidence of potential drug-drug interactions in an intensive care unit of a hospital, focusing on antimicrobial drugs. Methods This cross-sectional study analyzed electronic prescriptions of patients admitted to the intensive care unit of a teaching hospital between January 1 and March 31, 2014 and assessed potential drug-drug interactions associated with antimicrobial drugs. Antimicrobial drug consumption levels were expressed in daily doses per 100 patient-days. The search and classification of the interactions were based on the Micromedex® system. Results The daily prescriptions of 82 patients were analyzed, totaling 656 prescriptions. Antimicrobial drugs represented 25% of all prescription drugs, with meropenem, vancomycin and ceftriaxone being the most prescribed medications. According to the approach of daily dose per 100 patient-days, the most commonly used antimicrobial drugs were cefepime, meropenem, sulfamethoxazole + trimethoprim and ciprofloxacin. The mean number of interactions per patient was 2.6. Among the interactions, 51% were classified as contraindicated or significantly severe. Highly significant interactions (clinical value 1 and 2) were observed with a prevalence of 98%. Conclusion The current study demonstrated that antimicrobial drugs are frequently prescribed in intensive care units and present a very high number of potential drug-drug interactions, with most of them being considered highly significant. PMID:26761473

Drug-drug interactions and adverse drug reactions in polypharmacy among older adults: an integrative review 1

PubMed Central

Rodrigues, Maria Cristina Soares; de Oliveira, Cesar

2016-01-01

ABSTRACT Objective: to identify and summarize studies examining both drug-drug interactions (DDI) and adverse drug reactions (ADR) in older adults polymedicated. Methods: an integrative review of studies published from January 2008 to December 2013, according to inclusion and exclusion criteria, in MEDLINE and EMBASE electronic databases were performed. Results: forty-seven full-text studies including 14,624,492 older adults (≥ 60 years) were analyzed: 24 (51.1%) concerning ADR, 14 (29.8%) DDI, and 9 studies (19.1%) investigating both DDI and ADR. We found a variety of methodological designs. The reviewed studies reinforced that polypharmacy is a multifactorial process, and predictors and inappropriate prescribing are associated with negative health outcomes, as increasing the frequency and types of ADRs and DDIs involving different drug classes, moreover, some studies show the most successful interventions to optimize prescribing. Conclusions: DDI and ADR among older adults continue to be a significant issue in the worldwide. The findings from the studies included in this integrative review, added to the previous reviews, can contribute to the improvement of advanced practices in geriatric nursing, to promote the safety of older patients in polypharmacy. However, more research is needed to elucidate gaps. PMID:27598380

Resolving anaphoras for the extraction of drug-drug interactions in pharmacological documents

PubMed Central

2010-01-01

Background Drug-drug interactions are frequently reported in the increasing amount of biomedical literature. Information Extraction (IE) techniques have been devised as a useful instrument to manage this knowledge. Nevertheless, IE at the sentence level has a limited effect because of the frequent references to previous entities in the discourse, a phenomenon known as 'anaphora'. DrugNerAR, a drug anaphora resolution system is presented to address the problem of co-referring expressions in pharmacological literature. This development is part of a larger and innovative study about automatic drug-drug interaction extraction. Methods The system uses a set of linguistic rules drawn by Centering Theory over the analysis provided by a biomedical syntactic parser. Semantic information provided by the Unified Medical Language System (UMLS) is also integrated in order to improve the recognition and the resolution of nominal drug anaphors. Besides, a corpus has been developed in order to analyze the phenomena and evaluate the current approach. Each possible case of anaphoric expression was looked into to determine the most effective way of resolution. Results An F-score of 0.76 in anaphora resolution was achieved, outperforming significantly the baseline by almost 73%. This ad-hoc reference line was developed to check the results as there is no previous work on anaphora resolution in pharmalogical documents. The obtained results resemble those found in related-semantic domains. Conclusions The present approach shows very promising results in the challenge of accounting for anaphoric expressions in pharmacological texts. DrugNerAr obtains similar results to other approaches dealing with anaphora resolution in the biomedical domain, but, unlike these approaches, it focuses on documents reflecting drug interactions. The Centering Theory has proved being effective at the selection of antecedents in anaphora resolution. A key component in the success of this framework is the

Drug-nutrient interaction.

PubMed

Matsui, M S; Rozovski, S J

1982-01-01

The effect of certain drugs on nutrient metabolism is discussed. Antituberculotic drugs such as INH and cycloserine interfere with vitamin B6 metabolism and may produce a secondary niacin deficiency. Oral contraceptives interfere with the metabolism of folic acid and ascorbic acid, and in cases of deficient nutrition, they also seem to interfere with riboflavin. Anticonvulsants can act as folate antagonists and precipitate folic acid deficiency. Therefore, in some cases, supplementation with folate has been recommended simultaneously with anticonvulsant therapy. Cholestyramine therapy has been associated with malabsorption of vitamins; several reports suggest that cholestyramine affects absorption of the fat-soluble vitamins K and D and, in addition, may alter water-soluble vitamins, including folic acid. The study of the interaction of drugs and nutrients is an area that deserves a greater attention in the future, especially in groups where nutrient deficiencies may be prevalent.

Exploring the interaction between Salvia miltiorrhiza and human serum albumin: Insights from herb-drug interaction reports, computational analysis and experimental studies

NASA Astrophysics Data System (ADS)

Shao, Xin; Ai, Ni; Xu, Donghang; Fan, Xiaohui

2016-05-01

Human serum albumin (HSA) binding is one of important pharmacokinetic properties of drug, which is closely related to in vivo distribution and may ultimately influence its clinical efficacy. Compared to conventional drug, limited information on this transportation process is available for medicinal herbs, which significantly hampers our understanding on their pharmacological effects, particularly when herbs and drug are co-administrated as polytherapy to the ailment. Several lines of evidence suggest the existence of Salvia miltiorrhiza-Warfarin interaction. Since Warfarin is highly HSA bound in the plasma with selectivity to site I, it is critical to evaluate the possibility of HSA-related herb-drug interaction. Herein an integrated approach was employed to analyze the binding of chemicals identified in S. miltiorrhiza to HSA. Molecular docking simulations revealed filtering criteria for HSA site I compounds that include docking score and key molecular determinants for binding. For eight representative ingredients from the herb, their affinity and specificity to HSA site I was measured and confirmed fluorometrically, which helps to improve the knowledge of interaction mechanisms between this herb and HSA. Our results indicated that several compounds in S. miltiorrhiza were capable of decreasing the binding constant of Warfarin to HSA site I significantly, which may increase free drug concentration in vivo, contributing to the herb-drug interaction observed clinically. Furthermore, the significance of HSA mediated herb-drug interactions was further implied by manual mining on the published literatures on S. miltiorrhiza.

Toward a complete dataset of drug-drug interaction information from publicly available sources.

PubMed

Ayvaz, Serkan; Horn, John; Hassanzadeh, Oktie; Zhu, Qian; Stan, Johann; Tatonetti, Nicholas P; Vilar, Santiago; Brochhausen, Mathias; Samwald, Matthias; Rastegar-Mojarad, Majid; Dumontier, Michel; Boyce, Richard D

2015-06-01

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms

Cytochrome P450 enzyme mediated herbal drug interactions (Part 1)

PubMed Central

Wanwimolruk, Sompon; Prachayasittikul, Virapong

2014-01-01

It is well recognized that herbal supplements or herbal medicines are now commonly used. As many patients taking prescription medications are concomitantly using herbal supplements, there is considerable risk for adverse herbal drug interactions. Such interactions can enhance the risk for an individual patient, especially with regard to drugs with a narrow therapeutic index such as warfarin, cyclosporine A and digoxin. Herbal drug interactions can alter pharmacokinetic or/and pharmacodynamic properties of administered drugs. The most common pharmacokinetic interactions usually involve either the inhibition or induction of the metabolism of drugs catalyzed by the important enzymes, cytochrome P450 (CYP). The aim of the present article is to provide an updated review of clinically relevant metabolic CYP-mediated drug interactions between selected herbal supplements and prescription drugs. The commonly used herbal supplements selected include Echinacea, Ginkgo biloba, garlic, St. John's wort, goldenseal, and milk thistle. To date, several significant herbal drug interactions have their origins in the alteration of CYP enzyme activity by various phytochemicals. Numerous herbal drug interactions have been reported. Although the significance of many interactions is uncertain but several interactions, especially those with St. John’s wort, may have critical clinical consequences. St. John’s wort is a source of hyperforin, an active ingredient that has a strong affinity for the pregnane xenobiotic receptor (PXR). As a PXR ligand, hyperforin promotes expression of CYP3A4 enzymes in the small intestine and liver. This in turn causes induction of CYP3A4 and can reduce the oral bioavailability of many drugs making them less effective. The available evidence indicates that, at commonly recommended doses, other selected herbs including Echinacea, Ginkgo biloba, garlic, goldenseal and milk thistle do not act as potent or moderate inhibitors or inducers of CYP enzymes. A good

Investigating antimalarial drug interactions of emetine dihydrochloride hydrate using CalcuSyn-based interactivity calculations

PubMed Central

Matthews, Holly; Deakin, Jon; Rajab, May; Idris-Usman, Maryam

2017-01-01

The widespread introduction of artemisinin-based combination therapy has contributed to recent reductions in malaria mortality. Combination therapies have a range of advantages, including synergism, toxicity reduction, and delaying the onset of resistance acquisition. Unfortunately, antimalarial combination therapy is limited by the depleting repertoire of effective drugs with distinct target pathways. To fast-track antimalarial drug discovery, we have previously employed drug-repositioning to identify the anti-amoebic drug, emetine dihydrochloride hydrate, as a potential candidate for repositioned use against malaria. Despite its 1000-fold increase in in vitro antimalarial potency (ED50 47 nM) compared with its anti-amoebic potency (ED50 26–32 uM), practical use of the compound has been limited by dose-dependent toxicity (emesis and cardiotoxicity). Identification of a synergistic partner drug would present an opportunity for dose-reduction, thus increasing the therapeutic window. The lack of reliable and standardised methodology to enable the in vitro definition of synergistic potential for antimalarials is a major drawback. Here we use isobologram and combination-index data generated by CalcuSyn software analyses (Biosoft v2.1) to define drug interactivity in an objective, automated manner. The method, based on the median effect principle proposed by Chou and Talalay, was initially validated for antimalarial application using the known synergistic combination (atovaquone-proguanil). The combination was used to further understand the relationship between SYBR Green viability and cytocidal versus cytostatic effects of drugs at higher levels of inhibition. We report here the use of the optimised Chou Talalay method to define synergistic antimalarial drug interactivity between emetine dihydrochloride hydrate and atovaquone. The novel findings present a potential route to harness the nanomolar antimalarial efficacy of this affordable natural product. PMID:28257497

Herb-drug interactions. Interactions between saw palmetto and prescription medications.

PubMed

Bressler, Rubin

2005-11-01

Patients over age 50 typically present with one chronic disease per decade. Each chronic disease typically requires long-term drug therapy, meaning most older patients require several drugs to maintain health. Simultaneously, use of complementary and alternative medicine (CAM) has increased in the United States in the last 20 years, reaching 36% in 2002; herbal medicine use accounts for approximately 22% of all CAM use. Older adults often add herbal medicines to prescription medications, yet do not always inform their physicians. The drug metabolizing enzyme systems process all compounds foreign to the body, including prescription and herbal medications. Therefore use of both medicinals simultaneously has a potential for adverse interactions. This review, which discusses saw palmetto, is the last in a series covering the documented interactions among the top 5 efficacious herbal medicines and prescription drugs.

Metabolic mechanisms of drug-nutrient interactions.

PubMed

Hathcock, J N

1985-01-01

Metabolic mechanisms of nutrition and drug interactions include 1) the effects of diet on drug metabolism and action and 2) the effects of drugs on nutritional processes. The type, amount, and timing of foods consumed influence drug dissolution, absorption, distribution, metabolism, and excretion. High-fat meals enhance the absorption of griseofulvin and some other drugs. Milk and other sources of calcium inhibit absorption of tetracycline. High-fat meals increase plasma concentrations of free fatty acids and thereby displace many drugs from binding sites on plasma albumin. High-protein diets increase the activity of the mixed-function oxidase system and enhance the metabolism of numerous drugs. High-electrolyte intakes increase excretion of lithium and also diminish the action of diuretic agents. Bile acid sequestrants and some laxatives decrease lipid digestion and absorption, as well as absorption of the fat-soluble vitamins. Numerous drugs, including tetracycline and cholestyramine, bind iron and decrease its absorption. Coumarins inhibit the function of vitamin K. Phenobarbital and other anticonvulsants are inducers of cytochrome P-450 and the mixed-function oxidase system. Long-term treatment with these inducers can cause excessive metabolism and deficiency of vitamin D. Prooxidant drugs such as chloroquine, drugs detoxified by conjugation with glutathione, and alcohol can deplete reduced glutathione with consequent effects on amino acid transport and the redox status of cells. Acid-forming foods acidify the urine and increase the loss of alkaline drugs such as the amphetamines. Base-forming drugs increase the loss of acidic drugs such as barbiturates. The range of metabolic interactions of drugs and nutrients includes the full scope of physiological processes to which drugs and nutrients are subject.

Comparative evaluation of the drug interaction screening programs MediQ and ID PHARMA CHECK in neurological inpatients.

PubMed

Zorina, Olesya I; Haueis, Patrick; Semmler, Alexander; Marti, Isabelle; Gonzenbach, Roman R; Guzek, Markus; Kullak-Ublick, Gerd A; Weller, Michael; Russmann, Stefan

2012-08-01

The comparative evaluation of clinical decision support software (CDSS) programs regarding their sensitivity and positive predictive value for the identification of clinically relevant drug interactions. In this research, we used a cross-sectional study that identified potential drug interactions using the CDSS MediQ and the ID PHARMA CHECK in 484 neurological inpatients. Interactions were reclassified according to the Zurich Interaction System, a multidimensional classification that incorporates the Operational Classification of Drug Interactions. In 484 patients with 2812 prescriptions, MediQ and ID PHARMA CHECK generated a total of 1759 and 1082 alerts, respectively. MediQ identified 658 unique potentially interacting combinations, 8 classified as "high danger," 164 as "average danger," and 486 as "low danger." ID PHARMA CHECK detected 336 combinations assigned to one or several of 12 risk and management categories. Altogether, both CDSS issued alerts relating to 808 unique potentially interacting combinations. According to the Zurich Interaction System, 6 of these were contraindicated, 25 were provisionally contraindicated, 190 carried a conditional risk, and 587 had a minimal risk of adverse events. The positive predictive value for alerts having at least a conditional risk was 0.24 for MediQ and 0.48 for ID PHARMA CHECK. CDSS showed major differences in the identification and grading of interactions, and many interactions were only identified by one of the two CDSS. For both programs, only a small proportion of all identified interactions appeared clinically relevant, and the selected display of alerts that imply management changes is a key issue in the further development and local setup of such programs. Copyright © 2012 John Wiley & Sons, Ltd.

Alcohol effects on drug-nutrient interactions.

PubMed

Seitz, H K

1985-01-01

The interaction of ethanol with drugs and xenobiotics is complex because ethanol can affect any of the following steps; absorption, plasma protein binding, hepatic blood flow, distribution, hepatic uptake of drugs, and phase I and II hepatic metabolism. The ingestion of ethanol can lead to malabsorption of a variety of nutrients and can modify the absorption of various drugs. High concentrations of ethanol in conjunction with aspirin causes gastric mucosal damage. The principal effect of acute ethanol ingestion on drug metabolism is inhibition of microsomal drug metabolism. The synergistic effects of ethanol on central nervous system depressants can be explained by this mechanism. In contrast, chronic ethanol consumption increases mixed function oxidation and drug metabolism. The cross tolerance between ethanol and sedatives in chronic alcoholics may be due to this effect of alcohol. In addition, enhanced production of hepatotoxic products from certain drugs and xenobiotics and an increased activation of procarcinogens to carcinogens can result from this microsomal induction. The increased susceptibility to hepatotoxins and the enhanced carcinogenesis in the alcoholic may be explained by this fact. Other effects of the interaction between drugs and ethanol are the result of changes in organ susceptibility, best demonstrated for the central nervous system. Subsequently, the presence of liver disease has a great effect on drug metabolism in alcoholics.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2016-12-22

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was

New era in drug interaction evaluation: US Food and Drug Administration update on CYP enzymes, transporters, and the guidance process.

