In silico quantitative structure-toxicity relationship study of aromatic nitro compounds.
Pasha, Farhan Ahmad; Neaz, Mohammad Morshed; Cho, Seung Joo; Ansari, Mohiuddin; Mishra, Sunil Kumar; Tiwari, Sharvan
2009-05-01
Small molecules often have toxicities that are a function of molecular structural features. Minor variations in structural features can make large difference in such toxicity. Consequently, in silico techniques may be used to correlate such molecular toxicities with their structural features. Relative to nine different sets of aromatic nitro compounds having known observed toxicities against different targets, we developed ligand-based 2D quantitative structure-toxicity relationship models using 20 selected topological descriptors. The topological descriptors have several advantages such as conformational independency, facile and less time-consuming computation to yield good results. Multiple linear regression analysis was used to correlate variations of toxicity with molecular properties. The information index on molecular size, lopping centric index and Kier flexibility index were identified as fundamental descriptors for different kinds of toxicity, and further showed that molecular size, branching and molecular flexibility might be particularly important factors in quantitative structure-toxicity relationship analysis. This study revealed that topological descriptor-guided quantitative structure-toxicity relationship provided a very useful, cost and time-efficient, in silico tool for describing small-molecule toxicities.
Quantitative structure-toxicity relationship (QSTR) studies on the organophosphate insecticides.
Can, Alper
2014-11-04
Organophosphate insecticides are the most commonly used pesticides in the world. In this study, quantitative structure-toxicity relationship (QSTR) models were derived for estimating the acute oral toxicity of organophosphate insecticides to male rats. The 20 chemicals of the training set and the seven compounds of the external testing set were described by means of using descriptors. Descriptors for lipophilicity, polarity and molecular geometry, as well as quantum chemical descriptors for energy were calculated. Model development to predict toxicity of organophosphate insecticides in different matrices was carried out using multiple linear regression. The model was validated internally and externally. In the present study, QSTR model was used for the first time to understand the inherent relationships between the organophosphate insecticide molecules and their toxicity behavior. Such studies provide mechanistic insight about structure-toxicity relationship and help in the design of less toxic insecticides. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Quantitative Structure--Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure
Background: Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. Objective: In this study, a combinatorial QSAR approach has been employed for the creation of robust and predictive models of acute toxi...
Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang
2014-08-15
A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs. Copyright © 2014 Elsevier B.V. All rights reserved.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Reinert, K.H.
1987-12-01
Recent EPA scrutiny of acrylate and methacrylate monomers has resulted in restrictive consent orders and Significant New Use Rules under the Toxic Substances Control Act, based on structure-activity relationships using mouse skin painting studies. The concern is centered on human health issues regarding worker and consumer exposure. Environmental issues, such as aquatic toxicity, are still of concern. Understanding the relationships and environmental risks to aquatic organisms may improve the understanding of the potential risks to human health. This study evaluates the quantitative structure-activity relationships from measured log Kow's and log LC50's for Pimephales promelas (fathead minnow) and Carassius auratus (goldfish).more » Scientific support of the current regulations is also addressed. Two monomer classes were designated: acrylates and methacrylates. Spearman rank correlation and linear regression were run. Based on this study, an ecotoxicological difference exists between acrylates and methacrylates. Regulatory activities and scientific study should reflect this difference.« less
Comparative Analysis of Predictive Models for Liver Toxicity Using ToxCast Assays and Quantitative Structure-Activity Relationships Jie Liu1,2, Richard Judson1, Matthew T. Martin1, Huixiao Hong3, Imran Shah1 1National Center for Computational Toxicology (NCCT), US EPA, RTP, NC...
Jin, Xiangqin; Jin, Minghao; Sheng, Lianxi
2014-08-01
Although numerous chemicals have been identified to have significant toxicological effect on aquatic organisms, there is still lack of a reliable, high-throughput approach to evaluate, screen and monitor the presence of organic contaminants in aquatic system. In the current study, we proposed a synthetic pipeline to automatically model and predict the acute toxicity of chemicals to algae. In the procedure, a new alignment-free three dimensional (3D) structure characterization method was described and, with this method, several 3D-quantitative structure-toxicity relationship (3D-QSTR) models were developed, from which two were found to exhibit strong internal fitting ability and high external predictive power. The best model was established by Gaussian process (GP), which was further employed to perform extrapolation on a random compound library consisting of 1014 virtually generated substituted benzenes. It was found that (i) substitution number can only exert slight influence on chemical׳s toxicity, but low-substituted benzenes seem to have higher toxicity than those of high-substituted entities, and (ii) benzenes substituted by nitro group and halogens exhibit high acute toxicity as compared to other substituents such as methyl and carboxyl groups. Subsequently, several promising candidates suggested by computational prediction were assayed by using a standard algal growth inhibition test. Consequently, four substituted benzenes, namely 2,3-dinitrophenol, 2-chloro-4-nitroaniline, 1,2,3-trinitrobenzene and 3-bromophenol, were determined to have high acute toxicity to Scenedesmus obliquus, with their EC50 values of 2.5±0.8, 10.5±2.1, 1.4±0.2 and 42.7±5.4μmol/L, respectively. Copyright © 2014 Elsevier Ltd. All rights reserved.
Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.
Zhu, Hao; Martin, Todd M; Ye, Lin; Sedykh, Alexander; Young, Douglas M; Tropsha, Alexander
2009-12-01
Few quantitative structure-activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD(50)) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire data set was selected that included all 3472 compounds used in TOPKAT's training set. The remaining 3913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R(2) of linear regression between actual and predicted LD(50) values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R(2) ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD(50) for every compound using all five models. The consensus models afforded higher prediction accuracy for the external validation data set with the higher coverage as compared to individual constituent models. The validated consensus LD(50) models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.
Quantitative structure toxicity relationships for phenols in isolated rat hepatocytes.
Moridani, Majid Y; Siraki, Arno; O'Brien, Peter J
2003-05-06
Quantitative structure toxicity relationship (QSTR) equations were obtained to predict and describe the cytotoxicity of 31 phenols using logLD(50) as a concentration to induce 50% cytotoxicity of isolated rat hepatocytes in 2 h and logP as octanol/water partitioning: logLD(50) (microM)=-0.588(+/-0.059)logP+4.652(+/-0.153) (n=27, r(2)=0.801, s=0.261, P<1 x 10(-9)). Hydroquinone, catechol, 4-nitrophenol, and 2,4-dinitrophenol were outliers for this equation. When the ionization constant pK(a) was considered as a contributing factor a two-parameter QSTR equation was derived: logLD(50) (microM)=-0.595(+/-0.051)logP+0.197(+/-0.029)pK(a)+2.665(+/-0.281) (n=28, r(2)=0.859, s=0.218, P<1 x 10(-6)). Using sigma+, the Brown variation of the Hammet electronic constant, as a contributing parameter, the cytotoxicity of phenols towards hepatocytes were defined by logLD(50) (microM)=-0.594(+/-0.052)logP-0.552(+/-0.085)sigma+ +4.540(+/-0.132) (n=28, r(2)=0.853, s=0.223, P<1 x 10(-6)). Replacing sigma+ with the homolytic bond dissociation energy (BDE) for (X-PhOH+PhO.-->X-PhO.+PhOH) led to logLD(50) (microM)=-0.601(+/-0.066)logP-0.040(+/-0.018)BDE+4.611(+/-0.166) (n=23, r(2)=0.827, s=0.223, P<0.05). Hydroquinone, catechol and 2-nitrophenol were outliers for the above equations. Using redox potential and logP led to a new correlation: logLD(50) (microM)=-0.529(+/-0.135)logP+2.077(+/-0.892)E(p/2)+2.806(+/-0.592) (n=15, r(2)=0.561, s=0.383, P<0.05) with 4-nitrophenol as an outlier. Our findings indicate that phenols with higher lipophilicity, BDE, or sigma+ values or with lower pK(a) and redox potential were more toxic towards hepatocytes. We also showed that a collapse of hepatocyte mitochondrial membrane potential preceded the cytotoxicity of most phenols. Our study indicates that one or a combination of mechanisms; i.e. mitochondrial uncoupling, phenoxy radicals, or phenol metabolism to quinone methides and quinones, contribute to phenol cytotoxicity towards hepatocytes depending on
Wang, Qiang; Jia, Qingzhu; Yan, Lihong; Xia, Shuqian; Ma, Peisheng
2014-08-01
The aquatic toxicity value of hazardous contaminants plays an important role in the risk assessments of aquatic ecosystems. The following study presents a stable and accurate structure-toxicity relationship model based on the norm indexes for the prediction of toxicity value (log(LC50)) for 190 diverse narcotic pollutants (96 h LC50 data for Poecilia reticulata). Research indicates that this new model is very efficient and provides satisfactory results. The suggested prediction model is evidenced by R(2) (square correlation coefficient) and ARD (average relative difference) values of 0.9376 and 10.45%, respectively, for the training set, and 0.9264 and 13.90% for the testing set. Comparison results with reference models demonstrate that this new method, based on the norm indexes proposed in this work, results in significant improvements, both in accuracy and stability for predicting aquatic toxicity values of narcotic pollutants. Copyright © 2014 Elsevier Ltd. All rights reserved.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Liu, Z.T.; Wang, L.S.; Chen, S.P.
1996-12-31
The fundamental differentiation of toxicity is between reactive and nonreactive toxicity. Reactive toxicity is associated with a specific mechanism for the reaction with an enzyme or inhibition of a metabolic pathway, and nonreactive toxicity is related directly to the quantity of toxicant acting upon the cell. The quantitative structure-activity relationships (QSARs) have been successfully used in the nonreactive toxicity, such as prediction of the toxicity of nonreactive compounds based on their solubility in the lipids of organisms. The elements of molecular structure that are most closely related to nonreactive toxicity are those that describe the partitioning of the toxicant intomore » the organism, while QSARs for the reactive toxicity are less common in the environmental toxicology literature. With the recent increase in the use of synthetic substituted benzenes as industrial chemicals, the accurate analysis of the effect of reactive toxic chemicals has become recognized with QSAR. For this purpose, we selected the fish (Carassias auratus) as the test organism, measured the acute toxicity of 50% lethal concentration (LC{sub 50}) of the chemicals and the adenosine triphosphate (ATP) content of the liver cells for the organism. These determined the relationships of the acute toxicity of some substituted benzenes with their physicochemical structural parameters. The effects on the ATP content was also compared to predict biological reactivities of the chemicals, so as to find some clues to explain the mode of mechanism of the toxicity. 17 refs., 1 tab.« less
Shi, Weimin; Zhang, Xiaoya; Shen, Qi
2010-01-01
Quantitative structure-activity relationship (QSAR) study of chemokine receptor 5 (CCR5) binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas and toxicity of aromatic compounds have been performed. The gene expression programming (GEP) was used to select variables and produce nonlinear QSAR models simultaneously using the selected variables. In our GEP implementation, a simple and convenient method was proposed to infer the K-expression from the number of arguments of the function in a gene, without building the expression tree. The results were compared to those obtained by artificial neural network (ANN) and support vector machine (SVM). It has been demonstrated that the GEP is a useful tool for QSAR modeling. Copyright 2009 Elsevier Masson SAS. All rights reserved.
Kim, Kwang-Yon; Shin, Seong Eun; No, Kyoung Tai
2015-01-01
Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used
Yost, Erin E; Stanek, John; DeWoskin, Robert S; Burgoon, Lyle D
2016-07-19
The United States Environmental Protection Agency (EPA) identified 1173 chemicals associated with hydraulic fracturing fluids, flowback, or produced water, of which 1026 (87%) lack chronic oral toxicity values for human health assessments. To facilitate the ranking and prioritization of chemicals that lack toxicity values, it may be useful to employ toxicity estimates from quantitative structure-activity relationship (QSAR) models. Here we describe an approach for applying the results of a QSAR model from the TOPKAT program suite, which provides estimates of the rat chronic oral lowest-observed-adverse-effect level (LOAEL). Of the 1173 chemicals, TOPKAT was able to generate LOAEL estimates for 515 (44%). To address the uncertainty associated with these estimates, we assigned qualitative confidence scores (high, medium, or low) to each TOPKAT LOAEL estimate, and found 481 to be high-confidence. For 48 chemicals that had both a high-confidence TOPKAT LOAEL estimate and a chronic oral reference dose from EPA's Integrated Risk Information System (IRIS) database, Spearman rank correlation identified 68% agreement between the two values (permutation p-value =1 × 10(-11)). These results provide support for the use of TOPKAT LOAEL estimates in identifying and prioritizing potentially hazardous chemicals. High-confidence TOPKAT LOAEL estimates were available for 389 of 1026 hydraulic fracturing-related chemicals that lack chronic oral RfVs and OSFs from EPA-identified sources, including a subset of chemicals that are frequently used in hydraulic fracturing fluids.
Furuhama, A; Hasunuma, K; Aoki, Y
2015-01-01
In addition to molecular structure profiles, descriptors based on physicochemical properties are useful for explaining the eco-toxicities of chemicals. In a previous study we reported that a criterion based on the difference between the partition coefficient (log POW) and distribution coefficient (log D) values of chemicals enabled us to identify aromatic amines and phenols for which interspecies relationships with strong correlations could be developed for fish-daphnid and algal-daphnid toxicities. The chemicals that met the log D-based criterion were expected to have similar toxicity mechanisms (related to membrane penetration). Here, we investigated the applicability of log D-based criteria to the eco-toxicity of other kinds of chemicals, including aliphatic compounds. At pH 10, use of a log POW - log D > 0 criterion and omission of outliers resulted in the selection of more than 100 chemicals whose acute fish toxicities or algal growth inhibition toxicities were almost equal to their acute daphnid toxicities. The advantage of log D-based criteria is that they allow for simple, rapid screening and prioritizing of chemicals. However, inorganic molecules and chemicals containing certain structural elements cannot be evaluated, because calculated log D values are unavailable.
Kafoury, Ramzi M; Huang, Ming-Ju
2005-08-01
The sequence of events leading to ozone-induced airway inflammation is not well known. To elucidate the molecular and cellular events underlying ozone toxicity in the lung, we hypothesized that lipid ozonation products (LOPs) generated by the reaction of ozone with unsaturated fatty acids in the epithelial lining fluid and cell membranes play a key role in mediating ozone-induced airway inflammation. To test our hypothesis, we ozonized 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC) and generated LOPs. Confluent human bronchial epithelial cells were exposed to the derivatives of ozonized POPC-9-oxononanoyl, 9-hydroxy-9-hydroperoxynonanoyl, and 8-(5-octyl-1,2,4-trioxolan-3-yl-)octanoyl-at a concentration of 10 muM, and the activity of phospholipases A2 (PLA2), C (PLC), and D (PLD) was measured (1, 0.5, and 1 h, respectively). Quantitative structure-activity relationship (QSAR) models were utilized to predict the biological activity of LOPs in airway epithelial cells. The QSAR results showed a strong correlation between experimental and computed activity (r = 0.97, 0.98, 0.99, for PLA2, PLC, and PLD, respectively). The results indicate that QSAR models can be utilized to predict the biological activity of the various ozone-derived LOP species in the lung. Copyright 2005 Wiley Periodicals, Inc.
Zhu, Hao; Ye, Lin; Richard, Ann; Golbraikh, Alexander; Wright, Fred A; Rusyn, Ivan; Tropsha, Alexander
2009-08-01
Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public-private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC(50)) and in vivo rodent median lethal dose (LD(50)) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure-activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD(50) values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC(50) and LD(50). However, a linear IC(50) versus LD(50) correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC(50) and LD(50) values: One group comprises compounds with linear IC(50) versus LD(50) relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD(50) values from chemical descriptors. All models were extensively validated using special protocols. The novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only
Hamadache, Mabrouk; Benkortbi, Othmane; Hanini, Salah; Amrane, Abdeltif; Khaouane, Latifa; Si Moussa, Cherif
2016-02-13
Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides. Copyright © 2015 Elsevier B.V. All rights reserved.
Bogaerts, P; Bohatier, J; Bonnemoy, F
2001-07-01
Cytotoxicity and quantitative structure-activity relationships of 13 inorganic and 21 organic substances were determined using three bioassays performed on the ciliated protozoan Tetrahymena pyriformis and the luminescent bacterium Vibrio fischeri. The best concordance of toxicity results was observed between the T. pyriformis FDA--esterase activity and population growth inhibition tests for the organic compounds. The sensitivity of these two assays is compared with that of the Microtox test. The T. pyriformis FDA test showed a high sensitivity is most cases. The aim of the current research was to determine whether the relative toxicity of metal ions and organic molecules, with these three bioassays, was predictable using three ion characteristics and hydrophobicity, respectively. For metal ions, the variable that best modeled the toxicity data obtained with the two T. pyriformis tests was the softness index [sigma(p), i.e., (coordinate bond energy of the metal fluoride--coordinate bond energy of the metal iodide)/(coordinate bond energy of the metal fluoride)]. No correlation was found with the Microtox test. For organic compounds, a significant correlation was observed between the hydrophobicity coefficient and the toxicity data. This correlation is closer with the two tests using Tetrahymena. Copyright 2001 Academic Press.
The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...
Quantitative structure-activity relationships for organophosphates binding to acetylcholinesterase.
Ruark, Christopher D; Hack, C Eric; Robinson, Peter J; Anderson, Paul E; Gearhart, Jeffery M
2013-02-01
Organophosphates are a group of pesticides and chemical warfare nerve agents that inhibit acetylcholinesterase, the enzyme responsible for hydrolysis of the excitatory neurotransmitter acetylcholine. Numerous structural variants exist for this chemical class, and data regarding their toxicity can be difficult to obtain in a timely fashion. At the same time, their use as pesticides and military weapons is widespread, which presents a major concern and challenge in evaluating human toxicity. To address this concern, a quantitative structure-activity relationship (QSAR) was developed to predict pentavalent organophosphate oxon human acetylcholinesterase bimolecular rate constants. A database of 278 three-dimensional structures and their bimolecular rates was developed from 15 peer-reviewed publications. A database of simplified molecular input line entry notations and their respective acetylcholinesterase bimolecular rate constants are listed in Supplementary Material, Table I. The database was quite diverse, spanning 7 log units of activity. In order to describe their structure, 675 molecular descriptors were calculated using AMPAC 8.0 and CODESSA 2.7.10. Orthogonal projection to latent structures regression, bootstrap leave-random-many-out cross-validation and y-randomization were used to develop an externally validated consensus QSAR model. The domain of applicability was assessed by the William's plot. Six external compounds were outside the warning leverage indicating potential model extrapolation. A number of compounds had residuals >2 or <-2, indicating potential outliers or activity cliffs. The results show that the HOMO-LUMO energy gap contributed most significantly to the binding affinity. A mean training R (2) of 0.80, a mean test set R (2) of 0.76 and a consensus external test set R (2) of 0.66 were achieved using the QSAR. The training and external test set RMSE values were found to be 0.76 and 0.88. The results suggest that this QSAR model can be used in
Structure-toxicity relationships of acrylic monomers.
Autian, J
1975-01-01
Esters of acrylic acid, in particular methyl methacrylate, have wide applications in a number of industrial and consumer products, forming very desirable nonbreakable glass-like materials. In dentistry, the monomers are used to prepare dentures and a variety of filling and coating materials for the teeth. Surgeons utilize the monomers to prepare a cement which helps anchor prosthetic devices to bone. Special types of acrylic monomers such as the cyano derivatives have found a useful application as adhesive materials. Most of the acrylic acid esters are volatile substances and can produce various levels of toxicity if inhaled. A large number of workers thus exposed to the vapors of these esters can develop clinical symptoms and signs of toxicity. This paper will discuss the toxicity of a large number of acrylic esters, and will attempt to show structure-activity relationships where such data are available. General comments will also be made as to the potential health hazards this variety of esters may present to selected segments of the population. PMID:1175551
Structure-toxicity relationships of acrylic monomers.
Autian, J
1975-06-01
Esters of acrylic acid, in particular methyl methacrylate, have wide applications in a number of industrial and consumer products, forming very desirable nonbreakable glass-like materials. In dentistry, the monomers are used to prepare dentures and a variety of filling and coating materials for the teeth. Surgeons utilize the monomers to prepare a cement which helps anchor prosthetic devices to bone. Special types of acrylic monomers such as the cyano derivatives have found a useful application as adhesive materials. Most of the acrylic acid esters are volatile substances and can produce various levels of toxicity if inhaled. A large number of workers thus exposed to the vapors of these esters can develop clinical symptoms and signs of toxicity. This paper will discuss the toxicity of a large number of acrylic esters, and will attempt to show structure-activity relationships where such data are available. General comments will also be made as to the potential health hazards this variety of esters may present to selected segments of the population.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Beck, B.D.; Toole, A.P.; Callahan, B.G.
1991-12-01
Alkylphenols are a class of environmentally pervasive compounds, found both in natural (e.g., crude oils) and in anthropogenic (e.g., wood tar, coal gasification waste) materials. Despite the frequent environmental occurrence of these chemicals, there is a limited toxicity database on alkylphenols. The authors have therefore developed a 'toxicity equivalence approach' for alkylphenols which is based on their ability to inhibit, in a specific manner, the enzyme cyclooxygenase. Enzyme-inhibiting ability for individual alkylphenols can be estimated based on the quantitative structure-activity relationship developed by Dewhirst (1980) and is a function of the free hydroxyl group, electron-donating ring substituents, and hydrophobic aromaticmore » ring substituents. The authors evaluated the toxicological significance of cyclooxygenase inhibition by comparison of the inhibitory capacity of alkylphenols with the inhibitory capacity of acetylsalicylic acid, or aspirin, a compound whose low-level effects are due to cyclooxygenase inhibition. Since nearly complete absorption for alkylphenols and aspirin is predicted, based on estimates of hydrophobicity and fraction of charged molecules at gastrointestinal pHs, risks from alkylphenols can be expressed directly in terms of 'milligram aspirin equivalence,' without correction for absorption differences. They recommend this method for assessing risks of mixtures of alkylphenols, especially for those compounds with no chronic toxicity data.38 references.« less
Partitioning and lipophilicity in quantitative structure-activity relationships.
Dearden, J C
1985-01-01
The history of the relationship of biological activity to partition coefficient and related properties is briefly reviewed. The dominance of partition coefficient in quantitation of structure-activity relationships is emphasized, although the importance of other factors is also demonstrated. Various mathematical models of in vivo transport and binding are discussed; most of these involve partitioning as the primary mechanism of transport. The models describe observed quantitative structure-activity relationships (QSARs) well on the whole, confirming that partitioning is of key importance in in vivo behavior of a xenobiotic. The partition coefficient is shown to correlate with numerous other parameters representing bulk, such as molecular weight, volume and surface area, parachor and calculated indices such as molecular connectivity; this is especially so for apolar molecules, because for polar molecules lipophilicity factors into both bulk and polar or hydrogen bonding components. The relationship of partition coefficient to chromatographic parameters is discussed, and it is shown that such parameters, which are often readily obtainable experimentally, can successfully supplant partition coefficient in QSARs. The relationship of aqueous solubility with partition coefficient is examined in detail. Correlations are observed, even with solid compounds, and these can be used to predict solubility. The additive/constitutive nature of partition coefficient is discussed extensively, as are the available schemes for the calculation of partition coefficient. Finally the use of partition coefficient to provide structural information is considered. It is shown that partition coefficient can be a valuable structural tool, especially if the enthalpy and entropy of partitioning are available. PMID:3905374
DOE Office of Scientific and Technical Information (OSTI.GOV)
Huang, X.D.; Krylov, S.N.; Ren, L.
1997-11-01
Photoinduced toxicity of polycyclic aromatic hydrocarbons (PAHs) occurs via photosensitization reactions (e.g., generation of singlet-state oxygen) and by photomodification (photooxidation and/or photolysis) of the chemicals to more toxic species. The quantitative structure-activity relationship (QSAR) described in the companion paper predicted, in theory, that photosensitization and photomodification additively contribute to toxicity. To substantiate this QSAR modeling exercise it was necessary to show that toxicity can be described by empirically derived parameters. The toxicity of 16 PAHs to the duckweed Lemna gibba was measured as inhibition of leaf production in simulated solar radiation (a light source with a spectrum similar to thatmore » of sunlight). A predictive model for toxicity was generated based on the theoretical model developed in the companion paper. The photophysical descriptors required of each PAH for modeling were efficiency of photon absorbance, relative uptake, quantum yield for triplet-state formation, and the rate of photomodification. The photomodification rates of the PAHs showed a moderate correlation to toxicity, whereas a derived photosensitization factor (PSF; based on absorbance, triplet-state quantum yield, and uptake) for each PAH showed only a weak, complex correlation to toxicity. However, summing the rate of photomodification and the PSF resulted in a strong correlation to toxicity that had predictive value. When the PSF and a derived photomodification factor (PMF; based on the photomodification rate and toxicity of the photomodified PAHs) were summed, an excellent explanatory model of toxicity was produced, substantiating the additive contributions of the two factors.« less
Structure-Activity Relationship Studies and their Role in Predicting and Investigating Chemical Toxicity
Structure-activity relationships (SAR) represent attempts to generalize chemical information relative to biological activity for the twin purposes of generating insigh...
THE PRACTICE OF STRUCTURE ACTIVITY RELATIONSHIPS (SAR) IN TOXICOLOGY
Both qualitative and quantitative modeling methods relating chemical structure to biological activity, called structure-activity relationship analyses or SAR, are applied to the prediction and characterization of chemical toxicity. This minireview will discuss some generic issue...
Structure-activity relationships for chloro- and nitrophenol toxicity in the pollen tube growth test
DOE Office of Scientific and Technical Information (OSTI.GOV)
Schueuermann, G.; Somashekar, R.K.; Kristen, U.
Acute toxicity of 10 chlorophenols and 10 nitrophenols with identical substitution patterns is analyzed with the pollen tube growth (PTG) test. Concentration values of 50% growth inhibition (IC50) between 0.1 and 300 mg/L indicate that the absolute sensitivity of this alternative biotest is comparable to conventional aquatic test systems. Analysis of quantitative structure-activity relationships using lipophilicity (log K{sub ow}), acidity (pK{sub a}), and quantum chemical parameters to model intrinsic acidity, solvation interactions, and nucleophilicity reveals substantial differences between the intraseries trends of log IC50. With chlorophenols, a narcotic-type relationship is derived, which, however, shows marked differences in slope and interceptmore » when compared to reference regression equations for polar narcosis. Regression analysis of nitrophenol toxicity suggests interpretation in terms of two modes of action: oxidative uncoupling activity is associated with a pK{sub a} window from 3.8 to 8.5, and more acidic congeners with diortho-substitution show a transition from uncoupling to a narcotic mode of action with decreasing pK{sub a} and log K{sub ow}. Model calculations for phenol nucleophilicity suggest that differences in the phenol readiness for glucuronic acid conjugation as a major phase-II detoxication pathway have no direct influence on acute PTG toxicity of the compounds.« less
Designing a Quantitative Structure-Activity Relationship for the ...
Toxicokinetic models serve a vital role in risk assessment by bridging the gap between chemical exposure and potentially toxic endpoints. While intrinsic metabolic clearance rates have a strong impact on toxicokinetics, limited data is available for environmentally relevant chemicals including nearly 8000 chemicals tested for in vitro bioactivity in the Tox21 program. To address this gap, a quantitative structure-activity relationship (QSAR) for intrinsic metabolic clearance rate was developed to offer reliable in silico predictions for a diverse array of chemicals. Models were constructed with curated in vitro assay data for both pharmaceutical-like chemicals (ChEMBL database) and environmentally relevant chemicals (ToxCast screening) from human liver microsomes (2176 from ChEMBL) and human hepatocytes (757 from ChEMBL and 332 from ToxCast). Due to variability in the experimental data, a binned approach was utilized to classify metabolic rates. Machine learning algorithms, such as random forest and k-nearest neighbor, were coupled with open source molecular descriptors and fingerprints to provide reasonable estimates of intrinsic metabolic clearance rates. Applicability domains defined the optimal chemical space for predictions, which covered environmental chemicals well. A reduced set of informative descriptors (including relative charge and lipophilicity) and a mixed training set of pharmaceuticals and environmentally relevant chemicals provided the best intr
Nolte, Tom M; Peijnenburg, Willie J G M; Hendriks, A Jan; van de Meent, Dik
2017-07-01
After use and disposal of chemical products, many types of polymer particles end up in the aquatic environment with potential toxic effects to primary producers like green algae. In this study, we have developed Quantitative Structure-Activity Relationships (QSARs) for a set of highly structural diverse polymers which are capable to estimate green algae growth inhibition (EC50). The model (N = 43, R 2 = 0.73, RMSE = 0.28) is a regression-based decision tree using one structural descriptor for each of three polymer classes separated based on charge. The QSAR is applicable to linear homo polymers as well as copolymers and does not require information on the size of the polymer particle or underlying core material. Highly branched polymers, non-nitrogen cationic polymers and polymeric surfactants are not included in the model and thus cannot be evaluated. The model works best for cationic and non-ionic polymers for which cellular adsorption, disruption of the cell wall and photosynthesis inhibition were the mechanisms of action. For anionic polymers, specific properties of the polymer and test characteristics need to be known for detailed assessment. The data and QSAR results for anionic polymers, when combined with molecular dynamics simulations indicated that nutrient depletion is likely the dominant mode of toxicity. Nutrient depletion in turn, is determined by the non-linear interplay between polymer charge density and backbone flexibility. Copyright © 2017 Elsevier Ltd. All rights reserved.
Ying, Jiali; Zhang, Ting; Tang, Meng
2015-01-01
Metal oxide nanomaterials are widely used in various areas; however, the divergent published toxicology data makes it difficult to determine whether there is a risk associated with exposure to metal oxide nanomaterials. The application of quantitative structure activity relationship (QSAR) modeling in metal oxide nanomaterials toxicity studies can reduce the need for time-consuming and resource-intensive nanotoxicity tests. The nanostructure and inorganic composition of metal oxide nanomaterials makes this approach different from classical QSAR study; this review lists and classifies some structural descriptors, such as size, cation charge, and band gap energy, in recent metal oxide nanomaterials quantitative nanostructure activity relationship (QNAR) studies and discusses the mechanism of metal oxide nanomaterials toxicity based on these descriptors and traditional nanotoxicity tests. PMID:28347085
DOE Office of Scientific and Technical Information (OSTI.GOV)
Krylov, S.N.; Huang, X.D.; Zeiler, L.F.
1997-11-01
A quantitative structure-activity relationship model for the photoinduced toxicity of 16 polycyclic aromatic hydrocarbons (PAHs) to duckweed (Lemna gibba) in simulated solar radiation (SSR) was developed. Lemna gibba was chosen for this study because toxicity could be considered in two compartments: water column and leaf tissue. Modeling of photoinduced toxicity was described by photochemical reactions between PAHs and a hypothetical group of endogenous biomolecules (G) required for normal growth, with damage to G by PAHs and/or photomodified PAHs in SSR resulting in impaired growth. The reaction scheme includes photomodification of PAHs, uptake of PAHs into leaves, triplet-state formation of intactmore » PAHs, photosensitization reactions that damage G, and reactions between photomodified PAHs and G. The assumptions used were: the PAH photomodification rate is slower than uptake of chemicals into leaves, the PAH concentration in aqueous solution is nearly constant during a toxicity test, the fluence rate of actinic radiation is lower within leaves than in the aqueous phase, and the toxicity of intact PAHs in the dark is negligible. A series of differential equations describing the reaction kinetics of intact and photomodifed PAHs with G was derived. The resulting equation for PAH toxicity was a function of treatment period, initial PAH concentration, relative absorbance of SSR by each PAH, quantum yield for formation of triplet-state PAH, and rate of PAH photomodification. Data for growth in the presence of intact and photomodified PAHs were used to empirically solve for a photosensitization constant (PSC) and a photomodification constant (PMC) for each of the 16 PAHs tested. For 9 PAHs the PMC dominates and for 7 PAHs the PSC dominates.« less
Quantitative structure-activity relationships (QSARs) are being developed to predict the toxicological endpoints for untested chemicals similar in structure to chemicals that have known experimental toxicological data. Based on a very large number of predetermined descriptors, a...
Tiwari, Anjani K; Ojha, Himanshu; Kaul, Ankur; Dutta, Anupama; Srivastava, Pooja; Shukla, Gauri; Srivastava, Rakesh; Mishra, Anil K
2009-07-01
Nuclear magnetic resonance imaging is a very useful tool in modern medical diagnostics, especially when gadolinium (III)-based contrast agents are administered to the patient with the aim of increasing the image contrast between normal and diseased tissues. With the use of soft modelling techniques such as quantitative structure-activity relationship/quantitative structure-property relationship after a suitable description of their molecular structure, we have studied a series of phosphonic acid for designing new MRI contrast agent. Quantitative structure-property relationship studies with multiple linear regression analysis were applied to find correlation between different calculated molecular descriptors of the phosphonic acid-based chelating agent and their stability constants. The final quantitative structure-property relationship mathematical models were found as--quantitative structure-property relationship Model for phosphonic acid series (Model 1)--log K(ML) = {5.00243(+/-0.7102)}- MR {0.0263(+/-0.540)}n = 12 l r l = 0.942 s = 0.183 F = 99.165 quantitative structure-property relationship Model for phosphonic acid series (Model 2)--log K(ML) = {5.06280(+/-0.3418)}- MR {0.0252(+/- .198)}n = 12 l r l = 0.956 s = 0.186 F = 99.256.
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS FOR CHEMICAL REDUCTIONS OF ORGANIC CONTAMINANTS
Sufficient kinetic data on abiotic reduction reactions involving organic contaminants are now available that quantitative structure-activity relationships (QSARs) for these reactions can be developed. Over 50 QSARs have been reported, most in just the last few years, and they ar...
Distributed Structure Searchable Toxicity
The Distributed Structure Searchable Toxicity (DSSTox) online resource provides high quality chemical structures and annotations in association with toxicity data. It helps to build a data foundation for improved structure-activity relationships and predictive toxicology. DSSTox publishes summarized chemical activity representations for structure-activity modeling and provides a structure browser. This tool also houses the chemical inventories for the ToxCast and Tox21 projects.
Tong, Lidan; Guo, Lixin; Lv, Xiaojun; Li, Yu
2017-01-01
Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were established by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Experimental toxicity data in Poecilia reticulata (pLC 50 ) and physico-chemical properties for 12 polychlorinated phenols were used as dependent and as independent variables, respectively. Among the 12 polychlorinated phenols, nine were randomly selected and used as a training set to construct the 3D-QSAR models through the SYBYL-X software to predict the pLC 50 values of the remaining 8 polychlorinated phenols congeners, and the other three polychlorinated phenols were used as a test set to evaluate the 3D-QSAR models (the training set and test set were arranged randomly, shuffled 60 times). Pentachlorophenol (PCP), which is the most toxic among the 20 polychlorinated phenols used in this experiment, was selected as an example for modification using contour maps produced using the established 3D-QSAR models. The aim was to decrease its toxicity and bioconcentration, increase its biodegradation, and maintain or better its effectiveness as a pesticide. The 3D-QSAR models were robust and had good predictive abilities with cross-validation correlation coefficients (q 2 ) of 0.858-0.992 (>0.5), correlation coefficients (r 2 ) of 0.966-1.000 (>0.9), and standard errors of prediction (SEP) of 0.004-0.159. CoMFA showed that the toxicity of the polychlorinated phenols arose mainly from electrostatic (42.7-66.7%) and steric (33.3-7.3%) contributions. By comparison, CoMSIA showed that the toxicity of polychlorinated phenols was dominated by electrostatic (57.5-76.9%) and hydrophobic (19.8-25.7%) contributions, with lesser contributions from the steric (0.7-1.0%) hydrogen bond donor (0.1-20.3%), and hydrogen bond acceptor (0-0.9%). 3D-QSAR electrostatic contour maps were used to modify PCP and design 11 new compounds with lower toxicity. The effectiveness of each of
20180312 - Structure-based QSAR Models to Predict Systemic Toxicity Points of Departure (SOT)
Human health risk assessment associated with environmental chemical exposure is limited by the tens of thousands of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models base...
Bradbury, Steven P; Russom, Christine L; Ankley, Gerald T; Schultz, T Wayne; Walker, John D
2003-08-01
The use of quantitative structure-activity relationships (QSARs) in assessing potential toxic effects of organic chemicals on aquatic organisms continues to evolve as computational efficiency and toxicological understanding advance. With the ever-increasing production of new chemicals, and the need to optimize resources to assess thousands of existing chemicals in commerce, regulatory agencies have turned to QSARs as essential tools to help prioritize tiered risk assessments when empirical data are not available to evaluate toxicological effects. Progress in designing scientifically credible QSARs is intimately associated with the development of empirically derived databases of well-defined and quantified toxicity endpoints, which are based on a strategic evaluation of diverse sets of chemical structures, modes of toxic action, and species. This review provides a brief overview of four databases created for the purpose of developing QSARs for estimating toxicity of chemicals to aquatic organisms. The evolution of QSARs based initially on general chemical classification schemes, to models founded on modes of toxic action that range from nonspecific partitioning into hydrophobic cellular membranes to receptor-mediated mechanisms is summarized. Finally, an overview of expert systems that integrate chemical-specific mode of action classification and associated QSAR selection for estimating potential toxicological effects of organic chemicals is presented.
Assessing deep and shallow learning methods for quantitative prediction of acute chemical toxicity.
Liu, Ruifeng; Madore, Michael; Glover, Kyle P; Feasel, Michael G; Wallqvist, Anders
2018-05-02
Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to outperform other machine-learning methods for quantitative structure-activity relationship modeling of molecular properties. However, most of the reported performance evaluations relied on global performance metrics, such as the root mean squared error (RMSE) between the predicted and experimental values of all samples, without considering the impact of sample distribution across the activity spectrum. Here, we carried out an in-depth analysis of DNN performance for quantitative prediction of acute chemical toxicity using several datasets. We found that the overall performance of DNN models on datasets of up to 30,000 compounds was similar to that of random forest (RF) models, as measured by the RMSE and correlation coefficients between the predicted and experimental results. However, our detailed analyses demonstrated that global performance metrics are inappropriate for datasets with a highly uneven sample distribution, because they show a strong bias for the most populous compounds along the toxicity spectrum. For highly toxic compounds, DNN and RF models trained on all samples performed much worse than the global performance metrics indicated. Surprisingly, our variable nearest neighbor method, which utilizes only structurally similar compounds to make predictions, performed reasonably well, suggesting that information of close near neighbors in the training sets is a key determinant of acute toxicity predictions.
Toxicity Estimation Software Tool (TEST)
The Toxicity Estimation Software Tool (TEST) was developed to allow users to easily estimate the toxicity of chemicals using Quantitative Structure Activity Relationships (QSARs) methodologies. QSARs are mathematical models used to predict measures of toxicity from the physical c...
Developmental toxicity is a relevant endpoint for the comprehensive assessment of human health risk from chemical exposure. However, animal developmental toxicity studies remain unavailable for many environmental contaminants due to the complexity and cost of these types of analy...
A web accessible software tool is being developed to predict the toxicity of unknown chemicals for a wide variety of endpoints. The tool will enable a user to easily predict the toxicity of a query compound by simply entering its structure in a 2-dimensional (2-D) chemical sketc...
Compound toxicity screening and structure-activity relationship modeling in Escherichia coli.
Planson, Anne-Gaëlle; Carbonell, Pablo; Paillard, Elodie; Pollet, Nicolas; Faulon, Jean-Loup
2012-03-01
Synthetic biology and metabolic engineering are used to develop new strategies for producing valuable compounds ranging from therapeutics to biofuels in engineered microorganisms. When developing methods for high-titer production cells, toxicity is an important element to consider. Indeed the production rate can be limited due to toxic intermediates or accumulation of byproducts of the heterologous biosynthetic pathway of interest. Conversely, highly toxic molecules are desired when designing antimicrobials. Compound toxicity in bacteria plays a major role in metabolic engineering as well as in the development of new antibacterial agents. Here, we screened a diversified chemical library of 166 compounds for toxicity in Escherichia coli. The dataset was built using a clustering algorithm maximizing the chemical diversity in the library. The resulting assay data was used to develop a toxicity predictor that we used to assess the toxicity of metabolites throughout the metabolome. This new tool for predicting toxicity can thus be used for fine-tuning heterologous expression and can be integrated in a computational-framework for metabolic pathway design. Many structure-activity relationship tools have been developed for toxicology studies in eukaryotes [Valerio (2009), Toxicol Appl Pharmacol, 241(3): 356-370], however, to the best of our knowledge we present here the first E. coli toxicity prediction web server based on QSAR models (EcoliTox server: http://www.issb.genopole.fr/∼faulon/EcoliTox.php). Copyright © 2011 Wiley Periodicals, Inc.
Cunningham, Albert R; Carrasquer, C Alex; Mattison, Donald R
2009-01-01
The choice of therapeutic strategies for hyperthyroidism during pregnancy is limited. Surgery and radioiodine are typically avoided, leaving propylthiouracil and methimazole in the US. Carbimazole, a metabolic precursor of methimazole, is available in some countries outside of the US. In the US propylthiouracil is recommended because of concern about developmental toxicity from methimazole and carbimazole. Despite this recommendation, the data on developmental toxicity of all three agents are extremely limited and insufficient to support a policy given the broad use of methimazole and carbimazole around the world. In the absence of new human or animal data we describe the development of a new structure-activity relationship (SAR) model for developmental toxicity using the cat-SAR expert system. The SAR model was developed from data for 323 compounds evaluated for human developmental toxicity with 130 categorized as developmental toxicants and 193 as nontoxicants. Model cross-validation yielded a concordance between observed and predicted results between 79% to 81%. Based on this model, propylthiouracil, methimazole, and carbimazole were observed to share some structural features relating to human developmental toxicity. Thus given the need to treat women with Graves's disease during pregnancy, new molecules with minimized risk for developmental toxicity are needed. To help meet this challenge, the cat-SAR method would be a useful in screening new drug candidates for developmental toxicity as well as for investigating their mechanism of action.
Reino, José L; Saiz-Urra, Liane; Hernandez-Galan, Rosario; Aran, Vicente J; Hitchcock, Peter B; Hanson, James R; Gonzalez, Maykel Perez; Collado, Isidro G
2007-06-27
Fourteen benzohydrazides have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The best antifungal activity was observed for the N',N'-dibenzylbenzohydrazides 3b-d and for the N-aminoisoindoline-derived benzohydrazide 5. A quantitative structure-activity relationship (QSAR) study has been developed using a topological substructural molecular design (TOPS-MODE) approach to interpret the antifungal activity of these synthetic compounds. The model described 98.3% of the experimental variance, with a standard deviation of 4.02. The influence of an ortho substituent on the conformation of the benzohydrazides was investigated by X-ray crystallography and supported by QSAR study. Several aspects of the structure-activity relationships are discussed in terms of the contribution of different bonds to the antifungal activity, thereby making the relationships between structure and biological activity more transparent.
Zhou, Peng; Wang, Congcong; Tian, Feifei; Ren, Yanrong; Yang, Chao; Huang, Jian
2013-01-01
Quantitative structure-activity relationship (QSAR), a regression modeling methodology that establishes statistical correlation between structure feature and apparent behavior for a series of congeneric molecules quantitatively, has been widely used to evaluate the activity, toxicity and property of various small-molecule compounds such as drugs, toxicants and surfactants. However, it is surprising to see that such useful technique has only very limited applications to biomacromolecules, albeit the solved 3D atom-resolution structures of proteins, nucleic acids and their complexes have accumulated rapidly in past decades. Here, we present a proof-of-concept paradigm for the modeling, prediction and interpretation of the binding affinity of 144 sequence-nonredundant, structure-available and affinity-known protein complexes (Kastritis et al. Protein Sci 20:482-491, 2011) using a biomacromolecular QSAR (BioQSAR) scheme. We demonstrate that the modeling performance and predictive power of BioQSAR are comparable to or even better than that of traditional knowledge-based strategies, mechanism-type methods and empirical scoring algorithms, while BioQSAR possesses certain additional features compared to the traditional methods, such as adaptability, interpretability, deep-validation and high-efficiency. The BioQSAR scheme could be readily modified to infer the biological behavior and functions of other biomacromolecules, if their X-ray crystal structures, NMR conformation assemblies or computationally modeled structures are available.
Quantitative Structure-Antifungal Activity Relationships for cinnamate derivatives.
Saavedra, Laura M; Ruiz, Diego; Romanelli, Gustavo P; Duchowicz, Pablo R
2015-12-01
Quantitative Structure-Activity Relationships (QSAR) are established with the aim of analyzing the fungicidal activities of a set of 27 active cinnamate derivatives. The exploration of more than a thousand of constitutional, topological, geometrical and electronic molecular descriptors, which are calculated with Dragon software, leads to predictions of the growth inhibition on Pythium sp and Corticium rolfsii fungi species, in close agreement to the experimental values extracted from the literature. A set containing 21 new structurally related cinnamate compounds is prepared. The developed QSAR models are applied to predict the unknown fungicidal activity of this set, showing that cinnamates like 38, 28 and 42 are expected to be highly active for Pythium sp, while this is also predicted for 28 and 34 in C. rolfsii. Copyright © 2015 Elsevier Inc. All rights reserved.
Saavedra, Laura M; Romanelli, Gustavo P; Rozo, Ciro E; Duchowicz, Pablo R
2018-01-01
The insecticidal activity of a series of 62 plant derived molecules against the chikungunya, dengue and zika vector, the Aedes aegypti (Diptera:Culicidae) mosquito, is subjected to a Quantitative Structure-Activity Relationships (QSAR) analysis. The Replacement Method (RM) variable subset selection technique based on Multivariable Linear Regression (MLR) proves to be successful for exploring 4885 molecular descriptors calculated with Dragon 6. The predictive capability of the obtained models is confirmed through an external test set of compounds, Leave-One-Out (LOO) cross-validation and Y-Randomization. The present study constitutes a first necessary computational step for designing less toxic insecticides. Copyright © 2017 Elsevier B.V. All rights reserved.
Matta, Chérif F; Arabi, Alya A
2011-06-01
The use of electron density-based molecular descriptors in drug research, particularly in quantitative structure--activity relationships/quantitative structure--property relationships studies, is reviewed. The exposition starts by a discussion of molecular similarity and transferability in terms of the underlying electron density, which leads to a qualitative introduction to the quantum theory of atoms in molecules (QTAIM). The starting point of QTAIM is the topological analysis of the molecular electron-density distributions to extract atomic and bond properties that characterize every atom and bond in the molecule. These atomic and bond properties have considerable potential as bases for the construction of robust quantitative structure--activity/property relationships models as shown by selected examples in this review. QTAIM is applicable to the electron density calculated from quantum-chemical calculations and/or that obtained from ultra-high resolution x-ray diffraction experiments followed by nonspherical refinement. Atomic and bond properties are introduced followed by examples of application of each of these two families of descriptors. The review ends with a study whereby the molecular electrostatic potential, uniquely determined by the density, is used in conjunction with atomic properties to elucidate the reasons for the biological similarity of bioisosteres.
Ruan, Xiaofang; Zhang, Ruisheng; Yao, Xiaojun; Liu, Mancang; Fan, Botao
2007-03-01
Alkylphenols are a group of permanent pollutants in the environment and could adversely disturb the human endocrine system. It is therefore important to effectively separate and measure the alkylphenols. To guide the chromatographic analysis of these compounds in practice, the development of quantitative relationship between the molecular structure and the retention time of alkylphenols becomes necessary. In this study, topological, constitutional, geometrical, electrostatic and quantum-chemical descriptors of 44 alkylphenols were calculated using a software, CODESSA, and these descriptors were pre-selected using the heuristic method. As a result, three-descriptor linear model (LM) was developed to describe the relationship between the molecular structure and the retention time of alkylphenols. Meanwhile, the non-linear regression model was also developed based on support vector machine (SVM) using the same three descriptors. The correlation coefficient (R(2)) for the LM and SVM was 0.98 and 0. 92, and the corresponding root-mean-square error was 0. 99 and 2. 77, respectively. By comparing the stability and prediction ability of the two models, it was found that the linear model was a better method for describing the quantitative relationship between the retention time of alkylphenols and the molecular structure. The results obtained suggested that the linear model could be applied for the chromatographic analysis of alkylphenols with known molecular structural parameters.
Luilo, G B; Cabaniss, S E
2011-10-01
Chlorinating water which contains dissolved organic matter (DOM) produces disinfection byproducts, the majority of unknown structure. Hence, the total organic halide (TOX) measurement is used as a surrogate for toxic disinfection byproducts. This work derives a robust quantitative structure-property relationship (QSPR) for predicting the TOX formation potential of model compounds. Literature data for 49 compounds were used to train the QSPR in moles of chlorine per mole of compound (Cp) (mol-Cl/mol-Cp). The resulting QSPR has four descriptors, calibration [Formula: see text] of 0.72 and standard deviation of estimation of 0.43 mol-Cl/mol-Cp. Internal and external validation indicate that the QSPR has good predictive power and low bias (<1%). Applying this QSPR to predict TOX formation by DOM surrogates - tannic acid, two model fulvic acids and two agent-based model assemblages - gave a predicted TOX range of 136-184 µg-Cl/mg-C, consistent with experimental data for DOM, which ranged from 78 to 192 µg-Cl/mg-C. However, the limited structural variation in the training data may limit QSPR applicability; studies of more sulfur-containing compounds, heterocyclic compounds and high molecular weight compounds could lead to a more widely applicable QSPR.
Combinatorial QSAR Modeling of Rat Acute Toxicity by Oral Exposure
Quantitative Structure-Activity Relationship (QSAR) toxicity models have become popular tools for identifying potential toxic compounds and prioritizing candidates for animal toxicity tests. However, few QSAR studies have successfully modeled large, diverse mammalian toxicity end...
Jin, Zhinan; Kinkade, April; Behera, Ishani; Chaudhuri, Shuvam; Tucker, Kathryn; Dyatkina, Natalia; Rajwanshi, Vivek K; Wang, Guangyi; Jekle, Andreas; Smith, David B; Beigelman, Leo; Symons, Julian A; Deval, Jerome
2017-07-01
Recent cases of severe toxicity during clinical trials have been associated with antiviral ribonucleoside analogs (e.g. INX-08189 and balapiravir). Some have hypothesized that the active metabolites of toxic ribonucleoside analogs, the triphosphate forms, inadvertently target human mitochondrial RNA polymerase (POLRMT), thus inhibiting mitochondrial RNA transcription and protein synthesis. Others have proposed that the prodrug moiety released from the ribonucleoside analogs might instead cause toxicity. Here, we report the mitochondrial effects of several clinically relevant and structurally diverse ribonucleoside analogs including NITD-008, T-705 (favipiravir), R1479 (parent nucleoside of balapiravir), PSI-7851 (sofosbuvir), and INX-08189 (BMS-986094). We found that efficient substrates and chain terminators of POLRMT, such as the nucleoside triphosphate forms of R1479, NITD-008, and INX-08189, are likely to cause mitochondrial toxicity in cells, while weaker chain terminators and inhibitors of POLRMT such as T-705 ribonucleoside triphosphate do not elicit strong in vitro mitochondrial effects. Within a fixed 3'-deoxy or 2'-C-methyl ribose scaffold, changing the base moiety of nucleotides did not strongly affect their inhibition constant (K i ) against POLRMT. By swapping the nucleoside and prodrug moieties of PSI-7851 and INX-08189, we demonstrated that the cell-based toxicity of INX-08189 is mainly caused by the nucleoside component of the molecule. Taken together, these results show that diverse 2' or 4' mono-substituted ribonucleoside scaffolds cause mitochondrial toxicity. Given the unpredictable structure-activity relationship of this ribonucleoside liability, we propose a rapid and systematic in vitro screen combining cell-based and biochemical assays to identify the early potential for mitochondrial toxicity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.
Physiologically based pharmacokinetic (PBPK) models bridge the gap between in vitro assays and in vivo effects by accounting for the adsorption, distribution, metabolism, and excretion of xenobiotics, which is especially useful in the assessment of human toxicity. Quantitative st...
Wignall, Jessica A; Muratov, Eugene; Sedykh, Alexander; Guyton, Kathryn Z; Tropsha, Alexander; Rusyn, Ivan; Chiu, Weihsueh A
2018-05-01
Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q 2 of 0.25-0.45, mean model errors of 0.70-1.11 log 10 units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org. An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.
Hu, Jing; Wu, Tian-Ming; Li, Hong-Ze; Zuo, Ze-Ping; Zhao, Ying-Lan; Yang, Li
2017-08-01
Cisplatin is a widely used antineoplastic drug, while its nephrotoxicity limits the clinical application. Although several mechanisms contributing to nephrotoxicity have been reported, the direct protein targets are unclear. Herein we reported the synthesis of 29 cisplatin derivatives and the structure-toxicity relationship (STR) of these compounds with MTT assay in human renal proximal tubule cells (HK-2) and pig kidney epithelial cells (LLC-PK1). To the best of our knowledge, this study represented the first report regarding the structure-toxicity relationship (STR) of cisplatin derivatives. The potency of biotin-pyridine conjugated derivative 3 met the requirement for target identification, and the preliminary chemical proteomics results suggested that it is a promising tool for further target identification of cisplatin-induced nephrotoxicity. Copyright © 2017. Published by Elsevier Ltd.
2011-09-22
OPs) are a group of pesticides that inhibit enzymes such as acetylcholinesterase. Numerous OP structural variants exist and toxicity data can be...and human toxicity studies especially for OPs lacking experimental data. 15. SUBJECT TERMS QSAR Organophosphates...structure and mechanism of toxicity c) Linking QSAR and OP PBPK/PD 2. Methods a) Physiochemical Descriptors b) Regression Techniques 3. Results a
The taste of toxicity: A quantitative analysis of bitter and toxic molecules.
Nissim, Ido; Dagan-Wiener, Ayana; Niv, Masha Y
2017-12-01
The role of bitter taste-one of the few basic taste modalities-is commonly assumed to signal toxicity and alert animals against consuming harmful compounds. However, it is known that some toxic compounds are not bitter and that many bitter compounds have negligible toxicity while having important health benefits. Here we apply a quantitative analysis of the chemical space to shed light on the bitterness-toxicity relationship. Using the BitterDB dataset of bitter molecules, The BitterPredict prediction tool, and datasets of toxic compounds, we quantify the identity and similarity between bitter and toxic compounds. About 60% of the bitter compounds have documented toxicity and only 56% of the toxic compounds are known or predicted to be bitter. The LD 50 value distributions suggest that most of the bitter compounds are not very toxic, but there is a somewhat higher chance of toxicity for known bitter compounds compared to known nonbitter ones. Flavonoids and alpha acids are more common in the bitter dataset compared with the toxic dataset. In contrast, alkaloids are more common in the toxic datasets compared to the bitter dataset. Interestingly, no trend linking LD 50 values with the number of activated bitter taste receptors (TAS2Rs) subtypes is apparent in the currently available data. This is in accord with the newly discovered expression of TAS2Rs in several extra-oral tissues, in which they might be activated by yet unknown endogenous ligands and play non-gustatory physiological roles. These results suggest that bitter taste is not a very reliable marker for toxicity, and is likely to have other physiological roles. © 2017 IUBMB Life, 69(12):938-946, 2017. © 2017 International Union of Biochemistry and Molecular Biology.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Helguera, Aliuska Morales; Molecular Simulation and Drug Design, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara; Department of Chemistry, Central University of Las Villas, Santa Clara, 54830, Villa Clara
2008-09-01
In this work, Quantitative Structure-Activity Relationship (QSAR) modelling was used as a tool for predicting the carcinogenic potency of a set of 39 nitroso-compounds, which have been bioassayed in male rats by using the oral route of administration. The optimum QSAR model provided evidence of good fit and performance of predicitivity from training set. It was able to account for about 84% of the variance in the experimental activity and exhibited high values of the determination coefficients of cross validations, leave one out and bootstrapping (q{sup 2}{sub LOO} = 78.53 and q{sup 2}{sub Boot} = 74.97). Such a model wasmore » based on spectral moments weighted with Gasteiger-Marsilli atomic charges, polarizability and hydrophobicity, as well as with Abraham indexes, specifically the summation solute hydrogen bond basicity and the combined dipolarity/polarizability. This is the first study to have explored the possibility of combining Abraham solute descriptors with spectral moments. A reasonable interpretation of these molecular descriptors from a toxicological point of view was achieved by means of taking into account bond contributions. The set of relationships so derived revealed the importance of the length of the alkyl chains for determining carcinogenic potential of the chemicals analysed, and were able to explain the difference between mono-substituted and di-substituted nitrosoureas as well as to discriminate between isomeric structures with hydroxyl-alkyl and alkyl substituents in different positions. Moreover, they allowed the recognition of structural alerts in classical structures of two potent nitrosamines, consistent with their biotransformation. These results indicate that this new approach has the potential for improving carcinogenicity predictions based on the identification of structural alerts.« less
Ecological Structure Activity Relationships
Ecological Structure Activity Relationships, v1.00a, February 2009
ECOSAR (Ecological Structure Activity Relationships) is a personal computer software program that is used to estimate the toxicity of chemicals used in industry and discharged into water. The program predicts...
The toxic outcomes associated with environmental contaminants are often not due to the chemical form that was originally introduced into the environment, but rather to the chemical having undergone a transformation prior to reaching the vulnerable species. More importantly, the c...
Although ranking schemes based on exposure and toxicity have been developed to aid in the prioritization of research funds for identifying chemicals of regulatory concern, there are significant gaps in the availability of experimental toxicity data for most health endpoints. Pred...
NASA Astrophysics Data System (ADS)
Shevade, Abhijit V.; Ryan, Margaret A.; Homer, Margie L.; Zhou, Hanying; Manfreda, Allison M.; Lara, Liana M.; Yen, Shiao-Pin S.; Jewell, April D.; Manatt, Kenneth S.; Kisor, Adam K.
We have developed a Quantitative Structure-Activity Relationships (QSAR) based approach to correlate the response of chemical sensors in an array with molecular descriptors. A novel molecular descriptor set has been developed; this set combines descriptors of sensing film-analyte interactions, representing sensor response, with a basic analyte descriptor set commonly used in QSAR studies. The descriptors are obtained using a combination of molecular modeling tools and empirical and semi-empirical Quantitative Structure-Property Relationships (QSPR) methods. The sensors under investigation are polymer-carbon sensing films which have been exposed to analyte vapors at parts-per-million (ppm) concentrations; response is measured as change in film resistance. Statistically validated QSAR models have been developed using Genetic Function Approximations (GFA) for a sensor array for a given training data set. The applicability of the sensor response models has been tested by using it to predict the sensor activities for test analytes not considered in the training set for the model development. The validated QSAR sensor response models show good predictive ability. The QSAR approach is a promising computational tool for sensing materials evaluation and selection. It can also be used to predict response of an existing sensing film to new target analytes.