PubMed

Huang, Shiew-Mei; Strong, John M; Zhang, Lei; Reynolds, Kellie S; Nallani, Srikanth; Temple, Robert; Abraham, Sophia; Habet, Sayed Al; Baweja, Raman K; Burckart, Gilbert J; Chung, Sang; Colangelo, Philip; Frucht, David; Green, Martin D; Hepp, Paul; Karnaukhova, Elena; Ko, Hon-Sum; Lee, Jang-Ik; Marroum, Patrick J; Norden, Janet M; Qiu, Wei; Rahman, Atiqur; Sobel, Solomon; Stifano, Toni; Thummel, Kenneth; Wei, Xiao-Xiong; Yasuda, Sally; Zheng, Jenny H; Zhao, Hong; Lesko, Lawrence J

2008-06-01

Predicting clinically significant drug interactions during drug development is a challenge for the pharmaceutical industry and regulatory agencies. Since the publication of the US Food and Drug Administration's (FDA's) first in vitro and in vivo drug interaction guidance documents in 1997 and 1999, researchers and clinicians have gained a better understanding of drug interactions. This knowledge has enabled the FDA and the industry to progress and begin to overcome these challenges. The FDA has continued its efforts to evaluate methodologies to study drug interactions and communicate recommendations regarding the conduct of drug interaction studies, particularly for CYP-based and transporter-based drug interactions, to the pharmaceutical industry. A drug interaction Web site was established to document the FDA's current understanding of drug interactions (http://www.fda.gov/cder/drug/drugInteractions/default.htm). This report provides an overview of the evolution of the drug interaction guidances, includes a synopsis of the steps taken by the FDA to revise the original drug interaction guidance documents, and summarizes and highlights updated sections in the current guidance document, Drug Interaction Studies-Study Design, Data Analysis, and Implications for Dosing and Labeling.

Evaluation of documented drug interactions and contraindications associated with herbs and dietary supplements: a systematic literature review.

PubMed

Tsai, H-H; Lin, H-W; Simon Pickard, A; Tsai, H-Y; Mahady, G B

2012-11-01

The use of herbs and dietary supplements (HDS) alone or concomitantly with medications can potentially increase the risk of adverse events experienced by the patients. This review aims to evaluate the documented HDS-drug interactions and contraindications. A structured literature review was conducted on PubMed, EMBASE, Cochrane Library, tertiary literature and Internet. While 85 primary literatures, six books and two web sites were reviewed for a total of 1,491 unique pairs of HDS-drug interactions, 213 HDS entities and 509 medications were involved. HDS products containing St. John's Wort, magnesium, calcium, iron, ginkgo had the greatest number of documented interactions with medications. Warfarin, insulin, aspirin, digoxin, and ticlopidine had the greatest number of reported interactions with HDS. Medications affecting the central nervous system or cardiovascular system had more documented interactions with HDS. Of the 882 HDS-drug interactions being described its mechanism and severity, 42.3% were due to altered pharmacokinetics and 240 were described as major interactions. Of the 152 identified HDS contraindications, the most frequent involved gastrointestinal (16.4%), neurological (14.5%), and renal/genitourinary diseases (12.5%). Flaxseed, echinacea, and yohimbe had the largest number of documented contraindications. Although HDS-drug interactions and contraindications primarily concerned a relatively small subset of commonly used medications and HDS entities, this review provides the summary to identify patients, HDS products, and medications that are more susceptible to HDS-drug interactions and contraindications. The findings would facilitate the health-care professionals to communicate these documented interactions and contraindications to their patients and/or caregivers thereby preventing serious adverse events and improving desired therapeutic outcomes. © 2012 Blackwell Publishing Ltd.

Analysis of National Drug Code Identifiers in Ambulatory E-Prescribing.

PubMed

Dhavle, Ajit A; Ward-Charlerie, Stacy; Rupp, Michael T; Amin, Vishal P; Ruiz, Joshua

2015-11-01

Communication of an accurate and interpretable drug identifier between prescriber and pharmacist is critically important for realizing the potential benefits of electronic prescribing (e-prescribing) while minimizing its risk. The National Drug Code (NDC) is the most commonly used codified drug identifier in ambulatory care e-prescribing, but concerns have been raised regarding its use for this purpose.  To (a) assess the frequency of NDC identifier transmission in ambulatory e-prescribing; (b) characterize the type of NDC identifier transmitted (representative, repackaged, obsolete, private label, and unit dose); and (c) assess the level of agreement between drug descriptions corresponding to NDC identifiers in electronic prescriptions (e-prescriptions) and the free-text drug descriptions that were entered by prescribers.  We analyzed a sample of 49,997 e-prescriptions that were transmitted by ambulatory care prescribers to outlets of a national retail drugstore chain during a single day in April 2014. The First Databank MedKnowledge drug database was used as the primary reference data base to assess the frequency and types of NDC numbers in the e-prescription messages. The FDA's Comprehensive NDC Standard Product Labeling Data Elements File and the National Library of Medicine's RxNorm data file were used as secondary and tertiary references, respectively, to identify NDC numbers that could not be located in the primary reference file. Three experienced reviewers compared the free-text drug description that had been entered by the prescriber with the drug description corresponding to the NDC number from 1 of the 3 reference database files to identify discrepancies. Two licensed pharmacists with residency training and ambulatory care experience served as final adjudicators. A total of 42,602 e-prescriptions contained a value in the NDC field, of which 42,335 (84.71%) were found in 1 of the 3 study reference databases and were thus considered to be valid NDC

Prevalence of statin-drug interactions in older people: a systematic review.

PubMed

Thai, Michele; Reeve, Emily; Hilmer, Sarah N; Qi, Katie; Pearson, Sallie-Anne; Gnjidic, Danijela

2016-05-01

Statins are among the most frequently prescribed medications internationally. Older people are commonly prescribed multiple medications and are at an increased risk of drug-drug interactions, including statin-drug interactions. The aim of this study was to conduct a systematic review of current evidence on the prevalence of statin-drug interactions in older people. A systematic search of observational studies in Embase, Medline, and PubMed was conducted. Articles were included if they were published in English during the period July 2000-July 2014 and reported on the prevalence of statin-drug interactions in people over 65 years of age. Two reviewers independently assessed the articles for eligibility and extracted the data. The search returned 1556 eligible articles. A total of 19 articles met the inclusion criteria. In studies (n = 7) that focused on statin users only, the prevalence of potential statin-drug interactions assessed using different measures ranged from 0.19 to 33.0 %. In studies that examined drug interactions across a population of both statin users and non-users (n = 12), the prevalence of potential statin-drug interactions ranged from 0.1 to 7.1 % (n = 8), and the prevalence of clinically relevant statin-drug interactions ranged from 1.5 to 4 % (n = 4). Current published evidence suggests substantial variations in the prevalence of statin-drug interactions and their clinical relevance. Further studies are necessary to provide a better understanding of the prevalence of clinically significant statin-drug interactions, the medications most frequently contributing to statin-drug interactions, and impact on relevant clinical outcomes in older people.

Potential drug-drug interactions with antiepileptic drugs in Medicaid recipients.

PubMed

Dickson, Michael; Bramley, Thomas J; Kozma, Chris; Doshi, Dilesh; Rupnow, Marcia F T

2008-09-15

The frequency of potential drug-drug interactions (DDIs) between antiepileptic drugs (AEDs) and other (non-AED) medications in Medicaid patients taking newer AED monotherapy, older AED monotherapy, and combinations of AED treatment was studied. A retrospective, observational study was conducted using administrative claims obtained from South Carolina Medicaid. Patients were included in the analysis if they (1) had at least one prescription for an AED between January 1, 2004, and December 31, 2004, (2) were taking a specific AED for at least 60 days, (3) had at least one epilepsy diagnosis during the 6 months before or during the enrollment period, and (4) were enrolled in Medicaid for at least 11 of the 12 months of the follow-up period. Possible DDI exposure was defined as 10 days of overlap between an AED and a non-AED known to have the potential to cause a clinically relevant interaction. A total of 4955 patients met the inclusion criteria. Approximately 45% of patients receiving monotherapy with an older AED had a potential DDI, compared with 3.9% receiving a newer AED. An average of 0.08 potential DDI per year of exposure occurred in the newer AED monotherapy cohort compared with 1.18 in the older AED monotherapy cohort. The most common potential interaction category was a decreased concentration of the non-AED. Older AEDs were associated with a greater likelihood of a potential DDI than were newer AEDs. Further research is needed to elucidate the relationship between the occurrence of potential DDIs and actual clinically relevant consequences.

Family medicine residents' knowledge and attitudes about drug-nutrient interactions.

PubMed

Lasswell, A B; DeForge, B R; Sobal, J; Muncie, H L; Michocki, R

1995-04-01

The Joint Commission on Accreditation of Healthcare Organizations (JCAHO) requires that health professionals recognize the importance of drug-nutrient interactions and educate patients to prevent adverse effects. Drug-nutrient interactions are an important issue in medical practice, but it is not clear how or if physicians are trained in this issue. This investigation was a needs assessment that examined attitudes and knowledge about drug-nutrient interactions that was examined in a national sample of 834 family medicine residents in 56 residency programs. Most reported they had little or no formal training in drug-nutrient interactions in medical school (83%) or residency (80%). However, 79% believed it was the physician's responsibility to inform patients about drug-nutrient interactions, although many thought pharmacists (75%) and dietitians (66%) share this responsibility. Overall, residents correctly answered 61% +/- 19 of fourteen drug-nutrient interaction knowledge items. There was a slight increase in drug-nutrient knowledge as year of residency increased. Physicians' knowledge of drug-nutrient interactions may be improved by including nutrition education in the topics taught by physicians, nutritionists, and pharmacists using several educational strategies. Nutrition educators in particular can play a role in curriculum development about drug-nutrient interactions by developing, refining, and evaluating materials and educational tools. Nutrition educators need to provide this information in academic settings for the training of all health professionals as well as in patient education settings such as hospitals and public health clinics.

Cognitive enhancers (Nootropics). Part 3: drugs interacting with targets other than receptors or enzymes. Disease-modifying drugs. Update 2014.

PubMed

Froestl, Wolfgang; Pfeifer, Andrea; Muhs, Andreas

2014-01-01

Scientists working in the field of Alzheimer's disease and, in particular, cognitive enhancers, are very productive. The review "Drugs interacting with Targets other than Receptors or Enzymes. Disease-modifying Drugs" was accepted in October 2012. In the last 20 months, new targets for the potential treatment of Alzheimer's disease were identified. Enormous progress was realized in the pharmacological characterization of natural products with cognitive enhancing properties. This review covers the evolution of research in this field through May 2014.

[Interactions of food and drug metabolism].

PubMed

Delzenne, N M; Verbeeck, R K

2001-01-01

The nutritional state, and/or the ingestion of specific nutrients, is/are able to modify drug disposition, by interfering with drug absorption, distribution, storage, and metabolism. Recent data report that nutrients interfere with drug metabolism either by modifying key enzymes of phase I (cytochromeP450 dependent mixed function oxidase) and II (glucuronosyl, sulfonyl- ... transferases), or by modulating coenzymes availability (NADPH, UDPglucuronic acid...). Food components involved in drug metabolism modifications are either macro-nutrients (carbohydrates, lipids, proteins, ethanol), micronutriments (vitamins, minerals), or phytochemicals. Drug-nutrients interactions may be beneficials, and thus could constitute, i.e. a way to improve drug therapeutic index, or generate adverse effects.

A proteomic approach to identifying new drug targets (potentiating topoisomerase II poisons).

PubMed

Jenkins, J R

2008-10-01

Topoisomerase II poisons are an established part of best clinical practice for the treatment of a number of solid tumours and haematological malignancies. However, toxicity and resistance to chemotherapeutic drugs often complicate the treatment. Furthermore, topoisomerase II poisons can also induce sister chromatid exchange, chromosomal recombination and chromosome aberrations and are associated with a significant risk of secondary leukaemia. It would therefore be of great clinical benefit if the efficacy of topoisomerase II inhibitors could be enhanced without the increased toxic side effects. It is proposed that clinical agents targeting topoisomerase II can be enhanced by inhibiting proteins that modulate topoisomerase II. The aim is to identify proteins, that by the nature of their interaction with topoisomerase II, represent putative drug targets.

Pharmacokinetic and pharmacodynamic drug interactions with ethanol (alcohol).

PubMed

Chan, Lingtak-Neander; Anderson, Gail D

2014-12-01

Ethanol (alcohol) is one of the most widely used legal drugs in the world. Ethanol is metabolized by alcohol dehydrogenase (ADH) and the cytochrome P450 (CYP) 2E1 drug-metabolizing enzyme that is also responsible for the biotransformation of xenobiotics and fatty acids. Drugs that inhibit ADH or CYP2E1 are the most likely theoretical compounds that would lead to a clinically significant pharmacokinetic interaction with ethanol, which include only a limited number of drugs. Acute ethanol primarily alters the pharmacokinetics of other drugs by changing the rate and extent of absorption, with more limited effects on clearance. Both acute and chronic ethanol use can cause transient changes to many physiologic responses in different organ systems such as hypotension and impairment of motor and cognitive functions, resulting in both pharmacokinetic and pharmacodynamic interactions. Evaluating drug interactions with long-term use of ethanol is uniquely challenging. Specifically, it is difficult to distinguish between the effects of long-term ethanol use on liver pathology and chronic malnutrition. Ethanol-induced liver disease results in decreased activity of hepatic metabolic enzymes and changes in protein binding. Clinical studies that include patients with chronic alcohol use may be evaluating the effects of mild cirrhosis on liver metabolism, and not just ethanol itself. The definition of chronic alcohol use is very inconsistent, which greatly affects the quality of the data and clinical application of the results. Our study of the literature has shown that a significantly higher volume of clinical studies have focused on the pharmacokinetic interactions of ethanol and other drugs. The data on pharmacodynamic interactions are more limited and future research addressing pharmacodynamic interactions with ethanol, especially regarding the non-central nervous system effects, is much needed.

[Simple method for precognition of drug interaction between oral iron and phenolic hydroxyl group-containing drugs].

PubMed

Sunagane, Nobuyoshi; Yoshinobu, Etsuko; Murayama, Nobuko; Terawaki, Yasufumi; Kamimura, Naoki; Uruno, Tsutomu

2005-02-01

In the present study, we devised a simple method for detecting the drug interaction between oral iron preparations and phenolic hydroxyl group-containing drugs, using the coloring reaction as indicator, due to the formation of complexes or chelates. In the method, oral iron preparations and test drugs in amounts as much as single dose for adults were added to 10 ml of purified water to make sample suspensions for testing. Thirty minutes after mixing an oral iron suspension and a test drug suspension, the change of color in the mixture was observed macroscopically and graded as 0 to 3, with a marked color change judged as grade 3 and no color change as grade 0. Screening of 14 test drugs commonly used orally was carried out. When using sodium ferrous citrate preparations as oral iron, 5 were classified as grade 3, 2 as grade 2, 4 as grade 1, and 3 as grade 0, respectively. To verify usefulness of the method, the interactions suggested by screening were pharmacokinetically assessed by measuring serum concentrations of the drug in mice. When a levodopa or droxidopa preparation, judged as grade 3 in screening, was concomitantly administered with an iron preparation, a significant reduction in bioavailability of the test drug was observed, indicating possible drug interaction between the test drug and oral iron. Combined administration of an acetaminophen preparation, judged as grade 1, and oral iron preparation showed no influence on the bioavailability of the test drug, implying no detectable interactions between them. In conclusion, the simple method devised in the present study is useful for precognition of drug interactions between oral iron preparations and phenolic hydroxyl group-containing drugs, and the drugs with a higher grade in screening may induce drug interactions with oral iron.

Venetoclax (ABT-199) Might Act as a Perpetrator in Pharmacokinetic Drug-Drug Interactions.