Zhu, Hao; Ye, Lin; Richard, Ann; Golbraikh, Alexander; Wright, Fred A.; Rusyn, Ivan; Tropsha, Alexander
2009-01-01
Background Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Objective A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. Methods and results A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC50) and in vivo rodent median lethal dose (LD50) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure–activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD50 values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC50 and LD50. However, a linear IC50 versus LD50 correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC50 and LD50 values: One group comprises compounds with linear IC50 versus LD50 relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD50 values from chemical descriptors. All models were extensively validated using special protocols. Conclusions The novelty of this modeling approach is that it uses the relationships
Quantitative structure-property relationship modeling of Grätzel solar cell dyes.
Venkatraman, Vishwesh; Åstrand, Per-Olof; Alsberg, Bjørn Kåre
2014-01-30
With fossil fuel reserves on the decline, there is increasing focus on the design and development of low-cost organic photovoltaic devices, in particular, dye-sensitized solar cells (DSSCs). The power conversion efficiency (PCE) of a DSSC is heavily influenced by the chemical structure of the dye. However, as far as we know, no predictive quantitative structure-property relationship models for DSSCs with PCE as one of the response variables have been reported. Thus, we report for the first time the successful application of comparative molecular field analysis (CoMFA) and vibrational frequency-based eigenvalue (EVA) descriptors to model molecular structure-photovoltaic performance relationships for a set of 40 coumarin derivatives. The results show that the models obtained provide statistically robust predictions of important photovoltaic parameters such as PCE, the open-circuit voltage (V(OC)), short-circuit current (J(SC)) and the peak absorption wavelength λ(max). Some of our findings based on the analysis of the models are in accordance with those reported in the literature. These structure-property relationships can be applied to the rational structural design and evaluation of new photovoltaic materials. Copyright © 2013 Wiley Periodicals, Inc.
Current data regarding the structure-toxicity relationship of boron-containing compounds.
Farfán-García, E D; Castillo-Mendieta, N T; Ciprés-Flores, F J; Padilla-Martínez, I I; Trujillo-Ferrara, J G; Soriano-Ursúa, M A
2016-09-06
Boron is ubiquitous in nature, being an essential element of diverse cells. As a result, humans have had contact with boron containing compounds (BCCs) for a long time. During the 20th century, BCCs were developed as antiseptics, antibiotics, cosmetics and insecticides. Boric acid was freely used in the nosocomial environment as an antiseptic and sedative salt, leading to the death of patients and an important discovery about its critical toxicology for humans. Since then the many toxicological studies done in relation to BCCs have helped to establish the proper limits of their use. During the last 15 years, there has been a boom of research on the design and use of new, potent and efficient boron containing drugs, finding that the addition of boron to some known drugs increases their affinity and selectivity. This mini-review summarizes two aspects of BCCs: toxicological data found with experimental models, and the scarce but increasing data about the structure-activity relationship for toxicity and therapeutic use. As is the case with boron-free compounds, the biological activity of BCCs is related to their chemical structure. We discuss the use of new technology to discover potent and efficient BCCs for medicinal therapy by avoiding toxic effects. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Newman, M C; McCloskey, J T; Tatara, C P
1998-01-01
Ecological risk assessment can be enhanced with predictive models for metal toxicity. Modelings of published data were done under the simplifying assumption that intermetal trends in toxicity reflect relative metal-ligand complex stabilities. This idea has been invoked successfully since 1904 but has yet to be applied widely in quantitative ecotoxicology. Intermetal trends in toxicity were successfully modeled with ion characteristics reflecting metal binding to ligands for a wide range of effects. Most models were useful for predictive purposes based on an F-ratio criterion and cross-validation, but anomalous predictions did occur if speciation was ignored. In general, models for metals with the same valence (i.e., divalent metals) were better than those combining mono-, di-, and trivalent metals. The softness parameter (sigma p) and the absolute value of the log of the first hydrolysis constant ([symbol: see text] log KOH [symbol: see text]) were especially useful in model construction. Also, delta E0 contributed substantially to several of the two-variable models. In contrast, quantitative attempts to predict metal interactions in binary mixtures based on metal-ligand complex stabilities were not successful. PMID:9860900
Nie, Quandeng; Xu, Xiaoyi; Zhang, Qi; Ma, Yuying; Yin, Zheng; Shang, Luqing
2018-06-07
A three-dimensional quantitative structure-activity relationships model of enterovirus A71 3C protease inhibitors was constructed in this study. The protein-ligand interaction fingerprint was analyzed to generate a pharmacophore model. A predictive and reliable three-dimensional quantitative structure-activity relationships model was built based on the Flexible Alignment of AutoGPA. Moreover, three novel compounds (I-III) were designed and evaluated for their biochemical activity against 3C protease and anti-enterovirus A71 activity in vitro. III exhibited excellent inhibitory activity (IC 50 =0.031 ± 0.005 μM, EC 50 =0.036 ± 0.007 μM). Thus, this study provides a useful quantitative structure-activity relationships model to develop potent inhibitors for enterovirus A71 3C protease. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
De Benedetti, Pier G; Fanelli, Francesca
2018-03-21
Simple comparative correlation analyses and quantitative structure-kinetics relationship (QSKR) models highlight the interplay of kinetic rates and binding affinity as an essential feature in drug design and discovery. The choice of the molecular series, and their structural variations, used in QSKR modeling is fundamental to understanding the mechanistic implications of ligand and/or drug-target binding and/or unbinding processes. Here, we discuss the implications of linear correlations between kinetic rates and binding affinity constants and the relevance of the computational approaches to QSKR modeling. Copyright © 2018 Elsevier Ltd. All rights reserved.
Nandi, Sisir; Monesi, Alessandro; Drgan, Viktor; Merzel, Franci; Novič, Marjana
2013-10-30
In the present study, we show the correlation of quantum chemical structural descriptors with the activation barriers of the Diels-Alder ligations. A set of 72 non-catalysed Diels-Alder reactions were subjected to quantitative structure-activation barrier relationship (QSABR) under the framework of theoretical quantum chemical descriptors calculated solely from the structures of diene and dienophile reactants. Experimental activation barrier data were obtained from literature. Descriptors were computed using Hartree-Fock theory using 6-31G(d) basis set as implemented in Gaussian 09 software. Variable selection and model development were carried out by stepwise multiple linear regression methodology. Predictive performance of the quantitative structure-activation barrier relationship (QSABR) model was assessed by training and test set concept and by calculating leave-one-out cross-validated Q2 and predictive R2 values. The QSABR model can explain and predict 86.5% and 80% of the variances, respectively, in the activation energy barrier training data. Alternatively, a neural network model based on back propagation of errors was developed to assess the nonlinearity of the sought correlations between theoretical descriptors and experimental reaction barriers. A reasonable predictability for the activation barrier of the test set reactions was obtained, which enabled an exploration and interpretation of the significant variables responsible for Diels-Alder interaction between dienes and dienophiles. Thus, studies in the direction of QSABR modelling that provide efficient and fast prediction of activation barriers of the Diels-Alder reactions turn out to be a meaningful alternative to transition state theory based computation.
Du, Qi-Shi; Huang, Ri-Bo; Wei, Yu-Tuo; Pang, Zong-Wen; Du, Li-Qin; Chou, Kuo-Chen
2009-01-30
In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus. (c) 2008 Wiley Periodicals, Inc.
Comber, Mike H I; Walker, John D; Watts, Chris; Hermens, Joop
2003-08-01
The use of quantitative structure-activity relationships (QSARs) for deriving the predicted no-effect concentration of discrete organic chemicals for the purposes of conducting a regulatory risk assessment in Europe and the United States is described. In the United States, under the Toxic Substances Control Act (TSCA), the TSCA Interagency Testing Committee and the U.S. Environmental Protection Agency (U.S. EPA) use SARs to estimate the hazards of existing and new chemicals. Within the Existing Substances Regulation in Europe, QSARs may be used for data evaluation, test strategy indications, and the identification and filling of data gaps. To illustrate where and when QSARs may be useful and when their use is more problematic, an example, methyl tertiary-butyl ether (MTBE), is given and the predicted and experimental data are compared. Improvements needed for new QSARs and tools for developing and using QSARs are discussed.
Nakai, S; Li-Chan, E
1985-10-01
According to the original idea of quantitative structure-activity relationship, electric, hydrophobic, and structural parameters should be taken into consideration for elucidating functionality. Changes in these parameters are reflected in the property of protein solubility upon modification of whey proteins by heating. Although solubility is itself a functional property, it has been utilized to explain other functionalities of proteins. However, better correlations were obtained when hydrophobic parameters of the proteins were used in conjunction with solubility. Various treatments reported in the literature were applied to whey protein concentrate in an attempt to obtain whipping and gelling properties similar to those of egg white. Mapping simplex optimization was used to search for the best results. Improvement in whipping properties by pepsin hydrolysis may have been due to higher protein solubility, and good gelling properties resulting from polyphosphate treatment may have been due to an increase in exposable hydrophobicity. However, the results of angel food cake making were still unsatisfactory.
Quantitative Structure-Cytotoxicity Relationship of Oleoylamides.
Sakagami, Hiroshi; Uesawa, Yoshihiro; Ishihara, Mariko; Kagaya, Hajime; Kanamoto, Taisei; Terakubo, Shigemi; Nakashima, Hideki; Takao, Koichi; Sugita, Yoshiaki
2015-10-01
Eighteen oleoylamides were subjected to quantitative structure-activity relationship analysis based on their cytotoxicity, tumor selectivity and anti-HIV activity, in order to assess their biological activities. Cytotoxicity against four human oral squamous cell carcinoma (OSCC) cell lines and five human oral normal cells (gingival fibroblast, periodontal ligament fibroblast, pulp cell, oral keratinocyte, primary gingival epithelial cells) was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Tumor-selectivity (TS) was evaluated by the ratio of the mean 50% cytotoxic concentration (CC50) against normal human oral cells to that against OSCC cell lines. Potency-selectivity expression (PSE) was determined by the ratio of TS to CC50 against OSCC. Anti-HIV activity was evaluated by the ratio of CC50 to the concentration leading to 50% cytoprotection from HIV infection (EC50). Physicochemical, structural and quantum-chemical parameters were calculated based on the conformations optimized by the LowModeMD method. Among 18 derivatives, compounds 8: with a catechol group) and 18: with a (2-pyridyl)amino group) had the highest TS. On the other hand, doxorubicin and 5-fluorouracil (5-FU) were more highly cytotoxic to normal epithelial cells, displaying unexpectedly lower TS and PSE values. None of the compounds had anti-HIV activity. Among 330 chemical descriptors, 75, 73 and 19 descriptors significantly correlated to the cytotoxicity to normal and tumor cells, and TS, respectively. Multivariate statistics with chemical descriptors for molecular polarization and hydrophobicity may be useful for the evaluation of cytotoxicity and TS of oleoylamides. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.
Patlewicz, Grace Y; Basketter, David A; Pease, Camilla K Smith; Wilson, Karen; Wright, Zoe M; Roberts, David W; Bernard, Guillaume; Arnau, Elena Giménez; Lepoittevin, Jean-Pierre
2004-02-01
Fragrance substances represent a very diverse group of chemicals; a proportion of them are associated with the ability to cause allergic reactions in the skin. Efforts to find substitute materials are hindered by the need to undertake animal testing for determining both skin sensitization hazard and potency. One strategy to avoid such testing is through an understanding of the relationships between chemical structure and skin sensitization, so-called structure-activity relationships. In recent work, we evaluated 2 groups of fragrance chemicals -- saturated aldehydes and alpha,beta-unsaturated aldehydes. Simple quantitative structure-activity relationship (QSAR) models relating the EC3 values [derived from the local lymph node assay (LLNA)] to physicochemical properties were developed for both sets of aldehydes. In the current study, we evaluated an additional group of carbonyl-containing compounds to test the predictive power of the developed QSARs and to extend their scope. The QSAR models were used to predict EC3 values of 10 newly selected compounds. Local lymph node assay data generated for these compounds demonstrated that the original QSARs were fairly accurate, but still required improvement. Development of these QSAR models has provided us with a better understanding of the potential mechanisms of action for aldehydes, and hence how to avoid or limit allergy. Knowledge generated from this work is being incorporated into new/improved rules for sensitization in the expert toxicity prediction system, deductive estimation of risk from existing knowledge (DEREK).
Quantitative Prediction of Systemic Toxicity Points of Departure (OpenTox USA 2017)
Human health risk assessment associated with environmental chemical exposure is limited by the tens of thousands of chemicals little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative models based on chemical structure information, are c...
Quantitative structure activity relationship studies of mushroom tyrosinase inhibitors
NASA Astrophysics Data System (ADS)
Xue, Chao-Bin; Luo, Wan-Chun; Ding, Qi; Liu, Shou-Zhu; Gao, Xing-Xiang
2008-05-01
Here, we report our results from quantitative structure-activity relationship studies on tyrosinase inhibitors. Interactions between benzoic acid derivatives and tyrosinase active sites were also studied using a molecular docking method. These studies indicated that one possible mechanism for the interaction between benzoic acid derivatives and the tyrosinase active site is the formation of a hydrogen-bond between the hydroxyl (aOH) and carbonyl oxygen atoms of Tyr98, which stabilized the position of Tyr98 and prevented Tyr98 from participating in the interaction between tyrosinase and ORF378. Tyrosinase, also known as phenoloxidase, is a key enzyme in animals, plants and insects that is responsible for catalyzing the hydroxylation of tyrosine into o-diphenols and the oxidation of o-diphenols into o-quinones. In the present study, the bioactivities of 48 derivatives of benzaldehyde, benzoic acid, and cinnamic acid compounds were used to construct three-dimensional quantitative structure-activity relationship (3D-QSAR) models using comparative molecular field (CoMFA) and comparative molecular similarity indices (CoMSIA) analyses. After superimposition using common substructure-based alignments, robust and predictive 3D-QSAR models were obtained from CoMFA ( q 2 = 0.855, r 2 = 0.978) and CoMSIA ( q 2 = 0.841, r 2 = 0.946), with 6 optimum components. Chemical descriptors, including electronic (Hammett σ), hydrophobic (π), and steric (MR) parameters, hydrogen bond acceptor (H-acc), and indicator variable ( I), were used to construct a 2D-QSAR model. The results of this QSAR indicated that π, MR, and H-acc account for 34.9, 31.6, and 26.7% of the calculated biological variance, respectively. The molecular interactions between ligand and target were studied using a flexible docking method (FlexX). The best scored candidates were docked flexibly, and the interaction between the benzoic acid derivatives and the tyrosinase active site was elucidated in detail. We believe
Wu, Wensheng; Zhang, Canyang; Lin, Wenjing; Chen, Quan; Guo, Xindong; Qian, Yu; Zhang, Lijuan
2015-01-01
Self-assembled nano-micelles of amphiphilic polymers represent a novel anticancer drug delivery system. However, their full clinical utilization remains challenging because the quantitative structure-property relationship (QSPR) between the polymer structure and the efficacy of micelles as a drug carrier is poorly understood. Here, we developed a series of QSPR models to account for the drug loading capacity of polymeric micelles using the genetic function approximation (GFA) algorithm. These models were further evaluated by internal and external validation and a Y-randomization test in terms of stability and generalization, yielding an optimization model that is applicable to an expanded materials regime. As confirmed by experimental data, the relationship between microstructure and drug loading capacity can be well-simulated, suggesting that our models are readily applicable to the quantitative evaluation of the drug-loading capacity of polymeric micelles. Our work may offer a pathway to the design of formulation experiments.
Overview of T.E.S.T. (Toxicity Estimation Software Tool)
This talk provides an overview of T.E.S.T. (Toxicity Estimation Software Tool). T.E.S.T. predicts toxicity values and physical properties using a variety of different QSAR (quantitative structure activity relationship) approaches including hierarchical clustering, group contribut...
Lin, Wenjing; Chen, Quan; Guo, Xindong; Qian, Yu; Zhang, Lijuan
2015-01-01
Self-assembled nano-micelles of amphiphilic polymers represent a novel anticancer drug delivery system. However, their full clinical utilization remains challenging because the quantitative structure-property relationship (QSPR) between the polymer structure and the efficacy of micelles as a drug carrier is poorly understood. Here, we developed a series of QSPR models to account for the drug loading capacity of polymeric micelles using the genetic function approximation (GFA) algorithm. These models were further evaluated by internal and external validation and a Y-randomization test in terms of stability and generalization, yielding an optimization model that is applicable to an expanded materials regime. As confirmed by experimental data, the relationship between microstructure and drug loading capacity can be well-simulated, suggesting that our models are readily applicable to the quantitative evaluation of the drug-loading capacity of polymeric micelles. Our work may offer a pathway to the design of formulation experiments. PMID:25780923
Ivanciuc, Ovidiu
2013-06-01
Chemical and molecular graphs have fundamental applications in chemoinformatics, quantitative structureproperty relationships (QSPR), quantitative structure-activity relationships (QSAR), virtual screening of chemical libraries, and computational drug design. Chemoinformatics applications of graphs include chemical structure representation and coding, database search and retrieval, and physicochemical property prediction. QSPR, QSAR and virtual screening are based on the structure-property principle, which states that the physicochemical and biological properties of chemical compounds can be predicted from their chemical structure. Such structure-property correlations are usually developed from topological indices and fingerprints computed from the molecular graph and from molecular descriptors computed from the three-dimensional chemical structure. We present here a selection of the most important graph descriptors and topological indices, including molecular matrices, graph spectra, spectral moments, graph polynomials, and vertex topological indices. These graph descriptors are used to define several topological indices based on molecular connectivity, graph distance, reciprocal distance, distance-degree, distance-valency, spectra, polynomials, and information theory concepts. The molecular descriptors and topological indices can be developed with a more general approach, based on molecular graph operators, which define a family of graph indices related by a common formula. Graph descriptors and topological indices for molecules containing heteroatoms and multiple bonds are computed with weighting schemes based on atomic properties, such as the atomic number, covalent radius, or electronegativity. The correlation in QSPR and QSAR models can be improved by optimizing some parameters in the formula of topological indices, as demonstrated for structural descriptors based on atomic connectivity and graph distance.
Quantitative structure-cytotoxicity relationship of phenylpropanoid amides.
Shimada, Chiyako; Uesawa, Yoshihiro; Ishihara, Mariko; Kagaya, Hajime; Kanamoto, Taisei; Terakubo, Shigemi; Nakashima, Hideki; Takao, Koichi; Saito, Takayuki; Sugita, Yoshiaki; Sakagami, Hiroshi
2014-07-01
A total of 12 phenylpropanoid amides were subjected to quantitative structure-activity relationship (QSAR) analysis, based on their cytotoxicity, tumor selectivity and anti-HIV activity, in order to investigate on their biological activities. Cytotoxicity against four human oral squamous cell carcinoma (OSCC) cell lines and three human oral normal cells was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Tumor selectivity was evaluated by the ratio of the mean CC50 (50% cytotoxic concentration) against normal oral cells to that against OSCC cell lines. Anti-HIV activity was evaluated by the ratio of CC50 to EC50 (50% cytoprotective concentration from HIV infection). Physicochemical, structural, and quantum-chemical parameters were calculated based on the conformations optimized by the LowModeMD method followed by density functional theory (DFT) method. Twelve phenylpropanoid amides showed moderate cytotoxicity against both normal and OSCC cell lines. N-Caffeoyl derivatives coupled with vanillylamine and tyramine exhibited relatively higher tumor selectivity. Cytotoxicity against normal cells was correlated with descriptors related to electrostatic interaction such as polar surface area and chemical hardness, whereas cytotoxicity against tumor cells correlated with free energy, surface area and ellipticity. The tumor-selective cytotoxicity correlated with molecular size (surface area) and electrostatic interaction (the maximum electrostatic potential). The molecular size, shape and ability for electrostatic interaction are useful parameters for estimating the tumor selectivity of phenylpropanoid amides. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.
Nonparametric regression applied to quantitative structure-activity relationships
Constans; Hirst
2000-03-01
Several nonparametric regressors have been applied to modeling quantitative structure-activity relationship (QSAR) data. The simplest regressor, the Nadaraya-Watson, was assessed in a genuine multivariate setting. Other regressors, the local linear and the shifted Nadaraya-Watson, were implemented within additive models--a computationally more expedient approach, better suited for low-density designs. Performances were benchmarked against the nonlinear method of smoothing splines. A linear reference point was provided by multilinear regression (MLR). Variable selection was explored using systematic combinations of different variables and combinations of principal components. For the data set examined, 47 inhibitors of dopamine beta-hydroxylase, the additive nonparametric regressors have greater predictive accuracy (as measured by the mean absolute error of the predictions or the Pearson correlation in cross-validation trails) than MLR. The use of principal components did not improve the performance of the nonparametric regressors over use of the original descriptors, since the original descriptors are not strongly correlated. It remains to be seen if the nonparametric regressors can be successfully coupled with better variable selection and dimensionality reduction in the context of high-dimensional QSARs.
Wu, Bin; Song, Jinming; Li, Xuegang
2014-10-15
The objective of the present study was to examine the relationships between benthic community structure and toxic metals using bivariate/multivariate techniques at 17 sediment locations in Laizhou Bay, North China. Sediment chemical data were evaluated against geochemical background values and sediment quality guidelines, which identified Cu and As as contaminants of concern with a moderate potential for adverse effects. Benthic community data were subjected to non-metric multidimensional scaling, which generated four groups of stations. Spearman rank correlation was then employed to explore the relationships between the major axes of heavy metals and benthic community structure. However, weak and insignificant correlations were found between these axes, indicating that contaminants of concern may not be the primary explanatory factors. Polychaeta were abundant in southern Laizhou Bay, serving as a warning regarding the health status of the ecosystem. Integrated sediment quality assessment showed sediments from northern central locations were impaired, displaying less diverse benthos and higher metal contamination. Copyright © 2014 Elsevier Ltd. All rights reserved.
Quantitative structure-cytotoxicity relationship of piperic acid amides.
Shimada, Chiyako; Uesawa, Yoshihiro; Ishihara, Mariko; Kagaya, Hajime; Kanamoto, Taisei; Terakubo, Shigemi; Nakashima, Hideki; Takao, Koichi; Miyashiro, Takaki; Sugita, Yoshiaki; Sakagami, Hiroshi
2014-09-01
A total of 12 piperic acid amides, including piperine, were subjected to quantitative structure-activity relationship (QSAR) analysis, based on their cytotoxicity, tumor selectivity and anti-HIV activity, in order to find new biological activities. Cytotoxicity against four human oral squamous cell carcinoma (OSCC) cell lines and three human oral normal cells was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Tumor selectivity was evaluated by the ratio of the mean 50% cytotoxic concentration (CC50) against normal oral cells to that against OSCC cell lines. Anti-HIV activity was evaluated by the ratio of the CC50 to 50% HIV infection-cytoprotective concentration (EC50). Physicochemical, structural, and quantum-chemical parameters were calculated based on the conformations optimized by LowModeMD method followed by density functional theory method. All compounds showed low-to-moderate tumor selectivity, but no anti-HIV activity. N-Piperoyldopamine ( 8: ) which has a catechol moiety, showed the highest tumor selectivity, possibly due to its unique molecular shape and electrostatic interaction, especially its largest partial equalization of orbital electronegativities and vsurf descriptors. The present study suggests that molecular shape and ability for electrostatic interaction are useful parameters for estimating the tumor selectivity of piperic acid amides. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.
Dickenson, E R V; Drewes, J E
2010-01-01
Isotherms were determined for the adsorption of five pharmaceutical residues, primidone, carbamazepine, ibuprofen, naproxen and diclofenac, to Calgon Filtrasorb 300 powdered activated carbon (PAC). The sorption behavior was examined in ultra-pure and wastewater effluent organic matter (EfOM) matrices, where more sorption was observed in the ultra-pure water for PAC doses greater than 10 mg/L suggesting the presence of EfOM hinders the sorption of the pharmaceuticals to the PAC. Adsorption behaviors were described by the Freundlich isotherm model. Quantitative structure property relationships (QSPRs) in the form of polyparameter linear solvation energy relationships were developed for simulating the Freundlich adsorption capacity in both ultra-pure and EfOM matrices. The significant 3D-based descriptors for the QSPRs were the molar volume, polarizability and hydrogen-bond donor parameters.
Rusyn, Ivan; Sedykh, Alexander; Guyton, Kathryn Z.; Tropsha, Alexander
2012-01-01
Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR–like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage. PMID:22387746
Pradeep, Prachi; Povinelli, Richard J; Merrill, Stephen J; Bozdag, Serdar; Sem, Daniel S
2015-04-01
The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Relationship between Composition and Toxicity of Motor Vehicle Emission Samples
McDonald, Jacob D.; Eide, Ingvar; Seagrave, JeanClare; Zielinska, Barbara; Whitney, Kevin; Lawson, Douglas R.; Mauderly, Joe L.
2004-01-01
In this study we investigated the statistical relationship between particle and semivolatile organic chemical constituents in gasoline and diesel vehicle exhaust samples, and toxicity as measured by inflammation and tissue damage in rat lungs and mutagenicity in bacteria. Exhaust samples were collected from “normal” and “high-emitting” gasoline and diesel light-duty vehicles. We employed a combination of principal component analysis (PCA) and partial least-squares regression (PLS; also known as projection to latent structures) to evaluate the relationships between chemical composition of vehicle exhaust and toxicity. The PLS analysis revealed the chemical constituents covarying most strongly with toxicity and produced models predicting the relative toxicity of the samples with good accuracy. The specific nitro-polycyclic aromatic hydrocarbons important for mutagenicity were the same chemicals that have been implicated by decades of bioassay-directed fractionation. These chemicals were not related to lung toxicity, which was associated with organic carbon and select organic compounds that are present in lubricating oil. The results demonstrate the utility of the PCA/PLS approach for evaluating composition–response relationships in complex mixture exposures and also provide a starting point for confirming causality and determining the mechanisms of the lung effects. PMID:15531438
Quantitative studies on structure-DPPH• scavenging activity relationships of food phenolic acids.
Jing, Pu; Zhao, Shu-Juan; Jian, Wen-Jie; Qian, Bing-Jun; Dong, Ying; Pang, Jie
2012-11-01
Phenolic acids are potent antioxidants, yet the quantitative structure-activity relationships of phenolic acids remain unclear. The purpose of this study was to establish 3D-QSAR models able to predict phenolic acids with high DPPH• scavenging activity and understand their structure-activity relationships. The model has been established by using a training set of compounds with cross-validated q2 = 0.638/0.855, non-cross-validated r2 = 0.984/0.986, standard error of estimate = 0.236/0.216, and F = 139.126/208.320 for the best CoMFA/CoMSIA models. The predictive ability of the models was validated with the correlation coefficient r2(pred) = 0.971/0.996 (>0.6) for each model. Additionally, the contour map results suggested that structural characteristics of phenolics acids favorable for the high DPPH• scavenging activity might include: (1) bulky and/or electron-donating substituent groups on the phenol ring; (2) electron-donating groups at the meta-position and/or hydrophobic groups at the meta-/ortho-position; (3) hydrogen-bond donor/electron-donating groups at the ortho-position. The results have been confirmed based on structural analyses of phenolic acids and their DPPH• scavenging data from eight recent publications. The findings may provide deeper insight into the antioxidant mechanisms and provide useful information for selecting phenolic acids for free radical scavenging properties.
Sharma, Mukesh C; Sharma, S
2016-12-01
A series of 2-dihydro-4-quinazolin with potent highly selective inhibitors of inducible nitric oxide synthase activities was subjected to quantitative structure activity relationships (QSAR) analysis. Statistically significant equations with high correlation coefficient (r 2 = 0.8219) were developed. The k-nearest neighbor model has showed good cross-validated correlation coefficient and external validation values of 0.7866 and 0.7133, respectively. The selected electrostatic field descriptors the presence of blue ball around R1 and R4 in the quinazolinamine moiety showed electronegative groups favorable for nitric oxide synthase activity. The QSAR models may lead to the structural requirements of inducible nitric oxide compounds and help in the design of new compounds.
Naik, P K; Singh, T; Singh, H
2009-07-01
Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.
The mode of toxic action (MOA) has been recognized as a key determinant of chemical toxicity and as an alternative to chemical class-based predictive toxicity modeling. However, the development of quantitative structure activity relationship (QSAR) and other models has been limit...
Researchers facilitated evaluation of chemicals that lack chronic oral toxicity values using a QSAR model to develop estimates of potential toxicity for chemicals used in HF fluids or found in flowback or produced water
Liu, Fengping; Cao, Chenzhong; Cheng, Bin
2011-01-01
A quantitative structure–property relationship (QSPR) analysis of aliphatic alcohols is presented. Four physicochemical properties were studied: boiling point (BP), n-octanol–water partition coefficient (lg POW), water solubility (lg W) and the chromatographic retention indices (RI) on different polar stationary phases. In order to investigate the quantitative structure–property relationship of aliphatic alcohols, the molecular structure ROH is divided into two parts, R and OH to generate structural parameter. It was proposed that the property is affected by three main factors for aliphatic alcohols, alkyl group R, substituted group OH, and interaction between R and OH. On the basis of the polarizability effect index (PEI), previously developed by Cao, the novel molecular polarizability effect index (MPEI) combined with odd-even index (OEI), the sum eigenvalues of bond-connecting matrix (SX1CH) previously developed in our team, were used to predict the property of aliphatic alcohols. The sets of molecular descriptors were derived directly from the structure of the compounds based on graph theory. QSPR models were generated using only calculated descriptors and multiple linear regression techniques. These QSPR models showed high values of multiple correlation coefficient (R > 0.99) and Fisher-ratio statistics. The leave-one-out cross-validation demonstrated the final models to be statistically significant and reliable. PMID:21731451
A set of literature data was used to derive several quantitative structure-activity relationships (QSARs) to predict the rate constants for the microbial reductive dehalogenation of chlorinated aromatics. Dechlorination rate constants for 25 chloroaromatics were corrected for th...
Passino, Dora R.M.; Hickey, James P.; Frank, Anthony M.
1988-01-01
In the Laurentian Great Lakes, more than 300 contaminants have been identified in fish, other biota, water, and sediment. Current hazard assessment of these chemicals by the National Fisheries Research Center-Great Lakes is based on their toxicity, occurrence in the environment, and source. Although scientists at the Center have tested over 70 chemicals with the crustacean Daphnia pulex, the number of experimental data needed to screen the huge array of chemicals in the Great Lakes exceeds the practical capabilities of conducting bioassays. This limitation can be partly circumvented, however, by using mathematical models based on quantitative structure-activity relationships (QSAR) to provide rapid, inexpensive estimates of toxicity. Many properties of chemicals, including toxicity, bioaccumulation and water solubility are well correlated and can be predicted by equations of the generalized linear solvation energy relationships (LSER). The equation we used to model solute toxicity is Toxicity = constant + mVI/100 + s (π* + dδ) + bβm + aαm where VI = intrinsic (Van der Waals) molar volume; π* = molecular dipolarity/polarizability; δ = polarizability 'correction term'; βm = solute hydrogen bond acceptor basicity; and αm = solute hydrogen bond donor acidity. The subscript m designates solute monomer values for α and β. We applied the LSER model to 48-h acute toxicity data (measured as immobilization) for six classes of chemicals detected in Great Lakes fish. The following regression was obtained for Daphnia pulex (concentration = μM): log EC50 = 4.86 - 4.35 VI/100; N = 38, r2 = 0.867, sd = 0.403 We also used the LSER modeling approach to analyze to a large published data set of 24-h acute toxicity for Daphnia magna; the following regression resulted, for eight classes of compounds (concentration = mM): log EC50 = 3.88 - 4.52 VI/100 - 1.62 π* + 1.66 βm - 0.916 αm; N = 62, r2 = 0.859, sd = 0.375 In addition we developed computer software that identifies
Mahmoud, Waleed M M; Toolaram, Anju P; Menz, Jakob; Leder, Christoph; Schneider, Mandy; Kümmerer, Klaus
2014-02-01
The fate of thalidomide (TD) was investigated after irradiation with a medium-pressure Hg-lamp. The primary elimination of TD was monitored and structures of phototransformation products (PTPs) were assessed by LC-UV-FL-MS/MS. Environmentally relevant properties of TD and its PTPs as well as hydrolysis products (HTPs) were predicted using in silico QSAR models. Mutagenicity of TD and its PTPs was investigated in the Ames microplate format (MPF) aqua assay (Xenometrix, AG). Furthermore, a modified luminescent bacteria test (kinetic luminescent bacteria test (kinetic LBT)), using the luminescent bacteria species Vibrio fischeri, was applied for the initial screening of environmental toxicity. Additionally, toxicity of phthalimide, one of the identified PTPs, was investigated separately in the kinetic LBT. The UV irradiation eliminated TD itself without complete mineralization and led to the formation of several PTPs. TD and its PTPs did not exhibit mutagenic response in the Salmonella typhimurium strains TA 98, and TA 100 with and without metabolic activation. In contrast, QSAR analysis of PTPs and HTPs provided evidence for mutagenicity, genotoxicity and carcinogenicity using additional endpoints in silico software. QSAR analysis of different ecotoxicological endpoints, such as acute toxicity towards V. fischeri, provided positive alerts for several identified PTPs and HTPs. This was partially confirmed by the results of the kinetic LBT, in which a steady increase of acute and chronic toxicity during the UV-treatment procedure was observed for the photolytic mixtures at the highest tested concentration. Moreover, the number of PTPs within the reaction mixture that might be responsible for the toxification of TD during UV-treatment was successfully narrowed down by correlating the formation kinetics of PTPs with QSAR predictions and experimental toxicity data. Beyond that, further analysis of the commercially available PTP phthalimide indicated that transformation of
Toxicity Relationship Analysis Program (TRAP) Version 1.21
The Toxicity Relationship Analysis Program (TRAP) fits a sigmoidal toxic response versus exposure variable relationship to standard toxicity test data. It will analyze binary (e.g., survival) or continuous (e.g., growth, reproduction) biological effect variables as a function o...
Quantitative Structure-Cytotoxicity Relationship of Cinnamic Acid Phenetyl Esters.
Uesawa, Yoshihiro; Sakagami, Hiroshi; Okudaira, Noriyuki; Toda, Kazuhiro; Takao, Koichi; Kagaya, Hajime; Sugita, Yoshiaki
2018-02-01
Many phenolic acid phenethyl esters possess diverse biological effects including antioxidant, cytoprotective, anti-inflammation and anti-tumor activities. However, most previous antitumor studies have not considered the cytotoxicity against normal cells. Ten cinnamic acid phenetyl esters were subjected to quantitative structure-activity relationship (QSAR) analysis, based on their cytotoxicity and tumor-specificity, in order to find their new biological activities. Cytotoxicity against four human oral squamous cell carcinoma cell lines and three oral normal mesenchymal cells was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Tumor specificity (TS) was evaluated by the ratio of the mean 50% cytotoxic concentration (CC 50 ) against normal oral cells to that against human oral squamous cell carcinoma cell lines. Potency-selectivity expression (PSE) value was calculated by dividing the TS value by CC 50 against tumor cells. Apoptosis markers were detected by western blot analysis. Physicochemical, structural and quantum-chemical parameters were calculated based on the conformations optimized by force-field minimization. Western blot analysis demonstrated that [ 9 ] stimulated the cleavage of caspase-3, suggesting the induction of apoptosis. QSAR analysis demonstrated that TS values were correlated with shape, size and ionization potential. Chemical modification of the lead compound may be a potential choice for designing a new type of anticancer drugs. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.
Quantitative Predictive Models for Systemic Toxicity (SOT)
Models to identify systemic and specific target organ toxicity were developed to help transition the field of toxicology towards computational models. By leveraging multiple data sources to incorporate read-across and machine learning approaches, a quantitative model of systemic ...
Quantitative structure-activity relationship: promising advances in drug discovery platforms.
Wang, Tao; Wu, Mian-Bin; Lin, Jian-Ping; Yang, Li-Rong
2015-12-01
Quantitative structure-activity relationship (QSAR) modeling is one of the most popular computer-aided tools employed in medicinal chemistry for drug discovery and lead optimization. It is especially powerful in the absence of 3D structures of specific drug targets. QSAR methods have been shown to draw public attention since they were first introduced. In this review, the authors provide a brief discussion of the basic principles of QSAR, model development and model validation. They also highlight the current applications of QSAR in different fields, particularly in virtual screening, rational drug design and multi-target QSAR. Finally, in view of recent controversies, the authors detail the challenges faced by QSAR modeling and the relevant solutions. The aim of this review is to show how QSAR modeling can be applied in novel drug discovery, design and lead optimization. QSAR should intentionally be used as a powerful tool for fragment-based drug design platforms in the field of drug discovery and design. Although there have been an increasing number of experimentally determined protein structures in recent years, a great number of protein structures cannot be easily obtained (i.e., membrane transport proteins and G-protein coupled receptors). Fragment-based drug discovery, such as QSAR, could be applied further and have a significant role in dealing with these problems. Moreover, along with the development of computer software and hardware, it is believed that QSAR will be increasingly important.
Fish acute toxicity syndromes and their use in the QSAR approach to hazard assessment.
McKim, J M; Bradbury, S P; Niemi, G J
1987-01-01
Implementation of the Toxic Substances Control Act of 1977 creates the need to reliably establish testing priorities because laboratory resources are limited and the number of industrial chemicals requiring evaluation is overwhelming. The use of quantitative structure activity relationship (QSAR) models as rapid and predictive screening tools to select more potentially hazardous chemicals for in-depth laboratory evaluation has been proposed. Further implementation and refinement of quantitative structure-toxicity relationships in aquatic toxicology and hazard assessment requires the development of a "mode-of-action" database. With such a database, a qualitative structure-activity relationship can be formulated to assign the proper mode of action, and respective QSAR, to a given chemical structure. In this review, the development of fish acute toxicity syndromes (FATS), which are toxic-response sets based on various behavioral and physiological-biochemical measurements, and their projected use in the mode-of-action database are outlined. Using behavioral parameters monitored in the fathead minnow during acute toxicity testing, FATS associated with acetylcholinesterase (AChE) inhibitors and narcotics could be reliably predicted. However, compounds classified as oxidative phosphorylation uncouplers or stimulants could not be resolved. Refinement of this approach by using respiratory-cardiovascular responses in the rainbow trout, enabled FATS associated with AChE inhibitors, convulsants, narcotics, respiratory blockers, respiratory membrane irritants, and uncouplers to be correctly predicted. PMID:3297660
NASA Astrophysics Data System (ADS)
Wang, Youwei; Zhang, Wenqing; Chen, Lidong; Shi, Siqi; Liu, Jianjun
2017-12-01
Li-ion batteries are a key technology for addressing the global challenge of clean renewable energy and environment pollution. Their contemporary applications, for portable electronic devices, electric vehicles, and large-scale power grids, stimulate the development of high-performance battery materials with high energy density, high power, good safety, and long lifetime. High-throughput calculations provide a practical strategy to discover new battery materials and optimize currently known material performances. Most cathode materials screened by the previous high-throughput calculations cannot meet the requirement of practical applications because only capacity, voltage and volume change of bulk were considered. It is important to include more structure-property relationships, such as point defects, surface and interface, doping and metal-mixture and nanosize effects, in high-throughput calculations. In this review, we established quantitative description of structure-property relationships in Li-ion battery materials by the intrinsic bulk parameters, which can be applied in future high-throughput calculations to screen Li-ion battery materials. Based on these parameterized structure-property relationships, a possible high-throughput computational screening flow path is proposed to obtain high-performance battery materials.
Wang, Youwei; Zhang, Wenqing; Chen, Lidong; Shi, Siqi; Liu, Jianjun
2017-01-01
Li-ion batteries are a key technology for addressing the global challenge of clean renewable energy and environment pollution. Their contemporary applications, for portable electronic devices, electric vehicles, and large-scale power grids, stimulate the development of high-performance battery materials with high energy density, high power, good safety, and long lifetime. High-throughput calculations provide a practical strategy to discover new battery materials and optimize currently known material performances. Most cathode materials screened by the previous high-throughput calculations cannot meet the requirement of practical applications because only capacity, voltage and volume change of bulk were considered. It is important to include more structure-property relationships, such as point defects, surface and interface, doping and metal-mixture and nanosize effects, in high-throughput calculations. In this review, we established quantitative description of structure-property relationships in Li-ion battery materials by the intrinsic bulk parameters, which can be applied in future high-throughput calculations to screen Li-ion battery materials. Based on these parameterized structure-property relationships, a possible high-throughput computational screening flow path is proposed to obtain high-performance battery materials.
Harju, Mikael; Hamers, Timo; Kamstra, Jorke H; Sonneveld, Edwin; Boon, Jan P; Tysklind, Mats; Andersson, Patrik L
2007-04-01
In this work, quantitative structure-activity relationships (QSARs) were developed to aid human and environmental risk assessment processes for brominated flame retardants (BFRs). Brominated flame retardants, such as the high-production-volume chemicals polybrominated diphenyl ethers (PBDEs), tetrabromobisphenol A, and hexabromocyclododecane, have been identified as potential endocrine disruptors. Quantitative structure-activity relationship models were built based on the in vitro potencies of 26 selected BFRs. The in vitro assays included interactions with, for example, androgen, progesterone, estrogen, and dioxin (aryl hydrocarbon) receptor, plus competition with thyroxine for its plasma carrier protein (transthyretin), inhibition of estradiol sulfation via sulfotransferase, and finally, rate of metabolization. The QSAR modeling, a number of physicochemical parameters were calculated describing the electronic, lipophilic, and structural characteristics of the molecules. These include frontier molecular orbitals, molecular charges, polarities, log octanol/water partitioning coefficient, and two- and three-dimensional molecularproperties. Experimental properties were included and measured for PBDEs, such as their individual ultraviolet spectra (200-320 nm) and retention times on three different high-performance liquid chromatography columns and one nonpolar gas chromatography column. Quantitative structure-activity relationship models based on androgen antagonism and metabolic degradation rates generally gave similar results, suggesting that lower-brominated PBDEs with bromine substitutions in ortho positions and bromine-free meta- and para positions had the highest potencies and metabolic degradation rates. Predictions made for the constituents of the technical flame retardant Bromkal 70-5DE found BDE 17 to be a potent androgen antagonist and BDE 66, which is a relevant PBDE in environmental samples, to be only a weak antagonist.
Ponec, R; Amat, L; Carbó-Dorca, R
1999-05-01
Since the dawn of quantitative structure-properties relationships (QSPR), empirical parameters related to structural, electronic and hydrophobic molecular properties have been used as molecular descriptors to determine such relationships. Among all these parameters, Hammett sigma constants and the logarithm of the octanol-water partition coefficient, log P, have been massively employed in QSPR studies. In the present paper, a new molecular descriptor, based on quantum similarity measures (QSM), is proposed as a general substitute of these empirical parameters. This work continues previous analyses related to the use of QSM to QSPR, introducing molecular quantum self-similarity measures (MQS-SM) as a single working parameter in some cases. The use of MQS-SM as a molecular descriptor is first confirmed from the correlation with the aforementioned empirical parameters. The Hammett equation has been examined using MQS-SM for a series of substituted carboxylic acids. Then, for a series of aliphatic alcohols and acetic acid esters, log P values have been correlated with the self-similarity measure between density functions in water and octanol of a given molecule. And finally, some examples and applications of MQS-SM to determine QSAR are presented. In all studied cases MQS-SM appeared to be excellent molecular descriptors usable in general QSPR applications of chemical interest.
NASA Astrophysics Data System (ADS)
Ponec, Robert; Amat, Lluís; Carbó-dorca, Ramon
1999-05-01
Since the dawn of quantitative structure-properties relationships (QSPR), empirical parameters related to structural, electronic and hydrophobic molecular properties have been used as molecular descriptors to determine such relationships. Among all these parameters, Hammett σ constants and the logarithm of the octanol- water partition coefficient, log P, have been massively employed in QSPR studies. In the present paper, a new molecular descriptor, based on quantum similarity measures (QSM), is proposed as a general substitute of these empirical parameters. This work continues previous analyses related to the use of QSM to QSPR, introducing molecular quantum self-similarity measures (MQS-SM) as a single working parameter in some cases. The use of MQS-SM as a molecular descriptor is first confirmed from the correlation with the aforementioned empirical parameters. The Hammett equation has been examined using MQS-SM for a series of substituted carboxylic acids. Then, for a series of aliphatic alcohols and acetic acid esters, log P values have been correlated with the self-similarity measure between density functions in water and octanol of a given molecule. And finally, some examples and applications of MQS-SM to determine QSAR are presented. In all studied cases MQS-SM appeared to be excellent molecular descriptors usable in general QSPR applications of chemical interest.
Santoro, Adriana Leandra; Carrilho, Emanuel; Lanças, Fernando Mauro; Montanari, Carlos Alberto
2016-06-10
The pharmacokinetic properties of flavonoids with differing degrees of lipophilicity were investigated using immobilized artificial membranes (IAMs) as the stationary phase in high performance liquid chromatography (HPLC). For each flavonoid compound, we investigated whether the type of column used affected the correlation between the retention factors and the calculated octanol/water partition (log Poct). Three-dimensional (3D) molecular descriptors were calculated from the molecular structure of each compound using i) VolSurf software, ii) the GRID method (computational procedure for determining energetically favorable binding sites in molecules of known structure using a probe for calculating the 3D molecular interaction fields, between the probe and the molecule), and iii) the relationship between partition and molecular structure, analyzed in terms of physicochemical descriptors. The VolSurf built-in Caco-2 model was used to estimate compound permeability. The extent to which the datasets obtained from different columns differ both from each other and from both the calculated log Poct and the predicted permeability in Caco-2 cells was examined by principal component analysis (PCA). The immobilized membrane partition coefficients (kIAM) were analyzed using molecular descriptors in partial least square regression (PLS) and a quantitative structure-retention relationship was generated for the chromatographic retention in the cholesterol column. The cholesterol column provided the best correlation with the permeability predicted by the Caco-2 cell model and a good fit model with great prediction power was obtained for its retention data (R(2)=0.96 and Q(2)=0.85 with four latent variables). Copyright © 2015 Elsevier B.V. All rights reserved.
Mohr, Johannes A; Jain, Brijnesh J; Obermayer, Klaus
2008-09-01
Quantitative structure activity relationship (QSAR) analysis is traditionally based on extracting a set of molecular descriptors and using them to build a predictive model. In this work, we propose a QSAR approach based directly on the similarity between the 3D structures of a set of molecules measured by a so-called molecule kernel, which is independent of the spatial prealignment of the compounds. Predictors can be build using the molecule kernel in conjunction with the potential support vector machine (P-SVM), a recently proposed machine learning method for dyadic data. The resulting models make direct use of the structural similarities between the compounds in the test set and a subset of the training set and do not require an explicit descriptor construction. We evaluated the predictive performance of the proposed method on one classification and four regression QSAR datasets and compared its results to the results reported in the literature for several state-of-the-art descriptor-based and 3D QSAR approaches. In this comparison, the proposed molecule kernel method performed better than the other QSAR methods.
Ivanciuc, O; Ivanciuc, T; Klein, D J; Seitz, W A; Balaban, A T
2001-02-01
Quantitative structure-retention relationships (QSRR) represent statistical models that quantify the connection between the molecular structure and the chromatographic retention indices of organic compounds, allowing the prediction of retention indices of novel, not yet synthesized compounds, solely from their structural descriptors. Using multiple linear regression, QSRR models for the gas chromatographic Kováts retention indices of 129 alkylbenzenes are generated using molecular graph descriptors. The correlational ability of structural descriptors computed from 10 molecular matrices is investigated, showing that the novel reciprocal matrices give numerical indices with improved correlational ability. A QSRR equation with 5 graph descriptors gives the best calibration and prediction results, demonstrating the usefulness of the molecular graph descriptors in modeling chromatographic retention parameters. The sequential orthogonalization of descriptors suggests simpler QSRR models by eliminating redundant structural information.
Caballero, Julio; Fernández, Michael; Coll, Deysma
2010-12-01
Three-dimensional quantitative structure-activity relationship studies were carried out on a series of 28 organosulphur compounds as 15-lipoxygenase inhibitors using comparative molecular field analysis and comparative molecular similarity indices analysis. Quantitative information on structure-activity relationships is provided for further rational development and direction of selective synthesis. All models were carried out over a training set including 22 compounds. The best comparative molecular field analysis model only included steric field and had a good Q² = 0.789. Comparative molecular similarity indices analysis overcame the comparative molecular field analysis results: the best comparative molecular similarity indices analysis model also only included steric field and had a Q² = 0.894. In addition, this model predicted adequately the compounds contained in the test set. Furthermore, plots of steric comparative molecular similarity indices analysis field allowed conclusions to be drawn for the choice of suitable inhibitors. In this sense, our model should prove useful in future 15-lipoxygenase inhibitor design studies. © 2010 John Wiley & Sons A/S.
DISTRIBUTED STRUCTURE-SEARCHABLE TOXICITY ...
The ability to assess the potential genotoxicity, carcinogenicity, or other toxicity of pharmaceutical or industrial chemicals based on chemical structure information is a highly coveted and shared goal of varied academic, commercial, and government regulatory groups. These diverse interests often employ different approaches and have different criteria and use for toxicity assessments, but they share a need for unrestricted access to existing public toxicity data linked with chemical structure information. Currently, there exists no central repository of toxicity information, commercial or public, that adequately meets the data requirements for flexible analogue searching, SAR model development, or building of chemical relational databases (CRD). The Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network is being proposed as a community-supported, web-based effort to address these shared needs of the SAR and toxicology communities. The DSSTox project has the following major elements: 1) to adopt and encourage the use of a common standard file format (SDF) for public toxicity databases that includes chemical structure, text and property information, and that can easily be imported into available CRD applications; 2) to implement a distributed source approach, managed by a DSSTox Central Website, that will enable decentralized, free public access to structure-toxicity data files, and that will effectively link knowledgeable toxicity data s
Toxicity Evaluation of Engineered Nanomaterials: Risk Evaluation Tools (Phase 3 Studies)
2012-01-01
report. The second modeling approach was on quantitative structure activity relationships ( QSARs ). A manuscript entitled “Connecting the dots: Towards...expands rapidly. We proposed two types of mechanisms of toxic action supported by the nano- QSAR model , which collectively govern the toxicity of the...interpretative nano- QSAR model describing toxicity of 18 nano-metal oxides to a HaCaT cell line as a model for dermal exposure. In result, by the comparison of
Das, Sreeparna; Mitra, Indrani; Batuta, Shaikh; Niharul Alam, Md; Roy, Kunal; Begum, Naznin Ara
2014-11-01
A series of flavonoid analogues were synthesized and screened for the in vitro antioxidant activity through their ability to quench 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical. The activity of these compounds, measured in comparison to the well-known standard antioxidants (29-32), their precursors (38-42) and other bioactive moieties (38-42) resembling partially the flavone skeleton was analyzed further to develop Quantitative Structure-Activity Relationship (QSAR) models using the Genetic Function Approximation (GFA) technique. Based on the essential structural requirements predicted by the QSAR models, some analogues were designed, synthesized and tested for activity. The predicted and experimental activities of these compounds were well correlated. Flavone analogue 20 was found to be the most potent antioxidant. Copyright © 2014 Elsevier Ltd. All rights reserved.
In general, the accuracy of a predicted toxicity value increases with increase in similarity between the query chemical and the chemicals used to develop a QSAR model. A toxicity estimation methodology employing this finding has been developed. A hierarchical based clustering t...
Murumkar, Prashant R; Giridhar, Rajani; Yadav, Mange Ram
2008-04-01
A set of 29 benzothiadiazepine hydroxamates having selective tumor necrosis factor-alpha converting enzyme inhibitory activity were used to compare the quality and predictive power of 3D-quantitative structure-activity relationship, comparative molecular field analysis, and comparative molecular similarity indices models for the atom-based, centroid/atom-based, data-based, and docked conformer-based alignment. Removal of two outliers from the initial training set of molecules improved the predictivity of models. Among the 3D-quantitative structure-activity relationship models developed using the above four alignments, the database alignment provided the optimal predictive comparative molecular field analysis model for the training set with cross-validated r(2) (q(2)) = 0.510, non-cross-validated r(2) = 0.972, standard error of estimates (s) = 0.098, and F = 215.44 and the optimal comparative molecular similarity indices model with cross-validated r(2) (q(2)) = 0.556, non-cross-validated r(2) = 0.946, standard error of estimates (s) = 0.163, and F = 99.785. These models also showed the best test set prediction for six compounds with predictive r(2) values of 0.460 and 0.535, respectively. The contour maps obtained from 3D-quantitative structure-activity relationship studies were appraised for activity trends for the molecules analyzed. The comparative molecular similarity indices models exhibited good external predictivity as compared with that of comparative molecular field analysis models. The data generated from the present study helped us to further design and report some novel and potent tumor necrosis factor-alpha converting enzyme inhibitors.
Lassalle, Yannick; Kinani, Aziz; Rifai, Ahmad; Souissi, Yasmine; Clavaguera, Carine; Bourcier, Sophie; Jaber, Farouk; Bouchonnet, Stéphane
2014-05-30
Boscalid is a carboximide fungicide mainly used for vineyard protection as well as for tomato, apple, blueberry and various ornamental cultivations. The structural elucidation of by-products arising from the UV-visible photodegradation of boscalid has been investigated by gas chromatography/multi-stage mass spectrometry (GC/MS(n) ) and liquid chromatography/tandem mass spectrometry (LC/MS/MS) couplings. The potential toxicities of transformation products were estimated by in silico calculations. Aqueous solutions of boscalid were irradiated up to 150 min in a self-made reactor equipped with a mercury lamp. Analyses were carried out using a gas chromatograph coupled with an ion trap mass spectrometer operated in both electron ionization (EI) and chemical ionization (CI) modes and a liquid chromatograph coupled with a quadrupole time-of-flight (Q-TOF) mass spectrometer operated in electrospray ionization (ESI) mode. Multiple-stage collision-induced dissociation (CID) experiments were performed to establish dissociation pathways of ions. The QSAR (Quantitative Structure-Activity Relationship) T.E.S.T. program allowed the estimation of the toxicities of the by-products. Eight photoproducts were investigated. Chemical structures were proposed not only on the interpretation of multi-stage CID experiments, but also on kinetics data. These structures led us to suggest photodegradation pathways. Three photoproducts were finally detected in Lebanon in a real sample of grape leaves for which routine analysis had led to the detection of boscalid at 4 mg kg(-1). With one exception, the structures proposed for the photoproducts on the basis of mass spectra interpretation have not been reported in previous studies. In silico toxicity predictions showed that two photoproducts are potentially more toxic than the parent compound considering oral rat LD50 while five photoproducts may induce mutagenic toxicity. With the exception of one compound, all photoproducts may
Quantitative structure-activity relationship studies of threo-methylphenidate analogs.
Misra, Milind; Shi, Qing; Ye, Xiaocong; Gruszecka-Kowalik, Ewa; Bu, Wei; Liu, Zhanzhu; Schweri, Margaret M; Deutsch, Howard M; Venanzi, Carol A
2010-10-15
Complementary two-dimensional (2D) and three-dimensional (3D) Quantitative Structure-Activity Relationship (QSAR) techniques were used to derive a preliminary model for the dopamine transporter (DAT) binding affinity of 80 racemic threo-methylphenidate (MP) analogs. A novel approach based on using the atom-level E-state indices of the 14 common scaffold atoms in a sphere exclusion protocol was used to identify a test set for 2D- and 3D-QSAR model validation. Comparative Molecular Field Analysis (CoMFA) contour maps based on the structure-activity data of the training set indicate that the 2' position of the phenyl ring cannot tolerate much steric bulk and that addition of electron-withdrawing groups to the 3' or 4' positions of the phenyl ring leads to improved DAT binding affinity. In particular, the optimal substituents were found to be those whose bulk is mainly in the plane of the phenyl ring. Substituents with significant bulk above or below the plane of the ring led to decreased binding affinity. Suggested alterations to be explored in the design of new compounds are the placement at the 3' and 4' position of the phenyl ring of electron-withdrawing groups that lie chiefly in the plane of the ring, for example, halogen substituents on the 3',4'-benzo analog, 79. A complementary 2D-QSAR approach-partial least squares analysis using a reduced set of Molconn-Z descriptors-supports the CoMFA structure-activity interpretation that phenyl ring substitution is a major determinant of DAT binding affinity. The potential usefulness of the CoMFA models was demonstrated by the prediction of the binding affinity of methyl 2-(naphthalen-1-yl)-2-(piperidin-2-yl)acetate, an analog not in the original data set, to be in good agreement with the experimental value. Copyright © 2010 Elsevier Ltd. All rights reserved.
Distributed structure-searchable toxicity (DSSTox) public database network: a proposal.
Richard, Ann M; Williams, ClarLynda R
2002-01-29
The ability to assess the potential genotoxicity, carcinogenicity, or other toxicity of pharmaceutical or industrial chemicals based on chemical structure information is a highly coveted and shared goal of varied academic, commercial, and government regulatory groups. These diverse interests often employ different approaches and have different criteria and use for toxicity assessments, but they share a need for unrestricted access to existing public toxicity data linked with chemical structure information. Currently, there exists no central repository of toxicity information, commercial or public, that adequately meets the data requirements for flexible analogue searching, Structure-Activity Relationship (SAR) model development, or building of chemical relational databases (CRD). The distributed structure-searchable toxicity (DSSTox) public database network is being proposed as a community-supported, web-based effort to address these shared needs of the SAR and toxicology communities. The DSSTox project has the following major elements: (1) to adopt and encourage the use of a common standard file format (structure data file (SDF)) for public toxicity databases that includes chemical structure, text and property information, and that can easily be imported into available CRD applications; (2) to implement a distributed source approach, managed by a DSSTox Central Website, that will enable decentralized, free public access to structure-toxicity data files, and that will effectively link knowledgeable toxicity data sources with potential users of these data from other disciplines (such as chemistry, modeling, and computer science); and (3) to engage public/commercial/academic/industry groups in contributing to and expanding this community-wide, public data sharing and distribution effort. The DSSTox project's overall aims are to effect the closer association of chemical structure information with existing toxicity data, and to promote and facilitate structure
Saiz-Urra, Liane; Racero, Juan C; Macías-Sáchez, Antonio J; Hernández-Galán, Rosario; Hanson, James R; Perez-Gonzalez, Maykel; Collado, Isidro G
2009-03-25
Twenty-three clovane derivatives, nine described here for the first time, bearing substituents on carbon C-2, have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The results showed that compounds 9, 14, 16, and 18 bearing nitrogen atoms in the chain attached at C-2 displayed potent antifungal activity, whereas mercapto derivatives 13, 19, and 22 displayed low activity. The antifungal activity showed a clear structure-activity relationship (SAR) trend, which confirmed the importance of the nature of the C-2 chain on the antifungal activity. On the basis of these observations, the metabolism of compounds 8 and 14 by the fungus B. cinerea, and the metabolism of other clovanes by this fungus, described previously, a pro-drug action mechanism for 2-alkoxyclovane compounds is proposed. Quantitative structure-activity relationship (QSAR) studies were performed to rationalize the results and to suggest further optimization, using a topological sub-structural molecular design (TOPS-MODE) approach. The model displayed good fit and predictive capability, describing 85.5% of the experimental variance, with a standard deviation of 9.502 and yielding high values of cross-validation determination coefficients (q2CV-LOO = 0.784 and q2boot = 0.673). The most significant variables were the spectral moments weighted by bond dipole moment (Dip), hydrophobicity (Hyd), and the combined dipolarity/polarizability Abraham molecular descriptor (Ab-pi2H).
Sahoo, Sagarika; Adhikari, Chandana; Kuanar, Minati; Mishra, Bijay K
2016-01-01
Synthesis of organic compounds with specific biological activity or physicochemical characteristics needs a thorough analysis of the enumerable data set obtained from literature. Quantitative structure property/activity relationships have made it simple by predicting the structure of the compound with any optimized activity. For that there is a paramount data set of molecular descriptors (MD). This review is a survey on the generation of the molecular descriptors and its probable applications in QSP/AR. Literatures have been collected from a wide class of research journals, citable web reports, seminar proceedings and books. The MDs were classified according to their generation. The applications of the MDs on the QSP/AR have also been reported in this review. The MDs can be classified into experimental and theoretical types, having a sub classification of the later into structural and quantum chemical descriptors. The structural parameters are derived from molecular graphs or topology of the molecules. Even the pixel of the molecular image can be used as molecular descriptor. In QSPR studies the physicochemical properties include boiling point, heat capacity, density, refractive index, molar volume, surface tension, heat of formation, octanol-water partition coefficient, solubility, chromatographic retention indices etc. Among biological activities toxicity, antimalarial activity, sensory irritant, potencies of local anesthetic, tadpole narcosis, antifungal activity, enzyme inhibiting activity are some important parameters in the QSAR studies. The classification of the MDs is mostly generic in nature. The application of the MDs in QSP/AR also has a generic link. Experimental MDs are more suitable in correlation analysis than the theoretical ones but are more expensive for generation. In advent of sophisticated computational tools and experimental design proliferation of MDs is inevitable, but for a highly optimized MD, studies on generation of MD is an unending
Mendenhall, Jeffrey; Meiler, Jens
2016-02-01
Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.
Mendenhall, Jeffrey; Meiler, Jens
2016-01-01
Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery (LB-CADD) pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both Enrichment false positive rate (FPR) and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22–46% over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods. PMID:26830599
Comparison of Global and Mode of Action-Based Models for Aquatic Toxicity
The ability to estimate aquatic toxicity for a wide variety of chemicals is a critical need for ecological risk assessment and chemical regulation. The consensus in the literature is that mode of action (MOA) based QSAR (Quantitative Structure Activity Relationship) models yield ...
Oberg, Tomas
2004-01-01
Halogenated aliphatic compounds have many technical uses, but substances within this group are also ubiquitous environmental pollutants that can affect the ozone layer and contribute to global warming. The establishment of quantitative structure-property relationships is of interest not only to fill in gaps in the available database but also to validate experimental data already acquired. The three-dimensional structures of 240 compounds were modeled with molecular mechanics prior to the generation of empirical descriptors. Two bilinear projection methods, principal component analysis (PCA) and partial-least-squares regression (PLSR), were used to identify outliers. PLSR was subsequently used to build a multivariate calibration model by extracting the latent variables that describe most of the covariation between the molecular structure and the boiling point. Boiling points were also estimated with an extension of the group contribution method of Stein and Brown.
Manoj Kumar, Palanivelu; Karthikeyan, Chandrabose; Hari Narayana Moorthy, Narayana Subbiah; Trivedi, Piyush
2006-11-01
In the present paper, quantitative structure activity relationship (QSAR) approach was applied to understand the affinity and selectivity of a novel series of triaryl imidazole derivatives towards glucagon receptor. Statistically significant and highly predictive QSARs were derived for glucagon receptor inhibition by triaryl imidazoles using QuaSAR descriptors of molecular operating environment (MOE) employing computer-assisted multiple regression procedure. The generated QSAR models revealed that factors related to hydrophobicity, molecular shape and geometry predominantly influences glucagon receptor binding affinity of the triaryl imidazoles indicating the relevance of shape specific steric interactions between the molecule and the receptor. Further, QSAR models formulated for selective inhibition of glucagon receptor over p38 mitogen activated protein (MAP) kinase of the compounds in the series highlights that the same structural features, which influence the glucagon receptor affinity, also contribute to their selective inhibition.
Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships.
Xu, Yuting; Ma, Junshui; Liaw, Andy; Sheridan, Robert P; Svetnik, Vladimir
2017-10-23
Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision1,2 and natural language processing.3,4 In the past four years, DNNs have also generated promising results for quantitative structure-activity relationship (QSAR) tasks.5,6 Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR data sets. It was also found that multitask DNN models-those trained on and predicting multiple QSAR properties simultaneously-outperform DNNs trained separately on the individual data sets in many, but not all, tasks. To date there has been no satisfactory explanation of why the QSAR of one task embedded in a multitask DNN can borrow information from other unrelated QSAR tasks. Thus, using multitask DNNs in a way that consistently provides a predictive advantage becomes a challenge. In this work, we explored why multitask DNNs make a difference in predictive performance. Our results show that during prediction a multitask DNN does borrow "signal" from molecules with similar structures in the training sets of the other tasks. However, whether this borrowing leads to better or worse predictive performance depends on whether the activities are correlated. On the basis of this, we have developed a strategy to use multitask DNNs that incorporate prior domain knowledge to select training sets with correlated activities, and we demonstrate its effectiveness on several examples.
A relational learning approach to Structure-Activity Relationships in drug design toxicity studies.
Camacho, Rui; Pereira, Max; Costa, Vítor Santos; Fonseca, Nuno A; Adriano, Carlos; Simões, Carlos J V; Brito, Rui M M
2011-09-16
It has been recognized that the development of new therapeutic drugs is a complex and expensive process. A large number of factors affect the activity in vivo of putative candidate molecules and the propensity for causing adverse and toxic effects is recognized as one of the major hurdles behind the current "target-rich, lead-poor" scenario. Structure-Activity Relationship (SAR) studies, using relational Machine Learning (ML) algorithms, have already been shown to be very useful in the complex process of rational drug design. Despite the ML successes, human expertise is still of the utmost importance in the drug development process. An iterative process and tight integration between the models developed by ML algorithms and the know-how of medicinal chemistry experts would be a very useful symbiotic approach. In this paper we describe a software tool that achieves that goal--iLogCHEM. The tool allows the use of Relational Learners in the task of identifying molecules or molecular fragments with potential to produce toxic effects, and thus help in stream-lining drug design in silico. It also allows the expert to guide the search for useful molecules without the need to know the details of the algorithms used. The models produced by the algorithms may be visualized using a graphical interface, that is of common use amongst researchers in structural biology and medicinal chemistry. The graphical interface enables the expert to provide feedback to the learning system. The developed tool has also facilities to handle the similarity bias typical of large chemical databases. For that purpose the user can filter out similar compounds when assembling a data set. Additionally, we propose ways of providing background knowledge for Relational Learners using the results of Graph Mining algorithms. Copyright 2011 The Author(s). Published by Journal of Integrative Bioinformatics.
Kaiser, K L E
2007-01-01
This presentation will review the evolution of the workshops from a scientific and personal perspective. From their modest beginning in 1983, the workshops have developed into larger international meetings, regularly held every two years. Their initial focus on the aquatic sphere soon expanded to include properties and effects on atmospheric and terrestrial species, including man. Concurrent with this broadening of their scientific scope, the workshops have become an important forum for the early dissemination of all aspects of qualitative and quantitative structure-activity research in ecotoxicology and human health effects. Over the last few decades, the field of quantitative structure/activity relationships (QSARs) has quickly emerged as a major scientific method in understanding the properties and effects of chemicals on the environment and human health. From substances that only affect cell membranes to those that bind strongly to a specific enzyme, QSARs provides insight into the biological effects and chemical and physical properties of substances. QSARs are useful for delineating the quantitative changes in biological effects resulting from minor but systematic variations of the structure of a compound with a specific mode of action. In addition, more holistic approaches are being devised that result in our ability to predict the effects of structurally unrelated compounds with (potentially) different modes of action. Research in QSAR environmental toxicology has led to many improvements in the manufacturing, use, and disposal of chemicals. Furthermore, it has led to national policies and international agreements, from use restrictions or outright bans of compounds, such as polychlorinated biphenyls (PCBs), mirex, and highly chlorinated pesticides (e.g. DDT, dieldrin) for the protection of avian predators, to alternatives for ozone-depleting compounds, to better waste treatment systems, to more powerful and specific acting drugs. Most of the recent advances
Pharmacology-based toxicity assessment: towards quantitative risk prediction in humans.
Sahota, Tarjinder; Danhof, Meindert; Della Pasqua, Oscar
2016-05-01
Despite ongoing efforts to better understand the mechanisms underlying safety and toxicity, ~30% of the attrition in drug discovery and development is still due to safety concerns. Changes in current practice regarding the assessment of safety and toxicity are required to reduce late stage attrition and enable effective development of novel medicines. This review focuses on the implications of empirical evidence generation for the evaluation of safety and toxicity during drug development. A shift in paradigm is needed to (i) ensure that pharmacological concepts are incorporated into the evaluation of safety and toxicity; (ii) facilitate the integration of historical evidence and thereby the translation of findings across species as well as between in vitro and in vivo experiments and (iii) promote the use of experimental protocols tailored to address specific safety and toxicity questions. Based on historical examples, we highlight the challenges for the early characterisation of the safety profile of a new molecule and discuss how model-based methodologies can be applied for the design and analysis of experimental protocols. Issues relative to the scientific rationale are categorised and presented as a hierarchical tree describing the decision-making process. Focus is given to four different areas, namely, optimisation, translation, analytical construct and decision criteria. From a methodological perspective, the relevance of quantitative methods for estimation and extrapolation of risk from toxicology and safety pharmacology experimental protocols, such as points of departure and potency, is discussed in light of advancements in population and Bayesian modelling techniques (e.g. non-linear mixed effects modelling). Their use in the evaluation of pharmacokinetics (PK) and pharmacokinetic-pharmacodynamic relationships (PKPD) has enabled great insight into the dose rationale for medicines in humans, both in terms of efficacy and adverse events. Comparable benefits
Li, Zhenghua; Cheng, Fansheng; Xia, Zhining
2011-01-01
The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.
Quantitative systems toxicology
Bloomingdale, Peter; Housand, Conrad; Apgar, Joshua F.; Millard, Bjorn L.; Mager, Donald E.; Burke, John M.; Shah, Dhaval K.
2017-01-01
The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety. PMID:29308440
Fang, Jiansong; Pang, Xiaocong; Wu, Ping; Yan, Rong; Gao, Li; Li, Chao; Lian, Wenwen; Wang, Qi; Liu, Ai-lin; Du, Guan-hua
2016-05-01
A dataset of 67 berberine derivatives for the inhibition of butyrylcholinesterase (BuChE) was studied based on the combination of quantitative structure-activity relationships models, molecular docking, and molecular dynamics methods. First, a series of berberine derivatives were reported, and their inhibitory activities toward butyrylcholinesterase (BuChE) were evaluated. By 2D- quantitative structure-activity relationships studies, the best model built by partial least-square had a conventional correlation coefficient of the training set (R(2)) of 0.883, a cross-validation correlation coefficient (Qcv2) of 0.777, and a conventional correlation coefficient of the test set (Rpred2) of 0.775. The model was also confirmed by Y-randomization examination. In addition, the molecular docking and molecular dynamics simulation were performed to better elucidate the inhibitory mechanism of three typical berberine derivatives (berberine, C2, and C55) toward BuChE. The predicted binding free energy results were consistent with the experimental data and showed that the van der Waals energy term (ΔEvdw) difference played the most important role in differentiating the activity among the three inhibitors (berberine, C2, and C55). The developed quantitative structure-activity relationships models provide details on the fine relationship linking structure and activity and offer clues for structural modifications, and the molecular simulation helps to understand the inhibitory mechanism of the three typical inhibitors. In conclusion, the results of this study provide useful clues for new drug design and discovery of BuChE inhibitors from berberine derivatives. © 2015 John Wiley & Sons A/S.
MOLECULAR INTERACTION POTENTIALS FOR THE DEVELOPMENT OF STRUCTURE-ACTIVITY RELATIONSHIPS
Abstract
One reasonable approach to the analysis of the relationships between molecular structure and toxic activity is through the investigation of the forces and intermolecular interactions responsible for chemical toxicity. The interaction between the xenobiotic and the bio...
Dearden, John C
2003-08-01
Boiling point, vapor pressure, and melting point are important physicochemical properties in the modeling of the distribution and fate of chemicals in the environment. However, such data often are not available, and therefore must be estimated. Over the years, many attempts have been made to calculate boiling points, vapor pressures, and melting points by using quantitative structure-property relationships, and this review examines and discusses the work published in this area, and concentrates particularly on recent studies. A number of software programs are commercially available for the calculation of boiling point, vapor pressure, and melting point, and these have been tested for their predictive ability with a test set of 100 organic chemicals.
Quantitative structure-property relationship modeling of remote liposome loading of drugs.
Cern, Ahuva; Golbraikh, Alexander; Sedykh, Aleck; Tropsha, Alexander; Barenholz, Yechezkel; Goldblum, Amiram
2012-06-10
Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a data set including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and 5-fold external validation. The external prediction accuracy for binary models was as high as 91-96%; for continuous models the mean coefficient R(2) for regression between predicted versus observed values was 0.76-0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments. Copyright © 2011 Elsevier B.V. All rights reserved.
Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung
2016-06-01
The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates. Copyright © 2016. Published by Elsevier Inc.
Borkar, Mahesh R; Pissurlenkar, Raghuvir R S; Coutinho, Evans C
2013-11-15
Peptides play significant roles in the biological world. To optimize activity for a specific therapeutic target, peptide library synthesis is inevitable; which is a time consuming and expensive. Computational approaches provide a promising way to simply elucidate the structural basis in the design of new peptides. Earlier, we proposed a novel methodology termed HomoSAR to gain insight into the structure activity relationships underlying peptides. Based on an integrated approach, HomoSAR uses the principles of homology modeling in conjunction with the quantitative structural activity relationship formalism to predict and design new peptide sequences with the optimum activity. In the present study, we establish that the HomoSAR methodology can be universally applied to all classes of peptides irrespective of sequence length by studying HomoSAR on three peptide datasets viz., angiotensin-converting enzyme inhibitory peptides, CAMEL-s antibiotic peptides, and hAmphiphysin-1 SH3 domain binding peptides, using a set of descriptors related to the hydrophobic, steric, and electronic properties of the 20 natural amino acids. Models generated for all three datasets have statistically significant correlation coefficients (r(2)) and predictive r2 (r(pred)2) and cross validated coefficient ( q(LOO)2). The daintiness of this technique lies in its simplicity and ability to extract all the information contained in the peptides to elucidate the underlying structure activity relationships. The difficulties of correlating both sequence diversity and variation in length of the peptides with their biological activity can be addressed. The study has been able to identify the preferred or detrimental nature of amino acids at specific positions in the peptide sequences. Copyright © 2013 Wiley Periodicals, Inc.
Gao, Jia-Suo; Tong, Xu-Peng; Chang, Yi-Qun; He, Yu-Xuan; Mei, Yu-Dan; Tan, Pei-Hong; Guo, Jia-Liang; Liao, Guo-Chao; Xiao, Gao-Keng; Chen, Wei-Min; Zhou, Shu-Feng; Sun, Ping-Hua
2015-01-01
Factor IXa (FIXa), a blood coagulation factor, is specifically inhibited at the initiation stage of the coagulation cascade, promising an excellent approach for developing selective and safe anticoagulants. Eighty-four amidinobenzothiophene antithrombotic derivatives targeting FIXa were selected to establish three-dimensional quantitative structure-activity relationship (3D-QSAR) and three-dimensional quantitative structure-selectivity relationship (3D-QSSR) models using comparative molecular field analysis and comparative similarity indices analysis methods. Internal and external cross-validation techniques were investigated as well as region focusing and bootstrapping. The satisfactory q (2) values of 0.753 and 0.770, and r (2) values of 0.940 and 0.965 for 3D-QSAR and 3D-QSSR, respectively, indicated that the models are available to predict both the inhibitory activity and selectivity on FIXa against Factor Xa, the activated status of Factor X. This work revealed that the steric, hydrophobic, and H-bond factors should appropriately be taken into account in future rational design, especially the modifications at the 2'-position of the benzene and the 6-position of the benzothiophene in the R group, providing helpful clues to design more active and selective FIXa inhibitors for the treatment of thrombosis. On the basis of the three-dimensional quantitative structure-property relationships, 16 new potent molecules have been designed and are predicted to be more active and selective than Compound 33, which has the best activity as reported in the literature.
The number of chemicals released into the environment has significantly increased over the past few years, leading to increased risk of human exposure through inhalation, ingestion, or dermal uptake. In addition, the risk also increases with increasing toxicity of the chemical. ...
Zaio, Yésica P; Gatti, Gerardo; Ponce, Andrés A; Saavedra Larralde, Natalia A; Martinez, María J; Zunino, María P; Zygadlo, Julio A
2018-05-13
Insecticidal activity and repellent effects on adults of Sitophilus zeamais of 12 cinnamaldehyde-related compounds was evaluated by contact toxicity bioassays and a two-choice olfactometer, respectively. To determine non-toxicity in mammals, additionally, body weight, serum biochemical profiles, liver weight, physiological parameters, sperm motility and histopathological data were obtained as complementary information in C57BL/6 mice, treated with the best natural compound. Based on 24h LC 95 and LC 50 values, alpha-methyl-cinnamaldehyde and cinnamaldehyde, respectively, exhibited better insecticidal activity than the other compounds. The best repellent effect was observed with alpha-bromo-cinnamaldehyde, which even repelled at the lowest concentration studied (0.28 μM). The evaluation of a quantitative structure-activity relationship showed a linear relationship between the LC 50 values for adult weevil toxicity and dipolo with Q (difference between orbital electronegativity carbon 1 and orbital electronegativity carbon 3 of the molecule) values in cinnamaldehyde-related compounds. In addition, the polar surface and Log P descriptors also revealed a linear relationship with the S. zeamais repellent effect for cinnamaldehyde analogues. Besides, cinnamaldehyde did not show toxicity in the parameters evaluated in mice. From the phenylpropanoid components studied, the natural compound which had the best insecticidal and repellent activity against S. zeamais was cinnamaldehyde and presented no mammalian toxicity. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Huang, Xiao Yan; Shan, Zhi Jie; Zhai, Hong Lin; Li, Li Na; Zhang, Xiao Yun
2011-08-22
Heat shock protein 90 (Hsp90) takes part in the developments of several cancers. Novobiocin, a typically C-terminal inhibitor for Hsp90, will probably used as an important anticancer drug in the future. In this work, we explored the valuable information and designed new novobiocin derivatives based on a three-dimensional quantitative structure-activity relationship (3D QSAR). The comparative molecular field analysis and comparative molecular similarity indices analysis models with high predictive capability were established, and their reliabilities are supported by the statistical parameters. Based on the several important influence factors obtained from these models, six new novobiocin derivatives with higher inhibitory activities were designed and confirmed by the molecular simulation with our models, which provide the potential anticancer drug leads for further research.
Girgis, Adel S; Basta, Altaf H; El-Saied, Houssni; Mohamed, Mohamed A; Bedair, Ahmad H; Salim, Ahmad S
2018-03-01
A variety of fluorescence-active fluorinated pyrazolines 13-33 was synthesized in good yields through cyclocondensation reaction of propenones 1-9 with aryl hydrazines 10-12 . Some of the synthesized compounds provided promising fluorescence properties with quantum yield ( Φ ) higher than that of quinine sulfate (standard reference). Quantitative structure-property relationship studies were undertaken supporting the exhibited fluorescence properties and estimating the parameters governing properties. Five synthesized fluorescence-active pyrazolines ( 13 , 15 , 18 , 19 and 23 ) with variable Φ were selected for treating two types of paper sheets (Fabriano and Bible paper). These investigated fluorescence compounds, especially compounds 19 and 23 , provide improvements in strength properties of paper sheets. Based on the observed performance they can be used as markers in security documents.
NASA Astrophysics Data System (ADS)
Girgis, Adel S.; Basta, Altaf H.; El-Saied, Houssni; Mohamed, Mohamed A.; Bedair, Ahmad H.; Salim, Ahmad S.
2018-03-01
A variety of fluorescence-active fluorinated pyrazolines 13-33 was synthesized in good yields through cyclocondensation reaction of propenones 1-9 with aryl hydrazines 10-12. Some of the synthesized compounds provided promising fluorescence properties with quantum yield (Φ) higher than that of quinine sulfate (standard reference). Quantitative structure-property relationship studies were undertaken supporting the exhibited fluorescence properties and estimating the parameters governing properties. Five synthesized fluorescence-active pyrazolines (13, 15, 18, 19 and 23) with variable Φ were selected for treating two types of paper sheets (Fabriano and Bible paper). These investigated fluorescence compounds, especially compounds 19 and 23, provide improvements in strength properties of paper sheets. Based on the observed performance they can be used as markers in security documents.
DSSTOX (DISTRIBUTED STRUCTURE-SEARCHABLE ...
Distributed Structure-Searchable Toxicity Database Network Major trends affecting public toxicity information resources have the potential to significantly alter the future of predictive toxicology. Chemical toxicity screening is undergoing shifts towards greater use of more fundamental information on gene/protein expression patterns and bioactivity and bioassay profiles, the latter generated with highthroughput screening technologies. Curated, systematically organized, and webaccessible toxicity and biological activity data in association with chemical structures, enabling the integration of diverse data information domains, will fuel the next frontier of advancement for QSAR (quantitative structure-activity relationship) and data mining technologies. The DSSTox project is supporting progress towards these goals on many fronts, promoting the use of formalized and structure-annotated toxicity data models, helping to interface these efforts with QSAR modelers, linking data from diverse sources, and creating a large, quality reviewed, central chemical structure information resource linked to various toxicity data sources
In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects.
Cronin, Mark T D; Enoch, Steven J; Mellor, Claire L; Przybylak, Katarzyna R; Richarz, Andrea-Nicole; Madden, Judith C
2017-07-01
In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.
Nirouei, Mahyar; Ghasemi, Ghasem; Abdolmaleki, Parviz; Tavakoli, Abdolreza; Shariati, Shahab
2012-06-01
The antiviral drugs that inhibit human immunodeficiency virus (HIV) entry to the target cells are already in different phases of clinical trials. They prevent viral entry and have a highly specific mechanism of action with a low toxicity profile. Few QSAR studies have been performed on this group of inhibitors. This study was performed to develop a quantitative structure-activity relationship (QSAR) model of the biological activity of indole glyoxamide derivatives as inhibitors of the interaction between HIV glycoprotein gp120 and host cell CD4 receptors. Forty different indole glyoxamide derivatives were selected as a sample set and geometrically optimized using Gaussian 98W. Different combinations of multiple linear regression (MLR), genetic algorithms (GA) and artificial neural networks (ANN) were then utilized to construct the QSAR models. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear log (1/EC50) prediction. The results that were obtained using GA-ANN were compared with MLR-MLR and MLR-ANN models. A high predictive ability was observed for the MLR, MLR-ANN and GA-ANN models, with root mean sum square errors (RMSE) of 0.99, 0.91 and 0.67, respectively (N = 40). In summary, machine learning methods were highly effective in designing QSAR models when compared to statistical method.
Mager, P P; Rothe, H
1990-10-01
Multicollinearity of physicochemical descriptors leads to serious consequences in quantitative structure-activity relationship (QSAR) analysis, such as incorrect estimators and test statistics of regression coefficients of the ordinary least-squares (OLS) model applied usually to QSARs. Beside the diagnosis of the known simple collinearity, principal component regression analysis (PCRA) also allows the diagnosis of various types of multicollinearity. Only if the absolute values of PCRA estimators are order statistics that decrease monotonically, the effects of multicollinearity can be circumvented. Otherwise, obscure phenomena may be observed, such as good data recognition but low predictive model power of a QSAR model.
Jiménez-Moreno, Ester; Jiménez-Osés, Gonzalo; Gómez, Ana M; Santana, Andrés G; Corzana, Francisco; Bastida, Agatha; Jiménez-Barbero, Jesus; Asensio, Juan Luis
2015-11-13
CH/π interactions play a key role in a large variety of molecular recognition processes of biological relevance. However, their origins and structural determinants in water remain poorly understood. In order to improve our comprehension of these important interaction modes, we have performed a quantitative experimental analysis of a large data set comprising 117 chemically diverse carbohydrate/aromatic stacking complexes, prepared through a dynamic combinatorial approach recently developed by our group. The obtained free energies provide a detailed picture of the structure-stability relationships that govern the association process, opening the door to the rational design of improved carbohydrate-based ligands or carbohydrate receptors. Moreover, this experimental data set, supported by quantum mechanical calculations, has contributed to the understanding of the main driving forces that promote complex formation, underlining the key role played by coulombic and solvophobic forces on the stabilization of these complexes. This represents the most quantitative and extensive experimental study reported so far for CH/π complexes in water.
Distributed Structure-Searchable Toxicity (DSSTox) Database
The Distributed Structure-Searchable Toxicity network provides a public forum for publishing downloadable, structure-searchable, standardized chemical structure files associated with chemical inventories or toxicity data sets of environmental relevance.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Sadat Hayatshahi, Sayyed Hamed; Abdolmaleki, Parviz; Safarian, Shahrokh
2005-12-16
Logistic regression and artificial neural networks have been developed as two non-linear models to establish quantitative structure-activity relationships between structural descriptors and biochemical activity of adenosine based competitive inhibitors, toward adenosine deaminase. The training set included 24 compounds with known k {sub i} values. The models were trained to solve two-class problems. Unlike the previous work in which multiple linear regression was used, the highest of positive charge on the molecules was recognized to be in close relation with their inhibition activity, while the electric charge on atom N1 of adenosine was found to be a poor descriptor. Consequently, themore » previously developed equation was improved and the newly formed one could predict the class of 91.66% of compounds correctly. Also optimized 2-3-1 and 3-4-1 neural networks could increase this rate to 95.83%.« less
NASA Astrophysics Data System (ADS)
Jukić, Marijana; Rastija, Vesna; Opačak-Bernardi, Teuta; Stolić, Ivana; Krstulović, Luka; Bajić, Miroslav; Glavaš-Obrovac, Ljubica
2017-04-01
The aim of this study was to evaluate nine newly synthesized amidine derivatives of 3,4- ethylenedioxythiophene (3,4-EDOT) for their cytotoxic activity against a panel of human cancer cell lines and to perform a quantitative structure-activity relationship (QSAR) analysis for the antitumor activity of a total of 27 3,4-ethylenedioxythiophene derivatives. Induction of apoptosis was investigated on the selected compounds, along with delivery options for the optimization of activity. The best obtained QSAR models include the following group of descriptors: BCUT, WHIM, 2D autocorrelations, 3D-MoRSE, GETAWAY descriptors, 2D frequency fingerprint and information indices. Obtained QSAR models should be relieved in elucidation of important physicochemical and structural requirements for this biological activity. Highly potent molecules have a symmetrical arrangement of substituents along the x axis, high frequency of distance between N and O atoms at topological distance 9, as well as between C and N atoms at topological distance 10, and more C atoms located at topological distances 6 and 3. Based on the conclusion given in the QSAR analysis, a new compound with possible great activity was proposed.
Šoškić, Milan; Porobić, Ivana
2016-01-01
Retention factors for 31 indole derivatives, most of them with auxin activity, were determined by high-performance liquid chromatography, using bonded β-cyclodextrin as a stationary phase. A three-parameter QSPR (quantitative structure-property relationship) model, based on physico-chemical and structural descriptors was derived, which accounted for about 98% variations in the retention factors. The model suggests that the indole nucleus occupies the relatively apolar cavity of β-cyclodextrin while the carboxyl group of the indole -3-carboxylic acids makes hydrogen bonds with the hydroxyl groups of β-cyclodextrin. The length and flexibility of the side chain containing carboxyl group strongly affect the binding of these compounds to β-cyclodextrin. Non-acidic derivatives, unlike the indole-3-carboxylic acids, are poorly retained on the column. A reasonably well correlation was found between the retention factors of the indole-3-acetic acids and their relative binding affinities for human serum albumin, a carrier protein in the blood plasma. A less satisfactory correlation was obtained when the retention factors of the indole derivatives were compared with their affinities for auxin-binding protein 1, a plant auxin receptor. PMID:27124734
Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies.
Nendza, Monika; Müller, Martin; Wenzel, Andrea
2017-03-22
Fish acute toxicity studies are required for environmental hazard and risk assessment of chemicals by national and international legislations such as REACH, the regulations of plant protection products and biocidal products, or the GHS (globally harmonised system) for classification and labelling of chemicals. Alternative methods like QSARs (quantitative structure-activity relationships) can replace many ecotoxicity tests. However, complete substitution of in vivo animal tests by in silico methods may not be realistic. For the so-called baseline toxicants, it is possible to predict the fish acute toxicity with sufficient accuracy from log K ow and, hence, valid QSARs can replace in vivo testing. In contrast, excess toxicants and chemicals not reliably classified as baseline toxicants require further in silico, in vitro or in vivo assessments. Thus, the critical task is to discriminate between baseline and excess toxicants. For fish acute toxicity, we derived a scheme based on structural alerts and physicochemical property thresholds to classify chemicals as either baseline toxicants (=predictable by QSARs) or as potential excess toxicants (=not predictable by baseline QSARs). The step-wise approach identifies baseline toxicants (true negatives) in a precautionary way to avoid false negative predictions. Therefore, a certain fraction of false positives can be tolerated, i.e. baseline toxicants without specific effects that may be tested instead of predicted. Application of the classification scheme to a new heterogeneous dataset for diverse fish species results in 40% baseline toxicants, 24% excess toxicants and 36% compounds not classified. Thus, we can conclude that replacing about half of the fish acute toxicity tests by QSAR predictions is realistic to be achieved in the short-term. The long-term goals are classification criteria also for further groups of toxicants and to replace as many in vivo fish acute toxicity tests as possible with valid QSAR
Toxicity evaluation and prediction of toxic chemicals on activated sludge system.
Cai, Bijing; Xie, Li; Yang, Dianhai; Arcangeli, Jean-Pierre
2010-05-15
The gaps of data for evaluating toxicity of new or overloaded organic chemicals on activated sludge system resulted in the requirements for methodology of toxicity estimation. In this study, 24 aromatic chemicals typically existed in the industrial wastewater were selected and classified into three groups of benzenes, phenols and anilines. Their toxicity on activated sludge was then investigated. Two indexes of IC(50-M) and IC(50-S) were determined respectively from the respiration rates of activated sludge with different toxicant concentration at mid-term (24h) and short-term (30min) time intervals. Experimental results showed that the group of benzenes was the most toxic, followed by the groups of phenols and anilines. The values of IC(50-M) of the tested chemicals were higher than those of IC(50-S). In addition, quantitative structure-activity relationships (QSARs) models developed from IC(50-M) were more stable and accurate than those of IC(50-S). The multiple linear models based on molecular descriptors and K(ow) presented better reliability than single linear models based on K(ow). Among these molecular descriptors, E(lumo) was the most important impact factor for evaluation of mid-term toxicity. Copyright (c) 2009 Elsevier B.V. All rights reserved.
Dolan, Niamh; Gavin, Declan P; Eshwika, Ahmed; Kavanagh, Kevin; McGinley, John; Stephens, John C
2016-01-15
We report the synthesis, antibacterial evaluation of a series of thiourea-containing compounds. 1-(3,5-Bis(trifluoromethyl)phenyl)-3-((S)-(6-methoxyquinolin-4-yl)-((1S,2S,4S,5R)-5-vinylquinuclidin-2-yl)methyl)thiourea 5, was the most active against a range of Gram-positive and Gram-negative bacteria, and exhibited bacteriostatic activity against methicillin resistant Staphylococcus aureus (MRSA) comparable to that of the well-known antibacterial agent vancomycin. Quinoline thiourea 5 was subjected to a detailed structure-activity relationship study, with 5 and its derivatives evaluated for their bacteriostatic activity against both Gram-negative and Gram-positive bacteria. A number of structural features important for the overall activity of quinoline thiourea 5 have been identified. A selection of compounds, including 5, was also evaluated for their in vivo toxicity using the larvae of the Greater wax moth, Galleria mellonella. Compound 5, and a number of derivatives, were found to be non-toxic to the larvae of Galleria mellonella. A new class of antibiotic can result from the further development of this family of compounds. Copyright © 2015 Elsevier Ltd. All rights reserved.
In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects
Cronin, Mark T.D.; Enoch, Steven J.; Mellor, Claire L.; Przybylak, Katarzyna R.; Richarz, Andrea-Nicole; Madden, Judith C.
2017-01-01
In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given. PMID:28744348
Nargotra, Amit; Sharma, Sujata; Koul, Jawahir Lal; Sangwan, Pyare Lal; Khan, Inshad Ali; Kumar, Ashwani; Taneja, Subhash Chander; Koul, Surrinder
2009-10-01
Quantitative structure activity relationship (QSAR) analysis of piperine analogs as inhibitors of efflux pump NorA from Staphylococcus aureus has been performed in order to obtain a highly accurate model enabling prediction of inhibition of S. aureus NorA of new chemical entities from natural sources as well as synthetic ones. Algorithm based on genetic function approximation method of variable selection in Cerius2 was used to generate the model. Among several types of descriptors viz., topological, spatial, thermodynamic, information content and E-state indices that were considered in generating the QSAR model, three descriptors such as partial negative surface area of the compounds, area of the molecular shadow in the XZ plane and heat of formation of the molecules resulted in a statistically significant model with r(2)=0.962 and cross-validation parameter q(2)=0.917. The validation of the QSAR models was done by cross-validation, leave-25%-out and external test set prediction. The theoretical approach indicates that the increase in the exposed partial negative surface area increases the inhibitory activity of the compound against NorA whereas the area of the molecular shadow in the XZ plane is inversely proportional to the inhibitory activity. This model also explains the relationship of the heat of formation of the compound with the inhibitory activity. The model is not only able to predict the activity of new compounds but also explains the important regions in the molecules in quantitative manner.
Quantitative Structure – Property Relationship Modeling of Remote Liposome Loading Of Drugs
Cern, Ahuva; Golbraikh, Alexander; Sedykh, Aleck; Tropsha, Alexander; Barenholz, Yechezkel; Goldblum, Amiram
2012-01-01
Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a dataset including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and five-fold external validation. The external prediction accuracy for binary models was as high as 91–96%; for continuous models the mean coefficient R2 for regression between predicted versus observed values was 0.76–0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments. PMID:22154932
Yoon, Kyoung Jin P; Krull, Erik J; Morton, Christopher L; Bornmann, William G; Lee, Richard E; Potter, Philip M; Danks, Mary K
2003-11-01
7-Ethyl-10-[4-(1-piperidino)-1-piperidino]carbonyloxycamptothecin (irinotecan, CPT-11) is a camptothecin prodrug that is metabolized by carboxylesterases (CE) to the active metabolite 7-ethyl-10-hydroxycamptothecin (SN-38), a topoisomerase I inhibitor. CPT-11 has shown encouraging antitumor activity against a broad spectrum of tumor types in early clinical trials, but hematopoietic and gastrointestinal toxicity limit its administration. To increase the therapeutic index of CPT-11 and to develop other prodrug analogues for enzyme/prodrug gene therapy applications, our laboratories propose to develop camptothecin prodrugs that will be activated by specific CEs. Specific analogues might then be predicted to be activated, for example, predominantly by human liver CE(hCE1), by human intestinal CE (hiCE), or in gene therapy approaches using a rabbit liver CE (rCE). This study describes a molecular modeling approach to relate the structure of rCE-activated camptothecin prodrugs with their biological activation. Comparative molecular field analysis, comparative molecular similarity index analysis, and docking studies were used to predict the biological activity of a 4-benzylpiperazine derivative of CPT-11 [7-ethyl-10-[4-(1-benzyl)-1-piperazino]carbonyloxycamptothecin (BP-CPT)] in U373MG glioma cell lines transfected with plasmids encoding rCE or hiCE. BP-CPT has been reported to be activated more efficiently than CPT-11 by a rat serum esterase activity; however, three-dimensional quantitative structure-activity relationship studies predicted that rCE would activate BP-CPT less efficiently than CPT-11. This was confirmed by both growth inhibition experiments and kinetic studies. The method is being used to design camptothecin prodrugs predicted to be activated by specific CEs.
On the virtues of automated quantitative structure-activity relationship: the new kid on the block.
de Oliveira, Marcelo T; Katekawa, Edson
2018-02-01
Quantitative structure-activity relationship (QSAR) has proved to be an invaluable tool in medicinal chemistry. Data availability at unprecedented levels through various databases have collaborated to a resurgence in the interest for QSAR. In this context, rapid generation of quality predictive models is highly desirable for hit identification and lead optimization. We showcase the application of an automated QSAR approach, which randomly selects multiple training/test sets and utilizes machine-learning algorithms to generate predictive models. Results demonstrate that AutoQSAR produces models of improved or similar quality to those generated by practitioners in the field but in just a fraction of the time. Despite the potential of the concept to the benefit of the community, the AutoQSAR opportunity has been largely undervalued.
Dixon, Steven L; Duan, Jianxin; Smith, Ethan; Von Bargen, Christopher D; Sherman, Woody; Repasky, Matthew P
2016-10-01
We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.
Oja, M; Maran, U
2015-01-01
Absorption in gastrointestinal tract compartments varies and is largely influenced by pH. Therefore, considering pH in studies and analyses of membrane permeability provides an opportunity to gain a better understanding of the behaviour of compounds and to obtain good permeability estimates for prediction purposes. This study concentrates on relationships between the chemical structure and membrane permeability of acidic and basic drugs and drug-like compounds. The membrane permeability of 36 acidic and 61 basic compounds was measured using the parallel artificial membrane permeability assay (PAMPA) at pH 3, 5, 7.4 and 9. Descriptive and/or predictive single-parameter quantitative structure-permeability relationships were derived for all pH values. For acidic compounds, membrane permeability is mainly influenced by hydrogen bond donor properties, as revealed by models with r(2) > 0.8 for pH 3 and pH 5. For basic compounds, the best (r(2) > 0.7) structure-permeability relationships are obtained with the octanol-water distribution coefficient for pH 7.4 and pH 9, indicating the importance of partition properties. In addition to the validation set, the prediction quality of the developed models was tested with folic acid and astemizole, showing good matches between experimental and calculated membrane permeabilities at key pHs. Selected QSAR models are available at the QsarDB repository ( http://dx.doi.org/10.15152/QDB.166 ).
Extrapolation of toxic indices among test objects
Tichý, Miloň; Rucki, Marián; Roth, Zdeněk; Hanzlíková, Iveta; Vlková, Alena; Tumová, Jana; Uzlová, Rút
2010-01-01
Oligochaeta Tubifex tubifex, fish fathead minnow (Pimephales promelas), hepatocytes isolated from rat liver and ciliated protozoan are absolutely different organisms and yet their acute toxicity indices correlate. Correlation equations for special effects were developed for a large heterogeneous series of compounds (QSAR, quantitative structure-activity relationships). Knowing those correlation equations and their statistic evaluation, one can extrapolate the toxic indices. The reason is that a common physicochemical property governs the biological effect, namely the partition coefficient between two unmissible phases, simulated generally by n-octanol and water. This may mean that the transport of chemicals towards a target is responsible for the magnitude of the effect, rather than reactivity, as one would assume suppose. PMID:21331180
Framework for a Quantitative Systemic Toxicity Model (FutureToxII)
EPA’s ToxCast program profiles the bioactivity of chemicals in a diverse set of ~700 high throughput screening (HTS) assays. In collaboration with L’Oreal, a quantitative model of systemic toxicity was developed using no effect levels (NEL) from ToxRefDB for 633 chemicals with HT...
ERIC Educational Resources Information Center
Krein, Michael
2011-01-01
After decades of development and use in a variety of application areas, Quantitative Structure Property Relationships (QSPRs) and related descriptor-based statistical learning methods have achieved a level of infamy due to their misuse. The field is rife with past examples of overtrained models, overoptimistic performance assessment, and outright…
Drosos, Juan Carlos; Viola-Rhenals, Maricela; Vivas-Reyes, Ricardo
2010-06-25
Polycyclic aromatic compounds (PAHs) are of concern in environmental chemistry and toxicology. In the present work, a QSRR study was performed for 209 previously reported PAHs using quantum mechanics and other sources descriptors estimated by different approaches. The B3LYP/6-31G* level of theory was used for geometrical optimization and quantum mechanics related variables. A good linear relationship between gas-chromatographic retention index and electronic or topologic descriptors was found by stepwise linear regression analysis. The molecular polarizability (alpha) and the second order molecular connectivity Kier and Hall index ((2)chi) showed evidence of significant correlation with retention index by means of important squared coefficient of determination, (R(2)), values (R(2)=0.950 and 0.962, respectively). A one variable QSRR model is presented for each descriptor and both models demonstrates a significant predictive capacity established using the leave-many-out LMO (excluding 25% of rows) cross validation method's q(2) cross-validation coefficients q(2)(CV-LMO25%), (obtained q(2)(CV-LMO25%) 0.947 and 0.960, respectively). Furthermore, the physicochemical interpretation of selected descriptors allowed detailed explanation of the source of the observed statistical correlation. The model analysis suggests that only one descriptor is sufficient to establish a consistent retention index-structure relationship. Moderate or non-significant improve was observed for quantitative results or statistical validation parameters when introducing more terms in predictive equation. The one parameter QSRR proposed model offers a consistent scheme to predict chromatographic properties of PAHs compounds. Copyright 2010 Elsevier B.V. All rights reserved.
Is there a relationship between soil and groundwater toxicity?
Sheehan, P; Dewhurst, R E; James, S; Callaghan, A; Connon, R; Crane, M
2003-03-01
Part IIA of the Environmental Protection Act 1990 requires environmental regulators to assess the risk of contaminants leaching from soils into groundwater (DETR, 1999). This newly introduced legislation assumes a link between soil and groundwater chemistry, in which rainwater leaches contaminants from soil into the saturated zone. As the toxicity of both groundwater and overlying soils is dependent upon the chemicals present, their partitioning and their bioavailability, similar patterns of soil, leachates and groundwater toxicity should be observed at contaminated sites. Soil and groundwater samples were collected from different contaminated land sites in an urban area, and used to determine relationships between soil chemistry and toxicity, mobility of contaminants, and groundwater chemistry and toxicity. Soils were leached using water to mimic rainfall, and both the soils and leachates tested using bioassays. Soil bioassays were carried out using Eisenia fetida, whilst groundwater and leachates were tested using the Microtox test system and Daphnia magna 48 h acute tests. Analysis of the bioassay responses demonstrated that a number of the samples were toxic to test organisms, however, there were no significant statistical relationships between soil, groundwater and leachate toxicity. Nor were there significant correlations between soil, leachates and groundwater chemistry.
(Q)SARs to predict environmental toxicities: current status and future needs.
Cronin, Mark T D
2017-03-22
The current state of the art of (Quantitative) Structure-Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models.
Norinder, U; Högberg, T
1992-04-01
The advantageous approach of using an experimentally designed training set as the basis for establishing a quantitative structure-activity relationship with good predictive capability is described. The training set was selected from a fractional factorial design scheme based on a principal component description of physico-chemical parameters of aromatic substituents. The derived model successfully predicts the activities of additional substituted benzamides of 6-methoxy-N-(4-piperidyl)salicylamide type. The major influence on activity of the 3-substituent is demonstrated.
CHEMICAL STRUCTURE INDEXING OF TOXICITY DATA ON ...
Standardized chemical structure annotation of public toxicity databases and information resources is playing an increasingly important role in the 'flattening' and integration of diverse sets of biological activity data on the Internet. This review discusses public initiatives that are accelerating the pace of this transformation, with particular reference to toxicology-related chemical information. Chemical content annotators, structure locator services, large structure/data aggregator web sites, structure browsers, International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) codes, toxicity data models and public chemical/biological activity profiling initiatives are all playing a role in overcoming barriers to the integration of toxicity data, and are bringing researchers closer to the reality of a mineable chemical Semantic Web. An example of this integration of data is provided by the collaboration among researchers involved with the Distributed Structure-Searchable Toxicity (DSSTox) project, the Carcinogenic Potency Project, projects at the National Cancer Institute and the PubChem database. Standardizing chemical structure annotation of public toxicity databases
Wang, Hui; Jiang, Mingyue; Li, Shujun; Hse, Chung-Yun; Jin, Chunde; Sun, Fangli; Li, Zhuo
2017-09-01
Cinnamaldehyde amino acid Schiff base (CAAS) is a new class of safe, bioactive compounds which could be developed as potential antifungal agents for fungal infections. To design new cinnamaldehyde amino acid Schiff base compounds with high bioactivity, the quantitative structure-activity relationships (QSARs) for CAAS compounds against Aspergillus niger ( A. niger ) and Penicillium citrinum (P. citrinum) were analysed. The QSAR models ( R 2 = 0.9346 for A. niger , R 2 = 0.9590 for P. citrinum, ) were constructed and validated. The models indicated that the molecular polarity and the Max atomic orbital electronic population had a significant effect on antifungal activity. Based on the best QSAR models, two new compounds were designed and synthesized. Antifungal activity tests proved that both of them have great bioactivity against the selected fungi.
NASA Astrophysics Data System (ADS)
Shanty, Angamaly Antony; Mohanan, Puzhavoorparambil Velayudhan
2018-03-01
Phenolic heterocyclic imine based Schiff bases from Thiophene-2-carboxaldehyde and Pyrrole-2-carboxaldehyde were synthesized and characterized as novel antioxidants. The solvent effects of these Schiff bases were determined and compared with standard antioxidants, BHA employing DPPH assay and ABTS assay. Fixed reaction time and Steady state measurement were used for study. IC50 and EC50 were calculated. Structure-activity relationship revealed that the electron donating group in the phenolic ring increases the activity where as the electron withdrawing moiety decreases the activity. The Schiff base derivatives showed antioxidant property by two different pathways namely SPLET and HAT mechanisms in DPPH assay. While in ABTS method, the reaction between ABTS radical and Schiff bases involves electron transfer followed by proton transfer (ET-PT) mechanism. The cytotoxicity of these compounds has been evaluated by MTT assay. The results showed that all these compounds are non toxic in nature.
Classification of Chemicals Based On Structured Toxicity ...
Thirty years and millions of dollars worth of pesticide registration toxicity studies, historically stored as hardcopy and scanned documents, have been digitized into highly standardized and structured toxicity data within the Toxicity Reference Database (ToxRefDB). Toxicity-based classifications of chemicals were performed as a model application of ToxRefDB. These endpoints will ultimately provide the anchoring toxicity information for the development of predictive models and biological signatures utilizing in vitro assay data. Utilizing query and structured data mining approaches, toxicity profiles were uniformly generated for greater than 300 chemicals. Based on observation rate, species concordance and regulatory relevance, individual and aggregated effects have been selected to classify the chemicals providing a set of predictable endpoints. ToxRefDB exhibits the utility of transforming unstructured toxicity data into structured data and, furthermore, into computable outputs, and serves as a model for applying such data to address modern toxicological problems.
Deep neural nets as a method for quantitative structure-activity relationships.
Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir
2015-02-23
Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable.
Yang, Guang-Fu; Huang, Xiaoqin
2006-01-01
Over forty years have elapsed since Hansch and Fujita published their pioneering work of quantitative structure-activity relationships (QSAR). Following the introduction of Comparative Molecular Field Analysis (CoMFA) by Cramer in 1998, other three-dimensional QSAR methods have been developed. Currently, combination of classical QSAR and other computational techniques at three-dimensional level is of greatest interest and generally used in the process of modern drug discovery and design. During the last several decades, a number of different mythologies incorporating a range of molecular descriptors and different statistical regression ways have been proposed and successfully applied in developing of new drugs, thus QSAR method has been proven to be indispensable in not only the reliable prediction of specific properties of new compounds, but also the help to elucidate the possible molecular mechanism of the receptor-ligand interactions. Here, we review the recent developments in QSAR and their applications in rational drug design, focusing on the reasonable selection of novel molecular descriptors and the construction of predictive QSAR models by the help of advanced computational techniques.
NASA Astrophysics Data System (ADS)
Hirst, Jonathan D.; King, Ross D.; Sternberg, Michael J. E.
1994-08-01
One of the largest available data sets for developing a quantitative structure-activity relationship (QSAR) — the inhibition of dihydrofolate reductase (DHFR) by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazine derivatives — has been used for a sixfold cross-validation trial of neural networks, inductive logic programming (ILP) and linear regression. No statistically significant difference was found between the predictive capabilities of the methods. However, the representation of molecules by attributes, which is integral to the ILP approach, provides understandable rules about drug-receptor interactions.
Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta; Tambe, Sanjeev S
2015-11-13
The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R(2)) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science
Belka, Mariusz; Hewelt-Belka, Weronika; Sławiński, Jarosław; Bączek, Tomasz
2014-01-01
A set of 15 new sulphonamide derivatives, presenting antitumor activity have been subjected to a metabolic stability study. The results showed that besides products of biotransformation, some additional peaks occurred in chromatograms. Tandem mass spectrometry revealed the same mass and fragmentation pathway, suggesting that geometric isomerization occurred. Thus, to support this hypothesis, quantitative structure-retention relationships were applied. Human liver microsomes were used as an in vitro model of metabolism. The biotransformation reactions were tracked by liquid chromatography assay and additionally, fragmentation mass spectra were recorded. In silico molecular modeling at a semi-empirical level was conducted as a starting point for molecular descriptor calculations. A quantitative structure-retention relationship model was built applying multiple linear regression based on selected three-dimensional descriptors. The studied compounds revealed high metabolic stability, with a tendency to form hydroxylated biotransformation products. However, significant chemical instability in conditions simulating human body fluids was noticed. According to literature and MS data geometrical isomerization was suggested. The developed in sillico model was able to describe the relationship between the geometry of isomer pairs and their chromatographic retention properties, thus it supported the hypothesis that the observed pairs of peaks are most likely geometric isomers. However, extensive structural investigations are needed to fully identify isomers’ geometry. An effort to describe MS fragmentation pathways of novel chemical structures is often not enough to propose structures of potent metabolites and products of other chemical reactions that can be observed in compound solutions at early drug discovery studies. The results indicate that the relatively non-expensive and not time- and labor-consuming in sillico approach could be a good supportive tool assisting the
Sizochenko, Natalia; Rasulev, Bakhtiyor; Gajewicz, Agnieszka; Kuz'min, Victor; Puzyn, Tomasz; Leszczynski, Jerzy
2014-11-21
Many metal oxide nanoparticles are able to cause persistent stress to live organisms, including humans, when discharged to the environment. To understand the mechanism of metal oxide nanoparticles' toxicity and reduce the number of experiments, the development of predictive toxicity models is important. In this study, performed on a series of nanoparticles, the comparative quantitative-structure activity relationship (nano-QSAR) analyses of their toxicity towards E. coli and HaCaT cells were established. A new approach for representation of nanoparticles' structure is presented. For description of the supramolecular structure of nanoparticles the "liquid drop" model was applied. It is expected that a novel, proposed approach could be of general use for predictions related to nanomaterials. In addition, in our study fragmental simplex descriptors and several ligand-metal binding characteristics were calculated. The developed nano-QSAR models were validated and reliably predict the toxicity of all studied metal oxide nanoparticles. Based on the comparative analysis of contributed properties in both models the LDM-based descriptors were revealed to have an almost similar level of contribution to toxicity in both cases, while other parameters (van der Waals interactions, electronegativity and metal-ligand binding characteristics) have unequal contribution levels. In addition, the models developed here suggest different mechanisms of nanotoxicity for these two types of cells.
Background: Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Methods and results: A database co...
Önlü, Serli; Saçan, Melek Türker
2017-04-01
The authors modeled the 72-h algal toxicity data of hundreds of chemicals with different modes of action as a function of chemical structures. They developed mode of action-based local quantitative structure-toxicity relationship (QSTR) models for nonpolar and polar narcotics as well as a global QSTR model with a wide applicability potential for industrial chemicals and pharmaceuticals. The present study rigorously evaluated the generated models, meeting the Organisation for Economic Co-operation and Development principles of robustness, validity, and transparency. The proposed global model had a broad structural coverage for the toxicity prediction of diverse chemicals (some of which are high-production volume chemicals) with no experimental toxicity data. The global model is potentially useful for endpoint predictions, the evaluation of algal toxicity screening, and the prioritization of chemicals, as well as for the decision of further testing and the development of risk-management measures in a scientific and regulatory frame. Environ Toxicol Chem 2017;36:1012-1019. © 2016 SETAC. © 2016 SETAC.
Chung, Jou-Ku; Shen, Shuijie; Jiang, Zhiteng; Yuan, Wei; Zheng, Jiang
2012-01-01
Styrene is one of the most important industrial intermediates consumed in the world and is mainly used as a monomer for reinforced plastics and rubber. Styrene has been found to be hepatotoxic and pneumotoxic in humans and experimental animals. The toxicity of styrene is suggested to be metabolism-dependent. Styrene-7,8-oxide has been considered as the major metabolite responsible for styrene-induced cytotoxicity. The objective of the study was to investigate the correlation between cytotoxicity of styrene and chemical and biochemical properties of the vinyl group of styrene by development of structure activity relationships (SAR). 4-Fluorostyrene, 4-chlorostyrene and 4-bromostyrene were selected for the SAR study. Cytotoxicity of styrene and the halogenated styrene derivatives with an order of 4-bromostyrene > 4-chlorostyrene > 4-fluorostyrene ≈ styrene was observed in CYP2E1 transgenic cells. Similar orders in the efficiency of the metabolism of styrene and the halogenated styrene analogues to their oxides and in the electrophilicity of the corresponding oxides were observed. Additionally, the order of the potency of cellular glutathione depletion and the degree of protein adduction induced by styrene and the halogenated styrenes were consistent with that of their cytotoxicities. The wild-type cells were less susceptible to the toxicity of the corresponding model compounds than CYP2E1 cells. The present study provided insight into the roles of the biochemical and chemical properties of styrene in its cytotoxicity. PMID:22366341
Ochiai, K; Uemura, S; Shimizu, A; Okumoto, Y; Matoh, T
2008-06-01
Boron toxicity tolerance of rice plants was studied. Modern japonica subspecies such as Koshihikari, Nipponbare, and Sasanishiki were tolerant, whereas indica subspecies such as Kasalath and IR36 were intolerant to excessive application of boron (B), even though their shoot B contents under B toxicity were not significantly different. Recombinant inbred lines (RILs) of japonica Nekken-1 and indica IR36 were used for quantitative trait locus (QTL) analysis to identify the gene responsible for B toxicity tolerance. A major QTL that could explain 45% of the phenotypic variation was detected in chromosome 4. The QTL was confirmed using a population derived from a recombinant inbred line which is heterogenic at the QTL region. The QTL was also confirmed in other chromosome segment substitution lines (CSSLs).
DOE Office of Scientific and Technical Information (OSTI.GOV)
Tian, Dayong; Department of Chemical and Environmental Engineering, Anyang Institute of Technology, Anyang 455000; Lin, Zhifen, E-mail: lzhifen@tongji.edu.cn
Intracellular chemical reaction of chemical mixtures is one of the main reasons that cause synergistic or antagonistic effects. However, it still remains unclear what the influencing factors on the intracellular chemical reaction are, and how they influence on the toxicological mechanism of chemical mixtures. To reveal this underlying toxicological mechanism of chemical mixtures, a case study on mixture toxicity of cyanogenic toxicants and aldehydes to Photobacterium phosphoreum was employed, and both their joint effects and mixture toxicity were observed. Then series of two-step linear regressions were performed to describe the relationships between joint effects, the expected additive toxicities and descriptorsmore » of individual chemicals (including concentrations, binding affinity to receptors, octanol/water partition coefficients). Based on the quantitative relationships, the underlying joint toxicological mechanisms were revealed. The result shows that, for mixtures with their joint effects resulting from intracellular chemical reaction, their underlying toxicological mechanism depends on not only their interaction with target proteins, but also their transmembrane actions and their concentrations. In addition, two generic points of toxicological mechanism were proposed including the influencing factors on intracellular chemical reaction and the difference of the toxicological mechanism between single reactive chemicals and their mixtures. This study provided an insight into the understanding of the underlying toxicological mechanism for chemical mixtures with intracellular chemical reaction. - Highlights: • Joint effects of nitriles and aldehydes at non-equitoxic ratios were determined. • A novel descriptor, ligand–receptor interaction energy (E{sub binding}), was employed. • Quantitative relationships for mixtures were developed based on a novel descriptor. • The underlying toxic mechanism was revealed based on quantitative relationships.
Bairy, Santhosh Kumar; Suneel Kumar, B V S; Bhalla, Joseph Uday Tej; Pramod, A B; Ravikumar, Muttineni
2009-04-01
c-Src kinase play an important role in cell growth and differentiation and its inhibitors can be useful for the treatment of various diseases, including cancer, osteoporosis, and metastatic bone disease. Three dimensional quantitative structure-activity relationship (3D-QSAR) studies were carried out on quinazolin derivatives inhibiting c-Src kinase. Molecular field analysis (MFA) models with four different alignment techniques, namely, GLIDE, GOLD, LIGANDFIT and Least squares based methods were developed. glide based MFA model showed better results (Leave one out cross validation correlation coefficient r(2)(cv) = 0.923 and non-cross validation correlation coefficient r(2)= 0.958) when compared with other models. These results help us to understand the nature of descriptors required for activity of these compounds and thereby provide guidelines to design novel and potent c-Src kinase inhibitors.
FDA toxicity databases and real-time data entry.
Arvidson, Kirk B
2008-11-15
Structure-searchable electronic databases are valuable new tools that are assisting the FDA in its mission to promptly and efficiently review incoming submissions for regulatory approval of new food additives and food contact substances. The Center for Food Safety and Applied Nutrition's Office of Food Additive Safety (CFSAN/OFAS), in collaboration with Leadscope, Inc., is consolidating genetic toxicity data submitted in food additive petitions from the 1960s to the present day. The Center for Drug Evaluation and Research, Office of Pharmaceutical Science's Informatics and Computational Safety Analysis Staff (CDER/OPS/ICSAS) is separately gathering similar information from their submissions. Presently, these data are distributed in various locations such as paper files, microfiche, and non-standardized toxicology memoranda. The organization of the data into a consistent, searchable format will reduce paperwork, expedite the toxicology review process, and provide valuable information to industry that is currently available only to the FDA. Furthermore, by combining chemical structures with genetic toxicity information, biologically active moieties can be identified and used to develop quantitative structure-activity relationship (QSAR) modeling and testing guidelines. Additionally, chemicals devoid of toxicity data can be compared to known structures, allowing for improved safety review through the identification and analysis of structural analogs. Four database frameworks have been created: bacterial mutagenesis, in vitro chromosome aberration, in vitro mammalian mutagenesis, and in vivo micronucleus. Controlled vocabularies for these databases have been established. The four separate genetic toxicity databases are compiled into a single, structurally-searchable database for easy accessibility of the toxicity information. Beyond the genetic toxicity databases described here, additional databases for subchronic, chronic, and teratogenicity studies have been prepared.