PubMed

Weiss, Johanna; Gajek, Thomas; Köhler, Bruno Christian; Haefeli, Walter Emil

2016-02-24

Venetoclax (ABT-199) represents a specific B-cell lymphoma 2 (Bcl-2) inhibitor that is currently under development for the treatment of lymphoid malignancies. So far, there is no published information on its interaction potential with important drug metabolizing enzymes and drug transporters, or its efficacy in multidrug resistant (MDR) cells. We therefore scrutinized its drug-drug interaction potential in vitro. Inhibition of cytochrome P450 enzymes (CYPs) was quantified by commercial kits. Inhibition of drug transporters (P-glycoprotein (P-gp, ABCB1), breast cancer resistance protein (BCRP), and organic anion transporting polypeptides (OATPs)) was evaluated by the use of fluorescent probe substrates. Induction of drug transporters and drug metabolizing enzymes was quantified by real-time RT-PCR. The efficacy of venetoclax in MDR cells lines was evaluated with proliferation assays. Venetoclax moderately inhibited P-gp, BCRP, OATP1B1, OATP1B3, CYP3A4, and CYP2C19, whereas CYP2B6 activity was increased. Venetoclax induced the mRNA expression of CYP1A1, CYP1A2, UGT1A3, and UGT1A9. In contrast, expression of ABCB1 was suppressed, which might revert tumor resistance towards antineoplastic P-gp substrates. P-gp over-expression led to reduced antiproliferative effects of venetoclax. Effective concentrations for inhibition and induction lay in the range of maximum plasma concentrations of venetoclax, indicating that it might act as a perpetrator drug in pharmacokinetic drug-drug interactions.

Hypericum perforatum: pharmacokinetic, mechanism of action, tolerability, and clinical drug-drug interactions.

PubMed

Russo, Emilio; Scicchitano, Francesca; Whalley, Benjamin J; Mazzitello, Carmela; Ciriaco, Miriam; Esposito, Stefania; Patanè, Marinella; Upton, Roy; Pugliese, Michela; Chimirri, Serafina; Mammì, Maria; Palleria, Caterina; De Sarro, Giovambattista

2014-05-01

Hypericum perforatum (HP) belongs to the Hypericaceae family and is one of the oldest used and most extensively investigated medicinal herbs. The medicinal form comprises the leaves and flowering tops of which the primary ingredients of interest are naphthodianthrones, xanthones, flavonoids, phloroglucinols (e.g. hyperforin), and hypericin. Although several constituents elicit pharmacological effects that are consistent with HP's antidepressant activity, no single mechanism of action underlying these effects has thus far been found. Various clinical trials have shown that HP has a comparable antidepressant efficacy as some currently used antidepressant drugs in the treatment of mild/moderate depression. Interestingly, low-hyperforin-content preparations are effective in the treatment of depression. Moreover, HP is also used to treat certain forms of anxiety. However, HP can induce various cytochrome P450s isozymes and/or P-glycoprotein, of which many drugs are substrates and which are the main origin of HP-drug interactions. Here, we analyse the existing evidence describing the clinical consequence of HP-drug interactions. Although some of the reported interactions are based on findings from in vitro studies, the clinical importance of which remain to be demonstrated, others are based on case reports where causality can, in some cases, be determined to reveal clinically significant interactions that suggest caution, consideration, and disclosure of potential interactions prior to informed use of HP. Copyright © 2013 John Wiley & Sons, Ltd.

Evaluation of drug interaction microcomputer software: comparative study.

PubMed

Poirier, T I; Giudici, R

1991-01-01

Twelve drug interaction microcomputer software programs were evaluated and compared using general and specific criteria. This article summarizes and compares the features, ratings, advantages, and disadvantages of each program. Features of an ideal drug interaction program are noted. Recommended programs based on three price ranges are suggested.

Clinical Drug-Drug Pharmacokinetic Interaction Potential of Sucralfate with Other Drugs: Review and Perspectives.

PubMed

Sulochana, Suresh P; Syed, Muzeeb; Chandrasekar, Devaraj V; Mullangi, Ramesh; Srinivas, Nuggehally R

2016-10-01

Sucralfate, a complex of aluminium hydroxide with sulfated sucrose, forms a strong gastrointestinal tract (GIT) mucosal barrier with excellent anti-ulcer property. Because sucralfate does not undergo any significant oral absorption, sucralfate resides in the GIT for a considerable length of time. The unabsorbed sucralfate may alter the pharmacokinetics of the oral drugs by impeding its absorption and reducing the oral bioavailability. Because of the increased use of sucralfate, it was important to provide a reappraisal of the published clinical drug-drug interaction studies of sucralfate with scores of drugs. This review covers several category of drugs such as non-steroidal anti-inflammatory drugs, fluoroquinolones, histamine H2-receptor blockers, macrolides, anti-fungals, anti-diabetics, salicylic acid derivatives, steroidal anti-inflammatory drugs and provides pharmacokinetic data summary along with study design, objectives and key remarks. While the loss of oral bioavailability was significant for the fluoroquinolone class, it generally varied for other classes of drugs, suggesting that impact of the co-administration of sucralfate is manageable in clinical situations. Given the technology advancement in formulation development, it may be in order feasible to develop appropriate formulation strategies to either avoid or minimize the absorption-related issues when co-administered with sucralfate. It is recommended that consideration of both in vitro and preclinical studies may be in order to gauge the level of interaction of a drug with sucralfate. Such data may aid in the development of appropriate strategies to navigate the co-administration of sucralfate with other drugs in this age of polypharmacy.

[Drug-food interactions in internal medicine: What physicians should know?].

PubMed

Mouly, S; Morgand, M; Lopes, A; Lloret-Linares, C; Bergmann, J-F

2015-08-01

Orally administered medications may interact with various fruits, vegetables, herbal medicines, functional foods or dietary supplements. Drug-food interactions, which are mostly unknown from prescribers, including internists, may be responsible for changes in drug plasma concentrations, which may decrease efficacy or led to sometimes life-threatening toxicity. Aging, concomitant medications, transplant recipients, patients with cancer, malnutrition, HIV infection and those receiving enteral or parenteral feeding are at increased risk of drug-food interactions. This review focused on the most clinically relevant drug-food interactions, including those with grapefruit juice, Saint-John's Wort, enteral or parenteral nutrition, their respective consequences in the clinical setting in order to provide thoughtful information for internists in their routine clinical practice. Specific clinical settings are also detailed, such as the Ramadan or multiple medications especially in elderly patients. Drug-food interactions are also presented with respect to the main therapeutic families, including the non-steroidal anti-inflammatory drugs, analgesics, cardiovascular medications, warfarin as well as new oral anticoagulants, anticancer drugs and immunosuppressant medications. Considerable effort has been achieved to a better understanding of food-drug interactions and increase clinicians' ability to anticipate their occurrence and consequences in clinical practice. Describing the frequency of relevant food-drug interactions in internal medicine is paramount in order to optimize patient care and drug dosing on an individual basis as well as to increase patients and doctors information. Copyright © 2015 Société nationale française de médecine interne (SNFMI). Published by Elsevier SAS. All rights reserved.

Importance of multi-P450 inhibition in drug-drug interactions: evaluation of incidence, inhibition magnitude and prediction from in vitro data

PubMed Central

Isoherranen, Nina; Lutz, Justin D; Chung, Sophie P; Hachad, Houda; Levy, Rene H; Ragueneau-Majlessi, Isabelle

2012-01-01

Drugs that are mainly cleared by a single enzyme are considered more sensitive to drug-drug interactions (DDIs) than drugs cleared by multiple pathways. However, whether this is true when a drug cleared by multiple pathways is co-administered with an inhibitor of multiple P450 enzymes (multi-P450 inhibition) is not known. Mathematically, simultaneous equipotent inhibition of two elimination pathways that each contributes half of the drug clearance is equal to equipotent inhibition of a single pathway that clears the drug. However, simultaneous strong or moderate inhibition of two pathways by a single inhibitor is perceived as an unlikely scenario. The aim of this study was (i) to identify P450 inhibitors currently in clinical use that can inhibit more than one clearance pathway of an object drug in vivo, and (ii) to evaluate the magnitude and predictability of DDIs caused by these multi-P450 inhibitors. Multi-P450 inhibitors were identified using the Metabolism and Transport Drug Interaction Database™. A total of 38 multi-P450 inhibitors, defined as inhibitors that increased the AUC or decreased the clearance of probes of two or more P450’s, were identified. Seventeen (45 %) multi-P450 inhibitors were strong inhibitors of at least one P450 and an additional 12 (32 %) were moderate inhibitors of one or more P450s. Only one inhibitor (fluvoxamine) was a strong inhibitor of more than one enzyme. Fifteen of the multi-P450 inhibitors also inhibit drug transporters in vivo, but such data are lacking on many of the inhibitors. Inhibition of multiple P450 enzymes by a single inhibitor resulted in significant (>2-fold) clinical DDIs with drugs that are cleared by multiple pathways such as imipramine and diazepam while strong P450 inhibitors resulted in only weak DDIs with these object drugs. The magnitude of the DDIs between multi-P450 inhibitors and diazepam, imipramine and omeprazole could be predicted using in vitro data with similar accuracy as probe substrate

Potential drug interactions in patients given antiretroviral therapy.

PubMed

Santos, Wendel Mombaque Dos; Secoli, Silvia Regina; Padoin, Stela Maris de Mello

2016-11-21

to investigate potential drug-drug interactions (PDDI) in patients with HIV infection on antiretroviral therapy. a cross-sectional study was conducted on 161 adults with HIV infection. Clinical, socio demographic, and antiretroviral treatment data were collected. To analyze the potential drug interactions, we used the software Micromedex(r). Statistical analysis was performed by binary logistic regression, with a p-value of ≤0.05 considered statistically significant. of the participants, 52.2% were exposed to potential drug-drug interactions. In total, there were 218 potential drug-drug interactions, of which 79.8% occurred between drugs used for antiretroviral therapy. There was an association between the use of five or more medications and potential drug-drug interactions (p = 0.000) and between the time period of antiretroviral therapy being over six years and potential drug-drug interactions (p < 0.00). The clinical impact was prevalent sedation and cardiotoxicity. the PDDI identified in this study of moderate and higher severity are events that not only affect the therapeutic response leading to toxicity in the central nervous and cardiovascular systems, but also can interfere in tests used for detection of HIV resistance to antiretroviral drugs. investigar potenciais interações droga-droga (PDDI) em pacientes infectados com HIV em terapia de antirretroviral. um estudo de corte transversal foi conduzido em 161 pessoas infectadas com o HIV. Dados de tratamentos clínicos, sociodemográficos e antirretrovirais foram coletados. Para analisar a possível interação medicamentosa, nós usamos o software Micromedex(r). A análise estatística foi feita por regressão logística binária, com um valor P de ≤0.05, considerado estatisticamente significativo. dos participantes, 52.2% foram expostos a potenciais interações droga-droga. No total, houve 218 interações droga-droga, das quais 79.8% ocorreram entre drogas usadas para a terapia antirretroviral

MONITORING POTENTIAL DRUG INTERACTIONS AND REACTIONS VIA NETWORK ANALYSIS OF INSTAGRAM USER TIMELINES.

PubMed

Correia, Rion Brattig; Li, Lang; Rocha, Luis M

2016-01-01

Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products-including cannabis-which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015.We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that Instagram

Hepatic transporter drug-drug interactions: an evaluation of approaches and methodologies.

PubMed

Williamson, Beth; Riley, Robert J

2017-12-01

Drug-drug interactions (DDIs) continue to account for 5% of hospital admissions and therefore remain a major regulatory concern. Effective, quantitative prediction of DDIs will reduce unexpected clinical findings and encourage projects to frontload DDI investigations rather than concentrating on risk management ('manage the baggage') later in drug development. A key challenge in DDI prediction is the discrepancies between reported models. Areas covered: The current synopsis focuses on four recent influential publications on hepatic drug transporter DDIs using static models that tackle interactions with individual transporters and in combination with other drug transporters and metabolising enzymes. These models vary in their assumptions (including input parameters), transparency, reproducibility and complexity. In this review, these facets are compared and contrasted with recommendations made as to their application. Expert opinion: Over the past decade, static models have evolved from simple [I]/k i models to incorporate victim and perpetrator disposition mechanisms including the absorption rate constant, the fraction of the drug metabolised/eliminated and/or clearance concepts. Nonetheless, models that comprise additional parameters and complexity do not necessarily out-perform simpler models with fewer inputs. Further, consideration of the property space to exploit some drug target classes has also highlighted the fine balance required between frontloading and back-loading studies to design out or 'manage the baggage'.

Drug-target interaction prediction from PSSM based evolutionary information.

PubMed

Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

2016-01-01

The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

Current cytochrome P450 phenotyping methods applied to metabolic drug-drug interaction prediction in dogs.

PubMed

Mills, Beth Miskimins; Zaya, Matthew J; Walters, Rodney R; Feenstra, Kenneth L; White, Julie A; Gagne, Jason; Locuson, Charles W

2010-03-01

Recombinant cytochrome P450 (P450) phenotyping, different approaches for estimating fraction metabolized (f(m)), and multiple measures of in vivo inhibitor exposure were tested for their ability to predict drug interaction magnitude in dogs. In previous reports, midazolam-ketoconazole interaction studies in dogs have been attributed to inhibition of CYP3A pathways. However, in vitro phenotyping studies demonstrated higher apparent intrinsic clearances (CL(int,app)) of midazolam with canine CYP2B11 and CYP2C21. Application of activity correction factors and isoform hepatic abundance to liver microsome CL(int,app) values further implicated CYP2B11 (f(m) >or= 0.89) as the dog enzyme responsible for midazolam- and temazepam-ketoconazole interactions in vivo. Mean area under the curve (AUC) in the presence of the inhibitor/AUC ratios from intravenous and oral midazolam interaction studies were predicted well with unbound K(i) and estimates of unbound hepatic inlet inhibitor concentrations and intestinal metabolism using the AUC-competitive inhibitor relationship. No interactions were observed in vivo with bufuralol, although significant interactions with bufuralol were predicted with fluoxetine via CYP2D and CYP2C pathways (>2.45-fold) but not with clomipramine (<2-fold). The minor caffeine-fluvoxamine interaction (1.78-fold) was slightly higher than predicted values based on determination of a moderate f(m) value for CYP1A1, although CYP1A2 may also be involved in caffeine metabolism. The findings suggest promise for in vitro approaches to drug interaction assessment in dogs, but they also highlight the need to identify improved substrate and inhibitor probes for canine P450s.

Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

PubMed

Alaimo, Salvatore; Giugno, Rosalba; Pulvirenti, Alfredo

2016-01-01

The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

Predicting transporter-mediated drug interactions: Commentary on: "Pharmacokinetic evaluation of a drug transporter cocktail consisting of digoxin, furosemide, metformin and rosuvastatin" and "Validation of a microdose probe drug cocktail for clinical drug interaction assessments for drug transporters and CYP3A".

PubMed

Zhang, L; Sparreboom, A

2017-04-01

Transporters, expressed in various tissues, govern the absorption, distribution, metabolism, and excretion of drugs, and consequently their inherent safety and efficacy profiles. Drugs may interact with a transporter as a substrate and/or an inhibitor. Understanding transporter-mediated drug-drug interactions (DDIs), in addition to enzyme-mediated DDIs, is an integral part of risk assessment in drug development and regulatory review because the concomitant use of more than one medication in patients is common. © 2016 ASCPT.

DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

PubMed Central

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-01-01

Motivation: Identifying drug–target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug–target interactions of new candidate drugs or targets. Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. Availability: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307615

Drug-drug interaction between methotrexate and levetiracetam resulting in delayed methotrexate elimination.

PubMed

Bain, Emily; Birhiray, Ruemu E; Reeves, David J

2014-02-01

To report a case of delayed methotrexate (MTX) elimination while receiving concomitant levetiracetam. A 46-year-old man with relapsed osteosarcoma of the base of the skull receiving high-dose MTX tolerated his first cycle of MTX with elimination to nontoxic MTX levels (≤0.1 µmol/L) within 90 hours. After hospital discharge, the patient experienced seizures secondary to brain metastasis and started on levetiracetam, which was continued as maintenance therapy. The patient experienced delayed MTX elimination during cycles 2, 3, and 4 while receiving levetiracetam. On average, elimination to nontoxic MTX levels took 130 hours (106-144 hours). Before the fifth cycle of MTX, lorazepam was substituted for the levetiracetam. MTX was eliminated to nontoxic levels within 95 hours. During all cycles, the patient received standard supportive care and serum creatinine remained stable. No other drugs known to interact with MTX were administered. This possible drug interaction has only been reported once in the pediatric population. With a score of 6 on the Drug Interaction Probability Scale for evaluating causation of drug interactions, it is probable that the delayed MTX elimination was caused by an interaction with levetiracetam. Coadministration of levetiracetam and MTX may result in delayed elimination of MTX, increasing the likelihood of toxicity. Consideration should be given to temporarily switching from levetiracetam to another antiepileptic (ie, lorazepam) to prevent this interaction. This is particularly important in those experiencing delayed elimination with prior cycles of concomitant MTX and levetiracetam or those at greater risk for MTX toxicity.

Effects of drug-carrier interactions on drug dissolution from binary and ternary matrices

NASA Astrophysics Data System (ADS)

Iqbal, Zafar

formulations and perform appropriate physical characterizations. (2) Characterize the increase in drug dissolution rates resulting from solid solution formulations. (3) Relate the initial dissolution rates to the drug solubility. (4) Explain the solubility enhancement from solid solution dosage in terms of the drug polymer interactions using the extended thermodynamic model. Two poorly water soluble drugs, levonorgestrel (LEVO) and ethinyl estradiol (EE) were formulated in seven solid solution preparations comprised of four carrier systems. Materials used as carriers included various combinations of the polymers PVP K-30, Copovidone (COP), Poloxamer 182, and the surfactant TweenRTM 20. Additionally, ibuprofen (IBU) was used in three formulations consisting of various combinations of PVP K-30, Copovidone and TweenRTM 20. Formulations with various drug weight fractions (0.5%--30%) were prepared using the solvent evaporation technique. Each formulation was tested for dissolution using intrinsic dissolution apparatus (USP). The solid solutions were compressed into tablets into the sample die that maintained a constant surface area during the dissolution process. DSC, XRD and NIRS scans identified that the crystalline peaks of the drug disappeared with the addition of the polymer for all ratios of EE, indicating the formation of solid solutions (to within the limits of detection of the equipment). This was also observed for the LEVO dispersions up to 10% drug loading. At higher drug loading, solutions were formed but some small degree crystallinity was also present. For each experiment, the initial dissolution rates were obtained from the slope of the mass dissolved vs. time plots taken at early times, and volume normalized initial dissolution rates RV were calculated by dividing the initial dissolution rate by the volume fraction of the drug in the formulation. Comparison of the RV values for the various formulations with a reference RV (typically that of the pure drug or of the

Cytochrome P450 drug interactions with statin therapy.

PubMed

Goh, Ivanna Xin Wei; How, Choon How; Tavintharan, Subramaniam

2013-03-01

Statins are commonly used in the treatment of hyperlipidaemia. Although the benefits of statins are well-documented, they have the potential to cause myopathy and rhabdomyolysis due to the complex interactions of drugs, comorbidities and genetics. The cytochrome P450 family consists of major enzymes involved in drug metabolism and bioactivation. This article aims to highlight drug interactions involving statins, as well as provide updated recommendations and approaches regarding the safe and appropriate use of statins in the primary care setting.

Recommendations for selecting drug-drug interactions for clinical decision support.

PubMed

Tilson, Hugh; Hines, Lisa E; McEvoy, Gerald; Weinstein, David M; Hansten, Philip D; Matuszewski, Karl; le Comte, Marianne; Higby-Baker, Stefanie; Hanlon, Joseph T; Pezzullo, Lynn; Vieson, Kathleen; Helwig, Amy L; Huang, Shiew-Mei; Perre, Anthony; Bates, David W; Poikonen, John; Wittie, Michael A; Grizzle, Amy J; Brown, Mary; Malone, Daniel C

2016-04-15

Recommendations for including drug-drug interactions (DDIs) in clinical decision support (CDS) are presented. A conference series was conducted to improve CDS for DDIs. A work group consisting of 20 experts in pharmacology, drug information, and CDS from academia, government agencies, health information vendors, and healthcare organizations was convened to address (1) the process to use for developing and maintaining a standard set of DDIs, (2) the information that should be included in a knowledge base of standard DDIs, (3) whether a list of contraindicated drug pairs can or should be established, and (4) how to more intelligently filter DDI alerts. We recommend a transparent, systematic, and evidence-driven process with graded recommendations by a consensus panel of experts and oversight by a national organization. We outline key DDI information needed to help guide clinician decision-making. We recommend judicious classification of DDIs as contraindicated and more research to identify methods to safely reduce repetitive and less-relevant alerts. An expert panel with a centralized organizer or convener should be established to develop and maintain a standard set of DDIs for CDS in the United States. The process should be evidence driven, transparent, and systematic, with feedback from multiple stakeholders for continuous improvement. The scope of the expert panel's work should be carefully managed to ensure that the process is sustainable. Support for research to improve DDI alerting in the future is also needed. Adoption of these steps may lead to consistent and clinically relevant content for interruptive DDIs, thus reducing alert fatigue and improving patient safety. Copyright © 2016 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Biology of PXR: role in drug-hormone interactions

PubMed Central

Wang, Jing; Dai, Shu; Guo, Yan; Xie, Wen; Zhai, Yonggong

2014-01-01

Hormonal homeostasis is essential for a variety of physiological and pathological processes. Elimination and detoxification of xenobiotics, such as drugs introduced into the human body, could disrupt the balance of hormones due to the induction of drug metabolizing enzymes (DMEs) and transporters. Pregnane X receptor (PXR, NR1I2) functions as a master xenobiotic receptor involved in drug metabolism and drug-drug interactions by its coordinated transcriptional regulation of phase I and phase II DMEs and transporters. Recently, increasing evidences indicate that PXR can also mediate the endocrine disruptor function and thus impact the integrity of the endocrine system. This review focuses primarily on the recent advances in our understanding of the function of PXR in glucocorticoid, mineralocorticoid, androgen and estrogen homeostasis. The elucidation of PXR-mediated drug-hormone interactions might have important therapeutic implications in dealing with hormone-dependent diseases and safety assessment of drugs. PMID:26417296

Cytochrome P450 enzyme mediated herbal drug interactions (Part 2)

PubMed Central

Wanwimolruk, Sompon; Phopin, Kamonrat; Prachayasittikul, Virapong

2014-01-01

To date, a number of significant herbal drug interactions have their origins in the alteration of cytochrome P450 (CYP) activity by various phytochemicals. Among the most noteworthy are those involving St. John's wort and drugs metabolized by human CYP3A4 enzyme. This review article is the continued work from our previous article (Part 1) published in this journal (Wanwimolruk and Prachayasittikul, 2014[ref:133]). This article extends the scope of the review to six more herbs and updates information on herbal drug interactions. These include black cohosh, ginseng, grape seed extract, green tea, kava, saw palmetto and some important Chinese medicines are also presented. Even though there have been many studies to determine the effects of herbs and herbal medicines on the activity of CYP, most of them were in vitro and in animal studies. Therefore, the studies are limited in predicting the clinical relevance of herbal drug interactions. It appeared that the majority of the herbal medicines have no clear effects on most of the CYPs examined. For example, the existing clinical trial data imply that black cohosh, ginseng and saw palmetto are unlikely to affect the pharmacokinetics of conventional drugs metabolized by human CYPs. For grape seed extract and green tea, adverse herbal drug interactions are unlikely when they are concomitantly taken with prescription drugs that are CYP substrates. Although there were few clinical studies on potential CYP-mediated interactions produced by kava, present data suggest that kava supplements have the ability to inhibit CYP1A2 and CYP2E1 significantly. Therefore, caution should be taken when patients take kava with CYP1A2 or CYP2E1 substrate drugs as it may enhance their therapeutic and adverse effects. Despite the long use of traditional Chinese herbal medicines, little is known about the potential drug interactions with these herbs. Many popularly used Chinese medicines have been shown in vitro to significantly change the

Nuclear Receptors in Drug Metabolism, Drug Response and Drug Interactions

PubMed Central

Prakash, Chandra; Zuniga, Baltazar; Song, Chung Seog; Jiang, Shoulei; Cropper, Jodie; Park, Sulgi; Chatterjee, Bandana

2016-01-01

Orally delivered small-molecule therapeutics are metabolized in the liver and intestine by phase I and phase II drug-metabolizing enzymes (DMEs), and transport proteins coordinate drug influx (phase 0) and drug/drug-metabolite efflux (phase III). Genes involved in drug metabolism and disposition are induced by xenobiotic-activated nuclear receptors (NRs), i.e. PXR (pregnane X receptor) and CAR (constitutive androstane receptor), and by the 1α, 25-dihydroxy vitamin D3-activated vitamin D receptor (VDR), due to transactivation of xenobiotic-response elements (XREs) present in phase 0-III genes. Additional NRs, like HNF4-α, FXR, LXR-α play important roles in drug metabolism in certain settings, such as in relation to cholesterol and bile acid metabolism. The phase I enzymes CYP3A4/A5, CYP2D6, CYP2B6, CYP2C9, CYP2C19, CYP1A2, CYP2C8, CYP2A6, CYP2J2, and CYP2E1 metabolize >90% of all prescription drugs, and phase II conjugation of hydrophilic functional groups (with/without phase I modification) facilitates drug clearance. The conjugation step is mediated by broad-specificity transferases like UGTs, SULTs, GSTs. This review delves into our current understanding of PXR/CAR/VDR-mediated regulation of DME and transporter expression, as well as effects of single nucleotide polymorphism (SNP) and epigenome (specified by promoter methylation, histone modification, microRNAs, long non coding RNAs) on the expression of PXR/CAR/VDR and phase 0-III mediators, and their impacts on variable drug response. Therapeutic agents that target epigenetic regulation and the molecular basis and consequences (overdosing, underdosing, or beneficial outcome) of drug-drug/drug-food/drug-herb interactions are also discussed. Precision medicine requires understanding of a drug’s impact on DME and transporter activity and their NR-regulated expression in order to achieve optimal drug efficacy without adverse drug reactions. In future drug screening, new tools such as humanized mouse models and

Micro-Environmental Signature of The Interactions between Druggable Target Protein, Dipeptidyl Peptidase-IV, and Anti-Diabetic Drugs.

PubMed

Chakraborty, Chiranjib; Mallick, Bidyut; Sharma, Ashish Ranjan; Sharma, Garima; Jagga, Supriya; Doss, C George Priya; Nam, Ju-Suk; Lee, Sang-Soo

2017-01-01

Druggability of a target protein depends on the interacting micro-environment between the target protein and drugs. Therefore, a precise knowledge of the interacting micro-environment between the target protein and drugs is requisite for drug discovery process. To understand such micro-environment, we performed in silico interaction analysis between a human target protein, Dipeptidyl Peptidase-IV (DPP-4), and three anti-diabetic drugs (saxagliptin, linagliptin and vildagliptin). During the theoretical and bioinformatics analysis of micro-environmental properties, we performed drug-likeness study, protein active site predictions, docking analysis and residual interactions with the protein-drug interface. Micro-environmental landscape properties were evaluated through various parameters such as binding energy, intermolecular energy, electrostatic energy, van der Waals'+H-bond+desolvo energy (E VHD ) and ligand efficiency (LE) using different in silico methods. For this study, we have used several servers and software, such as Molsoft prediction server, CASTp server, AutoDock software and LIGPLOT server. Through micro-environmental study, highest log P value was observed for linagliptin (1.07). Lowest binding energy was also observed for linagliptin with DPP-4 in the binding plot. We also identified the number of H-bonds and residues involved in the hydrophobic interactions between the DPP-4 and the anti-diabetic drugs. During interaction, two H-bonds and nine residues, two H-bonds and eleven residues as well as four H-bonds and nine residues were found between the saxagliptin, linagliptin as well as vildagliptin cases and DPP-4, respectively. Our in silico data obtained for drug-target interactions and micro-environmental signature demonstrates linagliptin as the most stable interacting drug among the tested anti-diabetic medicines.

Microsomal metabolism of calycosin, formononetin and drug-drug interactions by dynamic microdialysis sampling and HPLC-DAD-MS analysis.

PubMed

Wen, Xiao-Dong; Qi, Lian-Wen; Li, Bin; Li, Ping; Yi, Ling; Wang, Ya-Qiong; Liu, E-Hu; Yang, Xiao-Lin

2009-08-15

A dynamic microdialysis sampling method with liquid chromatography-diode array detection and time-of-flight mass spectrometry (LC-DAD-TOF/MS) analysis was developed to investigate rat microsomal metabolisms of calycosin and formononetin, and their drug-drug interactions. Two hydroxylated metabolites from calycosin, and three hydroxylated or 4'-O-demethylated derivatives from formononetin were detected and identified after co-incubation with microsomes. Calibration curves offered linear ranges of two orders of magnitude with r(2)>0.999 for calycosin, formononetin and daidzein. The quantitative LC method provides a range of 0.028-0.034microg/mL for limits of detection, overall precision less than 5% and accuracy less than 3% by RSD. Besides, calycosin and formononetin were found to produce the depressive effect on the CYP450 enzyme reaction, and inhibit phase I enzyme reaction of each other when they are concurrent. Dynamic microdialysis sampling with LC-DAD-TOF/MS analysis developed in this work is a powerful tool for in vitro metabolism studies of drugs and metabolic interactions.

[Designing a tool to describe drug interactions and adverse events for learning and clinical routine].

PubMed

Auzéric, M; Bellemère, J; Conort, O; Roubille, R; Allenet, B; Bedouch, P; Rose, F-X; Juste, M; Charpiat, B

2009-11-01

Pharmacists play an important role in prescription analysis. They are involved in therapeutic drug monitoring, particularly for drugs with a narrow therapeutic index, prevention and management of drug interactions, and may be called in to identify side effects and adverse events related to drug therapy. For the polymedicated patient, the medical file, the list of prescribed drugs and the history of their administration may be insufficient to adequately assign the responsibility of a given adverse effect to one or more drugs. Graphical representations can sometimes be useful to describe and clarify a sequence of events. In addition, as part of their academic course, students have many occasions to hear about "side effects" and "drug interactions". However, in the academic setting, there are few opportunities to observe the evolution and the consequences of these events. In the course of their hospital training, these students are required to perform patient follow-up for pharmacotherapeutic or educational purposes and to comment case reports to physicians. The aim of this paper is to present a tool facilitating the graphic display of drug interaction consequences and side effects. This tool can be a useful aid for causality assessment. It structures the students' training course and helps them better understand the commentaries pharmacists provide for physicians. Further development of this tool should contribute to the prevention of adverse drug events.

Drug interactions with neuromuscular blockers.

PubMed

Feldman, S; Karalliedde, L

1996-10-01

Drugs administered to patients undergoing anaesthesia may complicate the use of the neuromuscular blockers that are given to provide good surgical conditions. The various sites of interaction include actions on motor nerve conduction and spinal reflexes, acetylcholine (ACh) synthesis, mobilisation and release, sensitivity of the motor end plate to ACh and the ease of propagation of the motor action potential. In addition, many drugs affect the pharmacokinetics of neuromuscular blockers, especially as most drugs depend to a greater or lesser extent upon renal excretion. The clinically significant interaction between nondepolarisers and depolarisers may be due to blockade of the pre-synaptic nicotinic receptors by the depolarisers, leading to decreased ACh mobilisation and release. Synergism between nondepolarisers probably results from post-synaptic receptor mechanisms. Volatile anaesthetic agents affect the sensitivity of the motor end-plate (post-synaptic receptor blockade) in addition to having effects on pre-synaptic nicotinic function. The effects of nondepolarisers are likely to be potentiated and their action prolonged by large doses of local anaesthetics due to depression of nerve conduction, depression of ACh formation, mobilisation and release, decreases in post-synaptic receptor channel opening times and reductions in muscular contraction. Most antibacterials have effects on pre-synaptic mechanisms. Procainamide and quinidine principally block nicotinic receptor channels. Magnesium has a marked inhibitory effect on ACh release. Calcium antagonists could theoretically interfere with neurotransmitter release and muscle contractility. Phenytoin and lithium decrease ACh release, whilst corticosteroids and furosemide (frusemide) tend to increase the release of the transmitter. Ecothiopate, tacrine, organophosphates, propanidid, metoclopramide and bambuterol depress cholinesterase activity and prolong the duration of the neuromuscular block. The probability of

Prediction of drug indications based on chemical interactions and chemical similarities.

PubMed

Huang, Guohua; Lu, Yin; Lu, Changhong; Zheng, Mingyue; Cai, Yu-Dong

2015-01-01

Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs.

Prediction of Drug Indications Based on Chemical Interactions and Chemical Similarities

PubMed Central

Huang, Guohua; Lu, Yin; Lu, Changhong; Cai, Yu-Dong

2015-01-01

Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs. PMID:25821813

Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution.