FDA toxicity databases and real-time data entry
DOE Office of Scientific and Technical Information (OSTI.GOV)
Arvidson, Kirk B.
Structure-searchable electronic databases are valuable new tools that are assisting the FDA in its mission to promptly and efficiently review incoming submissions for regulatory approval of new food additives and food contact substances. The Center for Food Safety and Applied Nutrition's Office of Food Additive Safety (CFSAN/OFAS), in collaboration with Leadscope, Inc., is consolidating genetic toxicity data submitted in food additive petitions from the 1960s to the present day. The Center for Drug Evaluation and Research, Office of Pharmaceutical Science's Informatics and Computational Safety Analysis Staff (CDER/OPS/ICSAS) is separately gathering similar information from their submissions. Presently, these data are distributedmore » in various locations such as paper files, microfiche, and non-standardized toxicology memoranda. The organization of the data into a consistent, searchable format will reduce paperwork, expedite the toxicology review process, and provide valuable information to industry that is currently available only to the FDA. Furthermore, by combining chemical structures with genetic toxicity information, biologically active moieties can be identified and used to develop quantitative structure-activity relationship (QSAR) modeling and testing guidelines. Additionally, chemicals devoid of toxicity data can be compared to known structures, allowing for improved safety review through the identification and analysis of structural analogs. Four database frameworks have been created: bacterial mutagenesis, in vitro chromosome aberration, in vitro mammalian mutagenesis, and in vivo micronucleus. Controlled vocabularies for these databases have been established. The four separate genetic toxicity databases are compiled into a single, structurally-searchable database for easy accessibility of the toxicity information. Beyond the genetic toxicity databases described here, additional databases for subchronic, chronic, and teratogenicity studies have been
Quantitative Relationships Involving Additive Differences: Numerical Resilience
ERIC Educational Resources Information Center
Ramful, Ajay; Ho, Siew Yin
2014-01-01
This case study describes the ways in which problems involving additive differences with unknown starting quantities, constrain the problem solver in articulating the inherent quantitative relationship. It gives empirical evidence to show how numerical reasoning takes over as a Grade 6 student instantiates the quantitative relation by resorting to…
Our study assesses the value of both in vitro assay and quantitative structure activity relationship (QSAR) data in predicting in vivo toxicity using numerous statistical models and approaches to process the data. Our models are built on datasets of (i) 586 chemicals for which bo...
Li, Jie; Sun, Jin; Cui, Shengmiao; He, Zhonggui
2006-11-03
Linear solvation energy relationships (LSERs) amended by the introduction of a molecular electronic factor were employed to establish quantitative structure-retention relationships using immobilized artificial membrane (IAM) chromatography, in particular ionizable solutes. The chromatographic indices, log k(IAM), were determined by HPLC on an IAM.PC.DD2 column for 53 structurally diverse compounds, including neutral, acidic and basic compounds. Unlike neutral compounds, the IAM chromatographic retention of ionizable compounds was affected by their molecular charge state. When the mean net charge per molecule (delta) was introduced into the amended LSER as the sixth variable, the LSER regression coefficient was significantly improved for the test set including ionizable solutes. The delta coefficients of acidic and basic compounds were quite different indicating that the molecular electronic factor had a markedly different impact on the retention of acidic and basic compounds on IAM column. Ionization of acidic compounds containing a carboxylic group tended to impair their retention on IAM, while the ionization of basic compounds did not have such a marked effect. In addition, the extra-interaction with the polar head of phospholipids might cause a certain change in the retention of basic compounds. A comparison of calculated and experimental retention indices suggested that the semi-empirical LSER amended by the addition of a molecular electronic factor was able to reproduce adequately the experimental retention factors of the structurally diverse solutes investigated.
Cunha, Jonathan Da; Lavaggi, María Laura; Abasolo, María Inés; Cerecetto, Hugo; González, Mercedes
2011-12-01
Hypoxic regions of tumours are associated with increased resistance to radiation and chemotherapy. Nevertheless, hypoxia has been used as a tool for specific activation of some antitumour prodrugs, named bioreductive agents. Phenazine dioxides are an example of such bioreductive prodrugs. Our 2D-quantitative structure activity relationship studies established that phenazine dioxides electronic and lipophilic descriptors are related to survival fraction in oxia or in hypoxia. Additionally, statistically significant models, derived by partial least squares, were obtained between survival fraction in oxia and comparative molecular field analysis standard model (r² = 0.755, q² = 0.505 and F = 26.70) or comparative molecular similarity indices analysis-combined steric and electrostatic fields (r² = 0.757, q² = 0.527 and F = 14.93), and survival fraction in hypoxia and comparative molecular field analysis standard model (r² = 0.736, q² = 0.521 and F = 18.63) or comparative molecular similarity indices analysis-hydrogen bond acceptor field (r² = 0.858, q² = 0.737 and F = 27.19). Categorical classification was used for the biological parameter selective cytotoxicity emerging also good models, derived by soft independent modelling of class analogy, with both comparative molecular field analysis standard model (96% of overall classification accuracy) and comparative molecular similarity indices analysis-steric field (92% of overall classification accuracy). 2D- and 3D-quantitative structure-activity relationships models provided important insights into the chemical and structural basis involved in the molecular recognition process of these phenazines as bioreductive agents and should be useful for the design of new structurally related analogues with improved potency. © 2011 John Wiley & Sons A/S.
Quantifying the relationship between sequence and three-dimensional structure conservation in RNA
2010-01-01
Background In recent years, the number of available RNA structures has rapidly grown reflecting the increased interest on RNA biology. Similarly to the studies carried out two decades ago for proteins, which gave the fundamental grounds for developing comparative protein structure prediction methods, we are now able to quantify the relationship between sequence and structure conservation in RNA. Results Here we introduce an all-against-all sequence- and three-dimensional (3D) structure-based comparison of a representative set of RNA structures, which have allowed us to quantitatively confirm that: (i) there is a measurable relationship between sequence and structure conservation that weakens for alignments resulting in below 60% sequence identity, (ii) evolution tends to conserve more RNA structure than sequence, and (iii) there is a twilight zone for RNA homology detection. Discussion The computational analysis here presented quantitatively describes the relationship between sequence and structure for RNA molecules and defines a twilight zone region for detecting RNA homology. Our work could represent the theoretical basis and limitations for future developments in comparative RNA 3D structure prediction. PMID:20550657
Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun
2018-01-01
During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future. PMID:29515993
NASA Astrophysics Data System (ADS)
Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun
2018-02-01
For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.
SAR STUDY OF NASAL TOXICITY: LESSONS FOR MODELING SMALL TOXICITY DATASETS
Most toxicity data, particularly from whole animal bioassays, are generated without the needs or capabilities of structure-activity relationship (SAR) modeling in mind. Some toxicity endpoints have been of sufficient regulatory concern to warrant large scale testing efforts (e.g....
A hierarchical clustering methodology for the estimation of toxicity.
Martin, Todd M; Harten, Paul; Venkatapathy, Raghuraman; Das, Shashikala; Young, Douglas M
2008-01-01
ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm-based technique is used to generate statistically valid QSAR models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using a Tetrahymena pyriformis acute toxicity data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results from the hierarchical clustering methodology were compared to the results from several different QSAR methodologies.
Quantitative Understanding of SHAPE Mechanism from RNA Structure and Dynamics Analysis.
Hurst, Travis; Xu, Xiaojun; Zhao, Peinan; Chen, Shi-Jie
2018-05-10
The selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) method probes RNA local structural and dynamic information at single nucleotide resolution. To gain quantitative insights into the relationship between nucleotide flexibility, RNA 3D structure, and SHAPE reactivity, we develop a 3D Structure-SHAPE Relationship model (3DSSR) to rebuild SHAPE profiles from 3D structures. The model starts from RNA structures and combines nucleotide interaction strength and conformational propensity, ligand (SHAPE reagent) accessibility, and base-pairing pattern through a composite function to quantify the correlation between SHAPE reactivity and nucleotide conformational stability. The 3DSSR model shows the relationship between SHAPE reactivity and RNA structure and energetics. Comparisons between the 3DSSR-predicted SHAPE profile and the experimental SHAPE data show correlation, suggesting that the extracted analytical function may have captured the key factors that determine the SHAPE reactivity profile. Furthermore, the theory offers an effective method to sieve RNA 3D models and exclude models that are incompatible with experimental SHAPE data.
NASA Astrophysics Data System (ADS)
Harper, Bryan; Thomas, Dennis; Chikkagoudar, Satish; Baker, Nathan; Tang, Kaizhi; Heredia-Langner, Alejandro; Lins, Roberto; Harper, Stacey
2015-06-01
The integration of rapid assays, large datasets, informatics, and modeling can overcome current barriers in understanding nanomaterial structure-toxicity relationships by providing a weight-of-the-evidence mechanism to generate hazard rankings for nanomaterials. Here, we present the use of a rapid, low-cost assay to perform screening-level toxicity evaluations of nanomaterials in vivo. Calculated EZ Metric scores, a combined measure of morbidity and mortality in developing embryonic zebrafish, were established at realistic exposure levels and used to develop a hazard ranking of diverse nanomaterial toxicity. Hazard ranking and clustering analysis of 68 diverse nanomaterials revealed distinct patterns of toxicity related to both the core composition and outermost surface chemistry of nanomaterials. The resulting clusters guided the development of a surface chemistry-based model of gold nanoparticle toxicity. Our findings suggest that risk assessments based on the size and core composition of nanomaterials alone may be wholly inappropriate, especially when considering complex engineered nanomaterials. Research should continue to focus on methodologies for determining nanomaterial hazard based on multiple sub-lethal responses following realistic, low-dose exposures, thus increasing the availability of quantitative measures of nanomaterial hazard to support the development of nanoparticle structure-activity relationships.
The relative toxic response of 27 selected phenols in the 96-hr acute flowthrough Pimephales promelas (fathead minnow) and the 48- to 60-hr chronic static Tetrahymena pyriformis (ciliate protozoan) test systems was evaluated. Log Kow-dependent linear regression analyses revealed ...
STRUCTURE TOXICITY IN RELATIONSHIPS FOR A,B-UNSATURATED ALCOHOLS IN FISH
Previous toxicity testing with fathead minnows (Pimephales promelas) indicated that some unsaturated acetylenic and allylic alcohols can be metabolically activated, via alcohol dehydrogenase, to highly toxic a,B-unsaturated aldehydes and ketones or allene derivatives. lthough sev...
Mao, Liang; Colosi, Lisa M; Gao, Shixiang; Huang, Qingguo
2011-07-15
We have verified in our previous work that lignin peroxidase (LiP) mediates effective removal of selected natural and synthetic estrogens. The efficiency of these reactions was greatly enhanced in the presence of veratryl alcohol (VA), a chemical that is produced along with LiP by certain white rot fungi, for example, Phanerochaete chrysosporium. In this study, we systematically evaluated the kinetic behaviors of LiP-mediated reactions for six endocrine disrupting compounds (EDCs), that is, steroid estrogens and their structural analogs, in both the presence and absence of VA. Resulting kinetic parameters were then correlated with structural features of LiP/substrate binding complexes, as quantified using molecular simulation, to create quantitative structure-activity relationship (QSAR) equations. These equations suggest that binding distance between a substrate's phenolic proton and δN of HIS47's imidazole ring plays an important role in modulating substrate reactivity toward LiP in both the presence and absence of VA. This information provides insight into an important enzymatic reaction process that occurs in the natural environment affecting EDC transformation, a process that may be used in engineered systems to achieve EDC removal from water.
Classification of Chemicals Based On Structured Toxicity Information
Thirty years and millions of dollars worth of pesticide registration toxicity studies, historically stored as hardcopy and scanned documents, have been digitized into highly standardized and structured toxicity data within the Toxicity Reference Database (ToxRefDB). Toxicity-bas...
Li, Linnan; Xie, Shaodong; Cai, Hao; Bai, Xuetao; Xue, Zhao
2008-08-01
Theoretical molecular descriptors were tested against logK(OW) values for polybrominated diphenyl ethers (PBDEs) using the Partial Least-Squares Regression method which can be used to analyze data with many variables and few observations. A quantitative structure-property relationship (QSPR) model was successfully developed with a high cross-validated value (Q(cum)(2)) of 0.961, indicating a good predictive ability and stability of the model. The predictive power of the QSPR model was further cross-validated. The values of logK(OW) for PBDEs are mainly governed by molecular surface area, energy of the lowest unoccupied molecular orbital and the net atomic charges on the oxygen atom. All these descriptors have been discussed to interpret the partitioning mechanism of PBDE chemicals. The bulk property of the molecules represented by molecular surface area is the leading factor, and K(OW) values increase with the increase of molecular surface area. Higher energy of the lowest unoccupied molecular orbital and higher net atomic charge on the oxygen atom of PBDEs result in smaller K(OW). The energy of the lowest unoccupied molecular orbital and the net atomic charge on PBDEs oxygen also play important roles in affecting the partition of PBDEs between octanol and water by influencing the interactions between PBDEs and solvent molecules.
Elsner, Martin; Hoelzer, Kathrin
2016-04-05
Much interest is directed at the chemical structure of hydraulic fracturing (HF) additives in unconventional gas exploitation. To bridge the gap between existing alphabetical disclosures by function/CAS number and emerging scientific contributions on fate and toxicity, we review the structural properties which motivate HF applications, and which determine environmental fate and toxicity. Our quantitative overview relied on voluntary U.S. disclosures evaluated from the FracFocus registry by different sources and on a House of Representatives ("Waxman") list. Out of over 1000 reported substances, classification by chemistry yielded succinct subsets able to illustrate the rationale of their use, and physicochemical properties relevant for environmental fate, toxicity and chemical analysis. While many substances were nontoxic, frequent disclosures also included notorious groundwater contaminants like petroleum hydrocarbons (solvents), precursors of endocrine disruptors like nonylphenols (nonemulsifiers), toxic propargyl alcohol (corrosion inhibitor), tetramethylammonium (clay stabilizer), biocides or strong oxidants. Application of highly oxidizing chemicals, together with occasional disclosures of putative delayed acids and complexing agents (i.e., compounds designed to react in the subsurface) suggests that relevant transformation products may be formed. To adequately investigate such reactions, available information is not sufficient, but instead a full disclosure of HF additives is necessary.
Development of a general baseline toxicity QSAR model for the fish embryo acute toxicity test.
Klüver, Nils; Vogs, Carolina; Altenburger, Rolf; Escher, Beate I; Scholz, Stefan
2016-12-01
Fish embryos have become a popular model in ecotoxicology and toxicology. The fish embryo acute toxicity test (FET) with the zebrafish embryo was recently adopted by the OECD as technical guideline TG 236 and a large database of concentrations causing 50% lethality (LC 50 ) is available in the literature. Quantitative Structure-Activity Relationships (QSARs) of baseline toxicity (also called narcosis) are helpful to estimate the minimum toxicity of chemicals to be tested and to identify excess toxicity in existing data sets. Here, we analyzed an existing fish embryo toxicity database and established a QSAR for fish embryo LC 50 using chemicals that were independently classified to act according to the non-specific mode of action of baseline toxicity. The octanol-water partition coefficient K ow is commonly applied to discriminate between non-polar and polar narcotics. Replacing the K ow by the liposome-water partition coefficient K lipw yielded a common QSAR for polar and non-polar baseline toxicants. This developed baseline toxicity QSAR was applied to compare the final mode of action (MOA) assignment of 132 chemicals. Further, we included the analysis of internal lethal concentration (ILC 50 ) and chemical activity (La 50 ) as complementary approaches to evaluate the robustness of the FET baseline toxicity. The analysis of the FET dataset revealed that specifically acting and reactive chemicals converged towards the baseline toxicity QSAR with increasing hydrophobicity. The developed FET baseline toxicity QSAR can be used to identify specifically acting or reactive compounds by determination of the toxic ratio and in combination with appropriate endpoints to infer the MOA for chemicals. Copyright © 2016 Elsevier Ltd. All rights reserved.
A Novel Two-Step Hierarchial Quantitative Structure-Activity ...
Background: Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Methods and results: A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC50) and in vivo rodent median lethal dose (LD50) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET ; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments) . The application of conventional quantitative structure–activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD50 values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC50 and LD50. However, a linear IC50 versus LD50 correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC50 and LD50 values: One group comprises compounds with linear IC50 versus LD50 relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models t
Luo, Jin; Hu, Jiwei; Wei, Xionghui; Fu, Liya; Li, Lingyun
2015-07-01
Dehalogenation is one of the highly important degradation reactions for halogenated organic compounds (HOCs) in the environment, which is also being developed as a potential type of the remediation technologies. In combination with the experimental results, intensive efforts have recently been devoted to the development of efficient theoretical methodologies (e.g. multi-scale simulation) to investigate the mechanisms for dehalogenation of HOCs. This review summarizes the structural characteristics of neutral molecules, anionic species and excited states of HOCs as well as their adsorption behavior on the surface of graphene and the Fe cluster. It discusses the key physiochemical properties (e.g. frontier orbital energies and thermodynamic properties) calculated at various levels of theory (e.g. semiempirical, ab initio, density functional theory (DFT) and the periodic DFT) as well as their connections to the reactivity and reaction pathway for the dehalogenation. This paper also reviews the advances in the linear and nonlinear quantitative structure-property relationship models for the dehalogenation kinetics of HOCs and in the mathematical modeling of the dehalogenation processes. Furthermore, prospects of further expansion and exploration of the current research fields are described in this article. Published by Elsevier Ltd.
Oszwałdowski, Sławomir; Timerbaev, Andrei R
2008-02-01
The relevance of the quantitative structure-activity relationship (QSAR) principle in MEKC and microemulsion EKC (MEEKC) of metal-ligand complexes was evaluated for a better understanding of analyte migration mechanism. A series of gallium chelates were applied as test solutes with available experimental migration data in order to reveal the molecular properties that govern the separation. The QSAR models operating with n-octanol-water partition coefficients or van der Waals volumes were found to be valid for estimation of the retention factors (log k') of neutral compounds when using only an aqueous MEEKC electrolyte. On the other hand, consistent approximations of log k' for both uncharged and charged complexes in either EKC mode (and also with hydro-organic BGEs) were achievable with two-parametric QSARs in which the dipole moment is additionally incorporated as a structural descriptor, reflecting the electrostatic solute-pseudostationary phase interaction. The theoretical analysis of significant molecular parameters in MEKC systems, in which the micellar BGE is modified with an organic solvent, confirmed that concomitant consideration of hydrophobic, electrostatic, and solvation factors is essential for explaining the migration behavior of neutral metal complexes.
Freitas, Mirlaine R; Matias, Stella V B G; Macedo, Renato L G; Freitas, Matheus P; Venturin, Nelson
2013-09-11
Two of major weeds affecting cereal crops worldwide are Avena fatua L. (wild oat) and Lolium rigidum Gaud. (rigid ryegrass). Thus, development of new herbicides against these weeds is required; in line with this, benzoxazinones, their degradation products, and analogues have been shown to be important allelochemicals and natural herbicides. Despite earlier structure-activity studies demonstrating that hydrophobicity (log P) of aminophenoxazines correlates to phytotoxicity, our findings for a series of benzoxazinone derivatives do not show any relationship between phytotoxicity and log P nor with other two usual molecular descriptors. On the other hand, a quantitative structure-activity relationship (QSAR) analysis based on molecular graphs representing structural shape, atomic sizes, and colors to encode other atomic properties performed very accurately for the prediction of phytotoxicities of these compounds against wild oat and rigid ryegrass. Therefore, these QSAR models can be used to estimate the phytotoxicity of new congeners of benzoxazinone herbicides toward A. fatua L. and L. rigidum Gaud.
TOWARDS REFINED USE OF TOXICITY DATA IN ...
In 2003, an International Life Sciences Institute (ILSI) Working Group examined the potential of statistically based structure-activity relationship (SAR) models for use in screening environmental contaminants for possible developmental toxicants. In 2003, an International Life Sciences Institute (ILSI) Working Group examined the potential of statistically based structure-activity relationship (SAR) models for use in screening environmental contaminants for possible developmental toxicants.
Fiori, Simona; Guzzetta, Andrea; Pannek, Kerstin; Ware, Robert S; Rossi, Giuseppe; Klingels, Katrijn; Feys, Hilde; Coulthard, Alan; Cioni, Giovanni; Rose, Stephen; Boyd, Roslyn N
2015-01-01
To provide first evidence of construct validity of a semi-quantitative scale for brain structural MRI (sqMRI scale) in children with unilateral cerebral palsy (UCP) secondary to periventricular white matter (PWM) lesions, by examining the relationship with hand sensorimotor function and whole brain structural connectivity. Cross-sectional study of 50 children with UCP due to PWM lesions using 3 T (MRI), diffusion MRI and assessment of hand sensorimotor function. We explored the relationship of lobar, hemispheric and global scores on the sqMRI scale, with fractional anisotropy (FA), as a measure of brain white matter microstructure, and with hand sensorimotor measures (Assisting Hand Assessment, AHA; Jebsen-Taylor Test for Hand Function, JTTHF; Melbourne Assessment of Unilateral Upper Limb Function, MUUL; stereognosis; 2-point discrimination). Lobar and hemispheric scores on the sqMRI scale contralateral to the clinical side of hemiplegia correlated with sensorimotor paretic hand function measures and FA of a number of brain structural connections, including connections of brain areas involved in motor control (postcentral, precentral and paracentral gyri in the parietal lobe). More severe lesions correlated with lower sensorimotor performance, with the posterior limb of internal capsule score being the strongest contributor to impaired hand function. The sqMRI scale demonstrates first evidence of construct validity against impaired motor and sensory function measures and brain structural connectivity in a cohort of children with UCP due to PWM lesions. More severe lesions correlated with poorer paretic hand sensorimotor function and impaired structural connectivity in the hemisphere contralateral to the clinical side of hemiplegia. The quantitative structural MRI scoring may be a useful clinical tool for studying brain structure-function relationships but requires further validation in other populations of CP.
The use of high-throughput screening techniques to evaluate mitochondrial toxicity.
Wills, Lauren P
2017-11-01
Toxicologists and chemical regulators depend on accurate and effective methods to evaluate and predict the toxicity of thousands of current and future compounds. Robust high-throughput screening (HTS) experiments have the potential to efficiently test large numbers of chemical compounds for effects on biological pathways. HTS assays can be utilized to examine chemical toxicity across multiple mechanisms of action, experimental models, concentrations, and lengths of exposure. Many agricultural, industrial, and pharmaceutical chemicals classified as harmful to human and environmental health exert their effects through the mechanism of mitochondrial toxicity. Mitochondrial toxicants are compounds that cause a decrease in the number of mitochondria within a cell, and/or decrease the ability of mitochondria to perform normal functions including producing adenosine triphosphate (ATP) and maintaining cellular homeostasis. Mitochondrial dysfunction can lead to apoptosis, necrosis, altered metabolism, muscle weakness, neurodegeneration, decreased organ function, and eventually disease or death of the whole organism. The development of HTS techniques to identify mitochondrial toxicants will provide extensive databases with essential connections between mechanistic mitochondrial toxicity and chemical structure. Computational and bioinformatics approaches can be used to evaluate compound databases for specific chemical structures associated with toxicity, with the goal of developing quantitative structure-activity relationship (QSAR) models and mitochondrial toxicophores. Ultimately these predictive models will facilitate the identification of mitochondrial liabilities in consumer products, industrial compounds, pharmaceuticals and environmental hazards. Copyright © 2017 Elsevier B.V. All rights reserved.
Das, Rudra Narayan; Roy, Kunal; Popelier, Paul L A
2015-11-01
The present study explores the chemical attributes of diverse ionic liquids responsible for their cytotoxicity in a rat leukemia cell line (IPC-81) by developing predictive classification as well as regression-based mathematical models. Simple and interpretable descriptors derived from a two-dimensional representation of the chemical structures along with quantum topological molecular similarity indices have been used for model development, employing unambiguous modeling strategies that strictly obey the guidelines of the Organization for Economic Co-operation and Development (OECD) for quantitative structure-activity relationship (QSAR) analysis. The structure-toxicity relationships that emerged from both classification and regression-based models were in accordance with the findings of some previous studies. The models suggested that the cytotoxicity of ionic liquids is dependent on the cationic surfactant action, long alkyl side chains, cationic lipophilicity as well as aromaticity, the presence of a dialkylamino substituent at the 4-position of the pyridinium nucleus and a bulky anionic moiety. The models have been transparently presented in the form of equations, thus allowing their easy transferability in accordance with the OECD guidelines. The models have also been subjected to rigorous validation tests proving their predictive potential and can hence be used for designing novel and "greener" ionic liquids. The major strength of the present study lies in the use of a diverse and large dataset, use of simple reproducible descriptors and compliance with the OECD norms. Copyright © 2015 Elsevier Ltd. All rights reserved.
Nandy, Ashis; Roy, Kunal; Saha, Achintya
2018-01-01
Metabolic syndrome is a matrix of different metabolic disorders which are the leading cause of death in human beings. Peroxysome proliferated activated receptor (PPAR) is a nuclear receptor involved in metabolism of fats and glucose. In order to explore structural requirements for selective PPAR modulators to control lipid and carbohydrate metabolism, the multi-cheminformatics studies have been performed. In silico modeling studies have been performed on a diverse set of PPAR modulators through quantitative structure-activity relationship (QSAR), pharmacophore mapping and docking studies. It is observed that the presence of an amide fragment (-CONHRPh) has a detrimental effect while an aliphatic ether linkage has a beneficial effect on PPARα modulation. On the other hand, the presence of an amide fragment has a positive effect on PPARδ modulation, but the aliphatic ether linkage and substituted aromatic ring in the molecular scaffold are very much essential for imparting potent and selective PPARγ modulation. Negative ionizable features (i.e. polar fragments) must be present in PPARδ and α modulators, but a hydrophobic feature is the prime requirement for PPARγ modulation. Here, the essential structural features have been explored for selective modulation of each subtype of PPAR in order to design new modulators with improved activity/selectivity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
Singh, Gagandeep; Sharma, Anuradha; Kaur, Harpreet; Ishar, Mohan Paul S
2016-02-01
Regio- and stereoselective 1,3-dipolar cycloadditions of C-(chrom-4-one-3-yl)-N-phenylnitrones (N) with different mono-substituted, disubstituted, and cyclic dipolarophiles were carried out to obtain substituted N-phenyl-3'-(chrom-4-one-3-yl)-isoxazolidines (1-40). All the synthesized compounds were assayed for their in vitro antibacterial activity and display significant inhibitory potential; in particular, compound 32 exhibited good inhibitory activity against Salmonella typhymurium-1 & Salmonella typhymurium-2 with minimum inhibitory concentration value of 1.56 μg/mL and also showed good potential against methicillin-resistant Staphylococcus aureus with minimum inhibitory concentration 3.12 μg/mL. Quantitative structure activity relationship investigations with stepwise multiple linear regression analysis and docking simulation studies have been performed for validation of the observed antibacterial potential of the investigated compounds for determination of the most important parameters regulating antibacterial activities. © 2015 John Wiley & Sons A/S.
Fiori, Simona; Guzzetta, Andrea; Pannek, Kerstin; Ware, Robert S.; Rossi, Giuseppe; Klingels, Katrijn; Feys, Hilde; Coulthard, Alan; Cioni, Giovanni; Rose, Stephen; Boyd, Roslyn N.
2015-01-01
Aim To provide first evidence of construct validity of a semi-quantitative scale for brain structural MRI (sqMRI scale) in children with unilateral cerebral palsy (UCP) secondary to periventricular white matter (PWM) lesions, by examining the relationship with hand sensorimotor function and whole brain structural connectivity. Methods Cross-sectional study of 50 children with UCP due to PWM lesions using 3 T (MRI), diffusion MRI and assessment of hand sensorimotor function. We explored the relationship of lobar, hemispheric and global scores on the sqMRI scale, with fractional anisotropy (FA), as a measure of brain white matter microstructure, and with hand sensorimotor measures (Assisting Hand Assessment, AHA; Jebsen–Taylor Test for Hand Function, JTTHF; Melbourne Assessment of Unilateral Upper Limb Function, MUUL; stereognosis; 2-point discrimination). Results Lobar and hemispheric scores on the sqMRI scale contralateral to the clinical side of hemiplegia correlated with sensorimotor paretic hand function measures and FA of a number of brain structural connections, including connections of brain areas involved in motor control (postcentral, precentral and paracentral gyri in the parietal lobe). More severe lesions correlated with lower sensorimotor performance, with the posterior limb of internal capsule score being the strongest contributor to impaired hand function. Conclusion The sqMRI scale demonstrates first evidence of construct validity against impaired motor and sensory function measures and brain structural connectivity in a cohort of children with UCP due to PWM lesions. More severe lesions correlated with poorer paretic hand sensorimotor function and impaired structural connectivity in the hemisphere contralateral to the clinical side of hemiplegia. The quantitative structural MRI scoring may be a useful clinical tool for studying brain structure–function relationships but requires further validation in other populations of CP. PMID:26106533
Li, Jie; Sun, Jin; He, Zhonggui
2007-01-26
We aimed to establish quantitative structure-retention relationship (QSRR) with immobilized artificial membrane (IAM) chromatography using easily understood and obtained physicochemical molecular descriptors and to elucidate which descriptors are critical to affect the interaction process between solutes and immobilized phospholipid membranes. The retention indices (logk(IAM)) of 55 structurally diverse drugs were determined on an immobilized artificial membrane column (IAM.PC.DD2) directly or obtained by extrapolation method for highly hydrophobic compounds. Ten simple physicochemical property descriptors (clogP, rings, rotatory bond, hydro-bond counting, etc.) of these drugs were collected and used to establish QSRR and predict the retention data by partial least squares regression (PLSR). Five descriptors, clogP, rotatory bond (RotB), rings, molecular weight (MW) and total surface area (TSA), were reserved by using the Variable Importance for Projection (VIP) values as criterion to build the final PLSR model. An external test set was employed to verify the QSRR based on the training set with the five variables, and QSRR by PLSR exhibited a satisfying predictive ability with R(p)=0.902 and RMSE(p)=0.400. Comparison of coefficients of centered and scaled variables by PLSR demonstrated that, for the descriptors studied, clogP and TSA have the most significant positive effect but the rotatable bond has significant negative effect on drug IAM chromatographic retention.
Dong, Pei-Pei; Ge, Guang-Bo; Zhang, Yan-Yan; Ai, Chun-Zhi; Li, Guo-Hui; Zhu, Liang-Liang; Luan, Hong-Wei; Liu, Xing-Bao; Yang, Ling
2009-10-16
Seven pairs of epimers and one pair of isomeric metabolites of taxanes, each pair of which have similar structures but different retention behaviors, together with additional 13 taxanes with different substitutions were chosen to investigate the quantitative structure-retention relationship (QSRR) of taxanes in ultra fast liquid chromatography (UFLC). Monte Carlo variable selection (MCVS) method was adopted to choose descriptors. The selected four descriptors were used to build QSRR model with multi-linear regression (MLR) and artificial neural network (ANN) modeling techniques. Both linear and nonlinear models show good predictive ability, of which ANN model was better with the determination coefficient R(2) for training, validation and test set being 0.9892, 0.9747 and 0.9840, respectively. The results of 100 times' leave-12-out cross validation showed the robustness of this model. All the isomers can be correctly differentiated by this model. According to the selected descriptors, the three dimensional structural information was critical for recognition of epimers. Hydrophobic interaction was the uppermost factor for retention in UFLC. Molecules' polarizability and polarity properties were also closely correlated with retention behaviors. This QSRR model will be useful for separation and identification of taxanes including epimers and metabolites from botanical or biological samples.
Magalhães, Uiaran de Oliveira; Souza, Alessandra Mendonça Teles de; Albuquerque, Magaly Girão; Brito, Monique Araújo de; Bello, Murilo Lamim; Cabral, Lucio Mendes; Rodrigues, Carlos Rangel
2013-01-01
Acquired immunodeficiency syndrome is a public health problem worldwide caused by the Human immunodeficiency virus (HIV). Treatment with antiretroviral drugs is the best option for viral suppression, reducing morbidity and mortality. However, viral resistance in HIV-1 therapy has been reported. HIV-1 integrase (IN) is an essential enzyme for effective viral replication and an attractive target for the development of new inhibitors. In the study reported here, two- and three-dimensional quantitative structure-activity relationship (2D/3D-QSAR) studies, applying hologram quantitative structure-activity relationship (HQSAR) and comparative molecular field analysis (CoMFA) methods, respectively, were performed on a series of tricyclic phthalimide HIV-1 IN inhibitors. The best HQSAR model (q (2) = 0.802, r (2) = 0.972) was obtained using atoms, bonds, and connectivity as the fragment distinction, a fragment size of 2-5 atoms, hologram length of 61 bins, and six components. The best CoMFA model (q (2) = 0.748, r (2) = 0.974) was obtained with alignment of all atoms of the tricyclic phthalimide moiety (alignment II). The HQSAR contribution map identified that the carbonyl-hydroxy-aromatic nitrogen motif made a positive contribution to the activity of the compounds. Furthermore, CoMFA contour maps suggested that bulky groups in meta and para positions in the phenyl ring would increase the biological activity of this class. The conclusions of this work may lead to a better understanding of HIV-1 IN inhibition and contribute to the design of new and more potent derivatives.
Quantitative Model of Systemic Toxicity Using ToxCast and ToxRefDB (SOT)
EPA’s ToxCast program profiles the bioactivity of chemicals in a diverse set of ~700 high throughput screening (HTS) assays. In collaboration with L’Oreal, a quantitative model of systemic toxicity was developed using no effect levels (NEL) from ToxRefDB for 633 chemicals with HT...
Structure/activity relationships for biodegradability and their role in environmental assessment
DOE Office of Scientific and Technical Information (OSTI.GOV)
Boethling, R.S.
1994-12-31
Assessment of biodegradability is an important part of the review process for both new and existing chemicals under the Toxic Substances Control Act. It is often necessary to estimate biodegradability because experimental data are unavailable. Structure/biodegradability relationships (SBR) are a means to this end. Quantitative SBR have been developed, but this approach has not been very useful because they apply only to a few narrowly defined classes of chemicals. In response to the need for more widely applicable methods, multivariate analysis has been used to develop biodegradability classification models. For example, recent efforts have produced four new models. Two calculatemore » the probability of rapid biodegradation and can be used for classification; the other two models allow semi-quantitative estimation of primary and ultimate biodegradation rates. All are based on multiple regressions against 36 preselected substructures plus molecular weight. Such efforts have been fairly successful by statistical criteria, but in general are hampered by a lack of large and consistent datasets. Knowledge-based expert systems may represent the next step in the evolution of SBR. In principle such systems need not be as severely limited by imperfect datasets. However, the codification of expert knowledge and reasoning is a critical prerequisite. Results of knowledge acquisition exercises and modeling based on them will also be described.« less
Quantitative MRI in refractory temporal lobe epilepsy: relationship with surgical outcomes
Bonilha, Leonardo
2015-01-01
Medically intractable temporal lobe epilepsy (TLE) remains a serious health problem. Across treatment centers, up to 40% of patients with TLE will continue to experience persistent postoperative seizures at 2-year follow-up. It is unknown why such a large number of patients continue to experience seizures despite being suitable candidates for resective surgery. Preoperative quantitative MRI techniques may provide useful information on why some patients continue to experience disabling seizures, and may have the potential to develop prognostic markers of surgical outcome. In this article, we provide an overview of how quantitative MRI morphometric and diffusion tensor imaging (DTI) data have improved the understanding of brain structural alterations in patients with refractory TLE. We subsequently review the studies that have applied quantitative structural imaging techniques to identify the neuroanatomical factors that are most strongly related to a poor postoperative prognosis. In summary, quantitative imaging studies strongly suggest that TLE is a disorder affecting a network of neurobiological systems, characterized by multiple and inter-related limbic and extra-limbic network abnormalities. The relationship between brain alterations and postoperative outcome are less consistent, but there is emerging evidence suggesting that seizures are less likely to remit with surgery when presurgical abnormalities are observed in the connectivity supporting brain regions serving as network nodes located outside the resected temporal lobe. Future work, possibly harnessing the potential from multimodal imaging approaches, may further elucidate the etiology of persistent postoperative seizures in patients with refractory TLE. Furthermore, quantitative imaging techniques may be explored to provide individualized measures of postoperative seizure freedom outcome. PMID:25853080
NASA Astrophysics Data System (ADS)
Khazaei, Ardeshir; Sarmasti, Negin; Seyf, Jaber Yousefi
2016-03-01
Quantitative structure activity relationship were used to study a series of curcumin-related compounds with inhibitory effect on prostate cancer PC-3 cells, pancreas cancer Panc-1 cells, and colon cancer HT-29 cells. Sphere exclusion method was used to split data set in two categories of train and test set. Multiple linear regression, principal component regression and partial least squares were used as the regression methods. In other hand, to investigate the effect of feature selection methods, stepwise, Genetic algorithm, and simulated annealing were used. In two cases (PC-3 cells and Panc-1 cells), the best models were generated by a combination of multiple linear regression and stepwise (PC-3 cells: r2 = 0.86, q2 = 0.82, pred_r2 = 0.93, and r2m (test) = 0.43, Panc-1 cells: r2 = 0.85, q2 = 0.80, pred_r2 = 0.71, and r2m (test) = 0.68). For the HT-29 cells, principal component regression with stepwise (r2 = 0.69, q2 = 0.62, pred_r2 = 0.54, and r2m (test) = 0.41) is the best method. The QSAR study reveals descriptors which have crucial role in the inhibitory property of curcumin-like compounds. 6ChainCount, T_C_C_1, and T_O_O_7 are the most important descriptors that have the greatest effect. With a specific end goal to design and optimization of novel efficient curcumin-related compounds it is useful to introduce heteroatoms such as nitrogen, oxygen, and sulfur atoms in the chemical structure (reduce the contribution of T_C_C_1 descriptor) and increase the contribution of 6ChainCount and T_O_O_7 descriptors. Models can be useful in the better design of some novel curcumin-related compounds that can be used in the treatment of prostate, pancreas, and colon cancers.
Luo, Wen; Medrek, Sarah; Misra, Jatin; Nohynek, Gerhard J
2007-02-01
The objective of this study was to construct and validate a quantitative structure-activity relationship model for skin absorption. Such models are valuable tools for screening and prioritization in safety and efficacy evaluation, and risk assessment of drugs and chemicals. A database of 340 chemicals with percutaneous absorption was assembled. Two models were derived from the training set consisting 306 chemicals (90/10 random split). In addition to the experimental K(ow) values, over 300 2D and 3D atomic and molecular descriptors were analyzed using MDL's QsarIS computer program. Subsequently, the models were validated using both internal (leave-one-out) and external validation (test set) procedures. Using the stepwise regression analysis, three molecular descriptors were determined to have significant statistical correlation with K(p) (R2 = 0.8225): logK(ow), X0 (quantification of both molecular size and the degree of skeletal branching), and SsssCH (count of aromatic carbon groups). In conclusion, two models to estimate skin absorption were developed. When compared to other skin absorption QSAR models in the literature, our model incorporated more chemicals and explored a large number of descriptors. Additionally, our models are reasonably predictive and have met both internal and external statistical validations.
Ali-Osman, F; Giblin, J; Berger, M; Murphy, M J; Rosenblum, M L
1985-09-01
antiglioma activity of nitrosoureas and (b) myelosuppression is at least partly linked with carbamoylation but that structural entities in the carbamoylating isocyanate rather than a quantitative degree of carbamoylation determine the degree of potential myelotoxicity.
Ruiz, Patricia; Begluitti, Gino; Tincher, Terry; Wheeler, John; Mumtaz, Moiz
2012-07-27
Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance's database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (LD₅₀) for determining relative toxicity of a number of substances. In general, the smaller the LD₅₀ value, the more toxic the chemical, and the larger the LD₅₀ value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s) of interest, during emergency responses, LD₅₀ values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD₅₀ models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD) are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD₅₀ values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.
Xiao, Ruiyang; Ye, Tiantian; Wei, Zongsu; Luo, Shuang; Yang, Zhihui; Spinney, Richard
2015-11-17
The sulfate radical anion (SO4•–) based oxidation of trace organic contaminants (TrOCs) has recently received great attention due to its high reactivity and low selectivity. In this study, a meta-analysis was conducted to better understand the role of functional groups on the reactivity between SO4•– and TrOCs. The results indicate that compounds in which electron transfer and addition channels dominate tend to exhibit a faster second-order rate constants (kSO4•–) than that of H–atom abstraction, corroborating the SO4•– reactivity and mechanisms observed in the individual studies. Then, a quantitative structure activity relationship (QSAR) model was developed using a sequential approach with constitutional, geometrical, electrostatic, and quantum chemical descriptors. Two descriptors, ELUMO and EHOMO energy gap (ELUMO–EHOMO) and the ratio of oxygen atoms to carbon atoms (#O:C), were found to mechanistically and statistically affect kSO4•– to a great extent with the standardized QSAR model: ln kSO4•– = 26.8–3.97 × #O:C – 0.746 × (ELUMO–EHOMO). In addition, the correlation analysis indicates that there is no dominant reaction channel for SO4•– reactions with various structurally diverse compounds. Our QSAR model provides a robust predictive tool for estimating emerging micropollutants removal using SO4•– during wastewater treatment processes.
In a previously published study, quantitative relationships were developed between landscape metrics and sediment contamination for 25 small estuarine systems within Chesapeake Bay. Nonparametric statistical analysis (rank transformation) was used to develop an empirical relation...
A Structure-Toxicity Study of Aß42 Reveals a New Anti-Parallel Aggregation Pathway
Vignaud, Hélène; Bobo, Claude; Lascu, Ioan; Sörgjerd, Karin Margareta; Zako, Tamotsu; Maeda, Mizuo; Salin, Benedicte; Lecomte, Sophie; Cullin, Christophe
2013-01-01
Amyloid beta (Aβ) peptides produced by APP cleavage are central to the pathology of Alzheimer’s disease. Despite widespread interest in this issue, the relationship between the auto-assembly and toxicity of these peptides remains controversial. One intriguing feature stems from their capacity to form anti-parallel ß-sheet oligomeric intermediates that can be converted into a parallel topology to allow the formation of protofibrillar and fibrillar Aβ. Here, we present a novel approach to determining the molecular aspects of Aß assembly that is responsible for its in vivo toxicity. We selected Aß mutants with varying intracellular toxicities. In vitro, only toxic Aß (including wild-type Aß42) formed urea-resistant oligomers. These oligomers were able to assemble into fibrils that are rich in anti-parallel ß-sheet structures. Our results support the existence of a new pathway that depends on the folding capacity of Aß . PMID:24244667
Oxidation of indometacin by ferrate (VI): kinetics, degradation pathways, and toxicity assessment.
Huang, Junlei; Wang, Yahui; Liu, Guoguang; Chen, Ping; Wang, Fengliang; Ma, Jingshuai; Li, Fuhua; Liu, Haijin; Lv, Wenying
2017-04-01
The oxidation of indometacin (IDM) by ferrate(VI) (Fe(VI)) was investigated to determine the reaction kinetics, transformation products, and changes in toxicity. The reaction between IDM and Fe(VI) followed first-order kinetics with respect to each reactant. The apparent second-order rate constants (k app ) decreased from 9.35 to 6.52 M -1 s -1 , as the pH of the solution increased from 7.0 to 10.0. The pH dependence of k app might be well explained by considering the species-specific rate constants of the reactions of IDM with Fe(VI). Detailed product studies using liquid chromatography-tandem mass spectrometry (LC-MS/MS) indicated that the oxidation products were primarily derived from the hydrolysis of amide linkages, the addition of hydroxyl groups, and electrophilic oxidation. The toxicity of the oxidation products was evaluated using the Microtox test, which indicated that transformation products exhibited less toxicity to the Vibrio fischeri bacteria. Quantitative structure-activity relationship (QSAR) analysis calculated by the ecological structure activity relationship (ECOSAR) revealed that all of the identified products exhibited lower acute and chronic toxicity than the parent pharmaceutical for fish, daphnid, and green algae. Furthermore, Fe(VI) was effective in the degradation IDM in water containing carbonate ions or fulvic acid (FA), and in lake water samples; however, higher Fe(VI) dosages would be required to completely remove IDM in lake water in contrast to deionized water.
Jing, Pu; Zhao, Shujuan; Ruan, Siyu; Sui, Zhongquan; Chen, Lihong; Jiang, Linlei; Qian, Bingjun
2014-02-15
The 3-dimensional quantitative structure activity relationship (3D-QSAR) models were established from 21 anthocyanins based on their oxygen radical absorbing capacity (ORAC) and were applied to predict anthocyanins in eggplant and radish for their ORAC values. The cross-validated q(2)=0.857/0.729, non-cross-validated r(2) = 0.958/0.856, standard error of estimate = 0.153/0.134, and F = 73.267/19.247 were for the best QSAR (CoMFA/CoMSIA) models, where the correlation coefficient r(2)pred = 0.998/0.997 (>0.6) indicated a high predictive ability for each. Additionally, the contour map results suggested that structural characteristics of anthocyanins favourable for the high ORAC. Four anthocyanins from eggplant and radish have been screened based on the QSAR models. Pelargonidin-3-[(6''-p-coumaroyl)-glucosyl(2 → 1)glucoside]-5-(6''-malonyl)-glucoside, delphinidin-3-rutinoside-5-glucoside, and delphinidin-3-[(4''-p-coumaroyl)-rhamnosyl(1 → 6)glucoside]-5-glucoside potential with high ORAC based the QSAR models were isolated and also confirmed for their relative high antioxidant ability, which might attribute to the bulky and/or electron-donating substituent at the 3-position in the C ring or/and hydrogen bond donor group/electron donating group on the R1 position in the B ring. Copyright © 2013 Elsevier Ltd. All rights reserved.
Development of structure-activity relationship for metal oxide nanoparticles
NASA Astrophysics Data System (ADS)
Liu, Rong; Zhang, Hai Yuan; Ji, Zhao Xia; Rallo, Robert; Xia, Tian; Chang, Chong Hyun; Nel, Andre; Cohen, Yoram
2013-05-01
Nanomaterial structure-activity relationships (nano-SARs) for metal oxide nanoparticles (NPs) toxicity were investigated using metrics based on dose-response analysis and consensus self-organizing map clustering. The NP cellular toxicity dataset included toxicity profiles consisting of seven different assays for human bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells, over a concentration range of 0.39-100 mg L-1 and exposure time up to 24 h, for twenty-four different metal oxide NPs. Various nano-SAR building models were evaluated, based on an initial pool of thirty NP descriptors. The conduction band energy and ionic index (often correlated with the hydration enthalpy) were identified as suitable NP descriptors that are consistent with suggested toxicity mechanisms for metal oxide NPs and metal ions. The best performing nano-SAR with the above two descriptors, built with support vector machine (SVM) model and of validated robustness, had a balanced classification accuracy of ~94%. An applicability domain for the present data was established with a reasonable confidence level of 80%. Given the potential role of nano-SARs in decision making, regarding the environmental impact of NPs, the class probabilities provided by the SVM nano-SAR enabled the construction of decision boundaries with respect to toxicity classification under different acceptance levels of false negative relative to false positive predictions.Nanomaterial structure-activity relationships (nano-SARs) for metal oxide nanoparticles (NPs) toxicity were investigated using metrics based on dose-response analysis and consensus self-organizing map clustering. The NP cellular toxicity dataset included toxicity profiles consisting of seven different assays for human bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells, over a concentration range of 0.39-100 mg L-1 and exposure time up to 24 h, for twenty-four different metal oxide NPs. Various nano-SAR building models were
Attene-Ramos, Matias S.; Huang, Ruili; Sakamuru, Srilatha; Witt, Kristine L.; Beeson, Gyda C.; Shou, Louie; Schnellmann, Rick G.; Beeson, Craig C.; Tice, Raymond R.; Austin, Christopher P.; Xia, Menghang
2014-01-01
A goal of the Tox21 program is to transit toxicity testing from traditional in vivo models to in vitro assays that assess how chemicals affect cellular responses and toxicity pathways. A critical contribution of the NIH Chemical Genomics center (NCGC) to the Tox21 program is the implementation of a quantitative high throughput screening (qHTS) approach, using cell- and biochemical-based assays to generate toxicological profiles for thousands of environmental compounds. Here, we evaluated the effect of chemical compounds on mitochondrial membrane potential in HepG2 cells by screening a library of 1,408 compounds provided by the National Toxicology Program (NTP) in a qHTS platform. Compounds were screened over 14 concentrations, and results showed that 91 and 88 compounds disrupted mitochondrial membrane potential after treatment for one or five h, respectively. Seventy-six compounds active at both time points were clustered by structural similarity, producing 11 clusters and 23 singletons. Thirty-eight compounds covering most of the active chemical space were more extensively evaluated. Thirty-six of the 38 compounds were confirmed to disrupt mitochondrial membrane potential using a fluorescence plate reader and 35 were confirmed using a high content imaging approach. Among the 38 compounds, 4 and 6 induced LDH release, a measure of cytotoxicity, at 1 or 5 h, respectively. Compounds were further assessed for mechanism of action (MOA) by measuring changes in oxygen consumption rate, which enabled identification of 20 compounds as uncouplers. This comprehensive approach allows for evaluation of thousands of environmental chemicals for mitochondrial toxicity and identification of possible MOAs. PMID:23895456
Wang, Xueding; Xu, Yilian; Yang, Lu; Lu, Xiang; Zou, Hao; Yang, Weiqing; Zhang, Yuanyuan; Li, Zicheng; Ma, Menglin
2018-03-01
A series of 1,3,5-triazines were synthesized and their UV absorption properties were tested. The computational chemistry methods were used to construct quantitative structure-property relationship (QSPR), which was used to computer aided design of new 1,3,5-triazines ultraviolet rays absorber compounds. The experimental UV absorption data are in good agreement with those predicted data using the Time-dependent density functional theory (TD-DFT) [B3LYP/6-311 + G(d,p)]. A suitable forecasting model (R > 0.8, P < 0.0001) was revealed. Predictive three-dimensional quantitative structure-property relationship (3D-QSPR) model was established using multifit molecular alignment rule of Sybyl program, which conclusion is consistent with the TD-DFT calculation. The exceptional photostability mechanism of such ultraviolet rays absorber compounds was studied and confirmed as principally banked upon their ability to undergo excited-state deactivation via an ultrafast excited-state proton transfer (ESIPT). The intramolecular hydrogen bond (IMHB) of 1,3,5-triazines compounds is the basis for the excited state proton transfer, which was explored by IR spectroscopy, UV spectra, structural and energetic aspects of different conformers and frontier molecular orbitals analysis.
Kleandrova, Valeria V; Luan, Feng; Speck-Planche, Alejandro; Cordeiro, M Natália D S
2015-01-01
The assessment of acute toxicity is one of the most important stages to ensure the safety of chemicals with potential applications in pharmaceutical sciences, biomedical research, or any other industrial branch. A huge and indiscriminate number of toxicity assays have been carried out on laboratory animals. In this sense, computational approaches involving models based on quantitative-structure activity/toxicity relationships (QSAR/QSTR) can help to rationalize time and financial costs. Here, we discuss the most significant advances in the last 6 years focused on the use of QSAR/QSTR models to predict acute toxicity of drugs/chemicals in laboratory animals, employing large and heterogeneous datasets. The advantages and drawbacks of the different QSAR/QSTR models are analyzed. As a contribution to the field, we introduce the first multitasking (mtk) QSTR model for simultaneous prediction of acute toxicity of compounds by considering different routes of administration, diverse breeds of laboratory animals, and the reliability of the experimental conditions. The mtk-QSTR model was based on artificial neural networks (ANN), allowing the classification of compounds as toxic or non-toxic. This model correctly classified more than 94% of the 1646 cases present in the whole dataset, and its applicability was demonstrated by performing predictions of different chemicals such as drugs, dietary supplements, and molecules which could serve as nanocarriers for drug delivery. The predictions given by the mtk-QSTR model are in very good agreement with the experimental results.
Hui Wang; Mingyue Jiang; Shujun Li; Chung-Yun Hse; Chunde Jin; Fangli Sun; Zhuo Li
2017-01-01
Cinnamaldehyde amino acid Schiff base (CAAS) is a new class of safe, bioactive compounds which could be developed as potential antifungal agents for fungal infections. To design new cinnamaldehyde amino acid Schiff base compounds with high bioactivity, the quantitative structureâactivity relationships (QSARs) for CAAS compounds against Aspergillus niger (A. niger) and...
The structure-activity relationship of inhibitors of serotonin uptake and receptor binding
NASA Astrophysics Data System (ADS)
Hansch, Corwin; Caldwell, Jonathan
1991-10-01
An analysis of five different datasets of inhibitors of serotonin uptake has yielded quantitative structure/ activity relationships (QSARs) which delineate the role of steric and hydrophobic properties essential for inhibition by phenylethylamine-type analogues.
Song, Yan; Feng, Jun; Dang, Ying; Zhao, Chao; Zheng, Jie; Ruan, Litao
2017-12-01
The aim of this study was to determine the relationship between plaque echo, thickness and neovascularization in different stenosis groups using quantitative and semi-quantitative contrast-enhanced ultrasound (CEUS) in patients with carotid atherosclerosis plaque. A total of 224 plaques were divided into mild stenosis (<50%; 135 plaques, 60.27%), moderate stenosis (50%-69%; 39 plaques, 17.41%) and severe stenosis (70%-99%; 50 plaques, 22.32%) groups. Quantitative and semi-quantitative methods were used to assess plaque neovascularization and determine the relationship between plaque echo, thickness and neovascularization. Correlation analysis revealed no relationship of neovascularization with plaque echo in the groups using either quantitative or semi-quantitative methods. Furthermore, there was no correlation of neovascularization with plaque thickness using the semi-quantitative method. The ratio of areas under the curve (RAUC) was negatively correlated with plaque thickness (r = -0.317, p = 0.001) in the mild stenosis group. With the quartile method, plaque thickness of the mild stenosis group was divided into four groups, with significant differences between the 1.5-2.2 mm and ≥3.5 mm groups (p = 0.002), 2.3-2.8 mm and ≥3.5 mm groups (p <0.001) and 2.9-3.4 mm and ≥3.5 mm groups (p <0.001). Both semi-quantitative and quantitative CEUS methods characterizing neovascularization of plaque are equivalent with respect to assessing relationships between neovascularization, echogenicity and thickness. However, the quantitative method could fail for plaque <3.5 mm because of motion artifacts. Copyright © 2017 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.
Gupta, Shikha; Basant, Nikita; Mohan, Dinesh; Singh, Kunwar P
2016-07-01
The persistence and the removal of organic chemicals from the atmosphere are largely determined by their reactions with the OH radical and O3. Experimental determinations of the kinetic rate constants of OH and O3 with a large number of chemicals are tedious and resource intensive and development of computational approaches has widely been advocated. Recently, ensemble machine learning (EML) methods have emerged as unbiased tools to establish relationship between independent and dependent variables having a nonlinear dependence. In this study, EML-based, temperature-dependent quantitative structure-reactivity relationship (QSRR) models have been developed for predicting the kinetic rate constants for OH (kOH) and O3 (kO3) reactions with diverse chemicals. Structural diversity of chemicals was evaluated using a Tanimoto similarity index. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation performed employing statistical checks. In test data, the EML QSRR models yielded correlation (R (2)) of ≥0.91 between the measured and the predicted reactivities. The applicability domains of the constructed models were determined using methods based on descriptors range, Euclidean distance, leverage, and standardization approaches. The prediction accuracies for the higher reactivity compounds were relatively better than those of the low reactivity compounds. Proposed EML QSRR models performed well and outperformed the previous reports. The proposed QSRR models can make predictions of rate constants at different temperatures. The proposed models can be useful tools in predicting the reactivities of chemicals towards OH radical and O3 in the atmosphere.
Tham, S Y; Agatonovic-Kustrin, S
2002-05-15
Quantitative structure-retention relationship(QSRR) method was used to model reversed-phase high-performance liquid chromatography (RP-HPLC) separation of 18 selected amino acids. Retention data for phenylthiocarbamyl (PTC) amino acids derivatives were obtained using gradient elution on ODS column with mobile phase of varying acetonitrile, acetate buffer and containing 0.5 ml/l of triethylamine (TEA). Molecular structure of each amino acid was encoded with 36 calculated molecular descriptors. The correlation between the molecular descriptors and the retention time of the compounds in the calibration set was established using the genetic neural network method. A genetic algorithm (GA) was used to select important molecular descriptors and supervised artificial neural network (ANN) was used to correlate mobile phase composition and selected descriptors with the experimentally derived retention times. Retention time values were used as the network's output and calculated molecular descriptors and mobile phase composition as the inputs. The best model with five input descriptors was chosen, and the significance of the selected descriptors for amino acid separation was examined. Results confirmed the dominant role of the organic modifier in such chromatographic systems in addition to lipophilicity (log P) and molecular size and shape (topological indices) of investigated solutes.
The goal of chemical toxicology research is utilizing short term bioassays and/or robust computational methods to predict in vivo toxicity endpoints for chemicals. The ToxCast program established at the US Environmental Protection Agency (EPA) is addressing this goal by using ca....
Bonano, J S; Banks, M L; Kolanos, R; Sakloth, F; Barnier, M L; Glennon, R A; Cozzi, N V; Partilla, J S; Baumann, M H; Negus, S S
2015-05-01
Methcathinone (MCAT) is a potent monoamine releaser and parent compound to emerging drugs of abuse including mephedrone (4-CH3 MCAT), the para-methyl analogue of MCAT. This study examined quantitative structure-activity relationships (QSAR) for MCAT and six para-substituted MCAT analogues on (a) in vitro potency to promote monoamine release via dopamine and serotonin transporters (DAT and SERT, respectively), and (b) in vivo modulation of intracranial self-stimulation (ICSS), a behavioural procedure used to evaluate abuse potential. Neurochemical and behavioural effects were correlated with steric (Es ), electronic (σp ) and lipophilic (πp ) parameters of the para substituents. For neurochemical studies, drug effects on monoamine release through DAT and SERT were evaluated in rat brain synaptosomes. For behavioural studies, drug effects were tested in male Sprague-Dawley rats implanted with electrodes targeting the medial forebrain bundle and trained to lever-press for electrical brain stimulation. MCAT and all six para-substituted analogues increased monoamine release via DAT and SERT and dose- and time-dependently modulated ICSS. In vitro selectivity for DAT versus SERT correlated with in vivo efficacy to produce abuse-related ICSS facilitation. In addition, the Es values of the para substituents correlated with both selectivity for DAT versus SERT and magnitude of ICSS facilitation. Selectivity for DAT versus SERT in vitro is a key determinant of abuse-related ICSS facilitation by these MCAT analogues, and steric aspects of the para substituent of the MCAT scaffold (indicated by Es ) are key determinants of this selectivity. © 2014 The British Pharmacological Society.