PubMed

Chakrabarti, Shaon; Michor, Franziska

2017-07-15

The identification of optimal drug administration schedules to battle the emergence of resistance is a major challenge in cancer research. The existence of a multitude of resistance mechanisms necessitates administering drugs in combination, significantly complicating the endeavor of predicting the evolutionary dynamics of cancers and optimal intervention strategies. A thorough understanding of the important determinants of cancer evolution under combination therapies is therefore crucial for correctly predicting treatment outcomes. Here we developed the first computational strategy to explore pharmacokinetic and drug interaction effects in evolutionary models of cancer progression, a crucial step towards making clinically relevant predictions. We found that incorporating these phenomena into our multiscale stochastic modeling framework significantly changes the optimum drug administration schedules identified, often predicting nonintuitive strategies for combination therapies. We applied our approach to an ongoing phase Ib clinical trial (TATTON) administering AZD9291 and selumetinib to EGFR-mutant lung cancer patients. Our results suggest that the schedules used in the three trial arms have almost identical efficacies, but slight modifications in the dosing frequencies of the two drugs can significantly increase tumor cell eradication. Interestingly, we also predict that drug concentrations lower than the MTD are as efficacious, suggesting that lowering the total amount of drug administered could lower toxicities while not compromising on the effectiveness of the drugs. Our approach highlights the fact that quantitative knowledge of pharmacokinetic, drug interaction, and evolutionary processes is essential for identifying best intervention strategies. Our method is applicable to diverse cancer and treatment types and allows for a rational design of clinical trials. Cancer Res; 77(14); 3908-21. ©2017 AACR . ©2017 American Association for Cancer Research.

Therapeutic effects of drug-nutrient interactions in the elderly.

PubMed

Roe, D A

1985-02-01

The elderly are the major drug users both because they need specific prescription drugs for control of chronic diseases and because they make excessive use of over-the-counter (OTC) drugs. Therapeutic drugs that are required may be discontinued because the individuals suffer side effects or because the drug is ineffective. Adverse drug reactions in the elderly may result from drug overuse or misuse, slowed drug metabolism or elimination secondary to aging or to age-related chronic disease, intake of alcohol, food-drug incompatibilities, or nutrient-drug interactions. The timing of drug intake in relation to food intake is an important determinant of therapeutic efficacy in the elderly. Food-drug interactions in the gastrointestinal tract may reduce drug absorption. Enteral formula feeding may also interfere with drug absorption. Conversely, absorption of certain drugs (e.g., thiazides) may be promoted by meal-induced slowing of gastric emptying time. Therapeutic diet prescription can influence drug responses in the elderly because the protein composition of the diet influences the rate of drug metabolism. Nutrient depletion secondary to the effect of drugs may be recognized as an important and often avoidable type of adverse drug reaction.

MONITORING POTENTIAL DRUG INTERACTIONS AND REACTIONS VIA NETWORK ANALYSIS OF INSTAGRAM USER TIMELINES

PubMed Central

CORREIA, RION BRATTIG; LI, LANG; ROCHA, LUIS M.

2015-01-01

Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this “Bibliome”, the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products—including cannabis—which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015. We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that

SELF-BLM: Prediction of drug-target interactions via self-training SVM.

PubMed

Keum, Jongsoo; Nam, Hojung

2017-01-01

Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are self-trained and final local classification models are constructed. When applied to four classes of proteins that include enzymes, G-protein coupled receptors (GPCRs), ion channels, and nuclear receptors, SELF-BLM showed the best performance for predicting not only known interactions but also potential interactions in three protein classes compare to other related studies. The implemented software and supporting data are available at https://github.com/GIST-CSBL/SELF-BLM.

Drug-drug interactions involving antidepressants: focus on desvenlafaxine.

PubMed

Low, Yvette; Setia, Sajita; Lima, Graca

2018-01-01

Psychiatric and physical conditions often coexist, and there is robust evidence that associates the frequency of depression with single and multiple physical conditions. More than half of patients with depression may have at least one chronic physical condition. Therefore, antidepressants are often used in cotherapy with other medications for the management of both psychiatric and chronic physical illnesses. The risk of drug-drug interactions (DDIs) is augmented by complex polypharmacy regimens and extended periods of treatment required, of which possible outcomes range from tolerability issues to lack of efficacy and serious adverse events. Optimal patient outcomes may be achieved through drug selection with minimal potential for DDIs. Desvenlafaxine is a serotonin-norepinephrine reuptake inhibitor approved for the treatment of adults with major depressive disorder. Pharmacokinetic studies of desvenlafaxine have shown a simple metabolic profile unique among antidepressants. This review examines the DDI profiles of antidepressants, particularly desvenlafaxine, in relation to drugs of different therapeutic areas. The summary and comparison of information available is meant to help clinicians in making informed decisions when using desvenlafaxine in patients with depression and comorbid chronic conditions.

Drug/Cell-line Browser: interactive canvas visualization of cancer drug/cell-line viability assay datasets.

PubMed

Duan, Qiaonan; Wang, Zichen; Fernandez, Nicolas F; Rouillard, Andrew D; Tan, Christopher M; Benes, Cyril H; Ma'ayan, Avi

2014-11-15

Recently, several high profile studies collected cell viability data from panels of cancer cell lines treated with many drugs applied at different concentrations. Such drug sensitivity data for cancer cell lines provide suggestive treatments for different types and subtypes of cancer. Visualization of these datasets can reveal patterns that may not be obvious by examining the data without such efforts. Here we introduce Drug/Cell-line Browser (DCB), an online interactive HTML5 data visualization tool for interacting with three of the recently published datasets of cancer cell lines/drug-viability studies. DCB uses clustering and canvas visualization of the drugs and the cell lines, as well as a bar graph that summarizes drug effectiveness for the tissue of origin or the cancer subtypes for single or multiple drugs. DCB can help in understanding drug response patterns and prioritizing drug/cancer cell line interactions by tissue of origin or cancer subtype. DCB is an open source Web-based tool that is freely available at: http://www.maayanlab.net/LINCS/DCB CONTACT: avi.maayan@mssm.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

[Terbinafine : Relevant drug interactions and their management].

PubMed

Dürrbeck, A; Nenoff, P

2016-09-01

The allylamine terbinafine is the probably most frequently prescribed systemic antifungal agent in Germany for the treatment of dermatomycoses and onychomycoses. According to the German drug law, terbinafine is approved for patients who are 18 years and older; however, this antifungal agent is increasingly used off-label for treatment of onychomycoses and tinea capitis in children. Terbinafine is associated with only a few interactions with other drugs, which is why terbinafine can generally be used without problems in older and multimorbid patients. Nevertheless, some potential interactions of terbinafine with certain drug substances are known, including substances of the group of antidepressants/antipsychotics and some cardiovascular drugs. Decisive for the relevance of interactions is-along with the therapeutic index of the substrate and the possible alternative degradation pathways-the genetically determined type of metabolism. When combining terbinafine with tricyclic antidepressants or selective serotonin reuptake inhibitors and serotonin/noradrenalin reuptake inhibitors, the clinical response and potential side effects must be monitored. Problematic is the use of terbinafine with simultaneous treatment with tamoxifen. The administration of potent CYP2D6 inhibitors leads to a diminished efficacy of tamoxifen because one of its most important active metabolites-endoxifen-is not sufficiently available. Therefore, combination of tamoxifen and terbinafine should be avoided. In conclusion, the number of substances which are able to cause clinically relevant interactions in case of simultaneously administration with terbinafine is clear and should be manageable in the dermatological office with adequate monitoring.

Relating drug–protein interaction network with drug side effects

PubMed Central

Mizutani, Sayaka; Pauwels, Edouard; Stoven, Véronique; Goto, Susumu; Yamanishi, Yoshihiro

2012-01-01

Motivation: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system–wide approaches for linking different scales of drug actions; namely drug-protein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs. Results: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the co-occurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug–targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles. Supplementary information: Datasets and all results are available at http://web.kuicr.kyoto-u.ac.jp/supp/smizutan/target-effect/. Availability: Software is available at the above supplementary website. Contact: yamanishi@bioreg.kyushu-u.ac.jp, or goto@kuicr.kyoto-u.ac.jp PMID:22962476

Drug interaction of levothyroxine with infant colic drops.

PubMed

Balapatabendi, Mihirani; Harris, David; Shenoy, Savitha D

2011-09-01

Infacol (Forest Laboratories UK, Kent, UK) is a widely available over-the-counter preparation used to relieve colic symptoms in neonates and infants. The active ingredient is simeticone. No drug interactions with simeticone are documented in the current summary of product characteristics. The authors report the case of an infant with confirmed congenital hypothyroidism on levothyroxine who experienced a possible drug interaction with simeticone. Despite adequate levothyroxine dosage, thyroid stimulating hormone (TSH) was high, suggesting undertreatment. Questioning revealed the child was taking Infacol drops before feeds while on levothyroxine. The colic drops were immediately discontinued and TSH promptly normalised with a reduction in thyroxine requirement to an age appropriate dosage. Drug interaction of thyroxine with simeticone has not been reported previously and is not listed in the British National Formulary for Children. Clinicians and parents need to be aware of this interaction to avoid unnecessary undertreatment and prevent potential long-term neurological sequelae.

A Novel Design for Drug-Drug Interaction Alerts Improves Prescribing Efficiency.

PubMed

Russ, Alissa L; Chen, Siying; Melton, Brittany L; Johnson, Elizabette G; Spina, Jeffrey R; Weiner, Michael; Zillich, Alan J

2015-09-01

Drug-drug interactions (DDIs) are common in clinical care and pose serious risks for patients. Electronic health records display DDI alerts that can influence prescribers, but the interface design of DDI alerts has largely been unstudied. In this study, the objective was to apply human factors engineering principles to alert design. It was hypothesized that redesigned DDI alerts would significantly improve prescribers' efficiency and reduce prescribing errors. In a counterbalanced, crossover study with prescribers, two DDI alert designs were evaluated. Department of Veterans Affairs (VA) prescribers were video recorded as they completed fictitious patient scenarios, which included DDI alerts of varying severity. Efficiency was measured from time-stamped recordings. Prescribing errors were evaluated against predefined criteria. Efficiency and prescribing errors were analyzed with the Wilcoxon signed-rank test. Other usability data were collected on the adequacy of alert content, prescribers' use of the DDI monograph, and alert navigation. Twenty prescribers completed patient scenarios for both designs. Prescribers resolved redesigned alerts in about half the time (redesign: 52 seconds versus original design: 97 seconds; p<.001). Prescribing errors were not significantly different between the two designs. Usability results indicate that DDI alerts might be enhanced by facilitating easier access to laboratory data and dosing information and by allowing prescribers to cancel either interacting medication directly from the alert. Results also suggest that neither design provided adequate information for decision making via the primary interface. Applying human factors principles to DDI alerts improved overall efficiency. Aspects of DDI alert design that could be further enhanced prior to implementation were also identified.

Development, validation and utility of an in vitro technique for assessment of potential clinical drug-drug interactions involving P-glycoprotein.

PubMed

Keogh, John P; Kunta, Jeevan R

2006-04-01

Regulatory interest is increasing for drug transporters generally and P-glycoprotein (Pgp) in particular, primarily in the area of drug-drug interactions. To aid in both identifying and discharging the potential liabilities associated with drug-transporter interactions, the pharmaceutical industry has a growing requirement for routine and robust non-clinical assays. An assay was designed, optimised and validated to determine the in vitro inhibitory potency of new chemical entities (NCEs) towards human Pgp-mediated transport. [3H]-Digoxin was established as a suitable probe substrate by investigating its characteristics in the in vitro system (MDCKII-MDR1 cells grown in 24-multiwell inserts). The inhibitory potencies (apparent IC50) of known Pgp inhibitors astemizole, GF120918, ketoconazole, itraconazole, quinidine, verapamil and quinine were determined over at least a 1000-fold concentration range. Validation was carried out using manual and automatic techniques. [3H]-Digoxin was found to be stable and have good mass balance in the system. In contrast to [A-->B] transport, [3H]-digoxin [B-->A] transport rates were readily measured with good reproducibility. There was no evidence of saturation of transport up to 10 microM digoxin and 30 nM digoxin was selected for routine assay use, reflecting clinical therapeutic concentrations. IC50 values ranged over approximately 100-fold with excellent reproducibility. Results from manual and automated versions were in close agreement. This method is suitable for routine use to assess the in vitro inhibitory potency of NCEs on Pgp-mediated digoxin transport. Comparison of IC50 values against clinical interaction profiles for the probe inhibitors indicated the in vitro assay is predictive of clinical digoxin-drug interactions mediated via Pgp.

Food intake attenuates the drug interaction between new quinolones and aluminum.

PubMed

Imaoka, Ayuko; Abiru, Kosuke; Akiyoshi, Takeshi; Ohtani, Hisakazu

2018-01-01

Intestinal absorption of new quinolones is decreased by oral administration of polyvalent metal cations. Some clinical studies have demonstrated this drug - drug interaction is more prominent under fasted condition. However, the effect of food intake on the extent of drug - drug interaction between new quinolones and metal cations remains to be investigated quantitatively and systematically. The aim of this study was to develop an animal model that enables to evaluate the effect of food intake on the extent of drug - drug interaction in the gastrointestinal tract by chelation and to apply the model to evaluate quantitatively the effect of food intake on the drug - drug interaction between two new quinolones, ofloxacin or ciprofloxacin and sucralfate. The rats were orally administered new quinolones (5.3 mg/kg of ofloxacin or 10 mg/kg of ciprofloxacin) with or without 13.3 mg/kg of sucralfate under fasted or fed condition and plasma concentration profiles of new quinolones were monitored. To the fed group, standard breakfast used in human studies was pasted and administered at a dose of 8.8 g/kg. The area under the plasma concentration - time curves (AUC 0-6 ) of ofloxacin and ciprofloxacin under the fasted condition were significantly decreased to 28.8 and 17.1% by co-administration of sucralfate, respectively. On the contrary, sucralfate moderately decreased the AUC 0-6 of ofloxacin and ciprofloxacin to 54.9 and 33.2%, respectively, under fed condition. The effects of sucralfate and food intake on the kinetics of ofloxacin in this study were well consistent with the results of previous clinical trial. The developed animal model quantitatively reproduced the effect of food intake on the drug - drug interaction between ofloxacin and sucralfate. The similar influences were observed for the drug - drug interaction between ciprofloxacin and sucralfate, suggesting that the extent of drug - drug interaction caused by chelation is generally attenuated by food intake.

In vitro drug interaction of levocetirizine and diclofenac: Theoretical and spectroscopic studies.

PubMed

Abo Dena, Ahmed S; Abdel Gaber, Sara A

2017-06-15

Levocetirizine dihydrochloride is known to interact with some anti-inflammatory drugs. We report here a comprehensive integrated theoretical and experimental study for the in vitro drug interaction between levocetirizine dihydrochloride (LEV) and diclofenac sodium (DIC). The interaction of the two drugs was confirmed by the molecular ion peak obtained from the mass spectrum of the product. Moreover, FTIR and 1 HNMR spectra of the individual drugs and their interaction product were inspected to allocate the possible sites of interaction. In addition, quantum mechanical DFT calculations were performed to search for the interaction sites and to verify the types of interactions deduced from the spectroscopic studies such as charge-transfer and non-bonding π-π interactions. It was found that the studied drugs interact with each other in aqueous solution via four types of interactions, namely, ion-pair formation, three weak hydrogen bonds, non-bonding π-π interactions and charge-transfer from DIC to LEV. Copyright © 2017 Elsevier B.V. All rights reserved.

The Two Faces of Social Interaction Reward in Animal Models of Drug Dependence.

PubMed

El Rawas, Rana; Saria, Alois

2016-03-01

Drug dependence is a serious health and social problem. Social factors can modify vulnerability to developing drug dependence, acting as risk factors or protective factors. Whereas stress and peer environment that encourage substance use may increase drug taking, strong attachments between family members and peer environment that do not experience drug use may protect against drug taking and, ultimately, drug dependence. The rewarding effects of drug abuse and social interaction can be evaluated using animal models. In this review we focus on evaluating social interaction reward in the conditioned place preference paradigm. We give an overview of how social interaction, if made available within the drug context, may facilitate, promote and interact with the drug's effects. However, social interaction, if offered alternatively outside the drug context, may have pronounced protective effects against drug abuse and relapse. We also address the importance of the weight difference parameter between the social partners in determining the positive or "agonistic" versus the hostile or "antagonistic" social interaction. We conclude that understanding social interaction reward and its subsequent effects on drug reward is sorely needed for therapeutic interventions against drug dependence.