Escher, Beate I; Baumer, Andreas; Bittermann, Kai; Henneberger, Luise; König, Maria; Kühnert, Christin; Klüver, Nils
2017-03-22
The Microtox assay, a bioluminescence inhibition assay with the marine bacterium Aliivibrio fischeri, is one of the most popular bioassays for assessing the cytotoxicity of organic chemicals, mixtures and environmental samples. Most environmental chemicals act as baseline toxicants in this short-term screening assay, which is typically run with only 30 min of exposure duration. Numerous Quantitative Structure-Activity Relationships (QSARs) exist for the Microtox assay for nonpolar and polar narcosis. However, typical water pollutants, which have highly diverse structures covering a wide range of hydrophobicity and speciation from neutral to anionic and cationic, are often outside the applicability domain of these QSARs. To include all types of environmentally relevant organic pollutants we developed a general baseline toxicity QSAR using liposome-water distribution ratios as descriptors. Previous limitations in availability of experimental liposome-water partition constants were overcome by reliable prediction models based on polyparameter linear free energy relationships for neutral chemicals and the COSMOmic model for charged chemicals. With this QSAR and targeted mixture experiments we could demonstrate that ionisable chemicals fall in the applicability domain. Most investigated water pollutants acted as baseline toxicants in this bioassay, with the few outliers identified as uncouplers or reactive toxicants. The main limitation of the Microtox assay is that chemicals with a high melting point and/or high hydrophobicity were outside of the applicability domain because of their low water solubility. We quantitatively derived a solubility cut-off but also demonstrated with mixture experiments that chemicals inactive on their own can contribute to mixture toxicity, which is highly relevant for complex environmental mixtures, where these chemicals may be present at concentrations below the solubility cut-off.
Silva, Valéria C; Almeida, Sônia M; Resgalla, Charrid; Masfaraud, Jean-François; Cotelle, Sylvie; Radetski, Claudemir M
2013-06-01
It is useful to test ecotoxicity and genotoxicity endpoints in the environmental impact assessment. Here, we compare and discuss ecotoxicity and genotoxicity effects in organisms in response to exposure to arsenate (As V) in solution. Eco(geno)toxicity responses in Aliivibrio fischeri, Lytechinus variegatus, Daphnia magna, Skeletonema costatum and Vicia faba were analyzed by assessing different endpoints: biomass growth, peroxidase activity, mitotic index, micronucleus frequency, and lethality in accordance with the international protocols. Quantitative sensitivity relationships (QSR) between these endpoints were established in order to rank endpoint sensitivity. The results for the QSR values based on the lowest observed effect concentration (LOEC) ratios varied from 2 (for ratio of root peroxidase activity to leaf peroxidase activity) to 2286 (for ratio of higher plant biomass growth to root peroxidase activity). The QSR values allowed the following sensitivity ranking to be established: higher plant enzymatic activity>daphnids≈echinoderms>bacteria≈algae>higher plant biomass growth. The LOEC values for the mitotic index and micronucleus frequency (LOEC=0.25mgAsL(-1)) were similar to the lowest LOEC values observed in aquatic organisms. This approach to the QSR of different endpoints could form the basis for monitoring and predicting early effects of pollutants before they give rise to significant changes in natural community structures. Copyright © 2013 Elsevier Inc. All rights reserved.
Saeed, Mohamed E M; Kadioglu, Onat; Seo, Ean-Jeong; Greten, Henry Johannes; Brenk, Ruth; Efferth, Thomas
2015-04-01
The antimalarial drug artemisinin has been shown to exert anticancer activity through anti-angiogenic effects. For further drug development, it may be useful to have derivatives with improved anti-angiogenic properties. We performed molecular docking of 52 artemisinin derivatives to vascular endothelial growth factor receptors (VEGFR1, VEGFR2), and VEGFA ligand using Autodock4 and AutodockTools-1.5.7.rc1 using the Lamarckian genetic algorithm. Quantitative structure-activity relationship (QSAR) analyses of the compounds prepared by Corina Molecular Networks were performed using the Molecular Operating Environment MOE 2012.10. A statistically significant inverse relationship was obtained between in silico binding energies to VEGFR1 and anti-angiogenic activity in vivo of a test-set of artemisinin derivatives (R=-0.843; p=0.035). This served as a control experiment to validate molecular docking predicting anti-angiogenc effects. Furthermore, 52 artemisinin derivatives were docked to VEGFR1 and in selected examples also to VEGFR2 and VEGFA. Higher binding affinities were calculated for receptors than for the ligand. The best binding affinities to VEGFR1 were found for an artemisinin dimer, 10-dihydroartemisinyl-2-propylpentanoate, and dihydroartemisinin α-hemisuccinate sodium salt. QSAR analyses revealed significant relationships between VEGFR1 binding energies and defined molecular descriptors of 35 artemisinins assigned to the training set (R=0.0848, p<0.0001) and 17 derivatives assigned to the test set (R=0.761, p<0.001). Molecular docking and QSAR calculations can be used to identify novel artemisinin derivatives with anti-angiogenic effects. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.
NASA Astrophysics Data System (ADS)
Smith, P. J.; Popelier, P. L. A.
2004-02-01
The present day abundance of cheap computing power enables the use of quantum chemical ab initio data in Quantitative Structure-Activity Relationships (QSARs). Optimised bond lengths are a new such class of descriptors, which we have successfully used previously in representing electronic effects in medicinal and ecological QSARs (enzyme inhibitory activity, hydrolysis rate constants and pKas). Here we use AM1 and HF/3-21G* bond lengths in conjunction with Partial Least Squares (PLS) and a Genetic Algorithm (GA) to predict the Corticosteroid-Binding Globulin (CBG) binding activity of the classic steroid data set, and the antibacterial activity of nitrofuran derivatives. The current procedure, which does not require molecular alignment, produces good r2 and q2 values. Moreover, it highlights regions in the common steroid skeleton deemed relevant to the active regions of the steroids and nitrofuran derivatives.
Ma, Ruoshui; Zhang, Xiumei; Wang, Yi; Zhang, Xiao
2018-04-27
The heterogeneous and complex structural characteristics of lignin present a significant challenge to predict its processability (e.g. depolymerization, modifications etc) to valuable products. This study provides a detailed characterization and comparison of structural properties of seven representative biorefinery lignin samples derived from forest and agricultural residues, which were subjected to representative pretreatment methods. A range of wet chemistry and spectroscopy methods were applied to determine specific lignin structural characteristics such as functional groups, inter-unit linkages and peak molecular weight. In parallel, oxidative depolymerization of these lignin samples to either monomeric phenolic compounds or dicarboxylic acids were conducted, and the product yields were quantified. Based on these results (lignin structural characteristics and monomer yields), we demonstrated for the first time to apply multiple-variable linear estimations (MVLE) approach using R statistics to gain insight toward a quantitative correlation between lignin structural properties and their conversion reactivity toward oxidative depolymerization to monomers. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
The Relationship between Quantitative and Qualitative Measures of Writing Skills.
ERIC Educational Resources Information Center
Howerton, Mary Lou P.; And Others
The relationships of quantitative measures of writing skills to overall writing quality as measured by the E.T.S. Composition Evaluation Scale (CES) were examined. Quantitative measures included indices of language productivity, vocabulary diversity, spelling, and syntactic maturity. Power of specific indices to account for variation in overall…
Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids.
Cao, Lingdi; Zhu, Peng; Zhao, Yongsheng; Zhao, Jihong
2018-06-15
Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S EP ) and charge distribution area (S σ-profile ) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R 2 and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot. Copyright © 2018 Elsevier B.V. All rights reserved.
Liu, Yitong
2018-05-18
An increased use of herbal dietary supplements has been associated with adverse liver effects such as elevated serum enzymes and liver failure. The safety assessment for herbal dietary supplements is challenging since they often contain complex mixtures of phytochemicals, most of which have unknown pharmacokinetic and toxicological properties. Rapid tools are needed to evaluate large numbers of phytochemicals for potential liver toxicity. The current study demonstrates a tiered approach combining identification of phytochemicals in liver toxic botanicals, followed by in silico quantitative structure-activity relationship (QSAR) evaluation of these phytochemicals for absorption (e.g. permeability), metabolism (cytochromes P450) and liver toxicity (e.g. elevated transaminases). First, 255 phytochemicals from 20 botanicals associated with clinical liver injury were identified, and the phytochemical structures were subsequently used for QSAR evaluation. Among these identified phytochemicals, 193 were predicted to be absorbed and then used to generate metabolites, which were both used to predict liver toxicity. Forty-eight phytochemicals were predicted as liver toxic, either due to parent phytochemicals or metabolites. Among them, nineteen phytochemicals have previous evidence of liver toxicity (e.g. pyrrolizidine alkaloids), while the majority were newly discovered (e.g. sesquiterpenoids). These findings help reveal new toxic phytochemicals in herbal dietary supplements and prioritize future toxicological testing. Published by Elsevier Ltd.
QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide.
Qin, Li-Tang; Chen, Yu-Han; Zhang, Xin; Mo, Ling-Yun; Zeng, Hong-Hu; Liang, Yan-Peng
2018-05-01
Antibiotics and pesticides may exist as a mixture in real environment. The combined effect of mixture can either be additive or non-additive (synergism and antagonism). However, no effective predictive approach exists on predicting the synergistic and antagonistic toxicities of mixtures. In this study, we developed a quantitative structure-activity relationship (QSAR) model for the toxicities (half effect concentration, EC 50 ) of 45 binary and multi-component mixtures composed of two antibiotics and four pesticides. The acute toxicities of single compound and mixtures toward Aliivibrio fischeri were tested. A genetic algorithm was used to obtain the optimized model with three theoretical descriptors. Various internal and external validation techniques indicated that the coefficient of determination of 0.9366 and root mean square error of 0.1345 for the QSAR model predicted that 45 mixture toxicities presented additive, synergistic, and antagonistic effects. Compared with the traditional concentration additive and independent action models, the QSAR model exhibited an advantage in predicting mixture toxicity. Thus, the presented approach may be able to fill the gaps in predicting non-additive toxicities of binary and multi-component mixtures. Copyright © 2018 Elsevier Ltd. All rights reserved.
Na, Young Eun; Kim, Soon-Il; Bang, Hea-Son; Kim, Byung-Seok; Ahn, Young-Joon
2011-06-10
The toxicity of two cassia oils, four cinnamon oils and (E)-cinnamaldehyde and (E)-cinnamic acid and 34 structurally related compounds to adult Dermanyssus gallinae (De Geer) collected from a poultry house was examined using a vapour-phase mortality bioassay. Results were compared with those of dichlorvos, a conventional acaricide. The cassia and cinnamon oils (cinnamon technical, cinnamon #500, cassia especial, cassia true, cinnamon bark and cinnamon green leaf) exhibited good fumigant toxicity (LD(50), 11.79-26.40 μg cm(-3)). α-Methyl-(E)-cinnamaldehyde (LD(50), 0.45 μg cm(-3)) and (E)-cinnamaldehyde (0.54 μg cm(-3)) were the most toxic compounds and the toxicity of these compounds was comparable to that of dichlorvos (0.30 μg cm(-3)). Potent fumigant toxicity was also observed in allyl cinnamate, ethyl-α-cyanocinnamate, (E)-2-methoxylcinnamic acid and (Z)-2-methoxylcinnamic acid (LD(50), 0.81-0.92 μg cm(-3)). Structure-activity relationships indicate that structural characteristics, such as types of functional groups and carbon skeleton rather than vapour pressure parameter, appear to play a role in determining toxicity. The essential oils and compounds described merit further study as potential acaricides for the control of D. gallinae populations as fumigants with contact action due to global efforts to reduce the level of highly toxic synthetic acaricides in the agricultural environment. Copyright © 2011. Published by Elsevier B.V.
NASA Astrophysics Data System (ADS)
Zhai, Mengting; Chen, Yan; Li, Jing; Zhou, Jun
2017-12-01
The molecular electrongativity distance vector (MEDV-13) was used to describe the molecular structure of benzyl ether diamidine derivatives in this paper, Based on MEDV-13, The three-parameter (M 3, M 15, M 47) QSAR model of insecticidal activity (pIC 50) for 60 benzyl ether diamidine derivatives was constructed by leaps-and-bounds regression (LBR) . The traditional correlation coefficient (R) and the cross-validation correlation coefficient (R CV ) were 0.975 and 0.971, respectively. The robustness of the regression model was validated by Jackknife method, the correlation coefficient R were between 0.971 and 0.983. Meanwhile, the independent variables in the model were tested to be no autocorrelation. The regression results indicate that the model has good robust and predictive capabilities. The research would provide theoretical guidance for the development of new generation of anti African trypanosomiasis drugs with efficiency and low toxicity.
Dostanić, J; Lončarević, D; Zlatar, M; Vlahović, F; Jovanović, D M
2016-10-05
A series of arylazo pyridone dyes was synthesized by changing the type of the substituent group in the diazo moiety, ranging from strong electron-donating to strong electron-withdrawing groups. The structural and electronic properties of the investigated dyes was calculated at the M062X/6-31+G(d,p) level of theory. The observed good linear correlations between atomic charges and Hammett σp constants provided a basis to discuss the transmission of electronic substituent effects through a dye framework. The reactivity of synthesized dyes was tested through their decolorization efficiency in TiO2 photocatalytic system (Degussa P-25). Quantitative structure-activity relationship analysis revealed a strong correlation between reactivity of investigated dyes and Hammett substituent constants. The reaction was facilitated by electron-withdrawing groups, and retarded by electron-donating ones. Quantum mechanical calculations was used in order to describe the mechanism of the photocatalytic oxidation reactions of investigated dyes and interpret their reactivities within the framework of the Density Functional Theory (DFT). According to DFT based reactivity descriptors, i.e. Fukui functions and local softness, the active site moves from azo nitrogen atom linked to benzene ring to pyridone carbon atom linked to azo bond, going from dyes with electron-donating groups to dyes with electron-withdrawing groups. Copyright © 2016 Elsevier B.V. All rights reserved.
1998-11-01
The majority (70%) of commercial ship hulls still use tributyltin ( TBT ) coatings, which also contain approximately 30% to 40% copper. The Navy spends...TECHNICAL DOCUMENT 3044 November 1998 Chemistry, Toxicity , and Bioavailability of Copper and Its Relationship to Regulation in the Marine Environment...participated in a Workshop on Chemistry, Toxicity , and Bioavailability of Copper and Its Relationship to Regulation in the Marine Environment. The goal
Auer, C M; Nabholz, J V; Baetcke, K P
1990-01-01
Section 5 of the Toxic Substances Control Act (TSCA) requires that manufacturers and importers of new chemicals must submit a Premanufacture Notification (PMN) to the U.S. Environmental Protection Agency 90 days before they intend to commence manufacture or import. Certain information such as chemical identity, uses, etc., must be included in the notification. The submission of test data on the new substance, however, is not required, although any available health and environmental information must be provided. Nonetheless, over half of all PMNs submitted to the agency do not contain any test data; because PMN chemicals are new, no test data is generally available in the scientific literature. Given this situation, EPA has had to develop techniques for hazard assessment that can be used in the presence of limited test data. EPA's approach has been termed "structure-activity relationships" (SAR) and involves three major components: the first is critical evaluation and interpretation of available toxicity data on the chemical; the second component involves evaluation of test data available on analogous substances and/or potential metabolites; and the third component involves the use of mathematical expressions for biological activity known as "quantitative structure-activity relationships" (QSARs). At present, the use of QSARs is limited to estimating physical chemical properties, environmental toxicity, and bioconcentration factors. An important overarching element in EPA's approach is the experience and judgment of scientific assessors in interpreting and integrating the available data and information. Examples are provided that illustrate EPA's approach to hazard assessment for PMN chemicals. PMID:2269224
DOE Office of Scientific and Technical Information (OSTI.GOV)
Benigni, R.; Andreoli, C.; Giuliani, A.
1989-01-01
The interrelationships among carcinogenicity, mutagenicity, acute toxicity (LD50), and a number of molecular descriptors were studied by computerized data analysis methods on the data base generated by the International Program for the Evaluation of Short-Term Test for Carcinogens (IPESTTC). With the use of statistical regression methods, three main associations were evidenced: (1) the well-known correlation between carcinogenicity and mutagenicity; (2) a correlation between mutagenicity and toxicity (LD50 ip in mice); and (3) a correlation between toxicity and a recently introduced estimator of the free energy of binding of the molecules to biological receptors. As expected on the basis of themore » large variety of chemical classes represented in the IPESTTC data base, no simple relationship between mutagenicity or carcinogenicity and chemical descriptors was found. To overcome this problem, a new pattern recognition method (REPAD), developed by us for structure-activity studies of noncongeneric chemicals, has been used. This allowed us to highlight a significant difference between the whole patterns of relationships among chemicophysical variables in the two groups to active (mutagenicity and/or carcinogenic) and inactive chemicals. This approach generated a classification rule able to correctly assign about 80% of carcinogens or mutagens.« less
Paulke, Alexander; Proschak, Ewgenij; Sommer, Kai; Achenbach, Janosch; Wunder, Cora; Toennes, Stefan W
2016-03-14
The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Chicu, Sergiu Adrian; Munteanu, Melania; Cîtu, Ioana; Soica, Codruta; Dehelean, Cristina; Trandafirescu, Cristina; Funar-Timofei, Simona; Ionescu, Daniela; Simu, Georgeta Maria
2014-07-08
Structure-toxicity relationships for a series of 75 azo and azo-anilide dyes and five diazonium salts were developed using Hydractinia echinata (H. echinata) as model species. In addition, based on these relationships, predictions for 58 other azo-dyes were made. The experimental results showed that the measured effectiveness Mlog(1/MRC50) does not depend on the number of azo groups or the ones corresponding to metobolites, but it is influenced by the number of anilide groups, as well as by the substituents' positions within molecules. The conformational analysis pointed out the intramolecular hydrogen bonds, especially the simple tautomerization of quinoidic (STOH) or aminoidic (STNH2) type. The effectiveness is strongly influenced by the "push-pull" electronic effect, specific to two hydroxy or amino groups separated by an azo moiety (double alternate tautomery, (DAT), to the -COOH or -SO3H groups which are located in ortho or para position with respect to the azo group. The levels of the lipophylic/hydrophilic, electronic and steric equilibriums, pointed out by the Mlog(1/MRC50) values, enabled the calculation of their average values Clog(1/MRC50) ("Köln model"), characteristic to one derivative class (class isotoxicity). The azo group reduction and the hydrolysis of the amido/peptidic group are two concurrent enzymatic reactions, which occur with different reaction rates and mechanisms. The products of the partial biodegradation are aromatic amines. No additive or synergic effects are noticed among them.
Nanoporous Cyanate Ester Resins: Structure-Gas Transport Property Relationships
NASA Astrophysics Data System (ADS)
Gusakova, Kristina; Fainleib, Alexander; Espuche, Eliane; Grigoryeva, Olga; Starostenko, Olga; Gouanve, Fabrice; Boiteux, Gisèle; Saiter, Jean-Marc; Grande, Daniel
2017-04-01
This contribution addresses the relationships between the structure and gas transport properties of nanoporous thermostable cyanate ester resins (CERs) derived from polycyclotrimerization of 1,1'-bis(4-cyanatophenyl)ethane in the presence of 30 or 50 wt% of inert high-boiling temperature porogens (i.e., dimethyl- or dibutyl phthalates), followed by their quantitative removal. The nanopores in the films obtained were generated via a chemically induced phase separation route with further porogen extraction from the densely crosslinked CERs. To ensure a total desorption of the porogen moieties from the networks, an additional short-term thermal annealing at 250 °C was performed. The structure and morphology of such nanoporous CER-based films were investigated by FTIR and SEM techniques, respectively. Further, the gas transport properties of CER films were analyzed after the different processing steps, and relationships between the material structure and the main gas transport parameters were established.
Structure-reactivity relationship of naphthenic acids in the photocatalytic degradation process.
de Oliveira Livera, Diogo; Leshuk, Tim; Peru, Kerry M; Headley, John V; Gu, Frank
2018-06-01
Bitumen extraction in Canada's oil sands generates oil sands process-affected water (OSPW) as a toxic by-product. Naphthenic acids (NAs) contribute to the water's toxicity, and treatment methods may need to be implemented to enable safe discharge. Heterogeneous photocatalysis is a promising advanced oxidation process (AOP) for OSPW remediation, however, its successful implementation requires understanding of the complicated relationship between structure and reactivity of NAs. This work aimed to study the effect of various structural properties of model compounds on the photocatalytic degradation kinetics via high resolution mass spectrometry (HRMS), including diamondoid structures, heteroatomic species, and degree of unsaturation. The rate of photocatalytic treatment increased significantly with greater structural complexity, namely with carbon number, aromaticity and degree of cyclicity, properties that render particular NAs recalcitrant to biodegradation. It is hypothesized that a superoxide radical-mediated pathway explains these observations and offers additional benefits over traditional hydroxyl radical-based AOPs. Detailed structure-reactivity investigations of NAs in photocatalysis have not previously been undertaken, and the results described herein illustrate the potential benefit of combining photocatalysis and biodegradation as a complete OSPW remediation technology. Copyright © 2018 Elsevier Ltd. All rights reserved.
TOWARDS REFINED USE OF TOXICITY DATA IN STATISTICALLY BASED SAR MODELS FOR DEVELOPMENTAL TOXICITY.
In 2003, an International Life Sciences Institute (ILSI) Working Group examined the potential of statistically based structure-activity relationship (SAR) models for use in screening environmental contaminants for possible developmental toxicants.
Kuang, Jiangmeng; Huang, Jun; Wang, Bin; Cao, Qiming; Deng, Shubo; Yu, Gang
2013-05-15
This work aimed to better understand the ozonation process of a typical antibiotic pharmaceutical, trimethoprim in aqueous solution. The parent compound was almost completely degraded with ozone dose up to 3.5 mg/L with no mineralization. Twenty one degradation products were identified using an electrospray quadrupole time-of-flight mass spectrometer. Several ozonation pathways were proposed including hydroxylation, demethylation, carbonylation, deamination and methylene group cleavage. Two species of luminescent bacteria Photobacterium phosphoreum and Vibrio qinghaiensis were selected to assess the toxicity of ozonation products. For P. phosphoreum, higher level of toxicity was observed compared to the parent compound, but a negligible toxicity change was observed for V. qinghaiensis, indicating different modes of action for the same water sample. This was further confirmed by quantitative structure-active relationship analysis. This work proves the dominant role of ozone rather than hydroxyl radicals in the reaction and the potential risk after ozonation. Copyright © 2013 Elsevier Ltd. All rights reserved.
Ahmadi, Mehdi; Shahlaei, Mohsen
2015-01-01
P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure-activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7-7-1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure-activity relationship model suggested is robust and satisfactory.
The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study
NASA Astrophysics Data System (ADS)
Kuz'min, Victor E.; Muratov, Eugene N.; Artemenko, Anatoly G.; Gorb, Leonid; Qasim, Mohammad; Leszczynski, Jerzy
2008-10-01
The present study applies the Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) for (i) evaluation of the influence of the characteristics of 28 nitroaromatic compounds (some of which belong to a widely known class of explosives) as to their toxicity; (ii) prediction of toxicity for new nitroaromatic derivatives; (iii) analysis of the effects of substituents in nitroaromatic compounds on their toxicity in vivo. The 50% lethal dose concentration for rats (LD50) was used to develop the QSAR models based on simplex representation of molecular structure. The preliminary 1D QSAR results show that even the information on the composition of molecules reveals the main tendencies of changes in toxicity. The statistic characteristics for partial least squares 2D QSAR models are quite satisfactory ( R 2 = 0.96-0.98; Q 2 = 0.91-0.93; R 2 test = 0.89-0.92), which allows us to carry out the prediction of activity for 41 novel compounds designed by the application of new combinations of substituents represented in the training set. The comprehensive analysis of toxicity changes as a function of substituent position and nature was carried out. Molecular fragments that promote and interfere with toxicity were defined on the basis of the obtained models. It was shown that the mutual influence of substituents in the benzene ring plays a crucial role regarding toxicity. The influence of different substituents on toxicity can be mediated via different C-H fragments of the aromatic ring.
Yamada, Takashi; Tanaka, Yushiro; Hasegawa, Ryuichi; Sakuratani, Yuki; Yamazoe, Yasushi; Ono, Atsushi; Hirose, Akihiko; Hayashi, Makoto
2014-12-01
We propose a category approach to assessing the testicular toxicity of chemicals with a similar structure to ethylene glycol methyl ether (EGME). Based on toxicity information for EGME and related chemicals and accompanied by adverse outcome pathway information on the testicular toxicity of EGME, this category was defined as chemicals that are metabolized to methoxy- or ethoxyacetic acid, a substance responsible for testicular toxicity. A Japanese chemical inventory was screened using the Hazard Evaluation Support System, which we have developed to support a category approach for predicting the repeated-dose toxicity of chemical substances. Quantitative metabolic information on the related chemicals was then considered, and seventeen chemicals were finally obtained from the inventory as a shortlist for the category. Available data in the literature shows that chemicals for which information is available on the metabolic formation of EGME, ethylene glycol ethyl ether, methoxy- or ethoxyacetic acid do in fact possess testicular toxicity, suggesting that testicular toxicity is a concern, due to metabolic activation, for the remaining chemicals. Our results clearly demonstrate practical utility of AOP-based category approach for predicting repeated-dose toxicity of chemicals. Copyright © 2014 Elsevier Inc. All rights reserved.
Cleuvers, Michael
2004-11-01
The ecotoxicity of the nonsteroidal anti-inflammatory drugs (NSAIDs) diclofenac, ibuprofen, naproxen, and acetylsalicylic acid (ASA) has been evaluated using acute Daphnia and algal tests. Toxicities were relatively low, with half-maximal effective concentration (EC50) values obtained using Daphnia in the range from 68 to 166 mg L(-1) and from 72 to 626 mg L(-1) in the algal test. Acute effects of these substances seem to be quite improbable. The quantitative structure-activity relationships (QSAR) approach showed that all substances act by nonpolar narcosis; thus, the higher the n-octanol/water partitioning coefficient (log Kow) of the substances, the higher is their toxicity. Mixture toxicity of the compounds could be accurately predicted using the concept of concentration addition. Toxicity of the mixture was considerable, even at concentrations at which the single substances showed no or only very slight effects, with some deviations in the Daphnia test, which could be explained by incompatibility of the very steep dose-response curves and the probit analysis of the data. Because pharmaceuticals in the aquatic environment occur usually as mixtures, an accurate prediction of the mixture toxicity is indispensable for environmental risk assessment.
Quantitative structure activity relationship and risk analysis of some pesticides in the goat milk.
Muhammad, Faqir; Awais, Mian Muhammad; Akhtar, Masood; Anwar, Muhammad Irfan
2013-01-04
The detection and quantification of different pesticides in the goat milk samples collected from different localities of Faisalabad, Pakistan was performed by HPLC using solid phase microextraction. The analysis showed that about 50% milk samples were contaminated with pesticides. The mean±SEM levels (ppm) of cyhalothrin, endosulfan, chlorpyrifos and cypermethrin were 0.34±0.007, 0.063±0.002, 0.034±0.002 and 0.092±0.002, respectively; whereas, methyl parathion was not detected in any of the analyzed samples. Quantitative structure activity relationship (QSAR) models were suggested to predict the residues of unknown pesticides in the goat milk using their known physicochemical characteristics including molecular weight (MW), melting point (MP), and log octanol to water partition coefficient (Ko/w) in relation to the characteristics such as pH, % fat, specific gravity and refractive index of goat milk. The analysis revealed good correlation coefficient (R2 = 0.985) for goat QSAR model. The coefficients for Ko/w and refractive index for the studied pesticides were higher in goat milk. This suggests that these are better determinants for pesticide residue prediction in the milk of these animals. Based upon the determined pesticide residues and their provisional tolerable daily intakes, risk analysis was also conducted which showed that daily intake levels of pesticide residues including cyhalothrin, chlorpyrifos and cypermethrin in present study are 2.68, 5.19 and 2.71 times higher, respectively in the goat milk. This intake of pesticide contaminated milk might pose health hazards to humans in this locality.
Quantitative structure activity relationship and risk analysis of some pesticides in the goat milk
2013-01-01
The detection and quantification of different pesticides in the goat milk samples collected from different localities of Faisalabad, Pakistan was performed by HPLC using solid phase microextraction. The analysis showed that about 50% milk samples were contaminated with pesticides. The mean±SEM levels (ppm) of cyhalothrin, endosulfan, chlorpyrifos and cypermethrin were 0.34±0.007, 0.063±0.002, 0.034±0.002 and 0.092±0.002, respectively; whereas, methyl parathion was not detected in any of the analyzed samples. Quantitative structure activity relationship (QSAR) models were suggested to predict the residues of unknown pesticides in the goat milk using their known physicochemical characteristics including molecular weight (MW), melting point (MP), and log octanol to water partition coefficient (Ko/w) in relation to the characteristics such as pH, % fat, specific gravity and refractive index of goat milk. The analysis revealed good correlation coefficient (R2 = 0.985) for goat QSAR model. The coefficients for Ko/w and refractive index for the studied pesticides were higher in goat milk. This suggests that these are better determinants for pesticide residue prediction in the milk of these animals. Based upon the determined pesticide residues and their provisional tolerable daily intakes, risk analysis was also conducted which showed that daily intake levels of pesticide residues including cyhalothrin, chlorpyrifos and cypermethrin in present study are 2.68, 5.19 and 2.71 times higher, respectively in the goat milk. This intake of pesticide contaminated milk might pose health hazards to humans in this locality. PMID:23369514
Kumar, Surendra; Singh, Vineet; Tiwari, Meena
2007-07-01
Selective inhibition of ciliary process enzyme i.e. Carbonic Anhydrase-II is an excellent approach in reducing elevated intraocular pressure, thus treating glaucoma. Due to characteristic physicochemical properties of sulphonamide (Inhibition of Carbonic Anhydrase), they are clinically effective against glaucoma. But the non-specificity of sulphonamide derivatives to isozyme, leads to a range of side effects. Presently, the absence of comparative studies related to the binding of the sulphonamides as inhibitors to CA isozymes limits their use. In this paper we have represented "Three Dimensional Quantitative Structure Activity Relationship" study to characterize structural features of Sulfamide derivative [RR'NSO(2)NH(2)] as inhibitors, that are required for selective binding of carbonic anhydrase isozymes (CAI and CAII). In the analysis, stepwise multiple linear regression was performed using physiochemical parameters as independent variable and CA-I and CA-II inhibitory activity as dependent variable, respectively. The best multiparametric QSAR model obtained for CA-I inhibitory activity shows good statistical significance (r= 0.9714) and predictability (Q(2)=0.8921), involving the Electronic descriptors viz. Highest Occupied Molecular Orbital, Lowest Unoccupied Molecular Orbital and Steric descriptors viz. Principal moment of Inertia at X axis. Similarly, CA-II inhibitory activity also shows good statistical significance (r=0.9644) and predictability (Q(2)=0.8699) involving aforementioned descriptors. The predictive power of the model was successfully tested externally using a set of six compounds as test set for CA-I inhibitory activity and a set of seven compounds in case of CA-II inhibitory activity with good predictive squared correlation coefficient, r(2)(pred)=0.6016 and 0.7662, respectively. Overview of analysis favours substituents with high electronegativity and less bulk at R and R' positions of the parent nucleus, provides a basis to design new
Rajkhowa, Sanchaita; Hussain, Iftikar; Hazarika, Kalyan K; Sarmah, Pubalee; Deka, Ramesh Chandra
2013-09-01
Artemisinin form the most important class of antimalarial agents currently available, and is a unique sesquiterpene peroxide occurring as a constituent of Artemisia annua. Artemisinin is effectively used in the treatment of drug-resistant Plasmodium falciparum and because of its rapid clearance of cerebral malaria, many clinically useful semisynthetic drugs for severe and complicated malaria have been developed. However, one of the major disadvantages of using artemisinins is their poor solubility either in oil or water and therefore, in order to overcome this difficulty many derivatives of artemisinin were prepared. A comparative study on the chemical reactivity of artemisinin and some of its derivatives is performed using density functional theory (DFT) calculations. DFT based global and local reactivity descriptors, such as hardness, chemical potential, electrophilicity index, Fukui function, and local philicity calculated at the optimized geometries are used to investigate the usefulness of these descriptors for understanding the reactive nature and reactive sites of the molecules. Multiple regression analysis is applied to build up a quantitative structure-activity relationship (QSAR) model based on the DFT based descriptors against the chloroquine-resistant, mefloquine-sensitive Plasmodium falciparum W-2 clone.
Raczak-Gutknecht, Joanna; Nasal, Antoni; Frąckowiak, Teresa; Kornicka, Anita; Sączewski, Franciszek; Wawrzyniak, Renata; Kubik, Łukasz; Kaliszan, Roman
2017-09-10
Imidazol(in)e derivatives, having the chemical structure similar to clonidine, exert diverse pharmacological activities connected with their interactions with alpha2-adrenergic receptors, e.g. hypotension, bradycardia, sedation as well as antinociceptive, anxiolytic, antiarrhythmic, muscle relaxant and mydriatic effects. The mechanism of pupillary dilation observed after systemic administration of imidazol(in)es to rats, mice and cats depends on the stimulation of postsynaptic alpha2-adrenoceptors within the brain. It was proved that the central nervous system (CNS)-localized I1-imidazoline receptors are not engaged in those effects. It appeared interesting to analyze the CNS-mediated pharmacodynamics of imidazole(in)e agents in terms of their chromatographic and calculation chemistry-derived parameters. In the present study a systematic determination and comparative pharmacometric analysis of mydriatic effects in rats were performed on a series of 20 imidazol(in)e agents, composed of the well-known drugs and of the substances used in experimental pharmacology. The eye pupil dilatory activities of the compounds were assessed in anesthetized Wistar rats according to the established Koss method. Among twenty imidazol(in)e derivatives studied, 18 produced diverse dose-dependent mydriatic effects. In the quantitative structure-activity relationships (QSAR) analysis, the pharmacological data (half maximum mydriatic effect - ED 50 in μmol/kg) were considered along with the structural parameters of the agents from molecular modeling. The theoretically calculated lipophilicity parameters, CLOGP, of imidazol(in)es, as well as their lipophilicity parameters from HPLC, logk w , were also considered. The attempts to derive statistically significant QSAR equations for a full series of the agents under study were unsuccessful. However, for a subgroup of eight apparently structurally related imidazol(in)es a significant relationship between log(1/ED 50 ) and logk w values was
Yoo, Hong Sik; Bradford, Blair U.; Kosyk, Oksana; Uehara, Takeki; Shymonyak, Svitlana; Collins, Leonard B.; Bodnar, Wanda M.; Ball, Louise M.; Gold, Avram; Rusyn, Ivan
2014-01-01
Trichloroethylene (TCE) is a well-known environmental and occupational toxicant that is classified as carcinogenic to humans based on the epidemiological evidence of an association with higher risk of renal cell carcinoma. A number of scientific issues critical for assessing human health risks from TCE remain unresolved, such as the amount of kidney-toxic glutathione conjugation metabolites formed, inter-species and -individual differences, and the mode of action for kidney carcinogenicity. We hypothesized that TCE metabolite levels in the kidney are associated with kidney-specific toxicity. Oral dosing with TCE was conducted in sub-acute (600 mg/kg/d; 5 days; 7 inbred mouse strains) and sub-chronic (100 or 400 mg/kg/d; 1, 2, or 4 weeks; 2 inbred mouse strains) designs. We evaluated the quantitative relationship between strain-, dose-, and time-dependent formation of TCE metabolites from cytochrome P450-mediated oxidation [trichloroacetic acid (TCA), dichloroacetic acid (DCA), and trichloroethanol] and glutathione conjugation [S-(1,2-dichlorovinyl)-L-cysteine and S-(1,2-dichlorovinyl)glutathione], and various kidney toxicity phenotypes. In sub-acute study, we observed inter-strain differences in TCE metabolite levels in the kidney. In addition, we found that in several strains kidney-specific effects of TCE included induction of peroxisome proliferator-marker genes Cyp4a10 and Acox1, increased cell proliferation, and expression of KIM-1, a marker of tubular damage and regeneration. In sub-chronic study, peroxisome proliferator-marker gene induction and kidney toxicity diminished while cell proliferative response was elevated in a dose-dependent manner in NZW/LacJ, but not C57BL/6J mice. Overall, we show that TCE metabolite levels in the kidney are associated with kidney-specific toxicity and that these effects are strain-dependent. PMID:25424545
Como, F; Carnesecchi, E; Volani, S; Dorne, J L; Richardson, J; Bassan, A; Pavan, M; Benfenati, E
2017-01-01
Ecological risk assessment of plant protection products (PPPs) requires an understanding of both the toxicity and the extent of exposure to assess risks for a range of taxa of ecological importance including target and non-target species. Non-target species such as honey bees (Apis mellifera), solitary bees and bumble bees are of utmost importance because of their vital ecological services as pollinators of wild plants and crops. To improve risk assessment of PPPs in bee species, computational models predicting the acute and chronic toxicity of a range of PPPs and contaminants can play a major role in providing structural and physico-chemical properties for the prioritisation of compounds of concern and future risk assessments. Over the last three decades, scientific advisory bodies and the research community have developed toxicological databases and quantitative structure-activity relationship (QSAR) models that are proving invaluable to predict toxicity using historical data and reduce animal testing. This paper describes the development and validation of a k-Nearest Neighbor (k-NN) model using in-house software for the prediction of acute contact toxicity of pesticides on honey bees. Acute contact toxicity data were collected from different sources for 256 pesticides, which were divided into training and test sets. The k-NN models were validated with good prediction, with an accuracy of 70% for all compounds and of 65% for highly toxic compounds, suggesting that they might reliably predict the toxicity of structurally diverse pesticides and could be used to screen and prioritise new pesticides. Copyright © 2016 Elsevier Ltd. All rights reserved.
Distributed Structure-Searchable Toxicity (DSSTox) Database Network: Making Public Toxicity Data Resources More Accessible and U sable for Data Exploration and SAR Development
Many sources of public toxicity data are not currently linked to chemical structure, are not ...
Huang, Ruili; Southall, Noel; Xia, Menghang; Cho, Ming-Hsuang; Jadhav, Ajit; Nguyen, Dac-Trung; Inglese, James; Tice, Raymond R.; Austin, Christopher P.
2009-01-01
In support of the U.S. Tox21 program, we have developed a simple and chemically intuitive model we call weighted feature significance (WFS) to predict the toxicological activity of compounds, based on the statistical enrichment of structural features in toxic compounds. We trained and tested the model on the following: (1) data from quantitative high–throughput screening cytotoxicity and caspase activation assays conducted at the National Institutes of Health Chemical Genomics Center, (2) data from Salmonella typhimurium reverse mutagenicity assays conducted by the U.S. National Toxicology Program, and (3) hepatotoxicity data published in the Registry of Toxic Effects of Chemical Substances. Enrichments of structural features in toxic compounds are evaluated for their statistical significance and compiled into a simple additive model of toxicity and then used to score new compounds for potential toxicity. The predictive power of the model for cytotoxicity was validated using an independent set of compounds from the U.S. Environmental Protection Agency tested also at the National Institutes of Health Chemical Genomics Center. We compared the performance of our WFS approach with classical classification methods such as Naive Bayesian clustering and support vector machines. In most test cases, WFS showed similar or slightly better predictive power, especially in the prediction of hepatotoxic compounds, where WFS appeared to have the best performance among the three methods. The new algorithm has the important advantages of simplicity, power, interpretability, and ease of implementation. PMID:19805409
Zhang, Shuqun; Hou, Bo; Yang, Huaiyu; Zuo, Zhili
2016-05-01
Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer's disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r (2) = 0.988, q (2) = 0.757, ONC = 6; r (2) = 0.966, q (2) = 0.645, ONC = 5; and r (2) = 0.957, q (2) = 0.736, ONC = 6. The predictabilities were validated using an external test sets, and the predictive r (2) values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.
Fatemi, Mohammad Hossein; Ghorbanzad'e, Mehdi
2009-11-01
Quantitative structure-property relationship models for the prediction of the nematic transition temperature (T (N)) were developed by using multilinear regression analysis and a feedforward artificial neural network (ANN). A collection of 42 thermotropic liquid crystals was chosen as the data set. The data set was divided into three sets: for training, and an internal and external test set. Training and internal test sets were used for ANN model development, and the external test set was used for evaluation of the predictive power of the model. In order to build the models, a set of six descriptors were selected by the best multilinear regression procedure of the CODESSA program. These descriptors were: atomic charge weighted partial negatively charged surface area, relative negative charged surface area, polarity parameter/square distance, minimum most negative atomic partial charge, molecular volume, and the A component of moment of inertia, which encode geometrical and electronic characteristics of molecules. These descriptors were used as inputs to ANN. The optimized ANN model had 6:6:1 topology. The standard errors in the calculation of T (N) for the training, internal, and external test sets using the ANN model were 1.012, 4.910, and 4.070, respectively. To further evaluate the ANN model, a crossvalidation test was performed, which produced the statistic Q (2) = 0.9796 and standard deviation of 2.67 based on predicted residual sum of square. Also, the diversity test was performed to ensure the model's stability and prove its predictive capability. The obtained results reveal the suitability of ANN for the prediction of T (N) for liquid crystals using molecular structural descriptors.
Qu, Yanfei; Ma, Yongwen; Wan, Jinquan; Wang, Yan
2018-06-01
The silicon oil-air partition coefficients (K SiO/A ) of hydrophobic compounds are vital parameters for applying silicone oil as non-aqueous-phase liquid in partitioning bioreactors. Due to the limited number of K SiO/A values determined by experiment for hydrophobic compounds, there is an urgent need to model the K SiO/A values for unknown chemicals. In the present study, we developed a universal quantitative structure-activity relationship (QSAR) model using a sequential approach with macro-constitutional and micromolecular descriptors for silicone oil-air partition coefficients (K SiO/A ) of hydrophobic compounds with large structural variance. The geometry optimization and vibrational frequencies of each chemical were calculated using the hybrid density functional theory at the B3LYP/6-311G** level. Several quantum chemical parameters that reflect various intermolecular interactions as well as hydrophobicity were selected to develop QSAR model. The result indicates that a regression model derived from logK SiO/A , the number of non-hydrogen atoms (#nonHatoms) and energy gap of E LUMO and E HOMO (E LUMO -E HOMO ) could explain the partitioning mechanism of hydrophobic compounds between silicone oil and air. The correlation coefficient R 2 of the model is 0.922, and the internal and external validation coefficient, Q 2 LOO and Q 2 ext , are 0.91 and 0.89 respectively, implying that the model has satisfactory goodness-of-fit, robustness, and predictive ability and thus provides a robust predictive tool to estimate the logK SiO/A values for chemicals in application domain. The applicability domain of the model was visualized by the Williams plot.
Akbar, Jamshed; Iqbal, Shahid; Batool, Fozia; Karim, Abdul; Chan, Kim Wei
2012-01-01
Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance. PMID:23203132
Toropova, Alla P; Schultz, Terry W; Toropov, Andrey A
2016-03-01
Data on toxicity toward Tetrahymena pyriformis is indicator of applicability of a substance in ecologic and pharmaceutical aspects. Quantitative structure-activity relationships (QSARs) between the molecular structure of benzene derivatives and toxicity toward T. pyriformis (expressed as the negative logarithms of the population growth inhibition dose, mmol/L) are established. The available data were randomly distributed three times into the visible training and calibration sets, and invisible validation sets. The statistical characteristics for the validation set are the following: r(2)=0.8179 and s=0.338 (first distribution); r(2)=0.8682 and s=0.341 (second distribution); r(2)=0.8435 and s=0.323 (third distribution). These models are built up using only information on the molecular structure: no data on physicochemical parameters, 3D features of the molecular structure and quantum mechanics descriptors are involved in the modeling process. Copyright © 2016 Elsevier B.V. All rights reserved.
Use of statistical and neural net approaches in predicting toxicity of chemicals.
Basak, S C; Grunwald, G D; Gute, B D; Balasubramanian, K; Opitz, D
2000-01-01
Hierarchical quantitative structure-activity relationships (H-QSAR) have been developed as a new approach in constructing models for estimating physicochemical, biomedicinal, and toxicological properties of interest. This approach uses increasingly more complex molecular descriptors in a graduated approach to model building. In this study, statistical and neural network methods have been applied to the development of H-QSAR models for estimating the acute aquatic toxicity (LC50) of 69 benzene derivatives to Pimephales promelas (fathead minnow). Topostructural, topochemical, geometrical, and quantum chemical indices were used as the four levels of the hierarchical method. It is clear from both the statistical and neural network models that topostructural indices alone cannot adequately model this set of congeneric chemicals. Not surprisingly, topochemical indices greatly increase the predictive power of both statistical and neural network models. Quantum chemical indices also add significantly to the modeling of this set of acute aquatic toxicity data.
Learning Quantitative Sequence-Function Relationships from Massively Parallel Experiments
NASA Astrophysics Data System (ADS)
Atwal, Gurinder S.; Kinney, Justin B.
2016-03-01
A fundamental aspect of biological information processing is the ubiquity of sequence-function relationships—functions that map the sequence of DNA, RNA, or protein to a biochemically relevant activity. Most sequence-function relationships in biology are quantitative, but only recently have experimental techniques for effectively measuring these relationships been developed. The advent of such "massively parallel" experiments presents an exciting opportunity for the concepts and methods of statistical physics to inform the study of biological systems. After reviewing these recent experimental advances, we focus on the problem of how to infer parametric models of sequence-function relationships from the data produced by these experiments. Specifically, we retrace and extend recent theoretical work showing that inference based on mutual information, not the standard likelihood-based approach, is often necessary for accurately learning the parameters of these models. Closely connected with this result is the emergence of "diffeomorphic modes"—directions in parameter space that are far less constrained by data than likelihood-based inference would suggest. Analogous to Goldstone modes in physics, diffeomorphic modes arise from an arbitrarily broken symmetry of the inference problem. An analytically tractable model of a massively parallel experiment is then described, providing an explicit demonstration of these fundamental aspects of statistical inference. This paper concludes with an outlook on the theoretical and computational challenges currently facing studies of quantitative sequence-function relationships.
Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse.
Bhhatarai, Barun; Gramatica, Paola
2011-05-01
Quantitative structure-activity relationship (QSAR) analyses were performed using the LD(50) oral toxicity data of per- and polyfluorinated chemicals (PFCs) on rodents: rat and mouse. PFCs are studied under the EU project CADASTER which uses the available experimental data for prediction and prioritization of toxic chemicals for risk assessment by using the in silico tools. The methodology presented here applies chemometrical analysis on the existing experimental data and predicts the toxicity of new compounds. QSAR analyses were performed on the available 58 mouse and 50 rat LD(50) oral data using multiple linear regression (MLR) based on theoretical molecular descriptors selected by genetic algorithm (GA). Training and prediction sets were prepared a priori from available experimental datasets in terms of structure and response. These sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the models were verified on 376 per- and polyfluorinated chemicals including those in REACH preregistration list. The rat and mouse endpoints were predicted by each model for the studied compounds, and finally 30 compounds, all perfluorinated, were prioritized as most important for experimental toxicity analysis under the project. In addition, cumulative study on compounds within the AD of all four models, including two earlier published models on LC(50) rodent analysis was studied and the cumulative toxicity trend was observed using principal component analysis (PCA). The similarities and the differences observed in terms of descriptors and chemical/mechanistic meaning encoded by descriptors to prioritize the most toxic compounds are highlighted.
SCF-MO computations have been performed on tetra- to octa-chlorinated dibenzo-p-dioxin congeners (PCDD) using an MNDO-PM3 Hamiltonian. Qualitative relationships were developed between empirical, international-toxic equivalence factors for PCDD congeners and their relati...
Kennicutt, A R; Morkowchuk, L; Krein, M; Breneman, C M; Kilduff, J E
2016-08-01
A quantitative structure-activity relationship was developed to predict the efficacy of carbon adsorption as a control technology for endocrine-disrupting compounds, pharmaceuticals, and components of personal care products, as a tool for water quality professionals to protect public health. Here, we expand previous work to investigate a broad spectrum of molecular descriptors including subdivided surface areas, adjacency and distance matrix descriptors, electrostatic partial charges, potential energy descriptors, conformation-dependent charge descriptors, and Transferable Atom Equivalent (TAE) descriptors that characterize the regional electronic properties of molecules. We compare the efficacy of linear (Partial Least Squares) and non-linear (Support Vector Machine) machine learning methods to describe a broad chemical space and produce a user-friendly model. We employ cross-validation, y-scrambling, and external validation for quality control. The recommended Support Vector Machine model trained on 95 compounds having 23 descriptors offered a good balance between good performance statistics, low error, and low probability of over-fitting while describing a wide range of chemical features. The cross-validated model using a log-uptake (qe) response calculated at an aqueous equilibrium concentration (Ce) of 1 μM described the training dataset with an r(2) of 0.932, had a cross-validated r(2) of 0.833, and an average residual of 0.14 log units.
NASA Astrophysics Data System (ADS)
Krein, Michael
After decades of development and use in a variety of application areas, Quantitative Structure Property Relationships (QSPRs) and related descriptor-based statistical learning methods have achieved a level of infamy due to their misuse. The field is rife with past examples of overtrained models, overoptimistic performance assessment, and outright cheating in the form of explicitly removing data to fit models. These actions do not serve the community well, nor are they beneficial to future predictions based on established models. In practice, in order to select combinations of descriptors and machine learning methods that might work best, one must consider the nature and size of the training and test datasets, be aware of existing hypotheses about the data, and resist the temptation to bias structure representation and modeling to explicitly fit the hypotheses. The definition and application of these best practices is important for obtaining actionable modeling outcomes, and for setting user expectations of modeling accuracy when predicting the endpoint values of unknowns. A wide variety of statistical learning approaches, descriptor types, and model validation strategies are explored herein, with the goals of helping end users understand the factors involved in creating and using QSPR models effectively, and to better understand relationships within the data, especially by looking at the problem space from multiple perspectives. Molecular relationships are commonly envisioned in a continuous high-dimensional space of numerical descriptors, referred to as chemistry space. Descriptor and similarity metric choice influence the partitioning of this space into regions corresponding to local structural similarity. These regions, known as domains of applicability, are most likely to be successfully modeled by a QSPR. In Chapter 2, the network topology and scaling relationships of several chemistry spaces are thoroughly investigated. Chemistry spaces studied include the
Structure-activity relationship of cyanine tau aggregation inhibitors
Chang, Edward; Congdon, Erin E.; Honson, Nicolette S.; Duff, Karen E.; Kuret, Jeff
2009-01-01
A structure-activity relationship for symmetrical cyanine inhibitors of human tau aggregation was elaborated using a filter trap assay. Antagonist activity depended on cyanine heterocycle, polymethine bridge length, and the nature of meso- and N-substituents. One potent member of the series, 3,3’-diethyl-9-methylthiacarbocyanine iodide (compound 11), retained submicromolar potency and had calculated physical properties consistent with blood-brain barrier and cell membrane penetration. Exposure of organotypic slices prepared from JNPL3 transgenic mice (which express human tau harboring the aggregation prone P301L tauopathy mutation) to compound 11 for one week revealed a biphasic dose response relationship. Low nanomolar concentrations decreased insoluble tau aggregates to half those observed in slices treated with vehicle alone. In contrast, high concentrations (≥300 nM) augmented tau aggregation and produced abnormalities in tissue tubulin levels. These data suggest that certain symmetrical carbocyanine dyes can modulate tau aggregation in the slice biological model at concentrations well below those associated with toxicity. PMID:19432420
Rojas, Cristian; Duchowicz, Pablo R; Tripaldi, Piercosimo; Pis Diez, Reinaldo
2015-11-27
A quantitative structure-property relationship (QSPR) was developed for modeling the retention index of 1184 flavor and fragrance compounds measured using a Carbowax 20M glass capillary gas chromatography column. The 4885 molecular descriptors were calculated using Dragon software, and then were simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceeded in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptor blocks, and the last one by analyzing only 3D-descriptor families. The models were validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-many-out were applied, together with Y-randomization and applicability domain analysis. The developed model was used to estimate the I of a set of 22 molecules. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the Randić-like index from reciprocal squared distance matrix has a high relevance for this purpose. Copyright © 2015 Elsevier B.V. All rights reserved.
Hocart, Simon J.; Liu, Huayin; Deng, Haiyan; De, Dibyendu; Krogstad, Frances M.; Krogstad, Donald J.
2011-01-01
Chloroquine (CQ) is a safe and economical 4-aminoquinoline (AQ) antimalarial. However, its value has been severely compromised by the increasing prevalence of CQ resistance. This study examined 108 AQs, including 68 newly synthesized compounds. Of these 108 AQs, 32 (30%) were active only against CQ-susceptible Plasmodium falciparum strains and 59 (55%) were active against both CQ-susceptible and CQ-resistant P. falciparum strains (50% inhibitory concentrations [IC50s], ≤25 nM). All AQs active against both CQ-susceptible and CQ-resistant P. falciparum strains shared four structural features: (i) an AQ ring without alkyl substitution, (ii) a halogen at position 7 (Cl, Br, or I but not F), (iii) a protonatable nitrogen at position 1, and (iv) a second protonatable nitrogen at the end of the side chain distal from the point of attachment to the AQ ring via the nitrogen at position 4. For activity against CQ-resistant parasites, side chain lengths of ≤3 or ≥10 carbons were necessary but not sufficient; they were identified as essential factors by visual comparison of 2-dimensional (2-D) structures in relation to the antiparasite activities of the AQs and were confirmed by computer-based 3-D comparisons and differential contour plots of activity against P. falciparum. The advantage of the method reported here (refinement of quantitative structure-activity relationship [QSAR] descriptors by random assignment of compounds to multiple training and test sets) is that it retains QSAR descriptors according to their abilities to predict the activities of unknown test compounds rather than according to how well they fit the activities of the compounds in the training sets. PMID:21383099
Mixture toxicity revisited from a toxicogenomic perspective.
Altenburger, Rolf; Scholz, Stefan; Schmitt-Jansen, Mechthild; Busch, Wibke; Escher, Beate I
2012-03-06
The advent of new genomic techniques has raised expectations that central questions of mixture toxicology such as for mechanisms of low dose interactions can now be answered. This review provides an overview on experimental studies from the past decade that address diagnostic and/or mechanistic questions regarding the combined effects of chemical mixtures using toxicogenomic techniques. From 2002 to 2011, 41 studies were published with a focus on mixture toxicity assessment. Primarily multiplexed quantification of gene transcripts was performed, though metabolomic and proteomic analysis of joint exposures have also been undertaken. It is now standard to explicitly state criteria for selecting concentrations and provide insight into data transformation and statistical treatment with respect to minimizing sources of undue variability. Bioinformatic analysis of toxicogenomic data, by contrast, is still a field with diverse and rapidly evolving tools. The reported combined effect assessments are discussed in the light of established toxicological dose-response and mixture toxicity models. Receptor-based assays seem to be the most advanced toward establishing quantitative relationships between exposure and biological responses. Often transcriptomic responses are discussed based on the presence or absence of signals, where the interpretation may remain ambiguous due to methodological problems. The majority of mixture studies design their studies to compare the recorded mixture outcome against responses for individual components only. This stands in stark contrast to our existing understanding of joint biological activity at the levels of chemical target interactions and apical combined effects. By joining established mixture effect models with toxicokinetic and -dynamic thinking, we suggest a conceptual framework that may help to overcome the current limitation of providing mainly anecdotal evidence on mixture effects. To achieve this we suggest (i) to design studies to
2017-01-01
Phenolic compounds and their derivatives are ubiquitous constituents of numerous synthetic and natural chemicals that exist in the environment. Their toxicity is mostly attributed to their hydrophobicity and/or the formation of free radicals. In a continuation of the study of phenolic toxicity in a systematic manner, we have examined the biological responses of Saccharomyces cerevisiae to a series of mostly monosubstituted phenols utilizing a quantitative structure–activity relationship (QSAR) approach. The biological end points included a growth assay that determines the levels of growth inhibition induced by the phenols as well as a yeast deletion (DEL) assay that assesses the ability of X-phenols to induce DNA damage or DNA breaks. The QSAR analysis of cell growth patterns determined by IC50 and IC80 values indicates that toxicity is delineated by a hydrophobic, parabolic model. The DEL assay was then utilized to detect genomic deletions in yeast. The increase in the genotoxicity was enhanced by the electrophilicity of the phenolic substituents that were strong electron donors as well as by minimal hydrophobicity. The electrophilicities are represented by Brown’s sigma plus values that are a variant of the Hammett sigma constants. A few mutant strains of genes involved in DNA repair were separately exposed to 2,6-di-tert-butyl-4-methyl-phenol (BHT) and butylated hydroxy anisole (BHA). They were subsequently screened for growth phenotypes. BHA-induced growth defects in most of the DNA repair null mutant strains, whereas BHT was unresponsive. PMID:29302629
The mechanism of dioxin toxicity: relationship to risk assessment.