[Molecular fundamentals of drug interactions in the therapy of colorectal cancer].

PubMed

Regulska, Katarzyna; Stanisz, Beata; Regulski, Miłosz; Gieremek, Paulina

2014-03-04

Rapid advances in the field of chemotherapy have resulted in the introduction of numerous antineoplastic drugs into clinical practice, which increased the efficiency of patient management. Also the prevalent use of combination treatment based on drug action synergy contributed to the improved clinical effect associated with cytotoxic drug administration. It seems, however, obvious that the multidirectional pharmacotherapy in oncology requires a thorough knowledge of drugs' pharmaceutical behavior in order to maximize their collective action and prevent the occurrence of unintended drug interactions that could potentially impair treatment effectiveness. In fact, drug interactions constitute a serious problem for current oncology primarily resulting from a narrow therapeutic index specific for the majority of anticancer drugs. This, in turn, indicates that even slight deviations of their pharmacokinetics could cause significant clinical consequences, manifested by alteration of the toxicological profile or reduction of therapeutic efficiency. Hence, the investigation of molecular aspects underlying the mechanisms of various drug interactions seems to be essential for proper and safe patient management. The present article is devoted to the extensive subject of drug interactions occurring in the therapy of colorectal cancer. It presents the available literature data on both positive and negative effects of interactions and it discusses their mechanisms complying with their classification into pharmacokinetic and pharmacodynamic ones.

The Two Faces of Social Interaction Reward in Animal Models of Drug Dependence

PubMed Central

Rawas, Rana El

2016-01-01

Drug dependence is a serious health and social problem. Social factors can modify vulnerability to developing drug dependence, acting as risk factors or protective factors. Whereas stress and peer environment that encourage substance use may increase drug taking, strong attachments between family members and peer environment that do not experience drug use may protect against drug taking and, ultimately, drug dependence. The rewarding effects of drug abuse and social interaction can be evaluated using animal models. In this review we focus on evaluating social interaction reward in the conditioned place preference paradigm. We give an overview of how social interaction, if made available within the drug context, may facilitate, promote and interact with the drug’s effects. However, social interaction, if offered alternatively outside the drug context, may have pronounced protective effects against drug abuse and relapse. We also address the importance of the weight difference parameter between the social partners in determining the positive or “agonistic” versus the hostile or “antagonistic” social interaction. We conclude that understanding social interaction reward and its subsequent effects on drug reward is sorely needed for therapeutic interventions against drug dependence. PMID:26088685

Drug Interactions of Direct-Acting Oral Anticoagulants.

PubMed

Fitzgerald, John Leonard; Howes, Laurence Guy

2016-09-01

In recent years, new direct-acting oral anticoagulants (DOACs) have been introduced into clinical practice that specifically inhibit either factor Ia or Xa. These drugs have, to a large extent, replaced warfarin for the treatment of venous thrombosis, pulmonary embolism, and non-valvular atrial fibrillation. They have potential advantages over warfarin in providing more stable anticoagulation and the lack of a need for regular venesection to monitor activity. They also have the promise of less drug and food interactions. All of these drugs are substrates for the permeability glycoprotein (P-gp) excretion system, and several are metabolised, in part, by cytochrome P450 (CYP) 3A4. This current article assesses the interactions that do or may occur with the DOACs, particularly with respect to the P-gp and CYP3A4 systems.

Pharmacokinetic drug-drug interaction and their implication in clinical management.

PubMed

Palleria, Caterina; Di Paolo, Antonello; Giofrè, Chiara; Caglioti, Chiara; Leuzzi, Giacomo; Siniscalchi, Antonio; De Sarro, Giovambattista; Gallelli, Luca

2013-07-01

Drug-drug interactions (DDIs) are one of the commonest causes of medication error in developed countries, particularly in the elderly due to poly-therapy, with a prevalence of 20-40%. In particular, poly-therapy increases the complexity of therapeutic management and thereby the risk of clinically important DDIs, which can both induce the development of adverse drug reactions or reduce the clinical efficacy. DDIs can be classify into two main groups: pharmacokinetic and pharmacodynamic. In this review, using Medline, PubMed, Embase, Cochrane library and Reference lists we searched articles published until June 30 2012, and we described the mechanism of pharmacokinetic DDIs focusing the interest on their clinical implications.

Mechanism-based inactivation of human cytochrome P450 enzymes: strategies for diagnosis and drug-drug interaction risk assessment.

PubMed

Venkatakrishnan, K; Obach, R S; Rostami-Hodjegan, A

2007-01-01

Among drugs that cause pharmacokinetic drug-drug interactions, mechanism-based inactivators of cytochrome P450 represent several of those agents that cause interactions of the greatest magnitude. In vitro inactivation kinetic data can be used to predict the potential for new drugs to cause drug interactions in the clinic. However, several factors exist, each with its own uncertainty, that must be taken into account in order to predict the magnitude of interactions reliably. These include aspects of in vitro experimental design, an understanding of relevant in vivo concentrations of the inactivator, and the extent to which the inactivated enzyme is involved in the clearance of the affected drug. Additionally, the rate of enzyme degradation in vivo is also an important factor that needs to be considered in the prediction of the drug interaction magnitudes. To address mechanism-based inactivation for new drugs, various in vitro experimental approaches have been employed. The selection of approaches for in vitro kinetic characterization of inactivation as well as in vitro-in vivo extrapolation should be guided by the purpose of the exercise and the stage of drug discovery and development, with an increase in the level of sophistication throughout the research and development process.

Potential for interaction of kava and St. John's wort with drugs.

PubMed

Singh, Yadhu N

2005-08-22

pharmacokinetic and/or pharmacodynamic interaction between drugs and kava or St. John's wort. This review provides a brief overview of the existing data on interactions of kava and St. John's wort with pharmaceutical agents and as a result reveals the urgent need for detailed investigations to identify clinically significant interactions for these herbal remedies that have the potential to cause adverse effects.

Drug-nutrient interactions in the intensive care unit: literature review and current recommendations.

PubMed

Heldt, Tatiane; Loss, Sergio Henrique

2013-01-01

To describe the interactions between drugs and nutrients and their frequency in the intensive care unit and to assess the professional team's awareness regarding this subject. The keywords "drug interactions" and "nutrition therapy" were searched in the PubMed (specifically MeSH) electronic database. The studies were systematically reviewed for descriptions of the types of interactions between drugs and nutrients, including their frequency and consequences. Sixty-seven articles were found. Among these, 20 articles were appropriate for the methodology adopted and accomplished the objectives of the study. Of these 20 articles, 14 articles described interactions between drugs and enteral nutrition, three described interactions between drugs and parenteral nutrition, and three described the importance and care required to avoid such interactions. The literature about drug and nutrient interactions is limited and suggests the inability of health care teams to recognize the potential for these interactions. Possibly, the elaboration of a protocol to evaluate drug-nutrient interactions will increase the safety and efficacy of therapeutics.

Interaction of cardiac troponin with cardiotonic drugs: a structural perspective.

PubMed

Li, Monica X; Robertson, Ian M; Sykes, Brian D

2008-04-25

Over the 40 years since its discovery, many studies have focused on understanding the role of troponin as a myofilament based molecular switch in regulating the Ca(2+)-dependent activation of striated muscle contraction. Recently, studies have explored the role of cardiac troponin as a target for cardiotonic agents. These drugs are clinically useful for treating heart failure, a condition in which the heart is no longer able to pump enough blood to other organs. These agents act via a mechanism that modulates the Ca(2+)-sensitivity of troponin; such a mode of action is therapeutically desirable because intracellular Ca(2+) concentration is not perturbed, preserving the regulation of other Ca(2+)-based signaling pathways. This review describes molecular details of the interaction of cardiac troponin with a variety of cardiotonic drugs. We present recent structural work that has identified the docking sites of several cardiotonic drugs in the troponin C-troponin I interface and discuss their relevance in the design of troponin based drugs for the treatment of heart disease.

Drug interactions among commonly used medications. Chart simplifies data from critical literature review.

PubMed Central

Crowther, N. R.; Holbrook, A. M.; Kenwright, R.; Kenwright, M.

1997-01-01

OBJECTIVE: To simplify risk assessment, we have developed a way to present critically appraised drug interaction information through a chart. DATA SOURCES: Fifty drugs most frequently prescribed by Canadian family physicians and 16 drugs and substances that frequently interact with these drugs were the basis for a literature review. Drug interaction textbooks and MEDLINE (from 1966 to 1994) were searched for documented interactions. Reports of additive effects and animal or in vitro studies were excluded. STUDY SELECTION: All reports of interactions were evaluated for clinical effect, clinical significance, and quality of evidence. SYNTHESIS: Of the 464 drug-drug or drug-substance pairs evaluated, 387 (83.4%) demonstrated an interaction, 59 (12.7%) documented no effect, and 18 (3.9%) pairs had conflicting evidence. Five percent of interactions were of major clinical significance; only 1.3% were of major clinical significance and supported by good-quality evidence. By using symbols, colours, and legends in a "grid-map" format, a large amount of drug interaction information was reduced to a single-page chart suitable for a desk reference or wall mounting. CONCLUSIONS: Our chart organizes a large amount of drug interaction information in a format that allows for rapid appreciation of outcome, clinical significance, and quality of evidence. PMID:9386884

Hyperforin in St. John's wort drug interactions.

PubMed

Madabushi, Rajanikanth; Frank, Bruno; Drewelow, Bernd; Derendorf, Hartmut; Butterweck, Veronika

2006-03-01

Recently, interactions of herbal medicines with synthetic drugs came into focus of particular interest. In the past 3 years, more than 50 papers were published regarding interactions between St. John's wort (Hypericum perforatum L.; SJW) and prescription drugs. Co-medication with SJW resulted in decreased plasma concentrations of a number of drugs including amitriptyline, cyclosporine, digoxin, indinavir, irinotecan, warfarin, phenprocoumon, alprazolam, dextrometorphane, simvastatin, and oral contraceptives. Sufficient evidence from interaction studies and case reports indicate that SJW is a potent inducer of cytochrome P450 enzymes (particularly CYP3A4) and/or P-glycoprotein. Recent studies could show that the degree of enzyme induction by SJW correlates strongly with the amount of hyperforin found in the product. Products that do not contain substantial amounts of hyperforin (<1%) have not been shown to produce clinically relevant enzyme induction. On the other hand, some evidence suggests that hyperforin may also contribute to the antidepressant activity of SJW. However, clinical studies using SJW preparations with a low hyperforin amount (<1%) clearly demonstrated the superiority of this plant extract over placebo and its equivalence to imipramine and fluoxetine in the treatment of mild to moderate forms of depression. In the present paper clinical significant SJW interactions are critically evaluated against the background of hyperforin.

Drug-target interaction prediction: A Bayesian ranking approach.

PubMed

Peska, Ladislav; Buza, Krisztian; Koller, Júlia

2017-12-01

In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

Studies on Pharmacokinetic Drug Interaction Potential of Vinpocetine

PubMed Central

Manda, Vamshi K.; Avula, Bharathi; Dale, Olivia R.; Chittiboyina, Amar G.; Khan, Ikhlas A.; Walker, Larry A.; Khan, Shabana I.

2015-01-01

Abstract Background Vinpocetine, a semi-synthetic derivative of vincamine, is a popular dietary supplement used for the treatment of several central nervous system related disorders. Despite its wide use, no pharmacokinetic drug interaction studies are reported in the literature. Due to increasing use of dietary supplements in combination with conventional drugs, the risk of adverse effects is on the rise. As a preliminary step to predict a possibility of drug interaction during concomitant use of vinpocetine and conventional drugs, this study was carried out to evaluate the effects of vinpocetine on three main regulators of pharmacokinetic drug interactions namely, cytochromes P450 (CYPs), P-glycoprotein (P-gp), and Pregnane X receptor (PXR). Methods Inhibition of CYPs was evaluated by employing recombinant enzymes. The inhibition of P-gp was determined by calcein-AM uptake method in transfected and wild type MDCKII cells. Modulation of PXR activity was monitored through a reporter gene assay in HepG2 cells. Results Vinpocetine showed a strong inhibition of P-gp (EC50 8 μM) and a moderate inhibition of recombinant CYP3A4 and CYP2D6 (IC50 2.8 and 6.5 μM) with no activity towards CYP2C9, CYP2C19 and CYP1A2 enzymes. In HLM, competitive inhibition of CYP3A4 (IC50 54 and Ki 19 μM) and non-competitive inhibition of CYP2D6 (IC50 19 and Ki 26 μM) was observed. Activation of PXR was observed only at the highest tested concentration of vinpocetine (30 μM) while lower doses were ineffective. Conclusion Strong inhibition of P-gp by vinpocetine is indicative of a possibility of drug interactions by altering the pharmacokinetics of drugs, which are the substrates of P-gp. However, the effects on CYPs and PXR indicate that vinpocetine may not affect CYP-mediated metabolism of drugs, as the inhibitory concentrations are much greater than the expected plasma concentrations in humans. PMID:28930203

Studies on Pharmacokinetic Drug Interaction Potential of Vinpocetine.

PubMed

Manda, Vamshi K; Avula, Bharathi; Dale, Olivia R; Chittiboyina, Amar G; Khan, Ikhlas A; Walker, Larry A; Khan, Shabana I

2015-06-05

Background: Vinpocetine, a semi-synthetic derivative of vincamine, is a popular dietary supplement used for the treatment of several central nervous system related disorders. Despite its wide use, no pharmacokinetic drug interaction studies are reported in the literature. Due to increasing use of dietary supplements in combination with conventional drugs, the risk of adverse effects is on the rise. As a preliminary step to predict a possibility of drug interaction during concomitant use of vinpocetine and conventional drugs, this study was carried out to evaluate the effects of vinpocetine on three main regulators of pharmacokinetic drug interactions namely, cytochromes P450 (CYPs), P-glycoprotein (P-gp), and Pregnane X receptor (PXR). Methods: Inhibition of CYPs was evaluated by employing recombinant enzymes. The inhibition of P-gp was determined by calcein-AM uptake method in transfected and wild type MDCKII cells. Modulation of PXR activity was monitored through a reporter gene assay in HepG2 cells. Results: Vinpocetine showed a strong inhibition of P-gp (EC 50 8 µM) and a moderate inhibition of recombinant CYP3A4 and CYP2D6 (IC 50 2.8 and 6.5 µM) with no activity towards CYP2C9, CYP2C19 and CYP1A2 enzymes. In HLM, competitive inhibition of CYP3A4 (IC 50 54 and K i 19 µM) and non-competitive inhibition of CYP2D6 (IC 50 19 and K i 26 µM) was observed. Activation of PXR was observed only at the highest tested concentration of vinpocetine (30 µM) while lower doses were ineffective. Conclusion: Strong inhibition of P-gp by vinpocetine is indicative of a possibility of drug interactions by altering the pharmacokinetics of drugs, which are the substrates of P-gp. However, the effects on CYPs and PXR indicate that vinpocetine may not affect CYP-mediated metabolism of drugs, as the inhibitory concentrations are much greater than the expected plasma concentrations in humans.

Results of a Doravirine-Atorvastatin Drug-Drug Interaction Study.