Birnbaum, L S
1994-01-01
Risk characterization involves hazard identification, determination of dose-response relationships, and exposure assessment. Improvement of the risk assessment process requires inclusion of the best available science. Recent findings in the area of dioxin toxicity have led to a major effort to reassess its risk. 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD), commonly referred to as "dioxin," is the most toxic member of a class of related chemicals including the polyhalogenated dibenzo-p-dioxins, dibenzofurans, biphenyls, naphthalenes, azo- and azoxy-benzenes, whose toxicities can be expressed as fractional equivalencies of TCDD. These chemicals exert their effects through interaction with a specific intracellular protein, the Ah receptor. While binding to the receptor is necessary, it is not sufficient to bring about a chain of events leading to various responses including enzyme induction, immunotoxicity, reproductive and endocrine effects, developmental toxicity, chloracne, tumor promotion, etc. Some of these responses appear to be linear at low doses. Immunotoxicity and effects on the reproductive system appear to be among the most sensitive responses. The Ah receptor functions as a transcriptional enhancer, interacting with a number of other regulatory proteins (heat shock proteins, kinases, translocases, DNA binding species). Interaction with specific base sequences in the DNA appear to be modulated by the presence of other growth factors, hormones and their receptors as well as other regulatory proteins. Thus, dioxin appears to function as a hormone, initiating a cascade of events that is dependent upon the environment of each cell and tissue. While Ah receptor variants exist, all vertebrates examined have demonstrated such a protein with similar numbers of receptors and binding affinity for TCDD. Most species respond similarly to dioxin and related compounds. While a given species may be an outlier for a given response, it will behave like other animals for
The structure-activity relationship in herbicidal monosubstituted sulfonylureas
DOE Office of Scientific and Technical Information (OSTI.GOV)
Li, Zheng-Ming; Ma, Yi; Guddat, Luke
The herbicide sulfonylurea (SU) belongs to one of the most important class of herbicides worldwide. It is well known for its ecofriendly, extreme low toxicity towards mammals and ultralow dosage application. The original inventor, G Levitt, set out structure-activity relationship (SAR) guidelines for SU structural design to attain superhigh bioactivity. A new approach to SU molecular design has been developed. After the analysis of scores of SU products by X-ray diffraction methodology and after greenhouse herbicidal screening of 900 novel SU structures synthesized in the authors laboratory, it was found that several SU structures containing a monosubstituted pyrimidine moiety retainmore » excellent herbicidal characteristics, which has led to partial revision of the Levitt guidelines. Among the novel SU molecules, monosulfuron and monosulfuron-ester have been developed into two new herbicides that have been officially approved for field application and applied in millet and wheat fields in China. A systematic structural study of the new substrate-target complex and the relative mode of action in comparison with conventional SU has been carried out. A new mode of action has been postulated.« less
Nowell, Lisa H.; Norman, Julia E.; Moran, Patrick W.; Martin, Jeffrey D.; Stone, Wesley W.
2014-01-01
Pesticide mixtures are common in streams with agricultural or urban influence in the watershed. The Pesticide Toxicity Index (PTI) is a screening tool to assess potential aquatic toxicity of complex pesticide mixtures by combining measures of pesticide exposure and acute toxicity in an additive toxic-unit model. The PTI is determined separately for fish, cladocerans, and benthic invertebrates. This study expands the number of pesticides and degradates included in previous editions of the PTI from 124 to 492 pesticides and degradates, and includes two types of PTI for use in different applications, depending on study objectives. The Median-PTI was calculated from median toxicity values for individual pesticides, so is robust to outliers and is appropriate for comparing relative potential toxicity among samples, sites, or pesticides. The Sensitive-PTI uses the 5th percentile of available toxicity values, so is a more sensitive screening-level indicator of potential toxicity. PTI predictions of toxicity in environmental samples were tested using data aggregated from published field studies that measured pesticide concentrations and toxicity to Ceriodaphnia dubia in ambient stream water. C. dubia survival was reduced to ≤ 50% of controls in 44% of samples with Median-PTI values of 0.1–1, and to 0% in 96% of samples with Median-PTI values > 1. The PTI is a relative, but quantitative, indicator of potential toxicity that can be used to evaluate relationships between pesticide exposure and biological condition.
Quantitative criticism of literary relationships
Dexter, Joseph P.; Katz, Theodore; Tripuraneni, Nilesh; Dasgupta, Tathagata; Kannan, Ajay; Brofos, James A.; Bonilla Lopez, Jorge A.; Schroeder, Lea A.; Casarez, Adriana; Rabinovich, Maxim; Haimson Lushkov, Ayelet; Chaudhuri, Pramit
2017-01-01
Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships (“intertextuality”) and contributes to cultural evolution. In this paper, we use techniques from stylometry and machine learning to address subjective literary critical questions about Latin literature, a corpus marked by an extraordinary concentration of intertextuality. Our work, which we term “quantitative criticism,” focuses on case studies involving two influential Roman authors, the playwright Seneca and the historian Livy. We find that four plays related to but distinct from Seneca’s main writings are differentiated from the rest of the corpus by subtle but important stylistic features. We offer literary interpretations of the significance of these anomalies, providing quantitative data in support of hypotheses about the use of unusual formal features and the interplay between sound and meaning. The second part of the paper describes a machine-learning approach to the identification and analysis of citational material that Livy loosely appropriated from earlier sources. We extend our approach to map the stylistic topography of Latin prose, identifying the writings of Caesar and his near-contemporary Livy as an inflection point in the development of Latin prose style. In total, our results reflect the integration of computational and humanistic methods to investigate a diverse range of literary questions. PMID:28373557
Quantitative criticism of literary relationships.
Dexter, Joseph P; Katz, Theodore; Tripuraneni, Nilesh; Dasgupta, Tathagata; Kannan, Ajay; Brofos, James A; Bonilla Lopez, Jorge A; Schroeder, Lea A; Casarez, Adriana; Rabinovich, Maxim; Haimson Lushkov, Ayelet; Chaudhuri, Pramit
2017-04-18
Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships ("intertextuality") and contributes to cultural evolution. In this paper, we use techniques from stylometry and machine learning to address subjective literary critical questions about Latin literature, a corpus marked by an extraordinary concentration of intertextuality. Our work, which we term "quantitative criticism," focuses on case studies involving two influential Roman authors, the playwright Seneca and the historian Livy. We find that four plays related to but distinct from Seneca's main writings are differentiated from the rest of the corpus by subtle but important stylistic features. We offer literary interpretations of the significance of these anomalies, providing quantitative data in support of hypotheses about the use of unusual formal features and the interplay between sound and meaning. The second part of the paper describes a machine-learning approach to the identification and analysis of citational material that Livy loosely appropriated from earlier sources. We extend our approach to map the stylistic topography of Latin prose, identifying the writings of Caesar and his near-contemporary Livy as an inflection point in the development of Latin prose style. In total, our results reflect the integration of computational and humanistic methods to investigate a diverse range of literary questions.
Jensen, G.E.; Niemelä, J.R.; Wedebye, E.B.; Nikolov, N.G.
2008-01-01
A special challenge in the new European Union chemicals legislation, Registration, Evaluation and Authorisation of Chemicals, will be the toxicological evaluation of chemicals for reproductive toxicity. Use of valid quantitative structure–activity relationships (QSARs) is a possibility under the new legislation. This article focuses on a screening exercise by use of our own and commercial QSAR models for identification of possible reproductive toxicants. Three QSAR models were used for reproductive toxicity for the endpoints teratogenic risk to humans (based on animal tests, clinical data and epidemiological human studies), dominant lethal effect in rodents (in vivo) and Drosophila melanogaster sex-linked recessive lethal effect. A structure set of 57,014 European Inventory of Existing Chemical Substances (EINECS) chemicals was screened. A total of 5240 EINECS chemicals, corresponding to 9.2%, were predicted as reproductive toxicants by one or more of the models. The chemicals predicted positive for reproductive toxicity will be submitted to the Danish Environmental Protection Agency as scientific input for a future updated advisory classification list with advisory classifications for concern for humans owing to possible developmental toxic effects: Xn (Harmful) and R63 (Possible risk of harm to the unborn child). The chemicals were also screened in three models for endocrine disruption. PMID:19061080
Distribution, relationship, and risk assessment of toxic heavy metals in walnuts and growth soil.
Han, Yongxiang; Ni, Zhanglin; Li, Shiliang; Qu, Minghua; Tang, Fubin; Mo, Runhong; Ye, Caifen; Liu, Yihua
2018-04-14
Walnut is one of the most popular nuts worldwide and contains various mineral nutrients. Little is known, however, about the relationship between toxic heavy metals in walnuts and growth soil. In this study, we investigated the distribution, relationship, and risk assessment of five toxic heavy metals-lead (Pb), arsenic (As), chromium (Cr), cadmium (Cd), and mercury (Hg)-in walnuts and growth soil in the main production areas of China. The results showed that the main heavy metal pollution in walnut and soil was Pb and Cd. Regionally, positive relationships existed between heavy metals and the pH and organic matter of soil. In addition, we observed a notable uptake effect between walnut and growth soil. In this study, we found a significant correlation (r = 0.786, P < 0.05) between the bioconcentration factors and the longitude of the sampling areas. The risks (total hazard quotients) of five heavy metals toward children and adults by dietary walnut consumption were 46.8 and 56.2%, respectively. The ability to identify toxic heavy metal pollution in walnuts and growth soil could be helpful to screen suitable planting sites to prevent and control heavy metal pollution and improve the quality and safety of walnut.
The most significant harmful algal bloom (HAB) toxin in terms of public health is commonly known as paralytic shellfish poisons (PSPs, "red tides" toxins). PSPs are neurotoxins produced by marine dinoflagellates and some cyanobacteria. PSPs comprise of over 21 natural toxins wi...
Nolte, Tom M; Ragas, Ad M J
2017-03-22
Many organic chemicals are ionizable by nature. After use and release into the environment, various fate processes determine their concentrations, and hence exposure to aquatic organisms. In the absence of suitable data, such fate processes can be estimated using Quantitative Structure-Property Relationships (QSPRs). In this review we compiled available QSPRs from the open literature and assessed their applicability towards ionizable organic chemicals. Using quantitative and qualitative criteria we selected the 'best' QSPRs for sorption, (a)biotic degradation, and bioconcentration. The results indicate that many suitable QSPRs exist, but some critical knowledge gaps remain. Specifically, future focus should be directed towards the development of QSPR models for biodegradation in wastewater and sediment systems, direct photolysis and reaction with singlet oxygen, as well as additional reactive intermediates. Adequate QSPRs for bioconcentration in fish exist, but more accurate assessments can be achieved using pharmacologically based toxicokinetic (PBTK) models. No adequate QSPRs exist for bioconcentration in non-fish species. Due to the high variability of chemical and biological species as well as environmental conditions in QSPR datasets, accurate predictions for specific systems and inter-dataset conversions are problematic, for which standardization is needed. For all QSPR endpoints, additional data requirements involve supplementing the current chemical space covered and accurately characterizing the test systems used.
Toxic Effect of Staphylococcal Lysins for Goldfish1
Kaplan, Milton T.; Appleman, Milo D.
1963-01-01
Goldfish died within 24 hr after intraperitoneal injections of 0.2 ml of Seitz filtrates of hemolytic Staphylococcus aureus cultures grown on Dolman and Wilson medium under increased CO2 pressure for 72 to 96 hr. Two lethal toxins differing in heat sensitivity, antigenicity, and degree of toxicity were demonstrated. Studies of the relationship between the lethal factors and the hemolysins in the filtrates suggested that α- and β-lysin were responsible for the lethal effects. Filtrates of nonhemolytic staphylococcal cultures were innocuous. Goldfish were suitable animals for detecting toxicity in staphylococcal culture filtrates and for quantitative studies of the toxins. The results were highly reproducible. PMID:14030754
NASA Astrophysics Data System (ADS)
Cho, Sehyeon; Choi, Min Ji; Kim, Minju; Lee, Sunhoe; Lee, Jinsung; Lee, Seok Joon; Cho, Haelim; Lee, Kyung-Tae; Lee, Jae Yeol
2015-03-01
A series of 3,4-dihydroquinazoline derivatives with anti-cancer activities against human lung cancer A549 cells were subjected to three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using the comparative molecular similarity indices analysis (CoMSIA) approaches. The most potent compound, 1 was used to align the molecules. As a result, the best prediction was obtained with CoMSIA combined the steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields (q2 = 0.720, r2 = 0.897). This model was validated by an external test set of 6 compounds giving satisfactory predictive r2 value of 0.923 as well as the scrambling stability test. This model would guide the design of potent 3,4-dihydroquinazoline derivatives as anti-cancer agent for the treatment of human lung cancer.
Gao, Jia-Suo; Tong, Xu-Peng; Chang, Yi-Qun; He, Yu-Xuan; Mei, Yu-Dan; Tan, Pei-Hong; Guo, Jia-Liang; Liao, Guo-Chao; Xiao, Gao-Keng; Chen, Wei-Min; Zhou, Shu-Feng; Sun, Ping-Hua
2015-01-01
Factor IXa (FIXa), a blood coagulation factor, is specifically inhibited at the initiation stage of the coagulation cascade, promising an excellent approach for developing selective and safe anticoagulants. Eighty-four amidinobenzothiophene antithrombotic derivatives targeting FIXa were selected to establish three-dimensional quantitative structure–activity relationship (3D-QSAR) and three-dimensional quantitative structure–selectivity relationship (3D-QSSR) models using comparative molecular field analysis and comparative similarity indices analysis methods. Internal and external cross-validation techniques were investigated as well as region focusing and bootstrapping. The satisfactory q2 values of 0.753 and 0.770, and r2 values of 0.940 and 0.965 for 3D-QSAR and 3D-QSSR, respectively, indicated that the models are available to predict both the inhibitory activity and selectivity on FIXa against Factor Xa, the activated status of Factor X. This work revealed that the steric, hydrophobic, and H-bond factors should appropriately be taken into account in future rational design, especially the modifications at the 2′-position of the benzene and the 6-position of the benzothiophene in the R group, providing helpful clues to design more active and selective FIXa inhibitors for the treatment of thrombosis. On the basis of the three-dimensional quantitative structure–property relationships, 16 new potent molecules have been designed and are predicted to be more active and selective than Compound 33, which has the best activity as reported in the literature. PMID:25848211
Use of fish embryo toxicity tests for the prediction of acute fish toxicity to chemicals.
Belanger, Scott E; Rawlings, Jane M; Carr, Gregory J
2013-08-01
The fish embryo test (FET) is a potential animal alternative for the acute fish toxicity (AFT) test. A comprehensive validation program assessed 20 different chemicals to understand intra- and interlaboratory variability for the FET. The FET had sufficient reproducibility across a range of potencies and modes of action. In the present study, the suitability of the FET as an alternative model is reviewed by relating FET and AFT. In total, 985 FET studies and 1531 AFT studies were summarized. The authors performed FET-AFT regressions to understand potential relationships based on physical-chemical properties, species choices, duration of exposure, chemical classes, chemical functional uses, and modes of action. The FET-AFT relationships are very robust (slopes near 1.0, intercepts near 0) across 9 orders of magnitude in potency. A recommendation for the predictive regression relationship is based on 96-h FET and AFT data: log FET median lethal concentration (LC50) = (0.989 × log fish LC50) - 0.195; n = 72 chemicals, r = 0.95, p < 0.001, LC50 in mg/L. A similar, not statistically different regression was developed for the entire data set (n = 144 chemicals, unreliable studies deleted). The FET-AFT regressions were robust for major chemical classes with suitably large data sets. Furthermore, regressions were similar to those for large groups of functional chemical categories such as pesticides, surfactants, and industrial organics. Pharmaceutical regressions (n = 8 studies only) were directionally correct. The FET-AFT relationships were not quantitatively different from acute fish-acute fish toxicity relationships with the following species: fathead minnow, rainbow trout, bluegill sunfish, Japanese medaka, and zebrafish. The FET is scientifically supportable as a rational animal alternative model for ecotoxicological testing of acute toxicity of chemicals to fish. Copyright © 2013 SETAC.
Feng, Qun; Li, Xiao-yu; Luan, Yong-fu; Sun, Sai-nan; Sun, Rong
2015-03-01
To study the effect of single administration of aqueous extracts from aconite on "dose-toxicity" relationship and "time-toxicity" relationship of mice hearts, through changes in electrocardiogram (ECG) and serum biochemical indexes. Mice were grouped according to different drug doses and time points, and orally administered with water extracts from aconite for once to observe the changes of mice ECG before and after the administration, calculate visceral indexes heart, liver and kidney, and detect levels of CK, LDH, BNP and CTn-I in serum. According to the "time-toxicity" relationship study, at 5 min after oral administration with aqueous extracts from aconite in mice, the heart rate of mice began rising, reached peak at 60 min and then slowly reduced; QRS, R amplitude, T duration and amplitude and QT interval declined at 5 min, reduced to the bottom at 60 min and then gradually elevated. The levels of CK, LDH, BNP and CTn-I in serum elevated at 5 min and reached the peak at 60 min, with no significant change in ratios of organs to body at different time points. On the basis of the "dose-toxicity" relationship, with the increase in single dose of aqueous extracts from aconite, the heart rate of mice. QRS, T duration and amplitude and QT interval declined gradually, and levels of CK, LDH, BNP and CTn-I in serum slowly elevated, with a certain dose dependence and no significant change in ratios of organs to body in mice. Single oral administration of different doses of aqueous extracts from aconite could cause different degrees of heart injury at different time points, with a certain dose dependence. Its peak time of toxicity is at 60 min after the administration of aqueous extracts from aconite.
Integrated scientific data bases review on asulacrine and associated toxicity.
Afzal, Attia; Sarfraz, Muhammad; Wu, Zimei; Wang, Guangji; Sun, Jianguo
2016-08-01
Asulacrine (ASL), a weakly basic and highly lipophilic drug was synthesized in 1980's in cancer research laboratory of Auckland by modifications to the acridine portion of amsacrine on 3-, 4- and 5-substitution patterns. In contrast to its precursor amsacrine (m-AMSA), ASL was effective not only against leukemia and Lewis lung tumor system but also a wide variety of solid tumor. Its metabolic pathway is not same to amsacrine hence different side effects, hepatotoxicity and excretion was observed. Asulacrine is under phase II clinical trials and has showed promising results but its toxicity especially phlebitis is stumbling block in its clinical implementation. This review is an effort to give a possible clue, based on scientifically proven results, to the researchers to solve the mystery of associated toxicity, phlebitis. Review covers the available literature on asulacrine and other acridine derivatives regarding pharmacology, pharmacokinetics, quantitative structure activity relationship and toxicology via electronic search using scientific databases like PubMed and others. To date, all abstracts and full-text articles were discussed and analyzed. The tabulated comparisons and circuitry mechanism of ASL are the added features of the review which give a complete understanding of hidden aspects of possible route cause of associated toxicity, the phlebitis. Copyright © 2016. Published by Elsevier Ireland Ltd.
The effect of leverage and/or influential on structure-activity relationships.
Bolboacă, Sorana D; Jäntschi, Lorentz
2013-05-01
In the spirit of reporting valid and reliable Quantitative Structure-Activity Relationship (QSAR) models, the aim of our research was to assess how the leverage (analysis with Hat matrix, h(i)) and the influential (analysis with Cook's distance, D(i)) of QSAR models may reflect the models reliability and their characteristics. The datasets included in this research were collected from previously published papers. Seven datasets which accomplished the imposed inclusion criteria were analyzed. Three models were obtained for each dataset (full-model, h(i)-model and D(i)-model) and several statistical validation criteria were applied to the models. In 5 out of 7 sets the correlation coefficient increased when compounds with either h(i) or D(i) higher than the threshold were removed. Withdrawn compounds varied from 2 to 4 for h(i)-models and from 1 to 13 for D(i)-models. Validation statistics showed that D(i)-models possess systematically better agreement than both full-models and h(i)-models. Removal of influential compounds from training set significantly improves the model and is recommended to be conducted in the process of quantitative structure-activity relationships developing. Cook's distance approach should be combined with hat matrix analysis in order to identify the compounds candidates for removal.
Acar, Evrim; Plopper, George E.; Yener, Bülent
2012-01-01
The structure/function relationship is fundamental to our understanding of biological systems at all levels, and drives most, if not all, techniques for detecting, diagnosing, and treating disease. However, at the tissue level of biological complexity we encounter a gap in the structure/function relationship: having accumulated an extraordinary amount of detailed information about biological tissues at the cellular and subcellular level, we cannot assemble it in a way that explains the correspondingly complex biological functions these structures perform. To help close this information gap we define here several quantitative temperospatial features that link tissue structure to its corresponding biological function. Both histological images of human tissue samples and fluorescence images of three-dimensional cultures of human cells are used to compare the accuracy of in vitro culture models with their corresponding human tissues. To the best of our knowledge, there is no prior work on a quantitative comparison of histology and in vitro samples. Features are calculated from graph theoretical representations of tissue structures and the data are analyzed in the form of matrices and higher-order tensors using matrix and tensor factorization methods, with a goal of differentiating between cancerous and healthy states of brain, breast, and bone tissues. We also show that our techniques can differentiate between the structural organization of native tissues and their corresponding in vitro engineered cell culture models. PMID:22479315
Lei, Tailong; Sun, Huiyong; Kang, Yu; Zhu, Feng; Liu, Hui; Zhou, Wenfang; Wang, Zhe; Li, Dan; Li, Youyong; Hou, Tingjun
2017-11-06
Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common adverse event for medications, natural supplements, and environmental chemicals. Despite its importance, there are only a few in silico models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures. Here, we developed a series of qualitative and quantitative structure-activity relationship (QSAR) models for predicting urinary tract toxicity. In our study, the recursive feature elimination method incorporated with random forests (RFE-RF) was used for dimension reduction, and then eight machine learning approaches were used for QSAR modeling, i.e., relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), C5.0 trees, eXtreme gradient boosting (XGBoost), AdaBoost.M1, SVM boosting (SVMBoost), and RVM boosting (RVMBoost). For building classification models, the synthetic minority oversampling technique was used to handle the imbalance data set problem. Among all the machine learning approaches, SVMBoost based on the RBF kernel achieves both the best quantitative (q ext 2 = 0.845) and qualitative predictions for the test set (MCC of 0.787, AUC of 0.893, sensitivity of 89.6%, specificity of 94.1%, and global accuracy of 90.8%). The application domains were then analyzed, and all of the tested chemicals fall within the application domain coverage. We also examined the structure features of the chemicals with large prediction errors. In brief, both the regression and classification models developed by the SVMBoost approach have reliable prediction capability for assessing chemical-induced urinary tract toxicity.
Wu, Zengxue; Zhang, Jian; Chen, Jixiang; Pan, Jianke; Zhao, Lei; Liu, Dengyue; Zhang, Awei; Chen, Jin; Hu, Deyu; Song, Baoan
2017-10-01
Ferulic acid and quinazoline derivatives possess good antiviral activities. In order to develop novel compounds with high antiviral activities, a series of ferulic acid ester derivatives containing quinazoline were synthesized and evaluated for their antiviral activities. Bioassays indicated that some of the compounds exhibited good antiviral activities in vivo against tobacco mosaic virus (TMV) and cucumber mosaic virus (CMV). One of the compounds demonstrated significant curative and protective activities against TMV and CMV, with EC 50 values of 162.14, 114.61 and 255.49, 138.81 mg L -1 , respectively, better than those of ningnanmycin (324.51, 168.84 and 373.88, 272.70 mg L -1 ). The values of q 2 and r 2 for comparative molecular field analysis and comparative molecular similarity index analysis in the TMV (0.508, 0.663 and 0.992, 0.930) and CMV (0.530, 0.626 and 0.997, 0.981) models presented good predictive abilities. Some of the title compounds demonstrated good antiviral activities. Three-dimensional quantitative structure-activity relationship models revealed that the antiviral activities depend on steric and electrostatic properties. These results could provide significant structural insights for the design of highly active ferulic acid derivatives. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.
Carbó-Dorca, Ramon; Gallegos, Ana; Sánchez, Angel J
2009-05-01
Classical quantitative structure-properties relationship (QSPR) statistical techniques unavoidably present an inherent paradoxical computational context. They rely on the definition of a Gram matrix in descriptor spaces, which is used afterwards to reduce the original dimension via several possible kinds of algebraic manipulations. From there, effective models for the computation of unknown properties of known molecular structures are obtained. However, the reduced descriptor dimension causes linear dependence within the set of discrete vector molecular representations, leading to positive semi-definite Gram matrices in molecular spaces. To resolve this QSPR dimensionality paradox (QSPR DP) here is proposed to adopt as starting point the quantum QSPR (QQSPR) computational framework perspective, where density functions act as infinite dimensional descriptors. The fundamental QQSPR equation, deduced from employing quantum expectation value numerical evaluation, can be approximately solved in order to obtain models exempt of the QSPR DP. The substitution of the quantum similarity matrix by an empirical Gram matrix in molecular spaces, build up with the original non manipulated discrete molecular descriptor vectors, permits to obtain classical QSPR models with the same characteristics as in QQSPR, that is: possessing a certain degree of causality and explicitly independent of the descriptor dimension. 2008 Wiley Periodicals, Inc.
Effects of toxic metals and chemicals on biofilm and biocorrosion.
Fang, Herbert H P; Xu, Li-Chong; Chan, Kwong-Yu
2002-11-01
Microbes in marine biofilms aggregated into clusters and increased the production of extracellular polymeric substances (EPS), by over 100% in some cases, when the seawater media containing toxic metals and chemicals, such as Cd(II), Cu(II), Pb(II), Zn(II), AI(III), Cr(III), glutaraldehyde, and phenol. The formation of microbial cluster and the increased production of EPS, which contained 84-92% proteins and 8-16% polysaccharides, accelerated the corrosion of the mild steel. However, there was no quantitative relationship between the degree of increased corrosion and the toxicity of metals/chemicals towards sulfate-reducing bacteria, or the increased EPS production.
Patel, H C; Tokarski, J S; Hopfinger, A J
1997-10-01
The purpose of this study was to identify the key physicochemical molecular properties of polymeric materials responsible for gaseous diffusion in the polymers. Quantitative structure-property relationships, QSPRs were constructed using a genetic algorithm on a training set of 16 polymers for which CO2, N2, O2 diffusion constants were measured. Nine physicochemical properties of each of the polymers were used in the trial basis set for QSPR model construction. The linear cross-correlation matrices were constructed and investigated for colinearity among the members of the training sets. Common water diffusion measures for a limited training set of six polymers was used to construct a "semi-QSPR" model. The bulk modulus of the polymer was overwhelmingly found to be the dominant physicochemical polymer property that governs CO2, N2 and O2 diffusion. Some secondary physicochemical properties controlling diffusion, including conformational entropy, were also identified as correlation descriptors. Very significant QSPR diffusion models were constructed for all three gases. Cohesive energy was identified as the main correlation physicochemical property with aqueous diffusion measures. The dominant role of polymer bulk modulus on gaseous diffusion makes it difficult to develop criteria for selective transport of gases through polymers. Moreover, high bulk moduli are predicted to be necessary for effective gas barrier materials. This property requirement may limit the processing and packaging features of the material. Aqueous diffusion in polymers may occur by a different mechanism than gaseous diffusion since bulk modulus does not correlate with aqueous diffusion, but rather cohesive energy of the polymer.
Dring, Ann M.; Anderson, Linnea E.; Qamar, Saima; Stoner, Matthew A.
2010-01-01
Constitutive androstane receptor (CAR) and pregnane X receptor (PXR) are closely related orphan nuclear receptor proteins that share several ligands and target overlapping sets of genes involved in homeostasis and all phases of drug metabolism. CAR and PXR are involved in the development of certain diseases, including diabetes, metabolic syndrome and obesity. Ligand screens for these receptors so far have typically focused on steroid hormone analogs with pharmacophore-based approaches, only to find relatively few new hits. Multiple CAR isoforms have been detected in human liver, with the most abundant being the constitutively active reference, CAR1, and the ligand-dependent isoform CAR3. It has been assumed that any compound that binds CAR1 should also activate CAR3, and so CAR3 can be used as a ligand-activated surrogate for CAR1 studies. The possibility of CAR3-specific ligands has not, so far, been addressed. To investigate the differences between CAR1, CAR3 and PXR, and to look for more CAR ligands that may be of use in quantitative structure-activity relationship (QSAR) studies, we performed a luciferase transactivation assay screen of 60 mostly non-steroid compounds. Known active compounds with different core chemistries were chosen as starting points and structural variants were rationally selected for screening. Distinct differences in agonist versus inverse agonist/antagonist effects were seen in 49 compounds that had some ligand effect on at least one receptor and 18 that had effects on all three receptors; eight were CAR1 ligands only, three were CAR3 only ligands and four affected PXR only. This work provides evidence for new CAR ligands, some of which have CAR3-specific effects, and provides observational data on CAR and PXR ligands with which to inform in silico strategies. Compounds that demonstrated unique activity on any one receptor are potentially valuable diagnostic tools for the investigation of in vivo molecular targets. PMID:20869355
ERIC Educational Resources Information Center
Hilk, Caroline Lual
2013-01-01
This series of meta-analyses investigates the effects of social interdependence (cooperative, competitive, and individualistic learning structures) on achievement and peer relationships among college students. This study quantitatively synthesized the literature on the effects of social interdependence on achievement and peer relationship outcomes…
Du, Hongying; Wang, Jie; Yao, Xiaojun; Hu, Zhide
2009-01-01
The heuristic method (HM) and support vector machine (SVM) were used to construct quantitative structure-retention relationship models by a series of compounds to predict the gradient retention times of reversed-phase high-performance liquid chromatography (HPLC) in three different columns. The aims of this investigation were to predict the retention times of multifarious compounds, to find the main properties of the three columns, and to indicate the theory of separation procedures. In our method, we correlated the retention times of many diverse structural analytes in three columns (Symmetry C18, Chromolith, and SG-MIX) with their representative molecular descriptors, calculated from the molecular structures alone. HM was used to select the most important molecular descriptors and build linear regression models. Furthermore, non-linear regression models were built using the SVM method; the performance of the SVM models were better than that of the HM models, and the prediction results were in good agreement with the experimental values. This paper could give some insights into the factors that were likely to govern the gradient retention process of the three investigated HPLC columns, which could theoretically supervise the practical experiment.
Filev, Filip; Oezcan, Ceprail; Feuerstacke, Jana; Linke, Stephan J; Wulff, Birgit; Hellwinkel, Olaf J C
2017-03-01
Dextran is added to corneal culture medium for at least 8 h prior to transplantation to ensure that the cornea is osmotically dehydrated. It is presumed that dextran has a certain toxic effect on corneal endothelium but the degree and the kinetics of this effect have not been quantified so far. We consider that such data regarding the toxicity of dextran on the corneal endothelium could have an impact on scheduling and logistics of corneal preparation in eye banking. In retrospective statistic analyses, we compared the progress of corneal endothelium (endothelium cell loss per day) of 1334 organ-cultured corneal explants in media with and without dextran. Also, the influence of donor-age, sex and cause of death on the observed dextran-mediated effect on endothelial cell counts was studied. Corneas cultured in dextran-free medium showed a mean endothelium cell count decrease of 0.7% per day. Dextran supplementation led to a mean endothelium cell loss of 2.01% per day; this reflects an increase by the factor of 2.9. The toxic impact of dextran was found to be time dependent; while the prevailing part of the effect was observed within the first 24 h after dextran-addition. Donor age, sex and cause of death did not seem to have an influence on the dextran-mediated toxicity. Based on these findings, we could design an algorithm which approximately describes the kinetics of dextran-toxicity. We reproduced the previously reported toxic effect of dextran on the corneal endothelium in vitro. Additionally, this is the first work that provides an algorithmic instrument for the semi-quantitative calculation of the putative endothelium cell count decrease in dextran containing medium for a given incubation time and could thus influence the time management and planning of corneal transplantations.
Lee, Yunho; von Gunten, Urs
2012-12-01
Various oxidants such as chlorine, chlorine dioxide, ferrate(VI), ozone, and hydroxyl radicals can be applied for eliminating organic micropollutant by oxidative transformation during water treatment in systems such as drinking water, wastewater, and water reuse. Over the last decades, many second-order rate constants (k) have been determined for the reaction of these oxidants with model compounds and micropollutants. Good correlations (quantitative structure-activity relationships or QSARs) are often found between the k-values for an oxidation reaction of closely related compounds (i.e. having a common organic functional group) and substituent descriptor variables such as Hammett or Taft sigma constants. In this study, we developed QSARs for the oxidation of organic and some inorganic compounds and organic micropollutants transformation during oxidative water treatment. A number of 18 QSARs were developed based on overall 412 k-values for the reaction of chlorine, chlorine dioxide, ferrate, and ozone with organic compounds containing electron-rich moieties such as phenols, anilines, olefins, and amines. On average, 303 out of 412 (74%) k-values were predicted by these QSARs within a factor of 1/3-3 compared to the measured values. For HO(·) reactions, some principles and estimation methods of k-values (e.g. the Group Contribution Method) are discussed. The developed QSARs and the Group Contribution Method could be used to predict the k-values for various emerging organic micropollutants. As a demonstration, 39 out of 45 (87%) predicted k-values were found within a factor 1/3-3 compared to the measured values for the selected emerging micropollutants. Finally, it is discussed how the uncertainty in the predicted k-values using the QSARs affects the accuracy of prediction for micropollutant elimination during oxidative water treatment. Copyright © 2012 Elsevier Ltd. All rights reserved.
Identifying and designing chemicals with minimal acute aquatic toxicity.
Kostal, Jakub; Voutchkova-Kostal, Adelina; Anastas, Paul T; Zimmerman, Julie Beth
2015-05-19
Industrial ecology has revolutionized our understanding of material stocks and flows in our economy and society. For this important discipline to have even deeper impact, we must understand the inherent nature of these materials in terms of human health and the environment. This paper focuses on methods to design synthetic chemicals to reduce their intrinsic ability to cause adverse consequence to the biosphere. Advances in the fields of computational chemistry and molecular toxicology in recent decades allow the development of predictive models that inform the design of molecules with reduced potential to be toxic to humans or the environment. The approach presented herein builds on the important work in quantitative structure-activity relationships by linking toxicological and chemical mechanistic insights to the identification of critical physical-chemical properties needed to be modified. This in silico approach yields design guidelines using boundary values for physiochemical properties. Acute aquatic toxicity serves as a model endpoint in this study. Defining value ranges for properties related to bioavailability and reactivity eliminates 99% of the chemicals in the highest concern for acute aquatic toxicity category. This approach and its future implementations are expected to yield very powerful tools for life cycle assessment practitioners and molecular designers that allow rapid assessment of multiple environmental and human health endpoints and inform modifications to minimize hazard.
Quantitative theory of hydrophobic effect as a driving force of protein structure
Perunov, Nikolay; England, Jeremy L
2014-01-01
Various studies suggest that the hydrophobic effect plays a major role in driving the folding of proteins. In the past, however, it has been challenging to translate this understanding into a predictive, quantitative theory of how the full pattern of sequence hydrophobicity in a protein shapes functionally important features of its tertiary structure. Here, we extend and apply such a phenomenological theory of the sequence-structure relationship in globular protein domains, which had previously been applied to the study of allosteric motion. In an effort to optimize parameters for the model, we first analyze the patterns of backbone burial found in single-domain crystal structures, and discover that classic hydrophobicity scales derived from bulk physicochemical properties of amino acids are already nearly optimal for prediction of burial using the model. Subsequently, we apply the model to studying structural fluctuations in proteins and establish a means of identifying ligand-binding and protein–protein interaction sites using this approach. PMID:24408023
tThe mode of toxic action (MOA) has been recognized as a key determinant of chemical toxicity andas an alternative to chemical class-based predictive toxicity modeling. However, the development ofquantitative structure activity relationship (QSAR) and other models has been limite...
Puri, Swati; Chickos, James S; Welsh, William J
2002-01-01
Three-dimensional Quantitative Structure-Property Relationship (QSPR) models have been derived using Comparative Molecular Field Analysis (CoMFA) to correlate the vaporization enthalpies of a representative set of polychlorinated biphenyls (PCBs) at 298.15 K with their CoMFA-calculated physicochemical properties. Various alignment schemes, such as inertial, as is, and atom fit, were employed in this study. The CoMFA models were also developed using different partial charge formalisms, namely, electrostatic potential (ESP) charges and Gasteiger-Marsili (GM) charges. The most predictive model for vaporization enthalpy (Delta(vap)H(m)(298.15 K)), with atom fit alignment and Gasteiger-Marsili charges, yielded r2 values 0.852 (cross-validated) and 0.996 (conventional). The vaporization enthalpies of PCBs increased with the number of chlorine atoms and were found to be larger for the meta- and para-substituted isomers. This model was used to predict Delta(vap)H(m)(298.15 K) of the entire set of 209 PCB congeners.
Böhme, Alexander; Thaens, Diana; Schramm, Franziska; Paschke, Albrecht; Schüürmann, Gerrit
2010-12-20
A recently introduced chemoassay has been used to determine second-order rate constants of the electrophile-nucleophile reaction of 15 α,β-unsaturated aldehydes with glutathione. The respective kGSH values vary for more than 3 orders of magnitude, and are within the range determined previously for 31 α,β-unsaturated ketones and esters. Structure-reactivity analyses yield distinct relationships between kGSH and structural features of the compounds. Moreover, increasing kGSH increases the aldehyde toxicity toward ciliates in terms of 48 h-EC50 values (effective concentration yielding 50% growth inhibition of Tetrahymena pyriformis within 48 h). A respective log-log regression equation including both kGSH and the octanol/water partition coefficient, Kow, yields a squared correlation coefficient of 0.96. Comparative analysis with corresponding data for 15 ketones and 16 esters reveals systematic differences between the three compound classes with regard to the individual contributions of hydrophobicity and electrophilic reactivity to aquatic toxicity. The former is particularly pronounced for aldehydes, while the ester toxicity is largely governed by reactivity, with ketones showing an intermediate pattern that is more similar to the one of esters than of aldehydes. It follows that within the Michael acceptor domain of α,β-unsaturated carbonyls, a distinction between aldehydes and nonaldehydic derivatives appears necessary when employing electrophilic reactivity as a component for the quantitative prediction of their reactive toxicity toward aquatic organisms.
Lipid reducing activity and toxicity profiles of a library of polyphenol derivatives.
Urbatzka, Ralph; Freitas, Sara; Palmeira, Andreia; Almeida, Tiago; Moreira, João; Azevedo, Carlos; Afonso, Carlos; Correia-da-Silva, Marta; Sousa, Emilia; Pinto, Madalena; Vasconcelos, Vitor
2018-05-10
Obesity is an increasing epidemic worldwide and novel treatments are urgently needed. Polyphenols are natural compounds derived from plants, which are known in particular for their antioxidant properties. However, some polyphenols were described to possess anti-obesity activities in vitro and in vivo. In this study, we aimed to screen a library of 85 polyphenol derivatives for their lipid reducing activity and toxicity. Compounds were analyzed at 5 μM with the zebrafish Nile red fluorescence fat metabolism assay and for general toxicity in vivo. To improve the safety profile, compounds were screened at 50 μM in murine preadipocytes in vitro for cytotoxicity. Obtained activity data were used to create a 2D-QSAR (quantitative structure activity relationship) model. 38 polyphenols showed strong lipid reducing activity. Toxicity analysis revealed that 18 of them did not show any toxicity in vitro or in vivo. QSAR analysis revealed the importance of the number of rings, fractional partial positively charged surface area, relative positive charge, relative number of oxygen atoms, and partial negative surface area for lipid-reducing activity. The five most potent compounds with EC 50 values in the nanomolar range for lipid reducing activity and without any toxic effects are strong candidates for future research and development into anti-obesity drugs. Molecular profiling for fasn, sirt1, mtp and ppary revealed one compound that reduced significantly fasn mRNA expression. Copyright © 2018 Elsevier Masson SAS. All rights reserved.
QSAR Modeling of Rat Acute Toxicity by Oral Exposure
Zhu, Hao; Martin, Todd M.; Ye, Lin; Sedykh, Alexander; Young, Douglas M.; Tropsha, Alexander
2009-01-01
Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. In this study, a comprehensive dataset of 7,385 compounds with their most conservative lethal dose (LD50) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire dataset was selected that included all 3,472 compounds used in the TOPKAT’s training set. The remaining 3,913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R2 of linear regression between actual and predicted LD50 values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R2 ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD50 for every compound using all 5 models. The consensus models afforded higher prediction accuracy for the external validation dataset with the higher coverage as compared to individual constituent models. The validated consensus LD50 models developed in this study can be used as reliable computational predictors of in vivo acute toxicity. PMID:19845371
Wang, Yi; Shao, Yonghua; Wang, Yangyang; Fan, Lingling; Yu, Xiang; Zhi, Xiaoyan; Yang, Chun; Qu, Huan; Yao, Xiaojun; Xu, Hui
2012-08-29
In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, 33 isoxazoline and oxime derivatives of podophyllotoxin modified in the C and D rings were synthesized and their structures were characterized by Proton nuclear magnetic resonance ((1)H NMR), high-resolution mass spectrometry (HRMS), electrospray ionization-mass spectrometry (ESI-MS), optical rotation, melting point (mp), and infrared (IR) spectroscopy. The stereochemical configurations of compounds 5e, 5f, and 9f were unambiguously determined by X-ray crystallography. Their insecticidal activity was evaluated against the pre-third-instar larvae of northern armyworm, Mythimna separata (Walker), in vivo. Compounds 5e, 9c, 11g, and 11h especially exhibited more promising insecticidal activity than toosendanin, a commercial botanical insecticide extracted from Melia azedarach . A genetic algorithm combined with multiple linear regression (GA-MLR) calculation is performed by the MOBY DIGS package. Five selected descriptors are as follows: one two-dimensional (2D) autocorrelation descriptor (GATS4e), one edge adjacency indice (EEig06x), one RDF descriptor (RDF080v), one three-dimensional (3D) MoRSE descriptor (Mor09v), and one atom-centered fragment (H-052) descriptor. Quantitative structure-activity relationship studies demonstrated that the insecticidal activity of these compounds was mainly influenced by many factors, such as electronic distribution, steric factors, etc. For this model, the standard deviation error in prediction (SDEP) is 0.0592, the correlation coefficient (R(2)) is 0.861, and the leave-one-out cross-validation correlation coefficient (Q(2)loo) is 0.797.
Kittelmann, Jörg; Lang, Katharina M H; Ottens, Marcel; Hubbuch, Jürgen
2017-01-27
Quantitative structure-activity relationship (QSAR) modeling for prediction of biomolecule parameters has become an established technique in chromatographic purification process design. Unfortunately available descriptor sets fail to describe the orientation of biomolecules and the effects of ionic strength in the mobile phase on the interaction with the stationary phase. The literature describes several special descriptors used for chromatographic retention modeling, all of these do not describe the screening of electrostatic potential by the mobile phase in use. In this work we introduce two new approaches of descriptor calculations, namely surface patches and plane projection, which capture an oriented binding to charged surfaces and steric hindrance of the interaction with chromatographic ligands with regard to electrostatic potential screening by mobile phase ions. We present the use of the developed descriptor sets for predictive modeling of Langmuir isotherms for proteins at different pH values between pH 5 and 10 and varying ionic strength in the range of 10-100mM. The resulting model has a high correlation of calculated descriptors and experimental results, with a coefficient of determination of 0.82 and a predictive coefficient of determination of 0.92 for unknown molecular structures and conditions. The agreement of calculated molecular interaction orientations with both, experimental results as well as molecular dynamic simulations from literature is shown. The developed descriptors provide the means for improved QSAR models of chromatographic processes, as they reflect the complex interactions of biomolecules with chromatographic phases. Copyright © 2016 Elsevier B.V. All rights reserved.
Satpathy, Raghunath; Guru, R K; Behera, R; Nayak, B
2015-01-01
Boswellic acid consists of a series of pentacyclic triterpene molecules that are produced by the plant Boswellia serrata. The potential applications of Bowsellic acid for treatment of cancer have been focused here. To predict the property of the bowsellic acid derivatives as anticancer compounds by various computational approaches. In this work, all total 65 derivatives of bowsellic acids from the PubChem database were considered for the study. After energy minimization of the ligands various types of molecular descriptors were computed and corresponding two-dimensional quantitative structure activity relationship (QSAR) models were obtained by taking Andrews coefficient as the dependent variable. Different types of comparative analysis were used for QSAR study are multiple linear regression, partial least squares, support vector machines and artificial neural network. From the study geometrical descriptors shows the highest correlation coefficient, which indicates the binding factor of the compound. To evaluate the anticancer property molecular docking study of six selected ligands based on Andrews affinity were performed with nuclear factor-kappa protein kinase (Protein Data Bank ID 4G3D), which is an established therapeutic target for cancers. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound.
The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity.
Colombo, Andrea; Benfenati, Emilio; Karelson, Mati; Maran, Uko
2008-06-01
One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (R(2)(cv) approximately 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.
Magnuson, Matthew L; Speth, Thomas F
2005-10-01
Granular activated carbon is a frequently explored technology for removing synthetic organic contaminants from drinking water sources. The success of this technology relies on a number of factors based not only on the adsorptive properties of the contaminant but also on properties of the water itself, notably the presence of substances in the water which compete for adsorption sites. Because it is impractical to perform field-scale evaluations for all possible contaminants, the pore surface diffusion model (PSDM) has been developed and used to predict activated carbon column performance using single-solute isotherm data as inputs. Many assumptions are built into this model to account for kinetics of adsorption and competition for adsorption sites. This work further evaluates and expands this model, through the use of quantitative structure-property relationships (QSPRs) to predict the effect of natural organic matter fouling on activated carbon adsorption of specific contaminants. The QSPRs developed are based on a combination of calculated topographical indices and quantum chemical parameters. The QSPRs were evaluated in terms of their statistical predictive ability,the physical significance of the descriptors, and by comparison with field data. The QSPR-enhanced PSDM was judged to give results better than what could previously be obtained.
Philipp, Bodo; Hoff, Malte; Germa, Florence; Schink, Bernhard; Beimborn, Dieter; Mersch-Sundermann, Volker
2007-02-15
Prediction of the biodegradability of organic compounds is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. We combined quantitative structure-activity relationships (QSAR) with the systematic collection of biochemical knowledge to establish rules for the prediction of aerobic biodegradation of N-heterocycles. Validated biodegradation data of 194 N-heterocyclic compounds were analyzed using the MULTICASE-method which delivered two QSAR models based on 17 activating (OSAR 1) and on 16 inactivating molecular fragments (GSAR 2), which were statistically significantly linked to efficient or poor biodegradability, respectively. The percentages of correct classifications were over 99% for both models, and cross-validation resulted in 67.9% (GSAR 1) and 70.4% (OSAR 2) correct predictions. Biochemical interpretation of the activating and inactivating characteristics of the molecular fragments delivered plausible mechanistic interpretations and enabled us to establish the following biodegradation rules: (1) Target sites for amidohydrolases and for cytochrome P450 monooxygenases enhance biodegradation of nonaromatic N-heterocycles. (2) Target sites for molybdenum hydroxylases enhance biodegradation of aromatic N-heterocycles. (3) Target sites for hydratation by an urocanase-like mechanism enhance biodegradation of imidazoles. Our complementary approach represents a feasible strategy for generating concrete rules for the prediction of biodegradability of organic compounds.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Harper, Bryan; Thomas, Dennis G.; Chikkagoudar, Satish
The integration of rapid assays, large data sets, informatics and modeling can overcome current barriers in understanding nanomaterial structure-toxicity relationships by providing a weight-of-the-evidence mechanism to generate hazard rankings for nanomaterials. Here we present the use of a rapid, low-cost assay to perform screening-level toxicity evaluations of nanomaterials in vivo. Calculated EZ Metric scores, a combined measure of morbidity and mortality, were established at realistic exposure levels and used to develop a predictive model of nanomaterial toxicity. Hazard ranking and clustering analysis of 68 diverse nanomaterials revealed distinct patterns of toxicity related to both core composition and outermost surface chemistrymore » of nanomaterials. The resulting clusters guided the development of a predictive model of gold nanoparticle toxicity to embryonic zebrafish. In addition, our findings suggest that risk assessments based on the size and core composition of nanomaterials alone may be wholly inappropriate, especially when considering complex engineered nanomaterials. These findings reveal the need to expeditiously increase the availability of quantitative measures of nanomaterial hazard and broaden the sharing of that data and knowledge to support predictive modeling. In addition, research should continue to focus on methodologies for developing predictive models of nanomaterial hazard based on sub-lethal responses to low dose exposures.« less
Harper, Bryan; Thomas, Dennis G.; Chikkagoudar, Satish; ...
2015-06-04
The integration of rapid assays, large data sets, informatics and modeling can overcome current barriers in understanding nanomaterial structure-toxicity relationships by providing a weight-of-the-evidence mechanism to generate hazard rankings for nanomaterials. Here we present the use of a rapid, low-cost assay to perform screening-level toxicity evaluations of nanomaterials in vivo. Calculated EZ Metric scores, a combined measure of morbidity and mortality, were established at realistic exposure levels and used to develop a predictive model of nanomaterial toxicity. Hazard ranking and clustering analysis of 68 diverse nanomaterials revealed distinct patterns of toxicity related to both core composition and outermost surface chemistrymore » of nanomaterials. The resulting clusters guided the development of a predictive model of gold nanoparticle toxicity to embryonic zebrafish. In addition, our findings suggest that risk assessments based on the size and core composition of nanomaterials alone may be wholly inappropriate, especially when considering complex engineered nanomaterials. These findings reveal the need to expeditiously increase the availability of quantitative measures of nanomaterial hazard and broaden the sharing of that data and knowledge to support predictive modeling. In addition, research should continue to focus on methodologies for developing predictive models of nanomaterial hazard based on sub-lethal responses to low dose exposures.« less
Mizukoshi, K; Nakamura, T; Oba, A
2016-08-01
The skin contains an undulating structure called the dermal papillary structure between the border of the epidermis and dermis. The physiological importance of the dermal papillary structures has been discussed, however, the dermal papillary structures have never been evaluated for their contribution to skin appearance. In this study, we investigated the correlation between the dermal papillary structure and skin color and elasticity. In addition, the relationship was validated with skin model experiments. The dermal papillary structures in the skin of the female cheek were quantitatively measured by in vivo confocal laser scanning microscopy images. In addition, the skin color and elasticity were measured at the same site. A skin model with dermal papilla-like structures was created by referring to the optical and shape properties of the skin using agar gel and a scattering sheet. Correlations were found between the dermal papillary structures and skin color irregularity and skin elasticity. These relationships were verified by the experiments employing a skin model. The results of this study indicated that the dermal papillary structure is also an important factor for skin appearance such as color and elasticity. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Salamat, Sara; Hutchings, John; Kwong, Clemens; Magnussen, John; Hancock, Mark J
2016-01-01
To assess the relationship between quantitative measures of disc height and signal intensity with the Pfirrmann disc degeneration scoring system and to test the inter-rater reliability of the quantitative measures. Participants were 76 people who had recently recovered from their last episode of acute low back pain and underwent MRI scan on a single 3T machine. At all 380 lumbar discs, quantitative measures of disc height and signal intensity were made by 2 independent raters and compared to Pfirrmann scores from a single radiologist. For quantitative measures of disc height and signal intensity a "raw" score and 2 adjusted ratios were calculated and the relationship with Pfirrmann scores was assessed. The inter-tester reliability of quantitative measures was also investigated. There was a strong linear relationship between quantitative disc signal intensity and Pfirrmann scores for grades 1-4, but not for grades 4 and 5. For disc height only, Pfirrmann grade 5 had significantly reduced disc height compared to all other grades. Results were similar regardless of whether raw or adjusted scores were used. Inter-rater reliability for the quantitative measures was excellent (ICC > 0.97). Quantitative measures of disc signal intensity were strongly related to Pfirrmann scores from grade 1 to 4; however disc height only differentiated between grade 4 and 5 Pfirrmann scores. Using adjusted ratios for quantitative measures of disc height or signal intensity did not significantly alter the relationship with Pfirrmann scores.
NASA Technical Reports Server (NTRS)
Johnson, H.; Kenley, R. A.; Rynard, C.; Golub, M. A.
1985-01-01
Quantitative structure-activity relationships were derived for acetyl- and butyrylcholinesterase inhibition by various organophosphorus esters. Bimolecular inhibition rate constants correlate well with hydrophobic substituent constants, and with the presence or absence of cationic groups on the inhibitor, but not with steric substituent constants. CNDO/2 calculations were performed on a separate set of organophosphorus esters, RR-primeP(O)X, where R and R-prime are alkyl and/or alkoxy groups and X is fluorine, chlorine or a phenoxy group. For each subset with the same X, the CNDO-derived net atomic charge at the central phosphorus atom in the ester correlates well with the alkaline hydrolysis rate constant. For the whole set of esters with different X, two equations were derived that relate either charge and leaving group steric bulk, or orbital energy and bond order to the hydrolysis rate constant.
Discriminating toxicant classes by mode of action. 1. (Eco)toxicity profiles.
Nendza, Monika; Wenzel, Andrea
2006-05-01
Predictive toxicology, particularly quantitative structure-activity relationships (QSARs), require classification of chemicals by mode of action (MOA). MOA is, however, not a constant property of a compound but it varies between species and may change with concentration and duration of exposure. A battery of MOA-specific in-vitro and low-complexity assays, featuring biomolecular targets for major classes of environmental pollutants, provides characteristic responses for (1.) classification of chemicals by MOA, (2.) identification of (eco)toxicity profiles of chemicals, (3.) identification of chemicals with specific MOAs, (4.) indication of most sensitive species, (5.) identification of chemicals that are outliers in QSARs and (6.) selection of appropriate QSARs for predictions. Chemicals covering nine distinct modes of toxic action (non-polar non-specific toxicants (n=14), polar non-specific toxicants (n=18), uncouplers of oxidative phosphorylation (n=25), inhibitors of photosynthesis (n=15), inhibitors of acetylcholinesterase (n=14), inhibitors of respiration (n=3), thiol-alkylating agents (n=9), reactives (irritants) (n=8), estrogen receptor agonists (n=9)) were tested for cytotoxicity in the neutralred assay, oxygen consumption in isolated mitochondria, oxygen production in algae, inhibition of AChE, reaction with GSH and activity in the yeast estrogen receptor assay. Data on in-vivo aquatic toxicity (LC50, EC50) towards fish, daphnids, algae and bacteria were collected from the literature for reasons of comparison and reference scaling. In the MOA-specific in-vitro test battery, most test chemicals are specifically active at low concentrations, though multiple effects do occur. Graphical and statistical evaluation of the individual classes versus MOA 1 (non-polar non-specific toxicants) identifies interactions related to predominant MOA. Discriminant analyses (DA) on subsets of the data revealed correct classifications between 70% (in-vivo data) and >90% (in
ERIC Educational Resources Information Center
Yu, Chong Ho
Although quantitative research methodology is widely applied by psychological researchers, there is a common misconception that quantitative research is based on logical positivism. This paper examines the relationship between quantitative research and eight major notions of logical positivism: (1) verification; (2) pro-observation; (3)…
Relationships between chemical hydrophobicity and toxicity have been shown for nearly 100 years in both mammals and fish, typically using the log of the octanol:water partition coefficient (kow). The current study reassessed the influence of mode of action (MOA) on aquatic toxici...
In-silico structure activity relationship study of toxicity endpoints by QSAR modeling (SOT)
Several thousand chemicals were tested in 700 toxicity-related in-vitro HTS bioassays through the EPA’s ToxCast and Tox21 projects. This chemical set only covers a portion of the chemical space of interest for environmental exposure, leading to a need to fill data gaps with alter...
Pinto, Erika G; Santos, Isabela O; Schmidt, Thomas J; Borborema, Samanta E T; Ferreira, Vitor F; Rocha, David R; Tempone, Andre G
2014-01-01
Naphtoquinones have been used as promising scaffolds for drug design studies against protozoan parasites. Considering the highly toxic and limited therapeutic arsenal, the global negligence with tropical diseases and the elevated prevalence of co-morbidities especially in developing countries, the parasitic diseases caused by various Leishmania species (leishmaniasis) became a significant public health threat in 98 countries. The aim of this work was the evaluation of antileishmanial in vitro potential of thirty-six 2-hydroxy-3-phenylsulfanylmethyl-[1,4]-naphthoquinones obtained by a three component reaction of lawsone, the appropriate aldehyde and thiols adequately substituted, exploiting the in situ generation of o-quinonemethides (o-QM) via the Knoevenagel condensation. The antileishmanial activity of the naphthoquinone derivatives was evaluated against promastigotes and intracellular amastigotes of Leishmania (Leishmania) infantum and their cytotoxicity was verified in mammalian cells. Among the thirty-six compounds, twenty-seven were effective against promastigotes, with IC50 values ranging from 8 to 189 µM; fourteen compounds eliminated the intracellular amastigotes, with IC50 values ranging from 12 to 65 µM. The compounds containing the phenyl groups at R1 and R2 and with the fluorine substituent at the phenyl ring at R2, rendered the most promising activity, demonstrating a selectivity index higher than 15 against amastigotes. A QSAR (quantitative structure activity relationship) analysis yielded insights into general structural requirements for activity of most compounds in the series. Considering the in vitro antileishmanial potential of 2-hydroxy-3-phenylsulfanylmethyl-[1,4]-naphthoquinones and their structure-activity relationships, novel lead candidates could be exploited in future drug design studies for leishmaniasis.
Zhang, Shuang; Xue, Xiwen; Zhang, Liangren; Zhang, Lihe; Liu, Zhenming
2015-12-01
In the past decade, the discovery, synthesis, and evaluation for hundreds of CD38 covalent and non-covalent inhibitors has been reported sequentially by our group and partners; however, a systematic structure-based guidance is still lacking for rational design of CD38 inhibitor. Here, we carried out a comparative analysis of pharmacophore features and quantitative structure-activity relationships for CD38 inhibitors. The results uncover that the essential interactions between key residues and covalent/non-covalent CD38 inhibitors include (i) hydrogen bond and hydrophobic interactions with residues Glu226 and Trp125, (ii) electrostatic or hydrogen bond interaction with the positively charged residue Arg127 region, and (iii) the hydrophobic interaction with residue Trp189. For covalent inhibitors, besides the covalent effect with residue Glu226, the electrostatic interaction with residue Arg127 is also necessary, while another hydrogen/non-bonded interaction with residues Trp125 and Trp189 can also be detected. By means of the SYBYL multifit alignment function, the best CoMFA and CoMSIA with CD38 covalent inhibitors presented cross-validated correlation coefficient values (q(2)) of 0.564 and 0.571, and non-cross-validated values (r(2)) of 0.967 and 0.971, respectively. The CD38 non-covalent inhibitors can be classified into five groups according to their chemical scaffolds, and the residues Glu226, Trp189, and Trp125 are indispensable for those non-covalent inhibitors binding to CD38, while the residues Ser126, Arg127, Asp155, Thr221, and Phe222 are also important. The best CoMFA and CoMSIA with the F12 analogues presented cross-validated correlation coefficient values (q(2)) of 0.469 and 0.454, and non-cross-validated values (r(2)) of 0.814 and 0.819, respectively. © 2015 John Wiley & Sons A/S.
Toxicity prediction of ionic liquids based on Daphnia magna by using density functional theory
NASA Astrophysics Data System (ADS)
Nu’aim, M. N.; Bustam, M. A.
2018-04-01
By using a model called density functional theory, the toxicity of ionic liquids can be predicted and forecast. It is a theory that allowing the researcher to have a substantial tool for computation of the quantum state of atoms, molecules and solids, and molecular dynamics which also known as computer simulation method. It can be done by using structural feature based quantum chemical reactivity descriptor. The identification of ionic liquids and its Log[EC50] data are from literature data that available in Ismail Hossain thesis entitled “Synthesis, Characterization and Quantitative Structure Toxicity Relationship of Imidazolium, Pyridinium and Ammonium Based Ionic Liquids”. Each cation and anion of the ionic liquids were optimized and calculated. The geometry optimization and calculation from the software, produce the value of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). From the value of HOMO and LUMO, the value for other toxicity descriptors were obtained according to their formulas. The toxicity descriptor that involves are electrophilicity index, HOMO, LUMO, energy gap, chemical potential, hardness and electronegativity. The interrelation between the descriptors are being determined by using a multiple linear regression (MLR). From this MLR, all descriptors being analyzed and the descriptors that are significant were chosen. In order to develop the finest model equation for toxicity prediction of ionic liquids, the selected descriptors that are significant were used. The validation of model equation was performed with the Log[EC50] data from the literature and the final model equation was developed. A bigger range of ionic liquids which nearly 108 of ionic liquids can be predicted from this model equation.
Persistence and dioxin-like toxicity of carbazole and chlorocarbazoles in soil.
Mumbo, John; Henkelmann, Bernhard; Abdelaziz, Ahmed; Pfister, Gerd; Nguyen, Nghia; Schroll, Reiner; Munch, Jean Charles; Schramm, Karl-Werner
2015-01-01
Halogenated carbazoles have recently been detected in soil and water samples, but their environmental effects and fate are unknown. Eighty-four soil samples obtained from a site with no recorded history of pollution were used to assess the persistence and dioxin-like toxicity of carbazole and chlorocarbazoles in soil under controlled conditions for 15 months. Soil samples were divided into two temperature conditions, 15 and 20 °C, both under fluctuating soil moisture conditions comprising 19 and 44 drying-rewetting cycles, respectively. This was characterized by natural water loss by evaporation and rewetting to -15 kPa. Accelerated solvent extraction (ASE) and cleanup were performed after incubation. Identification and quantification were done using high-resolution gas chromatogram/mass spectrometer (HRGC/MS), while dioxin-like toxicity was determined by ethoxyresorufin-O-deethylase (EROD) induction in H4IIA rat hepatoma cells assay and multidimensional quantitative structure-activity relationships (mQSAR) modelling. Carbazole, 3-chlorocarbazole and 3,6-dichlorocarbazole were detected including trichlorocarbazole not previously reported in soils. Carbazole and 3-chlorocarbazole showed significant dissipation at 15 °C but not at 20 °C incubating conditions indicating that low temperature could be suitable for dissipation of carbazole and chlorocarbazoles. 3,6-Dichlorocarbazole was resistant at both conditions. Trichlorocarbazole however exhibited a tendency to increase in concentration with time. 3-Chlorocarbazole, 3,6-dibromocarbazole and selected soil extracts exhibited EROD activity. Dioxin-like toxicity did not decrease significantly with time, whereas the sum chlorocarbazole toxic equivalence concentrations (∑TEQ) did not contribute significantly to the soil assay dioxin-like toxicity equivalent concentrations (TCDD-EQ). Carbazole and chlorocarbazoles are persistent with the latter also toxic in natural conditions.
Tian, Dayong; Lin, Zhifen; Yin, Daqiang; Zhang, Yalei; Kong, Deyang
2012-02-01
Environmental contaminants are usually encountered as mixtures, and many of these mixtures yield synergistic or antagonistic effects attributable to an intracellular chemical reaction that pose a potential threat on ecological systems. However, how atomic charges of individual chemicals determine their intracellular chemical reactions, and then determine the joint effects for mixtures containing reactive toxicants, is not well understood. To address this issue, the joint effects between cyanogenic toxicants and aldehydes on Photobacterium phosphoreum were observed in the present study. Their toxicological joint effects differed from one another. This difference is inherently related to the two atomic charges of the individual chemicals: the oxygen charge of -CHO (O(aldehyde toxicant)) in aldehyde toxicants and the carbon-atom charge of a carbon chain in the cyanogenic toxicant (C(cyanogenic toxicant)). Based on these two atomic charges, the following QSAR (quantitative structure-activity relationship) model was proposed: When (O(aldehyde toxicant) -C(cyanogenic toxicant) )> -0.125, the joint effect of equitoxic binary mixtures at median inhibition (TU, the sum of toxic units) can be calculated as TU = 1.00 ± 0.20; when (O(aldehyde toxicant) -C(cyanogenic toxicant) ) ≤ -0.125, the joint effect can be calculated using TU = - 27.6 x O (aldehyde toxicant) - 5.22 x C (cyanogenic toxicant) - 6.97 (n = 40, r = 0.887, SE = 0.195, F = 140, p < 0.001, q(2) (Loo) = 0.748; SE is the standard error of the regression, F is the F test statistic). The result provides insight into the relationship between the atomic charges and the joint effects for mixtures containing cyanogenic toxicants and aldehydes. This demonstrates that the essence of the joint effects resulting from intracellular chemical reactions depends on the atomic charges of individual chemicals. The present study provides a possible approach for the development of a QSAR model for mixtures containing reactive
Distinct C9orf72-Associated Dipeptide Repeat Structures Correlate with Neuronal Toxicity
Krans, Amy; Sawaya, Michael R.; Paulson, Henry L.; Todd, Peter K.; Barmada, Sami J.; Ivanova, Magdalena I.
2016-01-01
Hexanucleotide repeat expansions in C9orf72 are the most common inherited cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). The expansions elicit toxicity in part through repeat-associated non-AUG (RAN) translation of the intronic (GGGGCC)n sequence into dipeptide repeat-containing proteins (DPRs). Little is known, however, about the structural characteristics and aggregation propensities of the dipeptide units comprising DPRs. To address this question, we synthesized dipeptide units corresponding to the three sense-strand RAN translation products, analyzed their structures by circular dichroism, electron microscopy and dye binding assays, and assessed their relative toxicity when applied to primary cortical neurons. Short, glycine-arginine (GR)3 dipeptides formed spherical aggregates and selectively reduced neuronal survival compared to glycine-alanine (GA)3 and glycine-proline (GP)3 dipeptides. Doubling peptide length had little effect on the structure of GR or GP peptides, but (GA)6 peptides formed β-sheet rich aggregates that bound thioflavin T and Congo red yet lacked the typical fibrillar morphology of amyloids. Aging of (GA)6 dipeptides increased their β-sheet content and enhanced their toxicity when applied to neurons. We also observed that the relative toxicity of each tested dipeptide was proportional to peptide internalization. Our results demonstrate that different C9orf72-related dipeptides exhibit distinct structural properties that correlate with their relative toxicity. PMID:27776165
A Market-Basket Approach to Predict the Acute Aquatic Toxicity of Munitions and Energetic Materials.
Burgoon, Lyle D
2016-06-01
An ongoing challenge in chemical production, including the production of insensitive munitions and energetics, is the ability to make predictions about potential environmental hazards early in the process. To address this challenge, a quantitative structure activity relationship model was developed to predict acute fathead minnow toxicity of insensitive munitions and energetic materials. Computational predictive toxicology models like this one may be used to identify and prioritize environmentally safer materials early in their development. The developed model is based on the Apriori market-basket/frequent itemset mining approach to identify probabilistic prediction rules using chemical atom-pairs and the lethality data for 57 compounds from a fathead minnow acute toxicity assay. Lethality data were discretized into four categories based on the Globally Harmonized System of Classification and Labelling of Chemicals. Apriori identified toxicophores for categories two and three. The model classified 32 of the 57 compounds correctly, with a fivefold cross-validation classification rate of 74 %. A structure-based surrogate approach classified the remaining 25 chemicals correctly at 48 %. This result is unsurprising as these 25 chemicals were fairly unique within the larger set.
Handa, Koichi; Nakagome, Izumi; Yamaotsu, Noriyuki; Gouda, Hiroaki; Hirono, Shuichi
2015-01-01
The pregnane X receptor [PXR (NR1I2)] induces the expression of xenobiotic metabolic genes and transporter genes. In this study, we aimed to establish a computational method for quantifying the enzyme-inducing potencies of different compounds via their ability to activate PXR, for the application in drug discovery and development. To achieve this purpose, we developed a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) for predicting enzyme-inducing potencies, based on computer-ligand docking to multiple PXR protein structures sampled from the trajectory of a molecular dynamics simulation. Molecular mechanics-generalized born/surface area scores representing the ligand-protein-binding free energies were calculated for each ligand. As a result, the predicted enzyme-inducing potencies for compounds generated by the CoMFA model were in good agreement with the experimental values. Finally, we concluded that this 3D-QSAR model has the potential to predict the enzyme-inducing potencies of novel compounds with high precision and therefore has valuable applications in the early stages of the drug discovery process. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.
A Hierarchical Clustering Methodology for the Estimation of Toxicity
A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural sim...
Arning, Jürgen; Matzke, Marianne; Stolte, Stefan; Nehen, Frauke; Bottin-Weber, Ulrike; Böschen, Andrea; Abdulkarim, Salha; Jastorff, Bernd; Ranke, Johannes
2009-12-01
To demonstrate how baseline toxicity can be separated from other more specific modes of toxic action and to address possible pitfals when dealing with hydrophobic substances, the four isothiazol-3-one biocides N-methylisothiazol-3-one (MIT), 5-chloro-N-methylisothiazol-3-one (CIT), N-octylisothiazol-3-one (OIT), and 4,5-dichloro-N-octylisothiazol-3-one (DCOIT) as an example for reactive electrophilic xenobiotics were tested for their cytotoxic effects on the human hepatoblastoma cell line Hep G2, on the marine bacterium Vibrio fischeri, and on the limnic green alga Scenedesmus vacuolatus. In each of the three test systems, toxic effects were observed in a consistent pattern. The two chlorinated compounds and OIT were found to be significantly more toxic than MIT. As compared to baseline toxicants, the small and polar MIT and CIT exhibited pronounced excess toxicity in each of the three test systems that is presumably triggered by their intrinsic reactivity toward cellular thiols. In contrast, OIT and DCOIT showed mainly toxicities that could be explained by their hydrophobicity. Analyzing and comparing these results using the toxic ratio concept and with data that indicate a dramatic depletion of cellular glutathione levels after incubation with DCOIT reveals that for highly hydrophobic substances, baseline level toxicity in an assay for acute toxicity can lead to an oversight of other more specific modes of toxic action that may cause significant effects that might be less reversible than those caused by unreactive baseline toxicants. This possibility should be taken into account in the hazard assessment of chemicals that are both hydrophobic and reactive.
Paula, Stefan; Tabet, Michael R; Farr, Carol D; Norman, Andrew B; Ball, W James
2004-01-01
Human monoclonal antibodies (mAbs) designed for immunotherapy have a high potential for avoiding the complications that may result from human immune system responses to the introduction of nonhuman mAbs into patients. This study presents a characterization of cocaine/antibody interactions that determine the binding properties of the novel human sequence mAb 2E2 using three-dimensional quantitative structure-activity relationship (3D-QSAR) methodology. We have experimentally determined the binding affinities of mAb 2E2 for cocaine and 38 cocaine analogues. The K(d) of mAb 2E2 for cocaine was 4 nM, indicating a high affinity. Also, mAb 2E2 displayed good cocaine specificity, as reflected in its 10-, 1500-, and 25000-fold lower binding affinities for the three physiologically relevant cocaine metabolites benzoylecgonine, ecgonine methyl ester, and ecgonine, respectively. 3D-QSAR models of cocaine binding were developed by comparative molecular similarity index analysis (CoMSIA). A model of high statistical quality was generated showing that cocaine binds to mAb 2E2 in a sterically restricted binding site that leaves the methyl group attached to the ring nitrogen of cocaine solvent-exposed. The methyl ester group of cocaine appears to engage in attractive van der Waals interactions with mAb 2E2, whereas the phenyl group contributes to the binding primarily via hydrophobic interactions. The model further indicated that an increase in partial positive charge near the nitrogen proton and methyl ester carbonyl group enhances binding affinity and that the ester oxygen likely forms an intermolecular hydrogen bond with mAb 2E2. Overall, the cocaine binding properties of mAb 2E2 support its clinical potential for development as a treatment of cocaine overdose and addiction.
Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz
2015-10-06
In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.
Wang, Hong-Jaan; Pao, Li-Heng; Hsiong, Cheng-Huei; Shih, Tung-Yuan; Lee, Meei-Shyuan; Hu, Oliver Yoa-Pu
2014-03-01
This study aims to improve the drug oral bioavailability by co-administration with flavonoid inhibitors of the CYP2C isozyme and to establish qualitative and quantitative (QSAR) structure-activity relationships (SAR) between flavonoids and CYP2C. A total of 40 naturally occurring flavonoids were screened in vitro for CYP2C inhibition. Enzyme activity was determined by measuring conversion of tolbutamide to 4-hydroxytolbutamide by rat liver microsomes. The percent inhibition and IC50 of each flavonoid were calculated and used to develop SAR and QSAR. The most effective flavonoid was orally co-administered in vivo with a cholesterol-reducing drug, fluvastatin, which is normally metabolized by CYP2C. The most potent CYP2C inhibitor identified in vitro was tamarixetin (IC50 = 1.4 μM). This flavonoid enhanced the oral bioavailability of fluvastatin in vivo, producing a >2-fold increase in the area under the concentration-time curve and in the peak plasma concentration. SAR analysis indicated that the presence of a 2,3-double bond in the C ring, hydroxylation at positions 5, 6, and 7, and glycosylation had important effects on flavonoid-CYP2C interactions. These findings should prove useful for predicting the inhibition of CYP2C activity by other untested flavonoid-like compounds. In the present study, tamarixetin significantly inhibited CYP2C activity in vitro and in vivo. Thus, the use of tamarixetin could improve the therapeutic efficacy of drugs with low bioavailability.
2007-03-01
alkaloid piperine and 12 syn- thetic derivatives have been evaluated against epimas- tigote and amastigote forms of the protozoan parasite Trypanosoma...O. Kris- tiansen, P. Maienfisch, A. Pascual, and A. Rindlisbacher. 2001. Synthesis and structure-activity relationships of benzophenone hydrazone...Am. J. Trop. Med. Hyg. 22: 124Ð 129. Creemer, L. C., H. A. Kirst, J.W. Paschal, and T. V.Worden. 2000. Synthesis and insecticidal activity of spinosyn
Redmond, Haley; Thompson, Jonathan E
2011-04-21
In this work we describe and evaluate a simple scheme by which the refractive index (λ = 589 nm) of non-absorbing components common to secondary organic aerosols (SOA) may be predicted from molecular formula and density (g cm(-3)). The QSPR approach described is based on three parameters linked to refractive index-molecular polarizability, the ratio of mass density to molecular weight, and degree of unsaturation. After computing these quantities for a training set of 111 compounds common to atmospheric aerosols, multi-linear regression analysis was conducted to establish a quantitative relationship between the parameters and accepted value of refractive index. The resulting quantitative relationship can often estimate refractive index to ±0.01 when averaged across a variety of compound classes. A notable exception is for alcohols for which the model consistently underestimates refractive index. Homogenous internal mixtures can conceivably be addressed through use of either the volume or mole fraction mixing rules commonly used in the aerosol community. Predicted refractive indices reconstructed from chemical composition data presented in the literature generally agree with previous reports of SOA refractive index. Additionally, the predicted refractive indices lie near measured values we report for λ = 532 nm for SOA generated from vapors of α-pinene (R.I. 1.49-1.51) and toluene (R.I. 1.49-1.50). We envision the QSPR method may find use in reconstructing optical scattering of organic aerosols if mass composition data is known. Alternatively, the method described could be incorporated into in models of organic aerosol formation/phase partitioning to better constrain organic aerosol optical properties.
Potter, W R; Henderson, B W; Bellnier, D A; Pandey, R K; Vaughan, L A; Weishaupt, K R; Dougherty, T J
1999-11-01
An open three-compartment pharmacokinetic model was applied to the in vivo quantitative structure-activity relationship (QSAR) data of a homologous series of pyropheophorbide photosensitizers for photodynamic therapy (PDT). The physical model was a lipid compartment sandwiched between two identical aqueous compartments. The first compartment was assumed to clear irreversibly at a rate K0. The measured octanol-water partition coefficients, P(i) (where i is the number of carbons in the alkyl chain) and the clearance rate K0 determined the clearance kinetics of the drugs. Solving the coupled differential equations of the three-compartment model produced clearance kinetics for each of the sensitizers in each of the compartments. The third compartment was found to contain the target of PDT. This series of compounds is quite lipophilic. Therefore these drugs are found mainly in the second compartment. The drug level in the third compartment represents a small fraction of the tissue level and is thus not accessible to direct measurement by extraction. The second compartment of the model accurately predicted the clearance from the serum of mice of the hexyl ether of pyropheophorbide a, one member of this series of compounds. The diffusion and clearance rate constants were those found by fitting the pharmacokinetics of the third compartment to the QSAR data. This result validated the magnitude and mechanistic significance of the rate constants used to model the QSAR data. The PDT response to dose theory was applied to the kinetic behavior of the target compartment drug concentration. This produced a pharmacokinetic-based function connecting PDT response to dose as a function of time postinjection. This mechanistic dose-response function was fitted to published, single time point QSAR data for the pheophorbides. As a result, the PDT target threshold dose together with the predicted QSAR as a function of time postinjection was found.
Structure-activity relationships between sterols and their thermal stability in oil matrix.
Hu, Yinzhou; Xu, Junli; Huang, Weisu; Zhao, Yajing; Li, Maiquan; Wang, Mengmeng; Zheng, Lufei; Lu, Baiyi
2018-08-30
Structure-activity relationships between 20 sterols and their thermal stabilities were studied in a model oil system. All sterol degradations were found to be consistent with a first-order kinetic model with determination of coefficient (R 2 ) higher than 0.9444. The number of double bonds in the sterol structure was negatively correlated with the thermal stability of sterol, whereas the length of the branch chain was positively correlated with the thermal stability of sterol. A quantitative structure-activity relationship (QSAR) model to predict thermal stability of sterol was developed by using partial least squares regression (PLSR) combined with genetic algorithm (GA). A regression model was built with R 2 of 0.806. Almost all sterol degradation constants can be predicted accurately with R 2 of cross-validation equals to 0.680. Four important variables were selected in optimal QSAR model and the selected variables were observed to be related with information indices, RDF descriptors, and 3D-MoRSE descriptors. Copyright © 2018 Elsevier Ltd. All rights reserved.
Trushkov, V F; Perminov, K A; Sapozhnikova, V V; Ignatova, O L
2013-01-01
The connection of thermodynamic properties and parameters of toxicity of chemical substances was determined. Obtained data are used for the evaluation of toxicity and hygienic rate setting of chemical compounds. The relationship between enthalpy and toxicity of chemical compounds has been established. Orthogonal planning of the experiment was carried out in the course of the investigations. Equation of unified hygienic rate setting in combined, complex, conjunct influence on the organism is presented. Prospects of determination of toxicity and methodology of unified hygienic rate setting in combined, complex, conjunct influence on the organism are presented
Giesen, Daniel; van Gestel, Cornelis A M
2013-03-01
Quantitative structure-activity relationships (QSARs) are an established tool in environmental risk assessment and a valuable alternative to the exhaustive use of test animals under REACH. In this study a QSAR was developed for the toxicity of a series of six chloroanilines to the soil-dwelling collembolan Folsomia candida in standardized natural LUFA2.2 soil. Toxicity endpoints incorporated in the QSAR were the concentrations causing 10% (EC10) and 50% (EC50) reduction in reproduction of F. candida. Toxicity was based on concentrations in interstitial water estimated from nominal concentrations in the soil and published soil-water partition coefficients. Estimated effect concentrations were negatively correlated with the lipophilicity of the compounds. Interstitial water concentrations for both the EC10 and EC50 for four compounds were determined by using solid-phase microextraction (SPME). Measured and estimated concentrations were comparable only for tetra- and pentachloroaniline. With decreasing chlorination the disparity between modelled and actual concentrations increased. Optimisation of the QSAR therefore could not be accomplished, showing the necessity to move from total soil to (bio)available concentration measurements. Copyright © 2012 Elsevier Ltd. All rights reserved.
Henderson, B W; Bellnier, D A; Greco, W R; Sharma, A; Pandey, R K; Vaughan, L A; Weishaupt, K R; Dougherty, T J
1997-09-15
An in vivo quantitative structure-activity relationship (QSAR) study was carried out on a congeneric series of pyropheophorbide photosensitizers to identify structural features critical for their antitumor activity in photodynamic therapy (PDT). The structural elements evaluated in this study include the length and shape (alkyl, alkenyl, cyclic, and secondary analogs) of the ether side chain. C3H mice, harboring the radiation-induced fibrosarcoma tumor model, were used to study three biological response endpoints: tumor growth delay, tumor cell lethality, and vascular perfusion. All three endpoints revealed highly similar QSAR patterns that constituted a function of the alkyl ether chain length and drug lipophilicity, which is defined as the log of the octanol:water partition coefficient (log P). When the illumination of tumor, tumor cells, or cutaneous vasculature occurred 24 h after sensitizer administration, activities were minimal with analogs of log P < or = 5, increased dramatically between log P of 5-6, and peaked between log P of 5.6-6.6. Activities declined gradually with higher log P. The lack of activity of the least-lipophilic analogs was explained in large part by their poor biodistribution characteristics, which yielded negligible tumor and plasma drug levels at the time of treatment with light. The progressively lower potencies of the most lipophilic analogs cannot be explained through the overall tumor and plasma pharmacokinetics of photosensitizer because tumor and plasma concentrations progressively increased with lipophilicity. When compensated for differences in tumor photosensitizer concentration, the 1-hexyl derivative (optimal lipophilicity) was 5-fold more potent than the 1-dodecyl derivative (more lipophilic) and 3-fold more potent than the 1-pentyl analog (less lipophilic), indicating that, in addition to the overall tumor pharmacokinetics, pharmacodynamic factors may influence PDT activity. Drug lipophilicity was highly predictive for
DOE Office of Scientific and Technical Information (OSTI.GOV)
Mistry, N; D'Souza, W; Sornsen de Koste, J
2014-06-01
Purpose: Recently, there has been an interest in incorporating functional information in treatment planning especially in thoracic tumors. The rationale is that healthy lung regions need to be spared from radiation if possible to help achieve better control on toxicity. However, it is still unclear whether high functioning regions need to be spared or have more capacity to deal with the excessive radiation as compared to the compromised regions of the lung. Our goal with this work is to establish the tools by which we can establish a relationship between pre-treatment lung function, dose, and radiographic outcomes of lung toxicity.more » Methods: Treatment planning was performed using a single phase of a 4DCT scan, and follow-up anatomical CT scans were performed every 3 months for most patients. In this study, we developed the pipeline of tools needed to analyze such a large dataset, while trying to establish a relationship between function, dose, and outcome. Pre-treatment lung function was evaluated using a recently published technique that evaluates Fractional Regional Ventilation (FRV). All images including the FRV map and the individual follow-up anatomical CT images were all spatially matched to the planning CT using a diffusion based Demons image registration algorithm. Change in HU value was used as a metric to capture the effects of lung toxicity. To validate the findings, a radiologist evaluated the follow-up anatomical CT images and scored lung toxicity. Results: Initial experience in 1 patient shows a relationship between the pre-treatment lung function, dose and toxicity outcome. The results are also correlated to the findings by the radiologist who was blinded to the analysis or dose. Conclusion: The pipeline we have established to study this enables future studies in large retrospective studies. However, the tools are dependent on the fidelity of 4DCT reconstruction for accurate evaluation of regional ventilation. Patent Pending for the technique
Yoo, Hong Sik; Bradford, Blair U.; Kosyk, Oksana; Shymonyak, Svitlana; Uehara, Takeki; Collins, Leonard B.; Bodnar, Wanda M.; Ball, Louise M.; Gold, Avram; Rusyn, Ivan
2014-01-01
Trichloroethylene (TCE) is a widely used organic solvent. Although TCE is classified as carcinogenic to humans, substantial gaps remain in our understanding of inter-individual variability in TCE metabolism and toxicity, especially in the liver. We tested a hypothesis that amounts of oxidative metabolites of TCE in mouse liver are associated with liver-specific toxicity. Oral dosing with TCE was conducted in sub-acute (600 mg/kg/d; 5 days; 7 inbred mouse strains) and sub-chronic (100 or 400 mg/kg/d; 1, 2, or 4 weeks; 2 inbred mouse strains) designs. We evaluated the quantitative relationship between strain-, dose-, and time-dependent formation of TCE metabolites from cytochrome P450-mediated oxidation [trichloroacetic acid (TCA), dichloroacetic acid (DCA), and trichloroethanol] and glutathione conjugation [S-(1,2-dichlorovinyl)-L-cysteine and S-(1,2-dichlorovinyl)glutathione] in serum and liver, and various liver toxicity phenotypes. In sub-acute study, inter-strain variability in TCE metabolite amounts was observed in serum and liver. No induction of Cyp2e1 protein levels in liver was detected. Serum and liver levels of TCA and DCA were correlated with increased transcription of peroxisome proliferator-marker genes Cyp4a10 and Acox1, but not with degree of induction in hepatocellular proliferation. In sub-chronic study, serum and liver levels of oxidative metabolites gradually decreased over time despite continuous dosing. Liver protein levels of Cyp2e1, Adh and Aldh2 were unaffected by treatment with TCE. While the magnitude of induction of peroxisome proliferator-marker genes also declined, hepatocellular proliferation increased. This study offers a unique opportunity to provide a scientific data-driven rationale for some of the major assumptions in human health assessment of TCE. PMID:25424544
Structure-activity relationships of fluorinated dendrimers in DNA and siRNA delivery.
Wang, Mingming; Cheng, Yiyun
2016-12-01
Fluorinated dendrimers have shown great promise in gene delivery due to their high transfection efficacy and low cytotoxicity, however, the structure-activity relationships of these polymers still remain unknown. Herein, we synthesized a library of fluorinated dendrimers with different dendrimer generations and fluorination degrees and investigated their behaviors in both DNA and siRNA delivery. The results show that fluorination significantly improves the transfection efficacy of G4-G7 polyamidoamine dendrimers in DNA and siRNA delivery. Fluorination on generation 5 dendrimer yields the most efficient polymers in gene delivery, and the transfection efficacy of fluorinated dendrimers depends on fluorination degree. All the fluorinated dendrimers cause minimal toxicity on the transfected cells at their optimal transfection conditions. This study provides a general and facile strategy to prepare high efficient and low cytotoxic gene carriers based on fluorinated polymers. The structure-activity relationships of fluorinated dendrimers in gene delivery is still unknown and the behavior of fluorinated dendrimers in siRNA delivery has not yet been investigated. Herein, we synthesized a library of fluorinated PAMAM dendrimers with different dendrimer generations and fluorination degrees and investigated their behaviors in both DNA and siRNA delivery. The results clearly indicate that fluorination significantly improves the transfection efficacy of dendrimers in both DNA and siRNA delivery without causing additional toxicity. G5 PAMAM dendrimer is best scaffold to synthesize fluorinated dendrimers and the transfection efficacy of fluorinated dendrimers depends on fluorination degree. This systematic study provides a general and facile strategy to prepare high efficient and low cytotoxic gene carriers based on fluorinated polymers. Copyright © 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Quantitative dose-response assessment of inhalation exposures to toxic air pollutants
DOE Office of Scientific and Technical Information (OSTI.GOV)
Jarabek, A.M.; Foureman, G.L.; Gift, J.S.
1997-12-31
Implementation of the 1990 Clean Air Act Amendments, including evaluation of residual risks. requires accurate human health risk estimates of both acute and chronic inhalation exposures to toxic air pollutants. The U.S. Environmental Protection Agency`s National Center for Environmental Assessment, Research Triangle Park, NC, has a research program that addresses several key issues for development of improved quantitative approaches for dose-response assessment. This paper describes three projects underway in the program. Project A describes a Bayesian approach that was developed to base dose-response estimates on combined data sets and that expresses these estimates as probability density functions. A categorical regressionmore » model has been developed that allows for the combination of all available acute data, with toxicity expressed as severity categories (e.g., mild, moderate, severe), and with both duration and concentration as governing factors. Project C encompasses two refinements to uncertainty factors (UFs) often applied to extrapolate dose-response estimates from laboratory animal data to human equivalent concentrations. Traditional UFs have been based on analyses of oral administration and may not be appropriate for extrapolation of inhalation exposures. Refinement of the UF applied to account for the use of subchronic rather than chronic data was based on an analysis of data from inhalation exposures (Project C-1). Mathematical modeling using the BMD approach was used to calculate the dose-response estimates for comparison between the subchronic and chronic data so that the estimates were not subject to dose-spacing or sample size variability. The second UF that was refined for extrapolation of inhalation data was the adjustment for the use of a LOAEL rather than a NOAEL (Project C-2).« less
NASA Astrophysics Data System (ADS)
Davoudi, Bahar; Damodaran, Vani; Bizheva, Kostadinka; Yang, Victor; Dinniwell, Robert; Levin, Wilfred; Vitkin, Alex
2013-03-01
Late oral radiation toxicity is a common condition occurring in a considerable percentage of head and neck cancer patients after radiation therapy which reduces their quality of life. The current examination of these patients is based on a visual inspection of the surface of the oral cavity; however, it is well known that many of the complications start in the subsurface layers before any superficial manifestation. Considering the currently suboptimal examination techniques, we address this clinical problem by using optical coherence tomography (OCT) to monitor the subsurface oral layers with micron-scale resolution images. A spectral-domain OCT system and a specialized oral imaging probe were designed and built for a clinical study to image late oral radiation toxicity patients. In addition to providing qualitative 2D and 3D images of the subsurface oral layers, quantitative metrics were developed to assess the back-scattering and thickness properties of different layers. Metric derivations are explained and preliminary results from late radiation toxicity patients and healthy volunteers are presented and discussed.
Nazemi, S Majid; Amini, Morteza; Kontulainen, Saija A; Milner, Jaques S; Holdsworth, David W; Masri, Bassam A; Wilson, David R; Johnston, James D
2015-08-01
Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain initiation. Calculation of bone elastic moduli from image data is a basic step when constructing finite element models. However, different relationships between elastic moduli and imaged density (known as density-modulus relationships) have been reported in the literature. The objective of this study was to apply seven different trabecular-specific and two cortical-specific density-modulus relationships from the literature to finite element models of proximal tibia subchondral bone, and identify the relationship(s) that best predicted experimentally measured local subchondral structural stiffness with highest explained variance and least error. Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using published density-modulus relationships and mapped to corresponding finite element models. Proximal tibial structural stiffness values were compared to experimentally measured stiffness values from in-situ macro-indentation testing directly on the subchondral bone surface (47 indentation points). Regression lines between experimentally measured and finite element calculated stiffness had R(2) values ranging from 0.56 to 0.77. Normalized root mean squared error varied from 16.6% to 337.6%. Of the 21 evaluated density-modulus relationships in this study, Goulet combined with Snyder and Schneider or Rho appeared most appropriate for finite element modeling of local subchondral bone structural stiffness. Though, further studies are needed to optimize density-modulus relationships and improve finite element estimates of local subchondral bone structural stiffness. Copyright © 2015 Elsevier Ltd. All rights reserved.
Ríos, Stella Maris; Barquin, Mercedes; Katusich, Ofelia; Nudelman, Norma
2014-01-01
Oil spill in the Central Patagonian zone was studied to evaluate if any relationship exists between the parameters used to characterize weathering spilled oil and soil toxicity for two plant species and to evaluate if the phytotoxicity to local species would be a good index for the soil contamination. Nuclear magnetic resonance (NMR) structural indexes and column chromatography compositional indexes were determined to characterize the oil spill in the soil samples. Bioassays were also carried out using Lactuca sativa L (reference) and Atriplex lampa (native species) as test organisms. Measurements of the total petroleum hydrocarbon (TPH) and the electrical conductivity (EC) of the soil were carried out to evaluate the effect on the bioassays. The principal components analysis of the parameters determined by NMR, compositional indexes, EC, TPH, and toxicology data shows that the first three principal components accounted for the 78% of the total variance (40%, 25%, and 13% for the first, second, and third PC, respectively). A good agreement was found between information obtained by compositional indexes and NMR structural indexes. Soil toxicity increases with the increase of EC and TPH. Other factors, such as, the presence of branched and aromatic hydrocarbons is also significant. The statistical evaluation showed that the Euclidean distances (3D) between the background and each one of the samples might be a better indicator of the soil contamination, compared with chemical criterion of TPH.
Quantitative Methods in Psychology: Inevitable and Useless
Toomela, Aaro
2010-01-01
Science begins with the question, what do I want to know? Science becomes science, however, only when this question is justified and the appropriate methodology is chosen for answering the research question. Research question should precede the other questions; methods should be chosen according to the research question and not vice versa. Modern quantitative psychology has accepted method as primary; research questions are adjusted to the methods. For understanding thinking in modern quantitative psychology, two epistemologies should be distinguished: structural-systemic that is based on Aristotelian thinking, and associative-quantitative that is based on Cartesian–Humean thinking. The first aims at understanding the structure that underlies the studied processes; the second looks for identification of cause–effect relationships between the events with no possible access to the understanding of the structures that underlie the processes. Quantitative methodology in particular as well as mathematical psychology in general, is useless for answering questions about structures and processes that underlie observed behaviors. Nevertheless, quantitative science is almost inevitable in a situation where the systemic-structural basis of behavior is not well understood; all sorts of applied decisions can be made on the basis of quantitative studies. In order to proceed, psychology should study structures; methodologically, constructive experiments should be added to observations and analytic experiments. PMID:21833199
Quantitative methods in psychology: inevitable and useless.
Toomela, Aaro
2010-01-01
Science begins with the question, what do I want to know? Science becomes science, however, only when this question is justified and the appropriate methodology is chosen for answering the research question. Research question should precede the other questions; methods should be chosen according to the research question and not vice versa. Modern quantitative psychology has accepted method as primary; research questions are adjusted to the methods. For understanding thinking in modern quantitative psychology, two epistemologies should be distinguished: structural-systemic that is based on Aristotelian thinking, and associative-quantitative that is based on Cartesian-Humean thinking. The first aims at understanding the structure that underlies the studied processes; the second looks for identification of cause-effect relationships between the events with no possible access to the understanding of the structures that underlie the processes. Quantitative methodology in particular as well as mathematical psychology in general, is useless for answering questions about structures and processes that underlie observed behaviors. Nevertheless, quantitative science is almost inevitable in a situation where the systemic-structural basis of behavior is not well understood; all sorts of applied decisions can be made on the basis of quantitative studies. In order to proceed, psychology should study structures; methodologically, constructive experiments should be added to observations and analytic experiments.
Netzeva, Tatiana I; Gallegos Saliner, Ana; Worth, Andrew P
2006-05-01
The aim of the present study was to illustrate that it is possible and relatively straightforward to compare the domain of applicability of a quantitative structure-activity relationship (QSAR) model in terms of its physicochemical descriptors with a large inventory of chemicals. A training set of 105 chemicals with data for relative estrogenic gene activation, obtained in a recombinant yeast assay, was used to develop the QSAR. A binary classification model for predicting active versus inactive chemicals was developed using classification tree analysis and two descriptors with a clear physicochemical meaning (octanol-water partition coefficient, or log Kow, and the number of hydrogen bond donors, or n(Hdon)). The model demonstrated a high overall accuracy (90.5%), with a sensitivity of 95.9% and a specificity of 78.1%. The robustness of the model was evaluated using the leave-many-out cross-validation technique, whereas the predictivity was assessed using an artificial external test set composed of 12 compounds. The domain of the QSAR training set was compared with the chemical space covered by the European Inventory of Existing Commercial Chemical Substances (EINECS), as incorporated in the CDB-EC software, in the log Kow / n(Hdon) plane. The results showed that the training set and, therefore, the applicability domain of the QSAR model covers a small part of the physicochemical domain of the inventory, even though a simple method for defining the applicability domain (ranges in the descriptor space) was used. However, a large number of compounds are located within the narrow descriptor window.
Structure- and ligand-based structure-activity relationships for a series of inhibitors of aldolase.
Ferreira, Leonardo G; Andricopulo, Adriano D
2012-12-01
Aldolase has emerged as a promising molecular target for the treatment of human African trypanosomiasis. Over the last years, due to the increasing number of patients infected with Trypanosoma brucei, there is an urgent need for new drugs to treat this neglected disease. In the present study, two-dimensional fragment-based quantitative-structure activity relationship (QSAR) models were generated for a series of inhibitors of aldolase. Through the application of leave-one-out and leave-many-out cross-validation procedures, significant correlation coefficients were obtained (r²=0.98 and q²=0.77) as an indication of the statistical internal and external consistency of the models. The best model was employed to predict pKi values for a series of test set compounds, and the predicted values were in good agreement with the experimental results, showing the power of the model for untested compounds. Moreover, structure-based molecular modeling studies were performed to investigate the binding mode of the inhibitors in the active site of the parasitic target enzyme. The structural and QSAR results provided useful molecular information for the design of new aldolase inhibitors within this structural class.
deLorimier, Elaine; Coonrod, Leslie A.; Copperman, Jeremy; ...
2014-10-10
In this study, CUG repeat expansions in the 3' UTR of dystrophia myotonica protein kinase ( DMPK) cause myotonic dystrophy type 1 (DM1). As RNA, these repeats elicit toxicity by sequestering splicing proteins, such as MBNL1, into protein–RNA aggregates. Structural studies demonstrate that CUG repeats can form A-form helices, suggesting that repeat secondary structure could be important in pathogenicity. To evaluate this hypothesis, we utilized structure-stabilizing RNA modifications pseudouridine (Ψ) and 2'-O-methylation to determine if stabilization of CUG helical conformations affected toxicity. CUG repeats modified with Ψ or 2'-O-methyl groups exhibited enhanced structural stability and reduced affinity for MBNL1. Molecularmore » dynamics and X-ray crystallography suggest a potential water-bridging mechanism for Ψ-mediated CUG repeat stabilization. Ψ modification of CUG repeats rescued mis-splicing in a DM1 cell model and prevented CUG repeat toxicity in zebrafish embryos. This study indicates that the structure of toxic RNAs has a significant role in controlling the onset of neuromuscular diseases.« less
Safe, S
1993-01-01
Polychlorinated biphenyls (PCBs) are industrial compounds that have been detected as contaminants in almost every component of the global ecosystem including the air, water, sediments, fish, and wildlife and human adipose tissue, milk, and serum. PCBs in commercial products and environmental extracts are complex mixtures of isomers and congeners that can now be analyzed on a congener-specific basis using high-resolution gas chromatographic analysis. PCBs are metabolized primarily via mixed-function oxidases into a broad spectrum of metabolites. The results indicate that metabolic activation is not required for PCB toxicity, and the parent hydrocarbons are responsible for most of the biochemical and toxic responses elicited by these compounds. Some of these responses include developmental and reproductive toxicity, dermal toxicity, endocrine effects, hepatotoxicity, carcinogenesis, and the induction of diverse phase I and phase II drug-metabolizing enzymes. Many of the effects observed for the commercial PCBs are similar to those reported for 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and related compounds. Structure-function relationships for PCB congeners have identified two major structural classes of PCBs that elicit "TCDD-like" responses, namely, the coplanar PCBs (e.g., 3,3',4,4'-tetraCB, 3,3'4,4',5-pentaCB and 3,3',4,4',5,5'-hexaCB) and their mono-ortho coplanar derivatives. These compounds competitively bind to the TCDD or aryl hydrocarbon (Ah) receptor and exhibit Ah receptor agonist activity. In addition, other structural classes of PCBs elicit biochemical and toxic responses that are not mediated through the Ah receptor. The shor-term effects of PCBs on occupationally exposed humans appear to be reversible, and no consistent changes in overall mortality and cancer mortality have been reported. Recent studies have demonstrated that some developmental deficits in infants and children correlated with in utero exposure to PCBs; however, the etiologic agent
NASA Astrophysics Data System (ADS)
Li, Yao-Wang; Li, Bo; He, Jiguo; Qian, Ping
2011-07-01
A database consisting of 214 tripeptides which contain either His or Tyr residue was applied to study quantitative structure-activity relationships (QSAR) of antioxidative tripeptides. Partial Least-Squares Regression analysis (PLSR) was conducted using parameters individually of each amino acid descriptor, including Divided Physico-chemical Property Scores (DPPS), Hydrophobic, Electronic, Steric, and Hydrogen (HESH), Vectors of Hydrophobic, Steric, and Electronic properties (VHSE), Molecular Surface-Weighted Holistic Invariant Molecular (MS-WHIM), isotropic surface area-electronic charge index (ISA-ECI) and Z-scale, to describe antioxidative tripeptides as X-variables and antioxidant activities measured with ferric thiocyanate methods were as Y-variable. After elimination of outliers by Hotelling's T 2 method and residual analysis, six significant models were obtained describing the entire data set. According to cumulative squared multiple correlation coefficients ( R2), cumulative cross-validation coefficients ( Q2) and relative standard deviation for calibration set (RSD c), the qualities of models using DPPS, HESH, ISA-ECI, and VHSE descriptors are better ( R2 > 0.6, Q2 > 0.5, RSD c < 0.39) than that of models using MS-WHIM and Z-scale descriptors ( R2 < 0.6, Q2 < 0.5, RSD c > 0.44). Furthermore, the predictive ability of models using DPPS descriptor is best among the six descriptors systems (cumulative multiple correlation coefficient for predict set ( Rext2) > 0.7). It was concluded that the DPPS is better to describe the amino acid of antioxidative tripeptides. The results of DPPS descriptor reveal that the importance of the center amino acid and the N-terminal amino acid are far more than the importance of the C-terminal amino acid for antioxidative tripeptides. The hydrophobic (positively to activity) and electronic (negatively to activity) properties of the N-terminal amino acid are suggested to play the most important significance to activity, followed
DISTRIBUTED STRUCTURE-SEARCHABLE TOXICITY (DSSTOX) PUBLIC DATABASE NETWORK: A PROPOSAL
The ability to assess the potential genotoxicity, carcinogenicity, or other toxicity of pharmaceutical or industrial chemicals based on chemical structure information is a highly coveted and shared goal of varied academic, commercial, and government regulatory groups. These dive...
NASA Astrophysics Data System (ADS)
Ding, Y.; Chen, X.; Bi, R.; Zhang, L. H.; Li, L.; Zhao, M.
2016-12-01
Alkenones and sterols are useful biomarkers to construct past productivity and community structure changes in aquatic environments. Until now, the quantitative relationship between biomarker content and biomass in marine phytoplankton remains understudied, which hinders the quantitative reconstruction of ocean changes. In this study, we carried out laboratory culture experiments to determine the quantitative relationship between biomarker content and biomass under three temperatures (15°, 20° and 25°) and three N:P supply ratios (N:P=10:1, 24:1 and 63:1 mol mol-1) for three common phytoplankton groups, diatoms (Phaeodactylum tricornutum Bohlin, Skeletonema costatum, Chaetoceros muelleri), dinoflagellates (Karenia mikimotoi, Prorocentrum donghaiense, Prorocentrum minimum), and coccolithophores (Emiliania huxleyi). Alkenones were only detected in E. huxleyiand dinosterol was only detected in dinoflagellates, confirming that they are the biomarkers for these two groups of phytoplankton, respectively. Brassicasterol was detected in all three groups of phytoplankton, but its content was higher in diatoms, suggesting that it is still a useful biomarker for diatoms. Cell-normalized alkenone content (pg/cell) increases with increasing growth temperature by up to 30%; while the effect of nutrients on alkenone content is minimum. On the other hand, cell-normalized dinosterol content is not temperature dependent, but it is strongly affected by nutrient ratio changes. The effects of temperature and nutrients on cell-normalized brassicasterol content are phytoplankton dependent. For diatoms, the temperature effect is minimum while the nutrient effect is significant but also varies with temperatures. Our results have strong implications for understanding how different phytoplankton respond to global changes, and for more quantitative reconstruction of past productivity and community structure changes using these biomarkers.
Nagai, Takashi; Taya, Kiyoshi; Yoda, Ikuko
2016-02-01
The authors used 5 species of periphytic algae to conduct toxicity assays of 20 herbicides. The 5 tested species represent riverine primary producers most likely to be affected by herbicides. A fluorescence microplate toxicity assay was used as an efficient and economical high-throughput assay. Toxicity characteristics were analyzed, focusing on their relationship to herbicide mode of action. The relative differences between 50% and 10% effect concentrations depended on herbicide mode of action, rather than tested species. Moreover, a clear relationship between sensitive species and herbicide mode of action was also observed. Green alga was most sensitive to herbicides of 2 mode of action groups: inhibitors of protoporphyrinogen oxidase and very long-chain fatty acid synthesis. Diatoms were most sensitive to herbicides of 1 mode of action group: 4-hydroxyphenyl-pyruvate-dioxygenase inhibitors. Cyanobacterium was most sensitive to herbicides of 1 mode of action group: inhibitors of acetolactate synthase. The species sensitivity distribution based on obtained data was also analyzed. The slopes of the species sensitivity distribution significantly differed among modes of action, suggesting that difference in species sensitivity is specific to the mode of action. In particular, differences in species sensitivity were markedly large for inhibitors of acetolactate synthase, protoporphyrinogen oxidase, and very long-chain fatty acid synthesis. The results clearly showed that a single algal species cannot represent the sensitivity of an algal assemblage. Therefore, multispecies algal toxicity data are essential for substances with specific modes of action. © 2015 SETAC.
El-Kilany, Yeldez; Nahas, Nariman M; Al-Ghamdi, Mariam A; Badawy, Mohamed E I; El Ashry, El Sayed H
2015-01-01
Ethyl (benzimidazol-1-yl)acetate was subjected to hydrazinolysis with hydrazine hydrate to give (benzimidazol-1-yl)acetohydrazide. The latter was reacted with various aromatic aldehydes to give the respective arylidene (1H-benzimidazol-1-yl)acetohydrazones. Solutions of the prepared hydrazones were found to contain two geometric isomers. Similarly (2-methyl-benzimidazol-1-yl)acetohydrazide was reacted with various aldehydes to give the corresponding hydrazones. The antibacterial activity was evaluated in vitro by minimum inhibitory concentration (MIC) against Agrobacterium tumefaciens (A. tumefaciens), Erwinia carotovora (E. carotovora), Corynebacterium fascians (C. fascians) and Pseudomonas solanacearum (P. solanacearum). MIC result demonstrated that salicylaldehyde(1H-benzimidazol-1-yl)acetohydrazone (4) was the most active compound (MIC = 20, 35, 25 and 30 mg/L against A. tumefaciens, C. fascians, E. carotovora and P. solanacearum, respectively). Quantitative structure activity relationship (QSAR) investigation using Hansch analysis was applied to find out the correlation between antibacterial activity and physicochemical properties. Various physicochemical descriptors and experimentally determined MIC values for different microorganisms were used as independent and dependent variables, respectively. pMICs of the compounds exhibited good correlation (r = 0.983, 0.914, 0.960 and 0.958 for A. tumefaciens, C. fascians, E. carotovora and P. solanacearum, respectively) with the prediction made by the model. QSAR study revealed that the hydrophobic parameter (ClogP), the aqueous solubility (LogS), calculated molar refractivity, topological polar surface area and hydrogen bond acceptor were found to have overall significant correlation with antibacterial activity. The statistical results of training set, correlation coefficient (r and r (2)), the ratio between regression and residual variances (f, Fisher's statistic), the standard error of estimates and
RULES FOR DISTINGUISHING TOXICANTS THAT CAUSE TYPE (I) AND TYPE (II) NARCOSIS SYNDROMES
Narcosis is a non-specific reversible state of arrested activity of protoplasmic structures caused by a wide variety of organic chemicals. he vast majority of industrial organic chemicals can be characterized by a baseline structure-toxicity relationship as developed for diverse ...
ERIC Educational Resources Information Center
Danelczyk, Ewa Krystyna
2013-01-01
The purpose of this quantitative causal-comparative study was to investigate the relationship between the instructional effects of the interactive whiteboard and students' proficiency levels in eighth-grade science as evidenced by the state FCAT scores. A total of 46 eighth-grade science teachers in a South Florida public school district completed…
Acute toxicity of anionic and non-ionic surfactants to aquatic organisms.
Lechuga, M; Fernández-Serrano, M; Jurado, E; Núñez-Olea, J; Ríos, F
2016-03-01
The environmental risk of surfactants requires toxicity measurements. As different test organisms have different sensitivity to the toxics, it is necessary to establish the most appropriate organism to classify the surfactant as very toxic, toxic, harmful or safe, in order to establish the maximum permissible concentrations in aquatic ecosystems. We have determined the toxicity values of various anionic surfactants ether carboxylic derivatives using four test organisms: the freshwater crustacean Daphnia magna, the luminescent bacterium Vibrio fischeri, the microalgae Selenastrum capricornutum (freshwater algae) and Phaeodactylum tricornutum (seawater algae). In addition, in order to compare and classify the different families of surfactants, we have included a compilation of toxicity data of surfactants collected from literature. The results indicated that V. fischeri was more sensitive to the toxic effects of the surfactants than was D. magna or the microalgae, which was the least sensitive. This result shows that the most suitable toxicity assay for surfactants may be the one using V. fischeri. The toxicity data revealed considerable variation in toxicity responses with the structure of the surfactants regardless of the species tested. The toxicity data have been related to the structure of the surfactants, giving a mathematical relationship that helps to predict the toxic potential of a surfactant from its structure. Model-predicted toxicity agreed well with toxicity values reported in the literature for several surfactants previously studied. Predictive models of toxicity is a handy tool for providing a risk assessment that can be useful to establish the toxicity range for each surfactant and the different test organisms in order to select efficient surfactants with a lower impact on the aquatic environment. Copyright © 2015 Elsevier Inc. All rights reserved.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Johnson, A.R.; Bartell, S.M.
1988-06-01
The state of an ecosystem at any time t may be characterized by a multidimensional state vector x(t). Changes in state are represented by the trajectory traced out by x(t) over time. The effects of toxicant stress are summarized by the displacement of a perturbed state vector, x/sub p/(t), relative to an appropriate control, x/sub c/(t). Within a multivariate statistical framework, the response of an ecosystem to perturbation is conveniently quantified by the distance separating x/sub p/(t) from x/sub c/(t) as measured by a Mahalanobis metric. Use of the Mahalanobis metric requires that the covariance matrix associated with the controlmore » state vector be estimated. State space displacement analysis was applied to data on the response of aquatic microcosms and outdoor ponds to alkylphenols. Dose-response relationships were derived using calculated state space separations as integrated measures of the ecological effects of toxicant exposure. Inspection of the data also revealed that the covariance structure varied both with time and with toxicant exposure, suggesting that analysis of such changes might be a useful tool for probing control mechanisms underlying ecosystem dynamics. 90 refs., 53 figs., 9 tabs.« less
Structure of the toxic core of α-synuclein from invisible crystals
Rodriguez, Jose A.; Ivanova, Magdalena I.; Sawaya, Michael R.; ...
2015-09-09
We report that the protein α-synuclein is the main component of Lewy bodies, the neuron-associated aggregates seen in Parkinson disease and other neurodegenerative pathologies. An 11-residue segment, which we term NACore, appears to be responsible for amyloid formation and cytotoxicity of human α-synuclein. Here we describe crystals of NACore that have dimensions smaller than the wavelength of visible light and thus are invisible by optical microscopy. As the crystals are thousands of times too small for structure determination by synchrotron X-ray diffraction, we use micro-electron diffraction to determine the structure at atomic resolution. The 1.4 Å resolution structure demonstrates thatmore » this method can determine previously unknown protein structures and here yields, to our knowledge, the highest resolution achieved by any cryo-electron microscopy method to date. The structure exhibits protofibrils built of pairs of face-to-face β-sheets. X-ray fibre diffraction patterns show the similarity of NACore to toxic fibrils of full-length α-synuclein. Finally, the NACore structure, together with that of a second segment, inspires a model for most of the ordered portion of the toxic, full-length α-synuclein fibril, presenting opportunities for the design of inhibitors of α-synuclein fibrils.« less
Cantrell, Charles L; Klun, Jerome A; Pridgeon, Julia; Becnel, James; Green, Solomon; Fronczek, Frank R
2009-04-01
Callicarpenal (=13,14,15,16-tetranorclerod-3-en-12-al=[(1S,2R,4aR,8aR)-1,2,3,4,4a,7,8,8a-octahydro-1,2,4a,5-tetramethylnaphthalen-1-yl]acetaldehyde; 1) has previously demonstrated significant mosquito bite-deterring activity against Aedes aegypti and Anopheles stephensi in addition to repellent activity against host-seeking nymphs of the blacklegged tick, Ixodes scapularis. In the present study, structural modifications were performed on callicarpenal (1) in an effort to understand the functional groups necessary for maintaining and/or increasing its activity and to possibly lead to more effective insect control agents. All modifications in this study targeted the C(12) aldehyde or the C(3) alkene functionalities or combinations thereof. Mosquito biting deterrency appeared to be influenced most by C(3) alkene modification as evidenced by catalytic hydrogenation that resulted in a compound having significantly less effectiveness than 1 at a test amount of 25 nmol/cm2. Oxidation and/or reduction of the C(12) aldehyde did not diminish mosquito biting deterrency, but, at the same time, none of the modifications were more effective than 1 in deterring mosquito biting. Toxicities of synthesized compounds towards Ae. aegypti ranged from an LD50 value of 2.36 to 40.11 microg per mosquito. Similarly, LD95 values ranged from a low of 5.59 to a high of 104.9 microg.
Sanders, John M; Beshore, Douglas C; Culberson, J Christopher; Fells, James I; Imbriglio, Jason E; Gunaydin, Hakan; Haidle, Andrew M; Labroli, Marc; Mattioni, Brian E; Sciammetta, Nunzio; Shipe, William D; Sheridan, Robert P; Suen, Linda M; Verras, Andreas; Walji, Abbas; Joshi, Elizabeth M; Bueters, Tjerk
2017-08-24
High-throughput screening (HTS) has enabled millions of compounds to be assessed for biological activity, but challenges remain in the prioritization of hit series. While biological, absorption, distribution, metabolism, excretion, and toxicity (ADMET), purity, and structural data are routinely used to select chemical matter for further follow-up, the scarcity of historical ADMET data for screening hits limits our understanding of early hit compounds. Herein, we describe a process that utilizes a battery of in-house quantitative structure-activity relationship (QSAR) models to generate in silico ADMET profiles for hit series to enable more complete characterizations of HTS chemical matter. These profiles allow teams to quickly assess hit series for desirable ADMET properties or suspected liabilities that may require significant optimization. Accordingly, these in silico data can direct ADMET experimentation and profoundly impact the progression of hit series. Several prospective examples are presented to substantiate the value of this approach.
STRUCTURE-ACTIVITY APPROACHES AND DATA EXPLORATION TOOLS FOR PRIORITIZING AND ASSESSING THE TOXICITY OF HAZARDOUS AIR POLLUTANTS
Hazardous Air Pollutants (HAPs) refers to a set of structurally diverse environmental chemicals, many with limited toxicity data, that have...
Li, Jin J; Tai, Hong W; Yu, Yang; Wen, Yang; Wang, Xiao H; Zhao, Yuan H
2015-07-01
Toxicity data to fish and algae were used to investigate excess toxicity between species. Results show that chemicals exhibiting excess toxicity to fish also show excess toxicity to algae for most of the compounds. This indicates that they share the same mode of action between species. Similar relationships between logKOW and toxicities to fish and algae for baseline and less inert compounds suggest that they have similar critical body residues in the two species. Differences in excess toxicity for some compounds suggest that there is a difference of physiological structure and metabolism between fish and algae. Some reactive compounds (e.g. polyamines) exhibit greater toxic effects for algae than those for fish because of relatively low bio-uptake potential of these hydrophilic compounds in fish as compared with that in algae. Esters exhibiting greater toxicity in fish than that in algae indicate that metabolism can affect the discrimination of excess toxicity from baseline level. Algae growth inhibition is a very good surrogate for fish lethality. This is not only because overall toxicity sensitivity to algae is greater than that to fish, but also the excess toxicity calculated from algal toxicity can better reflect reactivity of compounds with target molecules than fish toxicity. Copyright © 2015 Elsevier B.V. All rights reserved.
Tertiary structural propensities reveal fundamental sequence/structure relationships.
Zheng, Fan; Zhang, Jian; Grigoryan, Gevorg
2015-05-05
Extracting useful generalizations from the continually growing Protein Data Bank (PDB) is of central importance. We hypothesize that the PDB contains valuable quantitative information on the level of local tertiary structural motifs (TERMs). We show that by breaking a protein structure into its constituent TERMs, and querying the PDB to characterize the natural ensemble matching each, we can estimate the compatibility of the structure with a given amino acid sequence through a metric we term "structure score." Considering submissions from recent Critical Assessment of Structure Prediction (CASP) experiments, we found a strong correlation (R = 0.69) between structure score and model accuracy, with poorly predicted regions readily identifiable. This performance exceeds that of leading atomistic statistical energy functions. Furthermore, TERM-based analysis of two prototypical multi-state proteins rapidly produced structural insights fully consistent with prior extensive experimental studies. We thus find that TERM-based analysis should have considerable utility for protein structural biology. Copyright © 2015 Elsevier Ltd. All rights reserved.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Rosenthal, David I.; Chambers, Mark S.; Fuller, Clifton D.
2008-11-01
Background: Intensity-modulated radiation therapy (IMRT) beams traverse nontarget normal structures not irradiated during three-dimensional conformal RT (3D-CRT) for head and neck cancer (HNC). This study estimates the doses and toxicities to nontarget structures during IMRT. Materials and Methods: Oropharyngeal cancer IMRT and 3D-CRT cases were reviewed. Dose-volume histograms (DVH) were used to evaluate radiation dose to the lip, cochlea, brainstem, occipital scalp, and segments of the mandible. Toxicity rates were compared for 3D-CRT, IMRT alone, or IMRT with concurrent cisplatin. Descriptive statistics and exploratory recursive partitioning analysis were used to estimate dose 'breakpoints' associated with observed toxicities. Results: A totalmore » of 160 patients were evaluated for toxicity; 60 had detailed DVH evaluation and 15 had 3D-CRT plan comparison. Comparing IMRT with 3D-CRT, there was significant (p {<=} 0.002) nonparametric differential dose to all clinically significant structures of interest. Thirty percent of IMRT patients had headaches and 40% had occipital scalp alopecia. A total of 76% and 38% of patients treated with IMRT alone had nausea and vomiting, compared with 99% and 68%, respectively, of those with concurrent cisplatin. IMRT had a markedly distinct toxicity profile than 3D-CRT. In recursive partitioning analysis, National Cancer Institute's Common Toxicity Criteria adverse effects 3.0 nausea and vomiting, scalp alopecia and anterior mucositis were associated with reconstructed mean brainstem dose >36 Gy, occipital scalp dose >30 Gy, and anterior mandible dose >34 Gy, respectively. Conclusions: Dose reduction to specified structures during IMRT implies an increased beam path dose to alternate nontarget structures that may result in clinical toxicities that were uncommon with previous, less conformal approaches. These findings have implications for IMRT treatment planning and research, toxicity assessment, and multidisciplinary patient
Cortes-Ciriano, Isidro
2016-01-01
Assessing compound toxicity at early stages of the drug discovery process is a crucial task to dismiss drug candidates likely to fail in clinical trials. Screening drug candidates against structural alerts, i.e. chemical fragments associated to a toxicological response prior or after being metabolized (bioactivation), has proved a valuable approach for this task. During the last decades, diverse algorithms have been proposed for the automatic derivation of structural alerts from categorical toxicity data sets. Here, the python library bioalerts is presented, which comprises functionalities for the automatic derivation of structural alerts from categorical (dichotomous), e.g. toxic/non-toxic, and continuous bioactivity data sets, e.g. [Formula: see text] or [Formula: see text] values. The library bioalerts relies on the RDKit implementation of the circular Morgan fingerprint algorithm to compute chemical substructures, which are derived by considering radial atom neighbourhoods of increasing bond radius. In addition to the derivation of structural alerts, bioalerts provides functionalities for the calculation of unhashed (keyed) Morgan fingerprints, which can be used in predictive bioactivity modelling with the advantage of allowing for a chemically meaningful deconvolution of the chemical space. Finally, bioalerts provides functionalities for the easy visualization of the derived structural alerts.