PubMed

Khalilieh, Sauzanne; Yee, Ka Lai; Sanchez, Rosa I; Triantafyllou, Ilias; Fan, Li; Maklad, Noha; Jordan, Heather; Martell, Maureen; Iwamoto, Marian

2017-02-01

Doravirine is a novel, highly potent, nonnucleoside reverse transcriptase inhibitor that is administered once daily and that is in development for the treatment of HIV-1 infection. In vitro and clinical data suggest that doravirine is unlikely to cause significant drug-drug interactions via major drug-metabolizing enzymes or transporters. As a common HIV-1 infection comorbidity, hypercholesterolemia is often treated with statins, including the commonly prescribed atorvastatin. Atorvastatin is subject to drug-drug interactions with cytochrome P450 3A4 (CYP3A4) inhibitors. Increased exposure due to CYP3A4 inhibition may lead to serious adverse events (AEs), including rhabdomyolysis. Furthermore, atorvastatin is a substrate for breast cancer resistance protein (BCRP), of which doravirine may be a weak inhibitor; this may increase atorvastatin exposure. The potential of doravirine to affect atorvastatin pharmacokinetics was investigated in a two-period, fixed-sequence study in healthy individuals. In period 1, a single dose of atorvastatin at 20 mg was administered followed by a 72-h washout. In period 2, doravirine at 100 mg was administered once daily for 8 days, with a single dose of atorvastatin at 20 mg concomitantly being administered on day 5. Sixteen subjects were enrolled, and 14 completed the trial; 2 discontinued due to AEs unrelated to the treatment. The atorvastatin area under the curve from time zero to infinity was similar with and without doravirine (geometric mean ratio [GMR] for doravirine-atorvastatin/atorvastatin, 0.98; 90% confidence interval [CI], 0.90 to 1.06), while the maximum concentration decreased by 33% (GMR for doravirine-atorvastatin/atorvastatin, 0.67; 90% CI, 0.52 to 0.85). These changes were deemed not to be clinically meaningful. Both of the study drugs were generally well tolerated. Doravirine had no clinically relevant effect on atorvastatin pharmacokinetics in healthy subjects, providing support for the coadministration of

Results of a Doravirine-Atorvastatin Drug-Drug Interaction Study

PubMed Central

Yee, Ka Lai; Sanchez, Rosa I.; Triantafyllou, Ilias; Fan, Li; Maklad, Noha; Jordan, Heather; Martell, Maureen; Iwamoto, Marian

2016-01-01

ABSTRACT Doravirine is a novel, highly potent, nonnucleoside reverse transcriptase inhibitor that is administered once daily and that is in development for the treatment of HIV-1 infection. In vitro and clinical data suggest that doravirine is unlikely to cause significant drug-drug interactions via major drug-metabolizing enzymes or transporters. As a common HIV-1 infection comorbidity, hypercholesterolemia is often treated with statins, including the commonly prescribed atorvastatin. Atorvastatin is subject to drug-drug interactions with cytochrome P450 3A4 (CYP3A4) inhibitors. Increased exposure due to CYP3A4 inhibition may lead to serious adverse events (AEs), including rhabdomyolysis. Furthermore, atorvastatin is a substrate for breast cancer resistance protein (BCRP), of which doravirine may be a weak inhibitor; this may increase atorvastatin exposure. The potential of doravirine to affect atorvastatin pharmacokinetics was investigated in a two-period, fixed-sequence study in healthy individuals. In period 1, a single dose of atorvastatin at 20 mg was administered followed by a 72-h washout. In period 2, doravirine at 100 mg was administered once daily for 8 days, with a single dose of atorvastatin at 20 mg concomitantly being administered on day 5. Sixteen subjects were enrolled, and 14 completed the trial; 2 discontinued due to AEs unrelated to the treatment. The atorvastatin area under the curve from time zero to infinity was similar with and without doravirine (geometric mean ratio [GMR] for doravirine-atorvastatin/atorvastatin, 0.98; 90% confidence interval [CI], 0.90 to 1.06), while the maximum concentration decreased by 33% (GMR for doravirine-atorvastatin/atorvastatin, 0.67; 90% CI, 0.52 to 0.85). These changes were deemed not to be clinically meaningful. Both of the study drugs were generally well tolerated. Doravirine had no clinically relevant effect on atorvastatin pharmacokinetics in healthy subjects, providing support for the coadministration of

Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study

PubMed Central

Ning, Yifan; Hernandez, Andres; Horn, John R; Jacobson, Rebecca; Boyce, Richard D

2016-01-01

Background Because vital details of potential pharmacokinetic drug-drug interactions are often described in free-text structured product labels, manual curation is a necessary but expensive step in the development of electronic drug-drug interaction information resources. The use of nonexperts to annotate potential drug-drug interaction (PDDI) mentions in drug product label annotation may be a means of lessening the burden of manual curation. Objective Our goal was to explore the practicality of using nonexpert participants to annotate drug-drug interaction descriptions from structured product labels. By presenting annotation tasks to both pharmacy experts and relatively naïve participants, we hoped to demonstrate the feasibility of using nonexpert annotators for drug-drug information annotation. We were also interested in exploring whether and to what extent natural language processing (NLP) preannotation helped improve task completion time, accuracy, and subjective satisfaction. Methods Two experts and 4 nonexperts were asked to annotate 208 structured product label sections under 4 conditions completed sequentially: (1) no NLP assistance, (2) preannotation of drug mentions, (3) preannotation of drug mentions and PDDIs, and (4) a repeat of the no-annotation condition. Results were evaluated within the 2 groups and relative to an existing gold standard. Participants were asked to provide reports on the time required to complete tasks and their perceptions of task difficulty. Results One of the experts and 3 of the nonexperts completed all tasks. Annotation results from the nonexpert group were relatively strong in every scenario and better than the performance of the NLP pipeline. The expert and 2 of the nonexperts were able to complete most tasks in less than 3 hours. Usability perceptions were generally positive (3.67 for expert, mean of 3.33 for nonexperts). Conclusions The results suggest that nonexpert annotation might be a feasible option for comprehensive

Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study.

PubMed

Hochheiser, Harry; Ning, Yifan; Hernandez, Andres; Horn, John R; Jacobson, Rebecca; Boyce, Richard D

2016-04-11

Because vital details of potential pharmacokinetic drug-drug interactions are often described in free-text structured product labels, manual curation is a necessary but expensive step in the development of electronic drug-drug interaction information resources. The use of nonexperts to annotate potential drug-drug interaction (PDDI) mentions in drug product label annotation may be a means of lessening the burden of manual curation. Our goal was to explore the practicality of using nonexpert participants to annotate drug-drug interaction descriptions from structured product labels. By presenting annotation tasks to both pharmacy experts and relatively naïve participants, we hoped to demonstrate the feasibility of using nonexpert annotators for drug-drug information annotation. We were also interested in exploring whether and to what extent natural language processing (NLP) preannotation helped improve task completion time, accuracy, and subjective satisfaction. Two experts and 4 nonexperts were asked to annotate 208 structured product label sections under 4 conditions completed sequentially: (1) no NLP assistance, (2) preannotation of drug mentions, (3) preannotation of drug mentions and PDDIs, and (4) a repeat of the no-annotation condition. Results were evaluated within the 2 groups and relative to an existing gold standard. Participants were asked to provide reports on the time required to complete tasks and their perceptions of task difficulty. One of the experts and 3 of the nonexperts completed all tasks. Annotation results from the nonexpert group were relatively strong in every scenario and better than the performance of the NLP pipeline. The expert and 2 of the nonexperts were able to complete most tasks in less than 3 hours. Usability perceptions were generally positive (3.67 for expert, mean of 3.33 for nonexperts). The results suggest that nonexpert annotation might be a feasible option for comprehensive labeling of annotated PDDIs across a broader

Identifying drugs that cause acute thrombocytopenia: an analysis using 3 distinct methods

PubMed Central

Reese, Jessica A.; Li, Xiaoning; Hauben, Manfred; Aster, Richard H.; Bougie, Daniel W.; Curtis, Brian R.; George, James N.

2010-01-01

Drug-induced immune thrombocytopenia (DITP) is often suspected in patients with acute thrombocytopenia unexplained by other causes, but documenting that a drug is the cause of thrombocytopenia can be challenging. To provide a resource for diagnosis of DITP and for drug safety surveillance, we analyzed 3 distinct methods for identifying drugs that may cause thrombocytopenia. (1) Published case reports of DITP have described 253 drugs suspected of causing thrombocytopenia; using defined clinical criteria, 87 (34%) were identified with evidence that the drug caused thrombocytopenia. (2) Serum samples from patients with suspected DITP were tested for 202 drugs; drug-dependent, platelet-reactive antibodies were identified for 67 drugs (33%). (3) The Food and Drug Administration's Adverse Event Reporting System database was searched for drugs associated with thrombocytopenia by use of data mining algorithms; 1444 drugs had at least 1 report associated with thrombocytopenia, and 573 (40%) drugs demonstrated a statistically distinctive reporting association with thrombocytopenia. Among 1468 drugs suspected of causing thrombocytopenia, 102 were evaluated by all 3 methods, and 23 of these 102 drugs had evidence for an association with thrombocytopenia by all 3 methods. Multiple methods, each with a distinct perspective, can contribute to the identification of drugs that can cause thrombocytopenia. PMID:20530792

Core drug-drug interaction alerts for inclusion in pediatric electronic health records with computerized prescriber order entry.

PubMed

Harper, Marvin B; Longhurst, Christopher A; McGuire, Troy L; Tarrago, Rod; Desai, Bimal R; Patterson, Al

2014-03-01

The study aims to develop a core set of pediatric drug-drug interaction (DDI) pairs for which electronic alerts should be presented to prescribers during the ordering process. A clinical decision support working group composed of Children's Hospital Association (CHA) members was developed. CHA Pharmacists and Chief Medical Information Officers participated. Consensus was reached on a core set of 19 DDI pairs that should be presented to pediatric prescribers during the order process. We have provided a core list of 19 high value drug pairs for electronic drug-drug interaction alerts to be recommended for inclusion as high value alerts in prescriber order entry software used with a pediatric patient population. We believe this list represents the most important pediatric drug interactions for practical implementation within computerized prescriber order entry systems.

Stress, alcohol and drug interaction: an update of human research.

PubMed

Uhart, Magdalena; Wand, Gary S

2009-01-01

A challenging question that continues unanswered in the field of addiction is why some individuals are more vulnerable to substance use disorders than others. Numerous risk factors for alcohol and other drugs of abuse, including exposure to various forms of stress, have been identified in clinical studies. However, the neurobiological mechanisms that underlie this relationship remain unclear. Critical neurotransmitters, hormones and neurobiological sites have been recognized, which may provide the substrates that convey individual differences in vulnerability to addiction. With the advent of more sophisticated measures of brain function in humans, such as functional imaging technology, the mechanisms and neural pathways involved in the interactions between drugs of abuse, the mesocorticolimbic dopamine system and stress systems are beginning to be characterized. This review provides a neuroadaptive perspective regarding the role of the hormonal and brain stress systems in drug addiction with a focus on the changes that occur during the transition from occasional drug use to drug dependence. We also review factors that contribute to different levels of hormonal/brain stress activation, which has implications for understanding individual vulnerability to drug dependence. Ultimately, these efforts may improve our chances of designing treatment strategies that target addiction at the core of the disorder.

Drug-Gut Microbiota Interactions: Implications for Neuropharmacology.

PubMed

Walsh, Jacinta; Griffin, Brendan T; Clarke, Gerard; Hyland, Niall P

2018-05-21

The fate and activity of drugs are frequently dictated not only by the host per se but also by the microorganisms present in the gastrointestinal tract. The gut microbiome is known to, both directly and indirectly, affect drug metabolism. More evidence now hints at the impact that drugs can have on the function and composition of the gut microbiome. Both microbiota-mediated alterations in drug metabolism and drug-mediated alterations in the gut microbiome can have beneficial or detrimental effects on the host. Greater insights into the mechanisms driving these reciprocal drug-gut microbiota interactions are needed, to guide the development of microbiome-targeted dietary or pharmacological interventions, with the potential to enhance drug efficacy or reduce drug side-effects. In this review, we explore the relationship between drugs and the gut microbiome, with a specific focus on potential mechanisms underpinning the drug-mediated alterations on the gut microbiome and the potential implications for psychoactive drugs. This article is protected by copyright. All rights reserved.

Pharmacokinetic Interactions between Drugs and Botanical Dietary Supplements.

PubMed

Sprouse, Alyssa A; van Breemen, Richard B

2016-02-01

The use of botanical dietary supplements has grown steadily over the last 20 years despite incomplete information regarding active constituents, mechanisms of action, efficacy, and safety. An important but underinvestigated safety concern is the potential for popular botanical dietary supplements to interfere with the absorption, transport, and/or metabolism of pharmaceutical agents. Clinical trials of drug-botanical interactions are the gold standard and are usually carried out only when indicated by unexpected consumer side effects or, preferably, by predictive preclinical studies. For example, phase 1 clinical trials have confirmed preclinical studies and clinical case reports that St. John's wort (Hypericum perforatum) induces CYP3A4/CYP3A5. However, clinical studies of most botanicals that were predicted to interact with drugs have shown no clinically significant effects. For example, clinical trials did not substantiate preclinical predictions that milk thistle (Silybum marianum) would inhibit CYP1A2, CYP2C9, CYP2D6, CYP2E1, and/or CYP3A4. Here, we highlight discrepancies between preclinical and clinical data concerning drug-botanical interactions and critically evaluate why some preclinical models perform better than others in predicting the potential for drug-botanical interactions. Gaps in knowledge are also highlighted for the potential of some popular botanical dietary supplements to interact with therapeutic agents with respect to absorption, transport, and metabolism. Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.

In Silico Repositioning-Chemogenomics Strategy Identifies New Drugs with Potential Activity against Multiple Life Stages of Schistosoma mansoni

PubMed Central

Neves, Bruno J.; Braga, Rodolpho C.; Bezerra, José C. B.; Cravo, Pedro V. L.; Andrade, Carolina H.

2015-01-01

Morbidity and mortality caused by schistosomiasis are serious public health problems in developing countries. Because praziquantel is the only drug in therapeutic use, the risk of drug resistance is a concern. In the search for new schistosomicidal drugs, we performed a target-based chemogenomics screen of a dataset of 2,114 proteins to identify drugs that are approved for clinical use in humans that may be active against multiple life stages of Schistosoma mansoni. Each of these proteins was treated as a potential drug target, and its amino acid sequence was used to interrogate three databases: Therapeutic Target Database (TTD), DrugBank and STITCH. Predicted drug-target interactions were refined using a combination of approaches, including pairwise alignment, conservation state of functional regions and chemical space analysis. To validate our strategy, several drugs previously shown to be active against Schistosoma species were correctly predicted, such as clonazepam, auranofin, nifedipine, and artesunate. We were also able to identify 115 drugs that have not yet been experimentally tested against schistosomes and that require further assessment. Some examples are aprindine, gentamicin, clotrimazole, tetrabenazine, griseofulvin, and cinnarizine. In conclusion, we have developed a systematic and focused computer-aided approach to propose approved drugs that may warrant testing and/or serve as lead compounds for the design of new drugs against schistosomes. PMID:25569258

Drug utilization, prescription errors and potential drug-drug interactions: an experience in rural Sri Lanka.

PubMed

Rathish, Devarajan; Bahini, Sivaswamy; Sivakumar, Thanikai; Thiranagama, Thilani; Abarajithan, Tharmarajah; Wijerathne, Buddhika; Jayasumana, Channa; Siribaddana, Sisira

2016-06-25

Prescription writing is a process which transfers the therapeutic message from the prescriber to the patient through the pharmacist. Prescribing errors, drug duplication and potential drug-drug interactions (pDDI) in prescriptions lead to medication error. Assessment of the above was made in prescriptions dispensed at State Pharmaceutical Corporation (SPC), Anuradhapura, Sri Lanka. A cross sectional study was conducted. Drugs were classified according to the WHO anatomical, therapeutic chemical classification system. A three point Likert scale, a checklist and Medscape online drug interaction checker were used to assess legibility, completeness and pDDIs respectively. Thousand prescriptions were collected. Majority were hand written (99.8 %) and from the private sector (73 %). The most frequently prescribed substance and subgroup were atorvastatin (4 %, n = 3668) and proton pump inhibitors (7 %, n = 3668) respectively. Out of the substances prescribed from the government and private sectors, 59 and 50 % respectively were available in the national list of essential medicines, Sri Lanka. Patients address (5 %), Sri Lanka Medical Council (SLMC) registration number (35 %), route (7 %), generic name (16 %), treatment symbol (48 %), diagnosis (41 %) and refill information (6 %) were seen in less than half of the prescriptions. Most were legible with effort (65 %) and illegibility was seen in 9 %. There was significant difference in omission and/or errors of generic name (P = 0.000), dose (P = 0.000), SLMC registration number (P = 0.000), and in evidence of pDDI (P = 0.009) with regards to the sector of prescribing. The commonest subgroup involved in duplication was non-steroidal anti-inflammatory drugs (NSAIDs) (43 %; 56/130). There were 1376 potential drug interactions (466/887 prescriptions). Most common pair causing pDDI was aspirin with losartan (4 %, n = 1376). Atorvastatin was the most frequently prescribed substance

An Overview of the Evidence and Mechanisms of Herb–Drug Interactions

PubMed Central

Fasinu, Pius S.; Bouic, Patrick J.; Rosenkranz, Bernd

2012-01-01

Despite the lack of sufficient information on the safety of herbal products, their use as alternative and/or complementary medicine is globally popular. There is also an increasing interest in medicinal herbs as precursor for pharmacological actives. Of serious concern is the concurrent consumption of herbal products and conventional drugs. Herb–drug interaction (HDI) is the single most important clinical consequence of this practice. Using a structured assessment procedure, the evidence of HDI presents with varying degree of clinical significance. While the potential for HDI for a number of herbal products is inferred from non-human studies, certain HDIs are well established through human studies and documented case reports. Various mechanisms of pharmacokinetic HDI have been identified and include the alteration in the gastrointestinal functions with consequent effects on drug absorption; induction and inhibition of metabolic enzymes and transport proteins; and alteration of renal excretion of drugs and their metabolites. Due to the intrinsic pharmacologic properties of phytochemicals, pharmacodynamic HDIs are also known to occur. The effects could be synergistic, additive, and/or antagonistic. Poor reporting on the part of patients and the inability to promptly identify HDI by health providers are identified as major factors limiting the extensive compilation of clinically relevant HDIs. A general overview and the significance of pharmacokinetic and pharmacodynamic HDI are provided, detailing basic mechanism, and nature of evidence available. An increased level of awareness of HDI is necessary among health professionals and drug discovery scientists. With the increasing number of plant-sourced pharmacological actives, the potential for HDI should always be assessed in the non-clinical safety assessment phase of drug development process. More clinically relevant research is also required in this area as current information on HDI is insufficient for clinical

Theoretical and experimental investigation of drug-polymer interaction and miscibility and its impact on drug supersaturation in aqueous medium.

PubMed

Baghel, Shrawan; Cathcart, Helen; O'Reilly, Niall J

2016-10-01

Amorphous solid dispersions (ASDs) have the potential to offer higher apparent solubility and bioavailability of BCS class II drugs. Knowledge of the solid state drug-polymer solubility/miscibility and their mutual interaction are fundamental requirements for the effective design and development of such systems. To this end, we have carried out a comprehensive investigation of various ASD systems of dipyridamole and cinnarizine in polyvinylpyrrolidone (PVP) and polyacrylic acid (PAA) at different drug loadings. Theoretical and experimental examinations (by implementing binary and ternary Flory-Huggins (F-H) theory) related to drug-polymer interaction/miscibility including solubility parameter approach, melting point depression method, phase diagram, drug-polymer interaction in the presence of moisture and the effect of drug loading on interaction parameter were performed. The information obtained from this study was used to predict the stability of ASDs at different drug loadings and under different thermal and moisture conditions. Thermal and moisture sorption analysis not only provided the composition-dependent interaction parameter but also predicted the composition dependent miscibility. DPM-PVP, DPM-PAA and CNZ-PAA systems have shown molecular level mixing over the complete range of drug loading. For CNZ-PVP, the presence of a single Tg at lower drug loadings (10, 20 and 35%w/w) indicates the formation of solid solution. However, drug recrystallization was observed for samples with higher drug weight fractions (50 and 65%w/w). Finally, the role of polymer in maintaining drug supersaturation has also been explored. It has been found that drug-polymer combinations capable of hydrogen-bonding in the solution state (DPM-PVP, DPM-PAA and CNZ-PAA) are more effective in preventing drug crystallization compared to the drug-polymer systems without such interaction (CNZ-PVP). The DPM-PAA system outperformed all other ASDs in various stability conditions (dry-state, in

Developing strategies for predicting hyperkalemia in potassium-increasing drug-drug interactions.

PubMed

Eschmann, Emmanuel; Beeler, Patrick Emanuel; Schneemann, Markus; Blaser, Jürg

2017-01-01

To compare different strategies predicting hyperkalemia (serum potassium level ≥5.5 mEq/l) in hospitalized patients for whom medications triggering potassium-increasing drug-drug interactions (DDIs) were ordered. We investigated 5 strategies that combined prediction triggered at onset of DDI versus continuous monitoring and taking into account an increasing number of patient parameters. The considered patient parameters were identified using generalized additive models, and the thresholds of the prediction strategies were calculated by applying Youden's J statistic to receiver operation characteristic curves. Half of the data served as the calibration set, half as the validation set. We identified 132 incidences of hyperkalemia induced by 8413 potentially severe potassium-increasing DDIs among 76 467 patients. The positive predictive value (PPV) of those strategies predicting hyperkalemia at the onset of DDI ranged from 1.79% (undifferentiated anticipation of hyperkalemia due to the DDI) to 3.02% (additionally considering the baseline serum potassium) and 3.10% (including further patient parameters). Continuous monitoring significantly increased the PPV to 8.25% (considering the current serum potassium) and 9.34% (additional patient parameters). Continuous monitoring of the risk for hyperkalemia based on current potassium level shows a better predictive power than predictions triggered at the onset of DDI. This contrasts with efforts to improve DDI alerts by taking into account more patient parameters at the time of ordering. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Drug-nutrient interactions in the intensive care unit: literature review and current recommendations

PubMed Central

Heldt, Tatiane; Loss, Sergio Henrique

2013-01-01

Objective To describe the interactions between drugs and nutrients and their frequency in the intensive care unit and to assess the professional team's awareness regarding this subject. Methods The keywords "drug interactions" and "nutrition therapy" were searched in the PubMed (specifically MeSH) electronic database. The studies were systematically reviewed for descriptions of the types of interactions between drugs and nutrients, including their frequency and consequences. Results Sixty-seven articles were found. Among these, 20 articles were appropriate for the methodology adopted and accomplished the objectives of the study. Of these 20 articles, 14 articles described interactions between drugs and enteral nutrition, three described interactions between drugs and parenteral nutrition, and three described the importance and care required to avoid such interactions. Conclusions The literature about drug and nutrient interactions is limited and suggests the inability of health care teams to recognize the potential for these interactions. Possibly, the elaboration of a protocol to evaluate drug-nutrient interactions will increase the safety and efficacy of therapeutics. PMID:23917982

2010 drug packaging review: identifying problems to prevent errors.

PubMed

2011-06-01

Prescrire's analyses showed that the quality of drug packaging in 2010 still left much to be desired. Potentially dangerous packaging remains a significant problem: unclear labelling is source of medication errors; dosing devices for some psychotropic drugs create a risk of overdose; child-proof caps are often lacking; and too many patient information leaflets are misleading or difficult to understand. Everything that is needed for safe drug packaging is available; it is now up to regulatory agencies and drug companies to act responsibly. In the meantime, health professionals can help their patients by learning to identify the pitfalls of drug packaging and providing safe information to help prevent medication errors.

Pharmacokinetic drug-drug interaction and their implication in clinical management

PubMed Central

Palleria, Caterina; Di Paolo, Antonello; Giofrè, Chiara; Caglioti, Chiara; Leuzzi, Giacomo; Siniscalchi, Antonio; De Sarro, Giovambattista; Gallelli, Luca

2013-01-01

Drug-drug interactions (DDIs) are one of the commonest causes of medication error in developed countries, particularly in the elderly due to poly-therapy, with a prevalence of 20-40%. In particular, poly-therapy increases the complexity of therapeutic management and thereby the risk of clinically important DDIs, which can both induce the development of adverse drug reactions or reduce the clinical efficacy. DDIs can be classify into two main groups: pharmacokinetic and pharmacodynamic. In this review, using Medline, PubMed, Embase, Cochrane library and Reference lists we searched articles published until June 30 2012, and we described the mechanism of pharmacokinetic DDIs focusing the interest on their clinical implications. PMID:24516494

Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration.

PubMed

Wu, Guangsheng; Liu, Juan; Wang, Caihua

2017-12-28

Prediction of drug-disease interactions is promising for either drug repositioning or disease treatment fields. The discovery of novel drug-disease interactions, on one hand can help to find novel indictions for the approved drugs; on the other hand can provide new therapeutic approaches for the diseases. Recently, computational methods for finding drug-disease interactions have attracted lots of attention because of their far more higher efficiency and lower cost than the traditional wet experiment methods. However, they still face several challenges, such as the organization of the heterogeneous data, the performance of the model, and so on. In this work, we present to hierarchically integrate the heterogeneous data into three layers. The drug-drug and disease-disease similarities are first calculated separately in each layer, and then the similarities from three layers are linearly fused into comprehensive drug similarities and disease similarities, which can then be used to measure the similarities between two drug-disease pairs. We construct a novel weighted drug-disease pair network, where a node is a drug-disease pair with known or unknown treatment relation, an edge represents the node-node relation which is weighted with the similarity score between two pairs. Now that similar drug-disease pairs are supposed to show similar treatment patterns, we can find the optimal graph cut of the network. The drug-disease pair with unknown relation can then be considered to have similar treatment relation with that within the same cut. Therefore, we develop a semi-supervised graph cut algorithm, SSGC, to find the optimal graph cut, based on which we can identify the potential drug-disease treatment interactions. By comparing with three representative network-based methods, SSGC achieves the highest performances, in terms of both AUC score and the identification rates of true drug-disease pairs. The experiments with different integration strategies also demonstrate that

Dendrimers in drug delivery and targeting: Drug-dendrimer interactions and toxicity issues

PubMed Central

Madaan, Kanika; Kumar, Sandeep; Poonia, Neelam; Lather, Viney; Pandita, Deepti

2014-01-01

Dendrimers are the emerging polymeric architectures that are known for their defined structures, versatility in drug delivery and high functionality whose properties resemble with biomolecules. These nanostructured macromolecules have shown their potential abilities in entrapping and/or conjugating the high molecular weight hydrophilic/hydrophobic entities by host-guest interactions and covalent bonding (prodrug approach) respectively. Moreover, high ratio of surface groups to molecular volume has made them a promising synthetic vector for gene delivery. Owing to these properties dendrimers have fascinated the researchers in the development of new drug carriers and they have been implicated in many therapeutic and biomedical applications. Despite of their extensive applications, their use in biological systems is limited due to toxicity issues associated with them. Considering this, the present review has focused on the different strategies of their synthesis, drug delivery and targeting, gene delivery and other biomedical applications, interactions involved in formation of drug-dendrimer complex along with characterization techniques employed for their evaluation, toxicity problems and associated approaches to alleviate their inherent toxicity. PMID:25035633

Drug Interactions with Clinafloxacin

PubMed Central

Randinitis, Edward J.; Alvey, Christine W.; Koup, Jeffery R.; Rausch, George; Abel, Robert; Bron, Nicola J.; Hounslow, Neil J.; Vassos, Artemios B.; Sedman, Allen J.

2001-01-01

Many fluoroquinolone antibiotics are inhibitors of cytochrome P450 enzyme systems and may produce potentially important drug interactions when administered with other drugs. Studies were conducted to determine the effect of clinafloxacin on the pharmacokinetics of theophylline, caffeine, warfarin, and phenytoin, as well as the effect of phenytoin on the pharmacokinetics of clinafloxacin. Concomitant administration of 200 or 400 mg of clinafloxacin reduces mean theophylline clearance by approximately 50 and 70%, respectively, and reduces mean caffeine clearance by 84%. (R)-Warfarin concentrations in plasma during clinafloxacin administration are 32% higher and (S)-warfarin concentrations do not change during clinafloxacin treatment. An observed late pharmacodynamic effect was most likely due to gut flora changes. Phenytoin has no effect on clinafloxacin pharmacokinetics, while phenytoin clearance is 15% lower during clinafloxacin administration. PMID:11502527

Individual variability in clinical effect and tolerability of opioid analgesics - Importance of drug interactions and pharmacogenetics.

PubMed

Solhaug, Vigdis; Molden, Espen

2017-10-01

As pain is often a comorbid condition, many patients use opioid analgesics in combination with several other drugs. This implies a generally increased risk of drug interactions, which along with inherent pharmacogenetic variability and other factors may cause differences in therapeutic response of opioids. To provide an overview of interactions and pharmacogenetic variability of relevance for individual differences in effect and tolerability of opioid analgesics, which physicians and other healthcare professionals should be aware of in clinical practice. The article was based on unsystematic searches in PubMed to identify literature highlighting the clinical impact of drug interactions and pharmacogenetics as sources of variable response of opioid analgesics. Cytochrome P450 (CYP)-mediated metabolism is an important process for both clinically relevant interactions and pharmacogenetic variability of several opioids. Concomitant use of CYP inhibitors (e.g. paroxetine, fluoxetine and bupropion) or inducers (e.g. carbamazepine, phenobarbital and phenytoin) could counteract the clinical effect or trigger side effects of analgesics in the same manner as genetically determined differences in CYP2D6-mediated metabolism of many opioids. Moreover, combination treatment with drugs that inhibit or induce P-glycoprotein (ABCB1), a blood-brain barrier efflux transporter, may alter the amount ('dose') of opioids distributed to the brain. At the pharmacodynamic level, it is crucial to be aware of the potential risk of interaction causing serotonergic syndrome when combining opioids and serotonergic drugs, in particular antidepressants inhibiting serotonin reuptake (SSRIs and SNRIs). Regarding pharmacogenetics at the receptor level of pain treatment, the knowledge is currently scarce, but an allelic variant of the μ1 opioid receptor (OPRM1) gene has been associated with higher dosage requirement to achieve analgesia. Drug interactions and pharmacogenetic differences may lead to

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2017-10-01

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive. This motivates us to derive a reduced feature set for prediction. In addition, since ensemble learning techniques are widely used to improve the classification performance, it is also worthwhile to design an ensemble learning framework to enhance the performance for drug-target interaction prediction. In this paper, we propose a framework for drug-target interaction prediction leveraging both feature dimensionality reduction and ensemble learning. First, we conducted feature subspacing to inject diversity into the classifier ensemble. Second, we applied three different dimensionality reduction methods to the subspaced features. Third, we trained homogeneous base learners with the reduced features and then aggregated their scores to derive the final predictions. For base learners, we selected two classifiers, namely Decision Tree and Kernel Ridge Regression, resulting in two variants of ensemble models, EnsemDT and EnsemKRR, respectively. In our experiments, we utilized AUC (Area under ROC Curve) as an evaluation metric. We compared our proposed methods with various state-of-the-art methods under 5-fold cross validation. Experimental results showed EnsemKRR achieving the highest AUC (94.3%) for predicting drug-target interactions. In addition, dimensionality reduction helped improve the performance of EnsemDT. In conclusion, our proposed methods produced significant improvements for drug-target interaction prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

Drug-nutrient interactions: a case and clinical guide.

PubMed

Plotnikoff, Gregory A

2011-10-01

Advances in pharmacokinetics and pharmacodynamics require new competencies related to pharmaceutical prescribing. First, both physicians and pharmacists need to recognize the potential negative impact of nutrients and dietary supplements on the absorption, metabolism, and utilization of prescription drugs. Second, physicians, even more than pharmacists, need to recognize the potential negative effects of pharmaceuticals on the absorption, metabolism, and utilization of nutrients. This article discusses common drug-nutrient interactions and presents a case that illustrates how unrecognized nutrient disruption may negatively affect a patient's health and potentially result in unnecessary prescribing of medications. In presenting the case, we also provide a conceptual framework for assessing and treating this patient and a summary of current knowledge regarding drug-nutrient interactions.

MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development | Office of Cancer Genomics

Cancer.gov

Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology.

OTC analgesics and drug interactions: clinical implications

PubMed Central

Fendrick, A Mark; Pan, Deborah E; Johnson, Grace E

2008-01-01

The risk of drug interactions with concurrent use of multiple medications is a clinically relevant issue. Many patients are unaware that over-the-counter (OTC) analgesics can cause potentially serious adverse effects when used in combination with other common medications such as anticoagulants, corticosteroids, or antihypertensive agents. Of particular significance is the increased risk of upper abdominal gastrointestinal adverse events in patients who take traditional nonsteroidal anti-inflammatory drugs (NSAIDs). This risk is dose dependent and further increased in patients who take more than one NSAID or use NSAIDs in combination with certain other medications. Some NSAIDs may also mitigate the antiplatelet benefits of aspirin and may increase blood pressure in patients with hypertension. Clinicians should be aware of potential drug interactions with OTC analgesics when prescribing new medications. Additionally, patients should be properly counseled on the appropriate and safe use of OTC analgesics. PMID:18257920

Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique.

PubMed

Hao, Ming; Wang, Yanli; Bryant, Stephen H

2016-02-25

Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision-recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. Published by Elsevier B.V.

UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria.

PubMed

Chanumolu, Sree Krishna; Rout, Chittaranjan; Chauhan, Rajinder S

2012-01-01

Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed. A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein's critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed. The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets. UniDrug-Target is expected to accelerate

Prolonged Drug-Drug Interaction between Terbinafine and Perphenazine.