Luo, Shuang; Wei, Zongsu; Spinney, Richard; Villamena, Frederick A; Dionysiou, Dionysios D; Chen, Dong; Tang, Chong-Jian; Chai, Liyuan; Xiao, Ruiyang
2018-02-15
Sulfate radical anion (SO 4 •- ) and hydroxyl radical (OH) based advanced oxidation technologies has been extensively used for removal of aromatic contaminants (ACs) in waters. In this study, we investigated the Gibbs free energy (ΔG SET ∘ ) of the single electron transfer (SET) reactions for 76 ACs with SO 4 •- and OH, respectively. The result reveals that SO 4 •- possesses greater propensity to react with ACs through the SET channel than OH. We hypothesized that the electron distribution within the molecule plays an essential role in determining the ΔG SET ∘ and subsequent SET reactions. To test the hypothesis, a quantitative structure-activity relationship (QSAR) model was developed for predicting ΔG SET ∘ using the highest occupied molecular orbital energies (E HOMO ), a measure of electron distribution and donating ability. The standardized QSAR models are reported to be ΔG ° SET =-0.97×E HOMO - 181 and ΔG ° SET =-0.97×E HOMO - 164 for SO 4 •- and OH, respectively. The models were internally and externally validated to ensure robustness and predictability, and the application domain and limitations were discussed. The single-descriptor based models account for 95% of the variability for SO 4 •- and OH. These results provide the mechanistic insight into the SET reaction pathway of radical and non-radical bimolecular reactions, and have important applications for radical based oxidation technologies to remove target ACs in different waters. Copyright © 2017 Elsevier B.V. All rights reserved.
Al-Fakih, A M; Algamal, Z Y; Lee, M H; Aziz, M
2018-05-01
A penalized quantitative structure-property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ([Formula: see text]) as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter ([Formula: see text]). The PBridge based model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], the Y-randomization test, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest [Formula: see text] of 0.959, [Formula: see text] of 0.953, [Formula: see text] of 0.949 and [Formula: see text] of 0.959, and the lowest [Formula: see text] and [Formula: see text]. For the test dataset, PBridge shows a higher [Formula: see text] of 0.945 and [Formula: see text] of 0.948, and a lower [Formula: see text] and [Formula: see text], indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.
Low, Yen S.; Sedykh, Alexander; Rusyn, Ivan; Tropsha, Alexander
2017-01-01
Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity. PMID:24805064
McCurry, Matthew R.; Mahony, Michael; Clausen, Phillip D.; Quayle, Michelle R.; Walmsley, Christopher W.; Jessop, Tim S.; Wroe, Stephen; Richards, Heather; McHenry, Colin R.
2015-01-01
Skull structure is intimately associated with feeding ability in vertebrates, both in terms of specific performance measures and general ecological characteristics. This study quantitatively assessed variation in the shape of the cranium and mandible in varanoid lizards, and its relationship to structural performance (von Mises strain) and interspecific differences in feeding ecology. Geometric morphometric and linear morphometric analyses were used to evaluate morphological differences, and finite element analysis was used to quantify variation in structural performance (strain during simulated biting, shaking and pulling). This data was then integrated with ecological classes compiled from relevant scientific literature on each species in order to establish structure-function relationships. Finite element modelling results showed that variation in cranial morphology resulted in large differences in the magnitudes and locations of strain in biting, shaking and pulling load cases. Gracile species such as Varanus salvadorii displayed high strain levels during shaking, especially in the areas between the orbits. All models exhibit less strain during pull back loading compared to shake loading, even though a larger force was applied (pull =30N, shake = 20N). Relationships were identified between the morphology, performance, and ecology. Species that did not feed on hard prey clustered in the gracile region of cranial morphospace and exhibited significantly higher levels of strain during biting (P = 0.0106). Species that fed on large prey clustered in the elongate area of mandible morphospace. This relationship differs from those that have been identified in other taxonomic groups such as crocodiles and mammals. This difference may be due to a combination of the open ‘space-frame’ structure of the varanoid lizard skull, and the ‘pull back’ behaviour that some species use for processing large prey. PMID:26106889
Mitchell, M.S.; Rutzmoser, S.H.; Wigley, T.B.; Loehle, C.; Gerwin, J.A.; Keyser, P.D.; Lancia, R.A.; Perry, R.W.; Reynolds, C.J.; Thill, R.E.; Weih, R.; White, D.; Wood, P.B.
2006-01-01
Little is known about factors that structure biodiversity on landscape scales, yet current land management protocols, such as forest certification programs, place an increasing emphasis on managing for sustainable biodiversity at landscape scales. We used a replicated landscape study to evaluate relationships between forest structure and avian diversity at both stand and landscape-levels. We used data on bird communities collected under comparable sampling protocols on four managed forests located across the Southeastern US to develop logistic regression models describing relationships between habitat factors and the distribution of overall richness and richness of selected guilds. Landscape models generated for eight of nine guilds showed a strong relationship between richness and both availability and configuration of landscape features. Diversity of topographic features and heterogeneity of forest structure were primary determinants of avian species richness. Forest heterogeneity, in both age and forest type, were strongly and positively associated with overall avian richness and richness for most guilds. Road density was associated positively but weakly with avian richness. Landscape variables dominated all models generated, but no consistent patterns in metrics or scale were evident. Model fit was strong for neotropical migrants and relatively weak for short-distance migrants and resident species. Our models provide a tool that will allow managers to evaluate and demonstrate quantitatively how management practices affect avian diversity on landscapes.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Le Calve, Benjamin; Lallemand, Benjamin; Perrone, Carmen
2011-07-01
The in vitro anticancer activity and toxicity of phyllostictine A, a novel oxazatricycloalkenone recently isolated from a plant-pathogenic fungus (Phyllosticta cirsii) was characterized in six normal and five cancer cell lines. Phyllostictine A displays in vitro growth-inhibitory activity both in normal and cancer cells without actual bioselectivity, while proliferating cells appear significantly more sensitive to phyllostictine A than non-proliferating ones. The main mechanism of action by which phyllostictine displays cytotoxic effects in cancer cells does not seem to relate to a direct activation of apoptosis. In the same manner, phyllostictine A seems not to bind or bond with DNA asmore » part of its mechanism of action. In contrast, phyllostictine A strongly reacts with GSH, which is a bionucleophile. The experimental data from the present study are in favor of a bonding process between GSH and phyllostictine A to form a complex though Michael attack at C=C bond at the acrylamide-like system. Considering the data obtained, two new hemisynthesized phyllostictine A derivatives together with three other natural phyllostictines (B, C and D) were also tested in vitro in five cancer cell lines. Compared to phyllostictine A, the two derivatives displayed a higher, phyllostictines B and D a lower, and phyllostictine C an almost equal, growth-inhibitory activity, respectively. These results led us to propose preliminary conclusions in terms of the structure-activity relationship (SAR) analyses for the anticancer activity of phyllostictine A and its related compounds, at least in vitro.« less
A novel logic-based approach for quantitative toxicology prediction.
Amini, Ata; Muggleton, Stephen H; Lodhi, Huma; Sternberg, Michael J E
2007-01-01
There is a pressing need for accurate in silico methods to predict the toxicity of molecules that are being introduced into the environment or are being developed into new pharmaceuticals. Predictive toxicology is in the realm of structure activity relationships (SAR), and many approaches have been used to derive such SAR. Previous work has shown that inductive logic programming (ILP) is a powerful approach that circumvents several major difficulties, such as molecular superposition, faced by some other SAR methods. The ILP approach reasons with chemical substructures within a relational framework and yields chemically understandable rules. Here, we report a general new approach, support vector inductive logic programming (SVILP), which extends the essentially qualitative ILP-based SAR to quantitative modeling. First, ILP is used to learn rules, the predictions of which are then used within a novel kernel to derive a support-vector generalization model. For a highly heterogeneous dataset of 576 molecules with known fathead minnow fish toxicity, the cross-validated correlation coefficients (R2CV) from a chemical descriptor method (CHEM) and SVILP are 0.52 and 0.66, respectively. The ILP, CHEM, and SVILP approaches correctly predict 55, 58, and 73%, respectively, of toxic molecules. In a set of 165 unseen molecules, the R2 values from the commercial software TOPKAT and SVILP are 0.26 and 0.57, respectively. In all calculations, SVILP showed significant improvements in comparison with the other methods. The SVILP approach has a major advantage in that it uses ILP automatically and consistently to derive rules, mostly novel, describing fragments that are toxicity alerts. The SVILP is a general machine-learning approach and has the potential of tackling many problems relevant to chemoinformatics including in silico drug design.
NASA Technical Reports Server (NTRS)
Cook, J. W.; Ewing, J. A.
1990-01-01
A quantitative relationship was determined between magnetic field strength (or magnetic flux) from photospheric magnetograph observations and the brightness temperature of solar fine-structure elements observed at 1600 A, where the predominant flux source is continuum emission from the solar temperature minimum region. A Kitt Peak magnetogram and spectroheliograph observations at 1600 A taken during a sounding rocket flight of the High Resolution Telescope and Spectrograph from December 11, 1987 were used. The statistical distributions of brightness temperature in the quiet sun at 1600 A, and absolute value of magnetic field strength in the same area were determined from these observations. Using a technique which obtains the best-fit relationship of a given functional form between these two histogram distributions, a quantitative relationship was determined between absolute value of magnetic field strength B and brightness temperature which is essentially linear from 10 to 150 G. An interpretation is suggested, in which a basal heating occurs generally, while brighter elements are produced in magnetic regions with temperature enhancements proportional to B.
Li, Yannan; Ning, Jing; Wang, Yan; Wang, Chao; Sun, Chengpeng; Huo, Xiaokui; Yu, Zhenlong; Feng, Lei; Zhang, Baojing; Tian, Xiangge; Ma, Xiaochi
2018-05-09
The high risk of herb-drug interactions (HDIs) mediated by the herbal medicines and dietary supplements which containing abundant flavonoids had become more and more frequent in our daily life. In our study, the inhibition activities of 44 different structures of flavonoids toward human CYPs were systemically evaluated for the first time. According to our results, a remarkable structure-dependent inhibition behavior toward CYP3A4 was observed in vitro. Some flavonoids such as licoflavone (12) and irilone (30) exhibited the selective inhibition toward CYP3 A4 rather than other major human CYPs. To illustrate the interaction mechanism, the inhibition kinetics of various compounds was further performed. Sophoranone (1), apigenin (10), baicalein (11), 5,4'-dihydroxy-3,6,7,8,3'-pentamethoxyflavone (15), myricetin (23) and kushenol K (38) remarkably inhibited the CYP3 A4-catalyzed bufalin 5'-hydroxylation reaction, with K i values of 2.17 ± 0.29, 6.15 ± 0.39, 9.18 ± 3.40, 2.30 ± 0.36, 5.00 ± 2.77 and 1.35 ± 0.25 μM, respectively. Importantly, compounds 1, 11, 15, 23 and 38 could significantly inhibit the metabolism of some clinical drugs in vitro, and these drug-drug interactions (DDIs) of myricetin (23) or kushenol K (38) with clinical drug diazepam were further verified in human primary hepatocytes, respectively. Finally, a quantitative structure-activity relationship (QSAR) of flavonoids with their inhibitory effects toward CYP3 A4 was established using computational methods. Our findings illustrated the high risk of herb-drug interactions (HDIs) caused by flavonoids and revealed the vital structures requirement of natural flavonoids for the HDIs with clinical drugs eliminated by CYP3 A4. Our research provided the useful guidance to safely and rationally use herbal medicines and dietary supplements containing rich natural flavonoids components. Copyright © 2018 Elsevier B.V. All rights reserved.
Hisaki, Tomoka; Aiba Née Kaneko, Maki; Yamaguchi, Masahiko; Sasa, Hitoshi; Kouzuki, Hirokazu
2015-04-01
Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental data is quantitative structure-activity relationship (QSAR) analysis. Here, we present QSAR models for prediction of maximum "no observed effect level" (NOEL) for repeated-dose, developmental and reproductive toxicities. NOEL values of 421 chemicals for repeated-dose toxicity, 315 for reproductive toxicity, and 156 for developmental toxicity were collected from Japan Existing Chemical Data Base (JECDB). Descriptors to predict toxicity were selected based on molecular orbital (MO) calculations, and QSAR models employing multiple independent descriptors as the input layer of an artificial neural network (ANN) were constructed to predict NOEL values. Robustness of the models was indicated by the root-mean-square (RMS) errors after 10-fold cross-validation (0.529 for repeated-dose, 0.508 for reproductive, and 0.558 for developmental toxicity). Evaluation of the models in terms of the percentages of predicted NOELs falling within factors of 2, 5 and 10 of the in-vivo-determined NOELs suggested that the model is applicable to both general chemicals and the subset of chemicals listed in International Nomenclature of Cosmetic Ingredients (INCI). Our results indicate that ANN models using in silico parameters have useful predictive performance, and should contribute to integrated risk assessment of systemic toxicity using a weight-of-evidence approach. Availability of predicted NOELs will allow calculation of the margin of safety, as recommended by the Scientific Committee on Consumer Safety (SCCS).
Toxicokinetic models serve a vital role in risk assessment by bridging the gap between chemical exposure and potentially toxic endpoints. While intrinsic metabolic clearance rates have a strong impact on toxicokinetics, limited data is available for environmentally relevant chemi...
Concu, Riccardo; Kleandrova, Valeria V; Speck-Planche, Alejandro; Cordeiro, M Natália D S
2017-09-01
Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological systems and their ecosystems. Toxicity testing is an essential step for assessing the potential risks of the NPs, but the experimental assays are often very expensive and usually too slow to flag the number of NPs that may cause adverse effects. In silico models centered on quantitative structure-activity/toxicity relationships (QSAR/QSTR) are alternative tools that have become valuable supports to risk assessment, rationalizing the search for safer NPs. In this work, we develop a unified QSTR-perturbation model based on artificial neural networks, aimed at simultaneously predicting general toxicity profiles of NPs under diverse experimental conditions. The model is derived from 54,371 NP-NP pair cases generated by applying the perturbation theory to a set of 260 unique NPs, and showed an accuracy higher than 97% in both training and validation sets. Physicochemical interpretation of the different descriptors in the model are additionally provided. The QSTR-perturbation model is then employed to predict the toxic effects of several NPs not included in the original dataset. The theoretical results obtained for this independent set are strongly consistent with the experimental evidence found in the literature, suggesting that the present QSTR-perturbation model can be viewed as a promising and reliable computational tool for probing the toxicity of NPs.
Predictive Model of Systemic Toxicity (SOT)
In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...
Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to either: (1) identify alternative toxicity measures (shorter duration) that may be used as...
Liu, Zhichao; Kelly, Reagan; Fang, Hong; Ding, Don; Tong, Weida
2011-07-18
The primary testing strategy to identify nongenotoxic carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize the chemicals for, supplement, or even replace the cancer bioassay. In silico approaches based on quantitative structure-activity relationships (QSAR) are rapid and inexpensive and thus have been investigated for such purposes. A slightly more expensive approach based on short-term animal studies with toxicogenomics (TGx) represents another attractive option for this application. Thus, the primary questions are how much better predictive performance using short-term TGx models can be achieved compared to that of QSAR models, and what length of exposure is sufficient for high quality prediction based on TGx. In this study, we developed predictive models for rodent liver carcinogenicity using gene expression data generated from short-term animal models at different time points and QSAR. The study was focused on the prediction of nongenotoxic carcinogenicity since the genotoxic chemicals can be inexpensively removed from further development using various in vitro assays individually or in combination. We identified 62 chemicals whose hepatocarcinogenic potential was available from the National Center for Toxicological Research liver cancer database (NCTRlcdb). The gene expression profiles of liver tissue obtained from rats treated with these chemicals at different time points (1 day, 3 days, and 5 days) are available from the Gene Expression Omnibus (GEO) database. Both TGx and QSAR models were developed on the basis of the same set of chemicals using the same modeling approach, a nearest-centroid method with a minimum redundancy and maximum relevancy-based feature selection with performance assessed using compound-based 5-fold cross-validation. We found that the TGx models outperformed QSAR in every aspect of modeling. For example, the
El Ashry, El Sayed H; El Nemr, Ahmed; Ragab, Safaa
2012-03-01
Quantum chemical calculations using the density functional theory (B3LYP/6-31G DFT) and semi-empirical AM1 methods were performed on ten pyridine derivatives used as corrosion inhibitors for mild steel in acidic medium to determine the relationship between molecular structure and their inhibition efficiencies. Quantum chemical parameters such as total negative charge (TNC) on the molecule, energy of highest occupied molecular orbital (E (HOMO)), energy of lowest unoccupied molecular orbital (E (LUMO)) and dipole moment (μ) as well as linear solvation energy terms, molecular volume (Vi) and dipolar-polarization (π) were correlated to corrosion inhibition efficiency of ten pyridine derivatives. A possible correlation between corrosion inhibition efficiencies and structural properties was searched to reduce the number of compounds to be selected for testing from a library of compounds. It was found that theoretical data support the experimental results. The results were used to predict the corrosion inhibition of 24 related pyridine derivatives.
EFFORTS TO EXPAND THE DSSTOX STRUCTURE-SEARCHABLE PUBLIC TOXICITY DATABASE NETWORK
A major goal of the DSSTox website is to improve the utility of published toxicity data across different fields of research. The largest barriers in the exploration of toxicity data by chemists and modelers are the lack of chemical structure annotation in the research literature ...
Zhang, Ding-kun; Li, Rui-sheng; Han, Xue; Li, Chun-yu; Zhao, Zhi-hao; Zhang, Hai-zhu; Yang, Ming; Wang, Jia-bo; Xiao, Xiao-he
2016-01-01
Complex chemical composition is an important reason for restricting herbal quality evaluation. Despite the multi-components determination method significantly promoted the progress of herbal quality evaluation, however, which mainly concerned the total amount of multiple components and ignored the activity variation between each one, and did not accurately reflect the biological activity of botanical medicines. In this manuscript, we proposed a toxicity calibrated contents determination method for hyper toxic aconite, called toxic constituents index (TCI). Initially, we determined the minimum lethal dose value of mesaconitine (MA), aconitine (AC), and hypaconitine (HA), and established the equation TCI = 100 × (0.3387 ×XMA + 0.4778 ×XAC + 0.1835 ×XHA). Then, 10 batches of aconite were selected and their evaluation results of toxic potency (TP), diester diterpenoid alkaloids (DDAs), and TCI were compared. Linear regression analysis result suggested that the relevance between TCI and TP was the highest and the correlation coefficient R was 0.954. Prediction error values study also indicated that the evaluation results of TCI was highly consistent with that of TP. Moreover, TCI and DDAs were both applied to evaluate 14 batches of aconite samples oriented different origins; from the different evaluation results, we found when the proportion of HA was reached 25% in DDAs, the pharmacopeia method could generate false positive results. All these results testified the accuracy and universality of TCI method. We believe that this study method is rather accurate, simple, and easy operation and it will be of great utility in studies of other foods and herbs. PMID:27378926
Toxic Substances List. 1972 Edition.
ERIC Educational Resources Information Center
Christensen, Herbert E., Ed.; And Others
The second edition of the Toxic Substances List, containing some 13,000 entries, is prepared annually by the National Institute for Occupational Safety and Health (NIOSH) in compliance with the Occupational Safety and Health Act of 1970. The purpose of the List is to identify all known toxic substances but not to quantitate the hazard. The List…
Su, Hanrui; Yu, Chunyang; Zhou, Yongfeng; Gong, Lidong; Li, Qilin; Alvarez, Pedro J J; Long, Mingce
2018-05-02
Tetra-amido macrocyclic ligand (TAML) activator is a functional analog of peroxidase enzymes, which activates hydrogen peroxide (H 2 O 2 ) to form high valence iron-oxo complexes that selectively degrade persistent aromatic organic contaminants (ACs) in water. Here, we develop quantitative structure-activity relationship (QSAR) models based on measured pseudo first-order kinetic rate coefficients (k obs ) of 29 ACs (e.g., phenols and pharmaceuticals) oxidized by TAML/H 2 O 2 at neutral and basic pH values to gain mechanistic insight on the selectivity and pH dependence of TAML/H 2 O 2 systems. These QSAR models infer that electron donating ability (E HOMO ) is the most important AC characteristic for TAML/H 2 O 2 oxidation, pointing to a rate-limiting single-electron transfer (SET) mechanism. Oxidation rates at pH 7 also depend on AC reactive indices such as f min - and qH + , which respectively represent propensity for electrophilic attack and the most positive net atomic charge on hydrogen atoms. At pH 10, TAML/H 2 O 2 is more reactive towards ACs with a lower hydrogen to carbon atoms ratio (#H:C), suggesting the significance of hydrogen atom abstraction. In addition, lnk obs of 14 monosubstituted phenols is negatively correlated with Hammett constants (σ) and exhibits similar sensitivity to substituent effects as horseradish peroxidase. Although accurately predicting degradation rates of specific ACs in complex wastewater matrices could be difficult, these QSAR models are statistically robust and help predict both relative degradability and reaction mechanism for TAML/H 2 O 2 -based treatment processes. Copyright © 2018 Elsevier Ltd. All rights reserved.
NASA Astrophysics Data System (ADS)
Lu, Xiaoyan; Tian, Yu; Zhao, Qinqin; Jin, Tingting; Xiao, Shun; Fan, Xiaohui
2011-02-01
Understanding the underlying properties-dependent interactions of nanostructures with biological systems is essential to nanotoxicological research. This study investigates the relationship between particle size and toxicity, and further reveals the mechanism of injury, using silica particles (SP) with diameters of 30, 70, and 300 nm (SP30, SP70, and SP300) as model materials. The biochemical compositions of liver tissues and serum of mice treated with SP30, SP70, and SP300 were analyzed by integrated metabonomics analysis based on gas chromatography-mass spectrometry (GC-MS) and in combination with pattern recognition approaches. Histopathological examinations and serum biochemical analysis were simultaneously performed. The toxicity induced by three different sizes of SP mainly involved hepatocytic necrosis, increased serum aminotransferase, and inflammatory cytokines. Moreover, the toxic effects of SP were dose-dependent for each particle size. The doses of SP30, SP70, and SP300 that were toxic to the liver were 10, 40, and 200 mg kg - 1, respectively. In this study, surface area has a greater effect than particle number on the toxicity of SP30, SP70, and SP300 in the liver. The disturbances in energy metabolism, amino acid metabolism, lipid metabolism, and nucleotide metabolism may be attributable to the hepatotoxicity induced by SP. In addition, no major differences were found in the response of biological systems caused by the different SP sizes among the metabolite profiles. The results suggest that not only nano-sized but also submicro-sized SP can cause similar extents of liver injury, which is dependent on the exposure dose, and the mechanism of toxicity may be almost the same.
Small molecule fluoride toxicity agonists.
Nelson, James W; Plummer, Mark S; Blount, Kenneth F; Ames, Tyler D; Breaker, Ronald R
2015-04-23
Fluoride is a ubiquitous anion that inhibits a wide variety of metabolic processes. Here, we report the identification of a series of compounds that enhance fluoride toxicity in Escherichia coli and Streptococcus mutans. These molecules were isolated by using a high-throughput screen (HTS) for compounds that increase intracellular fluoride levels as determined via a fluoride riboswitch reporter fusion construct. A series of derivatives were synthesized to examine structure-activity relationships, leading to the identification of compounds with improved activity. Thus, we demonstrate that small molecule fluoride toxicity agonists can be identified by HTS from existing chemical libraries by exploiting a natural fluoride riboswitch. In addition, our findings suggest that some molecules might be further optimized to function as binary antibacterial agents when combined with fluoride. Copyright © 2015 Elsevier Ltd. All rights reserved.
Small Molecule Fluoride Toxicity Agonists
Nelson1, James W.; Plummer, Mark S.; Blount, Kenneth F.; Ames, Tyler D.; Breaker, Ronald R.
2015-01-01
SUMMARY Fluoride is a ubiquitous anion that inhibits a wide variety of metabolic processes. Here we report the identification of a series of compounds that enhance fluoride toxicity in Escherichia coli and Streptococcus mutans. These molecules were isolated by using a high-throughput screen (HTS) for compounds that increase intracellular fluoride levels as determined via a fluoride riboswitch-reporter fusion construct. A series of derivatives were synthesized to examine structure-activity relationships, leading to the identification of compounds with improved activity. Thus, we demonstrate that small molecule fluoride toxicity agonists can be identified by HTS from existing chemical libraries by exploiting a natural fluoride riboswitch. In addition, our findings suggest that some molecules might be further optimized to function as binary antibacterial agents when combined with fluoride. PMID:25910244
An expert system for prediction of aquatic toxicity of contaminants
Hickey, James P.; Aldridge, Andrew J.; Passino, Dora R. May; Frank, Anthony M.; Hushon, Judith M.
1990-01-01
The National Fisheries Research Center-Great Lakes has developed an interactive computer program in muLISP that runs on an IBM-compatible microcomputer and uses a linear solvation energy relationship (LSER) to predict acute toxicity to four representative aquatic species from the detailed structure of an organic molecule. Using the SMILES formalism for a chemical structure, the expert system identifies all structural components and uses a knowledge base of rules based on an LSER to generate four structure-related parameter values. A separate module then relates these values to toxicity. The system is designed for rapid screening of potential chemical hazards before laboratory or field investigations are conducted and can be operated by users with little toxicological background. This is the first expert system based on LSER, relying on the first comprehensive compilation of rules and values for the estimation of LSER parameters.
PROPOSED ST ANDARD TO GREA TL Y EXP AND PUBLIC ACCESS AND EXPLORATION OF TOXICITY DATA: EVALUATION OF STRUCTURE DATA FILE FORMAT
The ability to assess the potential toxicity of environmental, pharmaceutical, or industrial chemicals based on chemical structure in...
THE FUTURE OF TOXICOLOGY-PREDICTIVE TOXICOLOGY: AN EXPANDED VIEW OF CHEMICAL TOXICITY
A chemistry approach to predictive toxicology relies on structure−activity relationship (SAR) modeling to predict biological activity from chemical structure. Such approaches have proven capabilities when applied to well-defined toxicity end points or regions of chemical space. T...
Relationship among visual field, blood flow, and neural structure measurements in glaucoma.
Hwang, John C; Konduru, Ranjith; Zhang, Xinbo; Tan, Ou; Francis, Brian A; Varma, Rohit; Sehi, Mitra; Greenfield, David S; Sadda, Srinivas R; Huang, David
2012-05-17
To determine the relationship among visual field, neural structural, and blood flow measurements in glaucoma. Case-control study. Forty-seven eyes of 42 patients with perimetric glaucoma were age-matched with 27 normal eyes of 27 patients. All patients underwent Doppler Fourier-domain optical coherence tomography to measure retinal blood flow and standard glaucoma evaluation with visual field testing and quantitative structural imaging. Linear regression analysis was performed to analyze the relationship among visual field, blood flow, and structure, after all variables were converted to logarithmic decibel scale. Retinal blood flow was reduced in glaucoma eyes compared to normal eyes (P < 0.001). Visual field loss was correlated with both reduced retinal blood flow and structural loss of rim area and retinal nerve fiber layer (RNFL). There was no correlation or paradoxical correlation between blood flow and structure. Multivariate regression analysis revealed that reduced blood flow and structural loss are independent predictors of visual field loss. Each dB decrease in blood flow was associated with at least 1.62 dB loss in mean deviation (P ≤ 0.001), whereas each dB decrease in rim area and RNFL was associated with 1.15 dB and 2.56 dB loss in mean deviation, respectively (P ≤ 0.03). There is a close link between reduced retinal blood flow and visual field loss in glaucoma that is largely independent of structural loss. Further studies are needed to elucidate the causes of the vascular dysfunction and potential avenues for therapeutic intervention. Blood flow measurement may be useful as an independent assessment of glaucoma severity.
NASA Astrophysics Data System (ADS)
Fujimori, Takashi; Takigami, Hidetaka; Takaoka, Masaki
2013-04-01
Heavy metals and toxic chlorinated aromatic compounds (aromatic-Cls) such as dioxins and polychlorinated biphenyls (PCBs) are found at high concentrations and persist in surface soil at wire burning sites (WBSs) in developing countries in which various wire cables are recycled to yield pure metals. Chlorine K-edge near-edge X-ray absorption fine structure (NEXAFS) is used to detect the specific chemical form of Cl and estimate its amount using a spectrum jump in the solid phase. Quantitative X-ray speciation of Cl was applied to study the mechanisms of aromatic-Cls formation in surface soil at WBSs in Southeast Asia. Relationships between aromatic-Cls and chlorides of heavy metals were evaluated because heavy metals are promoters of the thermochemical solid-phase formation of aromatic-Cls.
NASA Astrophysics Data System (ADS)
Alloui, Mebarka; Belaidi, Salah; Othmani, Hasna; Jaidane, Nejm-Eddine; Hochlaf, Majdi
2018-03-01
We performed benchmark studies on the molecular geometry, electron properties and vibrational analysis of imidazole using semi-empirical, density functional theory and post Hartree-Fock methods. These studies validated the use of AM1 for the treatment of larger systems. Then, we treated the structural, physical and chemical relationships for a series of imidazole derivatives acting as angiotensin II AT1 receptor blockers using AM1. QSAR studies were done for these imidazole derivatives using a combination of various physicochemical descriptors. A multiple linear regression procedure was used to design the relationships between molecular descriptor and the activity of imidazole derivatives. Results validate the derived QSAR model.
ERIC Educational Resources Information Center
Hines, Denise Williams
2009-01-01
The use of electronic personal health records is becoming increasingly more popular as healthcare providers, healthcare and government leaders, and patients are seeking ways to improve healthcare quality and to decrease costs (Abrahamsen, 2007). This quantitative, descriptive correlational study examined the relationship between the degree of…
A Quantitative review of relationships between Ecosystem services
NASA Astrophysics Data System (ADS)
Lee, H.; Lautenbach, S.
2014-12-01
Each decision in natural resources management can generate trade-offs with respect to the provisioning of ecosystem services (ES). If the increase of one ES happens directly or indirectly at the cost of another ES, an attempt to maximize the provision of a single ES will lead to suboptimal results. However, decisions in natural resources management are often made without considering such trade-offs, despite their crucial role toward supporting better decision-making. The research on trade-offs between ES has gained some attention in the scientific community. However, a synthesis on existing knowledge and knowledge gaps is missing so far. We aim at closing that gap by a quantitative review of recent literature on trade-offs of ES. We looked at the pairs of ES that have been studied in ~100 case studies that report on trade-offs between ES. If a case study analyzed more than one ES pair, we looked at all pairwise combinations. We categorized relationships between these pairs of ES into the categories "trade-off", "synergy" or "no-effect". Most pairs of ES had a clear association with one category: the majority of case studies that studied a specific pair of ES identified the same category of relationship between the two ES. Pairs of regulating services were typically synergetic in relationship, whereas provisioning services and regulating services typically showed a trade-off. However, for several pairs of ES we were not able to identify a dominate category of relationship. Our hypothesis is that this relates to either the scale of the analysis, the land system where the analysis took place or the method used to quantify the relationship. The number of case studies for each pair of ES was spread unevenly. This hinders the support for a conclusive statement drawn for the pairs. Our results showed further that the method used to identify the relationship between services had a strong effect on the direction of the effect. This suggests that researchers should consider
Taraji, Maryam; Haddad, Paul R; Amos, Ruth I J; Talebi, Mohammad; Szucs, Roman; Dolan, John W; Pohl, Chris A
2017-02-07
A design-of-experiment (DoE) model was developed, able to describe the retention times of a mixture of pharmaceutical compounds in hydrophilic interaction liquid chromatography (HILIC) under all possible combinations of acetonitrile content, salt concentration, and mobile-phase pH with R 2 > 0.95. Further, a quantitative structure-retention relationship (QSRR) model was developed to predict retention times for new analytes, based only on their chemical structures, with a root-mean-square error of prediction (RMSEP) as low as 0.81%. A compound classification based on the concept of similarity was applied prior to QSRR modeling. Finally, we utilized a combined QSRR-DoE approach to propose an optimal design space in a quality-by-design (QbD) workflow to facilitate the HILIC method development. The mathematical QSRR-DoE model was shown to be highly predictive when applied to an independent test set of unseen compounds in unseen conditions with a RMSEP value of 5.83%. The QSRR-DoE computed retention time of pharmaceutical test analytes and subsequently calculated separation selectivity was used to optimize the chromatographic conditions for efficient separation of targets. A Monte Carlo simulation was performed to evaluate the risk of uncertainty in the model's prediction, and to define the design space where the desired quality criterion was met. Experimental realization of peak selectivity between targets under the selected optimal working conditions confirmed the theoretical predictions. These results demonstrate how discovery of optimal conditions for the separation of new analytes can be accelerated by the use of appropriate theoretical tools.
Zheng, Jie; Liang, Guizhao
2015-01-01
Phenolic acids and derivatives have potential biological functions, however, little is known about the structure-activity relationships and the underlying action mechanisms of these phenolic acids to date. Herein we investigate the structure-thermodynamics-antioxidant relationships of 20 natural phenolic acids and derivatives using DPPH• scavenging assay, density functional theory calculations at the B3LYP/6-311++G(d,p) levels of theory, and quantitative structure-activity relationship (QSAR) modeling. Three main working mechanisms (HAT, SETPT and SPLET) are explored in four micro-environments (gas-phase, benzene, water and ethanol). Computed thermodynamics parameters (BDE, IP, PDE, PA and ETE) are compared with the experimental radical scavenging activities against DPPH•. Available theoretical and experimental investigations have demonstrated that the extended delocalization and intra-molecular hydrogen bonds are the two main contributions to the stability of the radicals. The C = O or C = C in COOH, COOR, C = CCOOH and C = CCOOR groups, and orthodiphenolic functionalities are shown to favorably stabilize the specific radical species to enhance the radical scavenging activities, while the presence of the single OH in the ortho position of the COOH group disfavors the activities. HAT is the thermodynamically preferred mechanism in the gas phase and benzene, whereas SPLET in water and ethanol. Furthermore, our QSAR models robustly represent the structure-activity relationships of these explored compounds in polar media. PMID:25803685
Chen, Yuzhen; Xiao, Huizhi; Zheng, Jie; Liang, Guizhao
2015-01-01
Phenolic acids and derivatives have potential biological functions, however, little is known about the structure-activity relationships and the underlying action mechanisms of these phenolic acids to date. Herein we investigate the structure-thermodynamics-antioxidant relationships of 20 natural phenolic acids and derivatives using DPPH• scavenging assay, density functional theory calculations at the B3LYP/6-311++G(d,p) levels of theory, and quantitative structure-activity relationship (QSAR) modeling. Three main working mechanisms (HAT, SETPT and SPLET) are explored in four micro-environments (gas-phase, benzene, water and ethanol). Computed thermodynamics parameters (BDE, IP, PDE, PA and ETE) are compared with the experimental radical scavenging activities against DPPH•. Available theoretical and experimental investigations have demonstrated that the extended delocalization and intra-molecular hydrogen bonds are the two main contributions to the stability of the radicals. The C = O or C = C in COOH, COOR, C = CCOOH and C = CCOOR groups, and orthodiphenolic functionalities are shown to favorably stabilize the specific radical species to enhance the radical scavenging activities, while the presence of the single OH in the ortho position of the COOH group disfavors the activities. HAT is the thermodynamically preferred mechanism in the gas phase and benzene, whereas SPLET in water and ethanol. Furthermore, our QSAR models robustly represent the structure-activity relationships of these explored compounds in polar media.
Prior studies exploring the quantitative relationship between landscape structure metrics and the ecological condition of receiving waters have used a variety of sampling units (e.g. a watershed, or a buffer around a sampling station) at a variety of spatial scales to generate la...
IN SILICO MODELLING OF HAZARDOUS ENDPOINTS: CURRENT PROBLEMS AND PROSPECTIVES
The primary hurdles for Quantitative Structure-Activity Relationships (QSARs) to overcome their acceptance for regulatory purposes will be discussed. They include (a) the development of more mechanistic representations of chemical structure, (b) the classification of toxicity pa...
Modelling interactions of toxicants and density dependence in wildlife populations
Schipper, Aafke M.; Hendriks, Harrie W.M.; Kauffman, Matthew J.; Hendriks, A. Jan; Huijbregts, Mark A.J.
2013-01-01
toxicant impacts until a critical threshold is crossed. In our study population, toxicant-induced changes were observed in the equilibrium number of nonbreeding rather than breeding birds, suggesting that monitoring efforts including both life stages are needed to timely detect population declines. Further, by combining quantitative exposure–response relationships with a wildlife demographic model, we provided a method to quantify critical toxicant thresholds for wildlife population persistence.
Standard developmental toxicology bioassays are designed to identify agents with the potential to induce adverse effects and include dose levels that induce maternal toxicity. The work reported here was undertaken to evaluate the relationship of maternal and fetal toxicity. It co...
Liao, Quan; Yao, Jianhua; Yuan, Shengang
2007-05-01
The study of prediction of toxicity is very important and necessary because measurement of toxicity is typically time-consuming and expensive. In this paper, Recursive Partitioning (RP) method was used to select descriptors. RP and Support Vector Machines (SVM) were used to construct structure-toxicity relationship models, RP model and SVM model, respectively. The performances of the two models are different. The prediction accuracies of the RP model are 80.2% for mutagenic compounds in MDL's toxicity database, 83.4% for compounds in CMC and 84.9% for agrochemicals in in-house database respectively. Those of SVM model are 81.4%, 87.0% and 87.3% respectively.
NASA Astrophysics Data System (ADS)
Woolfrey, John R.; Avery, Mitchell A.; Doweyko, Arthur M.
1998-03-01
Two three-dimensional quantitative structure-activity relationship (3D-QSAR) methods, comparative molecular field analysis (CoMFA) and hypothetical active site lattice (HASL), were compared with respect to the analysis of a training set of 154 artemisinin analogues. Five models were created, including a complete HASL and two trimmed versions, as well as two CoMFA models (leave-one-out standard CoMFA and the guided-region selection protocol). Similar r2 and q2 values were obtained by each method, although some striking differences existed between CoMFA contour maps and the HASL output. Each of the four predictive models exhibited a similar ability to predict the activity of a test set of 23 artemisinin analogues, although some differences were noted as to which compounds were described well by either model.
Dose-finding designs using a novel quasi-continuous endpoint for multiple toxicities
Ezzalfani, Monia; Zohar, Sarah; Qin, Rui; Mandrekar, Sumithra J; Deley, Marie-Cécile Le
2013-01-01
The aim of a phase I oncology trial is to identify a dose with an acceptable safety profile. Most phase I designs use the dose-limiting toxicity, a binary endpoint, to assess the unacceptable level of toxicity. The dose-limiting toxicity might be incomplete for investigating molecularly targeted therapies as much useful toxicity information is discarded. In this work, we propose a quasi-continuous toxicity score, the total toxicity profile (TTP), to measure quantitatively and comprehensively the overall severity of multiple toxicities. We define the TTP as the Euclidean norm of the weights of toxicities experienced by a patient, where the weights reflect the relative clinical importance of each grade and toxicity type. We propose a dose-finding design, the quasi-likelihood continual reassessment method (CRM), incorporating the TTP score into the CRM, with a logistic model for the dose–toxicity relationship in a frequentist framework. Using simulations, we compared our design with three existing designs for quasi-continuous toxicity score (the Bayesian quasi-CRM with an empiric model and two nonparametric designs), all using the TTP score, under eight different scenarios. All designs using the TTP score to identify the recommended dose had good performance characteristics for most scenarios, with good overdosing control. For a sample size of 36, the percentage of correct selection for the quasi-likelihood CRM ranged from 80% to 90%, with similar results for the quasi-CRM design. These designs with TTP score present an appealing alternative to the conventional dose-finding designs, especially in the context of molecularly targeted agents. PMID:23335156
Development of whole sediment toxicity identification and evaluation (TIEs) methods has been under way for approximately four years. These methods are necessary to define cause and effect relationships in toxic sediments during ecological risk assessments, remediation and disposa...
Structural basis of membrane disruption and cellular toxicity by α-synuclein oligomers.
Fusco, Giuliana; Chen, Serene W; Williamson, Philip T F; Cascella, Roberta; Perni, Michele; Jarvis, James A; Cecchi, Cristina; Vendruscolo, Michele; Chiti, Fabrizio; Cremades, Nunilo; Ying, Liming; Dobson, Christopher M; De Simone, Alfonso
2017-12-15
Oligomeric species populated during the aggregation process of α-synuclein have been linked to neuronal impairment in Parkinson's disease and related neurodegenerative disorders. By using solution and solid-state nuclear magnetic resonance techniques in conjunction with other structural methods, we identified the fundamental characteristics that enable toxic α-synuclein oligomers to perturb biological membranes and disrupt cellular function; these include a highly lipophilic element that promotes strong membrane interactions and a structured region that inserts into lipid bilayers and disrupts their integrity. In support of these conclusions, mutations that target the region that promotes strong membrane interactions by α-synuclein oligomers suppressed their toxicity in neuroblastoma cells and primary cortical neurons. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
Card, Marcella L; Gomez-Alvarez, Vicente; Lee, Wen-Hsiung; Lynch, David G; Orentas, Nerija S; Lee, Mari Titcombe; Wong, Edmund M; Boethling, Robert S
2017-03-22
Chemical property estimation is a key component in many industrial, academic, and regulatory activities, including in the risk assessment associated with the approximately 1000 new chemical pre-manufacture notices the United States Environmental Protection Agency (US EPA) receives annually. The US EPA evaluates fate, exposure and toxicity under the 1976 Toxic Substances Control Act (amended by the 2016 Frank R. Lautenberg Chemical Safety for the 21 st Century Act), which does not require test data with new chemical applications. Though the submission of data is not required, the US EPA has, over the past 40 years, occasionally received chemical-specific data with pre-manufacture notices. The US EPA has been actively using this and publicly available data to develop and refine predictive computerized models, most of which are housed in EPI Suite™, to estimate chemical properties used in the risk assessment of new chemicals. The US EPA develops and uses models based on (quantitative) structure-activity relationships ([Q]SARs) to estimate critical parameters. As in any evolving field, (Q)SARs have experienced successes, suffered failures, and responded to emerging trends. Correlations of a chemical structure with its properties or biological activity were first demonstrated in the late 19 th century and today have been encapsulated in a myriad of quantitative and qualitative SARs. The development and proliferation of the personal computer in the late 20 th century gave rise to a quickly increasing number of property estimation models, and continually improved computing power and connectivity among researchers via the internet are enabling the development of increasingly complex models.
Zhang, Qingqing; Huo, Mengqi; Zhang, Yanling; Qiao, Yanjiang; Gao, Xiaoyan
2018-06-01
High-resolution mass spectrometry (HRMS) provides a powerful tool for the rapid analysis and identification of compounds in herbs. However, the diversity and large differences in the content of the chemical constituents in herbal medicines, especially isomerisms, are a great challenge for mass spectrometry-based structural identification. In the current study, a new strategy for the structural characterization of potential new phthalide compounds was proposed by isomer structure predictions combined with a quantitative structure-retention relationship (QSRR) analysis using phthalide compounds in Chuanxiong as an example. This strategy consists of three steps. First, the structures of phthalide compounds were reasonably predicted on the basis of the structure features and MS/MS fragmentation patterns: (1) the collected raw HRMS data were preliminarily screened by an in-house database; (2) the MS/MS fragmentation patterns of the analogous compounds were summarized; (3) the reported phthalide compounds were identified, and the structures of the isomers were reasonably predicted. Second, the QSRR model was established and verified using representative phthalide compound standards. Finally, the retention times of the predicted isomers were calculated by the QSRR model, and the structures of these peaks were rationally characterized by matching retention times of the detected chromatographic peaks and the predicted isomers. A multiple linear regression QSRR model in which 6 physicochemical variables were screened was built using 23 phthalide standards. The retention times of the phthalide isomers in Chuanxiong were well predicted by the QSRR model combined with reasonable structure predictions (R 2 =0.955). A total of 81 peaks were detected from Chuanxiong and assigned to reasonable structures, and 26 potential new phthalide compounds were structurally characterized. This strategy can improve the identification efficiency and reliability of homologues in complex
Universal structural parameter to quantitatively predict metallic glass properties
Ding, Jun; Cheng, Yong-Qiang; Sheng, Howard; ...
2016-12-12
Quantitatively correlating the amorphous structure in metallic glasses (MGs) with their physical properties has been a long-sought goal. Here we introduce flexibility volume' as a universal indicator, to bridge the structural state the MG is in with its properties, on both atomic and macroscopic levels. The flexibility volume combines static atomic volume with dynamics information via atomic vibrations that probe local configurational space and interaction between neighbouring atoms. We demonstrate that flexibility volume is a physically appropriate parameter that can quantitatively predict the shear modulus, which is at the heart of many key properties of MGs. Moreover, the new parametermore » correlates strongly with atomic packing topology, and also with the activation energy for thermally activated relaxation and the propensity for stress-driven shear transformations. These correlations are expected to be robust across a very wide range of MG compositions, processing conditions and length scales.« less
Passino-Reader, D.R.; Hickey, J.P.; Ogilvie, L.M.
1997-01-01
The objectives of this study were (1) to determine the toxicity of several types of polycyclic hydrocarbons characteristic of Great Lakes samples to Daphnia pulex, a Great Lakes zooplankter, (2) to investigate the influence of different structural characteristics on toxicity, and (3) to determine the linear solvation energy relationship (LSER) parameters and model that describe these compounds. These results will be related to comparative toxicity of other Great Lakes environmental compounds and to their application in site specific risk assessment.
White, Paul A; Johnson, George E
2016-05-01
Applied genetic toxicology is undergoing a transition from qualitative hazard identification to quantitative dose-response analysis and risk assessment. To facilitate this change, the Health and Environmental Sciences Institute (HESI) Genetic Toxicology Technical Committee (GTTC) sponsored a workshop held in Lancaster, UK on July 10-11, 2014. The event included invited speakers from several institutions and the contents was divided into three themes-1: Point-of-departure Metrics for Quantitative Dose-Response Analysis in Genetic Toxicology; 2: Measurement and Estimation of Exposures for Better Extrapolation to Humans and 3: The Use of Quantitative Approaches in Genetic Toxicology for human health risk assessment (HHRA). A host of pertinent issues were discussed relating to the use of in vitro and in vivo dose-response data, the development of methods for in vitro to in vivo extrapolation and approaches to use in vivo dose-response data to determine human exposure limits for regulatory evaluations and decision-making. This Special Issue, which was inspired by the workshop, contains a series of papers that collectively address topics related to the aforementioned themes. The Issue includes contributions that collectively evaluate, describe and discuss in silico, in vitro, in vivo and statistical approaches that are facilitating the shift from qualitative hazard evaluation to quantitative risk assessment. The use and application of the benchmark dose approach was a central theme in many of the workshop presentations and discussions, and the Special Issue includes several contributions that outline novel applications for the analysis and interpretation of genetic toxicity data. Although the contents of the Special Issue constitutes an important step towards the adoption of quantitative methods for regulatory assessment of genetic toxicity, formal acceptance of quantitative methods for HHRA and regulatory decision-making will require consensus regarding the
Mondal Roy, Sutapa; Roy, Debesh R; Sahoo, Suban K
2015-11-01
The applicability of Density Functional Theory (DFT) based descriptors for the development of quantitative structure-toxicity relationships (QSTR) is assessed for two different series of toxic aromatic compounds, viz., polyhalogenated dibenzo-p-dioxins (PHDDs) and phenols (PHs). A series of 20 compounds each for PHDDs and PHs with their experimental toxicities (IC50 and IGC50) is chosen in the present study to develop DFT based efficient quantum chemical parameters (QCPs) for explaining the toxin potential of the considered compounds. A systematic analysis to find out the electron donation/acceptance nature of these selected compounds with the considered model biosystems, viz., nucleic acid (NA) bases and DNA base pairs, is performed to identify potential QCPs. Accordingly, PHDDs is found to be electron acceptors whereas phenols as donors, during their interaction with biosystems. Two parameter regression model is carried out comprising global charge transfer (ΔN), and local Fukui Function's for nucleophilic attack (fk(+)) for PHDDs and the same for electrophilic attack (fk(-)) in case of PHs. It is heartening to note that our chosen descriptors, viz, charge transfer (ΔN) and Fukui Function (fk(±)) plays a crucial role by explaining more than 90% of the observed toxic behavior (in terms of correlation-coefficient, R) of PHDDs and PHs. The developed QCPs, viz., ΔN and fk(±) can be added as the new descriptors in the QSTR parlance. Copyright © 2015 Elsevier Inc. All rights reserved.
Short-tailed shrews: Toxicity and residue relationships of DDT, dieldrin, and endrin
Blus, L.J.
1978-01-01
Experiments involving dietary toxicity and residue relationships of DDT, dieldrin, and endrin were conducted with short-tailed shrews. Dietary concentrations of DDT dissolved in vegetable oils were usually more toxic than diets containing comparable amounts of powdered DDT. Younger shrews, particularly females, were more tolerant of powdered DDT than older animals; yet, there were no conspicuous age differences in toxicity of DDT dissolved in oils. In comparison to other mammals, short-tailed shrews are not unusually sensitive to DDT, dieldrin, or endrin on the basis of two-week feeding tests. The influence of age and sex on toxicity of DDT, endrin, and dieldrin was sometimes more important than body weight. Of those shrews of the same age and sex that were fed the same dietary dosage, heavier shrews were more tolerant than lighter individuals; and, heavier shrews tended to lose a greater percentage of body weight before death. There was a range of 15 to 105 DDT equivalents in brains of shrews dying on dietary dosages of DDT. Six shrews fed a high level of DDT seemed to have unusual metabolite capabilities and died with apparent lethal levels of DDD in their brains. Levels of dieldrin in brains of shrews that died on a dietary dosage of dieldrin ranged from 3.7 to 12.6 ppm. In the rates of gain and loss experiments where shrews were given diets containing 400 ppm DDT or 50 ppm dieldrin up to 17 days, high residues were noted in tissues of shrews after two weeks on a contaminated diet and a few died at that time. After shrews were placed on clean food, it was determined that >50% of the dieldrin residues in carcass and brain were lost in 50% of residues of DDT and metabolites in brains after 2 weeks on clean food; males lost nearly 50% of residues in carcasses after two weeks on clean food compared with a loss of only 11% in females.
Improved building up a model of toxicity towards Pimephales promelas by the Monte Carlo method.
Toropova, Alla P; Toropov, Andrey A; Raskova, Maria; Raska, Ivan
2016-12-01
By optimization of so-called correlation weights of attributes of simplified molecular input-line entry system (SMILES) quantitative structure - activity relationships (QSAR) for toxicity towards Pimephales promelas are established. A new SMILES attribute has been utilized in this work. This attribute is a molecular descriptor, which reflects (i) presence of different kinds of bonds (double, triple, and stereo chemical bonds); (ii) presence of nitrogen, oxygen, sulphur, and phosphorus atoms; and (iii) presence of fluorine, chlorine, bromine, and iodine atoms. The statistical characteristics of the best model are the following: n=226, r 2 =0.7630, RMSE=0.654 (training set); n=114, r 2 =0.7024, RMSE=0.766 (calibration set); n=226, r 2 =0.6292, RMSE=0.870 (validation set). A new criterion to select a preferable split into the training and validation sets are suggested and discussed. Copyright © 2016 Elsevier B.V. All rights reserved.
ERIC Educational Resources Information Center
May-Vollmar, Kelly
2017-01-01
Purpose: The purpose of this quantitative correlation study was to identify whether there is a relationship between emotional intelligence and effective leadership practices, specifically with school administrators in Southern California K-12 public schools. Methods: This study was conducted using a quantitative descriptive design, correlation…
Wang, Xiao H.; Yu, Yang; Huang, Tao; Qin, Wei C.; Su, Li M.; Zhao, Yuan H.
2016-01-01
Investigations on the relationship of toxicities between species play an important role in the understanding of toxic mechanisms to environmental organisms. In this paper, the toxicity data of 949 chemicals to fish and 1470 chemicals to V. fischeri were used to investigate the modes of action (MOAs) between species. The results show that although there is a positive interspecies correlation, the relationship is poor. Analysis on the excess toxicity calculated from toxic ratios (TR) shows that many chemicals have close toxicities and share the same MOAs between the two species. Linear relationships between the toxicities and octanol/water partition coefficient (log KOW) for baseline and less inert compounds indicate that the internal critical concentrations (CBRs) approach a constant both to fish and V. fischeri for neutral hydrophobic compounds. These compounds share the same toxic mechanisms and bio-uptake processes between species. On the other hand, some hydrophilic compounds exhibit different toxic effects with greatly different log TR values between V. fischeri and fish species. These hydrophilic compounds were identified as reactive MOAs to V. fischeri, but not to fish. The interspecies correlation is improved by adding a hydrophobic descriptor into the correlation equation. This indicates that the differences in the toxic ratios between fish and V. fischeri for these hydrophilic compounds can be partly attributed to the differences of bioconcentration between the two species, rather than the differences of reactivity with the target macromolecules. These hydrophilic compounds may more easily pass through the cell membrane of V. fischeri than the gill and skin of fish, react with the target macromolecules and exhibit excess toxicity. The compounds with log KOW > 7 exhibiting very low toxicity (log TR < –1) to both species indicate that the bioconcentration potential of a chemical plays a very important role in the identification of excess toxicity and MOAs
Traditional toxicity testing provides insight into the mechanisms underlying toxicological responses but requires a high investment in a large number of resources. The new paradigm of testing approaches involves rapid screening studies able to evaluate thousands of chemicals acro...
Chen, Shaodan; Li, Xiangmin; Yong, Tianqiao; Wang, Zhanggen; Su, Jiyan; Jiao, Chunwei; Xie, Yizhen; Yang, Burton B
2017-02-07
We conducted a study of Ganoderma lucidum metabolites and isolated 35 lanostane-type triterpenoids, including 5 new ganoderols (1-5). By spectroscopy, we compared the structures of these compounds with known related compounds in this group. All of the isolated compounds were assayed for their effect against the human breast carcinoma cell line MDA-MB-231 and hepatocellular carcinoma cell line HepG2. Corresponding three-dimensional quantitative structure-activity relationship (3D-QSAR) models were built and analyzed using Discovery Studio. These results provide further evidence for anti-cancer constituents within Ganoderma lucidum, and may provide a theoretical foundation for designing novel therapeutic compounds.
Toxic β-Amyloid (Aβ) Alzheimer's Ion Channels: From Structure to Function and Design
NASA Astrophysics Data System (ADS)
Nussinov, Ruth
2012-02-01
Full-length amyloid beta peptides (Aβ1-40/42) form neuritic amyloid plaques in Alzheimer's disease (AD) patients and are implicated in AD pathology. Recent biophysical and cell biological studies suggest a direct mechanism of amyloid beta toxicity -- ion channel mediated loss of calcium homeostasis. Truncated amyloid beta fragments (Aβ11-42 and Aβ17-42), commonly termed as non-amyloidogenic are also found in amyloid plaques of Alzheimer's disease (AD) and in the preamyloid lesions of Down's syndrome (DS), a model system for early onset AD study. Very little is known about the structure and activity of these smaller peptides although they could be key AD and DS pathological agents. Using complementary techniques of explicit solvent molecular dynamics (MD) simulations, atomic force microscopy (AFM), channel conductance measurements, cell calcium uptake assays, neurite degeneration and cell death assays, we have shown that non-amyloidogenic Aβ9-42 and Aβ17-42 peptides form ion channels with loosely attached subunits and elicit single channel conductances. The subunits appear mobile suggesting insertion of small oligomers, followed by dynamic channel assembly and dissociation. These channels allow calcium uptake in APP-deficient cells and cause neurite degeneration in human cortical neurons. Channel conductance, calcium uptake and neurite degeneration are selectively inhibited by zinc, a blocker of amyloid ion channel activity. Thus truncated Aβ fragments could account for undefined roles played by full length Aβs and provide a novel mechanism of AD and DS pathology. The emerging picture from our large-scale simulations is that toxic ion channels formed by β-sheets are highly polymorphic, and spontaneously break into loosely interacting dynamic units (though still maintaining ion channel structures as imaged with AFM), that associate and dissociate leading to toxic ion flux. This sharply contrasts intact conventional gated ion channels that consist of tightly
Reproducibility of Quantitative Structural and Physiological MRI Measurements
2017-08-09
per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and...Journal Article 3. DATES COVERED (From – To) January 2015 – July 2017 4. TITLE AND SUBTITLE Reproducibility of Quantitative Structural and...NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) USAF School of Aerospace Medicine Aeromedical Research Dept/FHOH 2510 Fifth St., Bldg
El-Ansary, Afaf K; Bacha, Abir Ben; Ayahdi, Layla Y Al-
2011-09-01
This study aims to clarify the relationship between blood Pb(2+) concentration as a ubiquitous environmental pollutant and plasma neurotransmitters as biochemical parameters that reflect brain function in Saudi autistic patients. RBC's lead content together with plasma concentration of gamma aminobutyric acid (GABA), serotonin (5HT) and dopamine (DA) were measured in 25 Saudi autistic patients and compared to 16 age-matching control samples. The obtained data recorded that Saudi autistic patients have a remarkable higher levels of Pb(2+) and significantly elevated levels of GABA, 5HT and DA compared to healthy subjects. ROC analysis revealed satisfactory values of specificity and sensitivity of the measured parameters. This study suggests that postnatal lead toxicity in autistic patients of Saudi Arabia could represent a causative factor in the pathogenesis of autism. Elevated GABA, 5HT and DA were discussed in relation to the chronic lead toxicity recorded in the investigated autistic samples. Copyright © 2011. Published by Elsevier Inc.
Quantitative Restoration of the Evolution of Mantle Structures Using Data Assimilation
NASA Astrophysics Data System (ADS)
Ismail-Zadeh, A.; Schubert, G.; Tsepelev, I.
2008-12-01
Rapid progress in imaging deep Earth structures and in studies of physical and chemical properties of mantle rocks facilitates research in assimilation of data related to mantle dynamics. We present a quantitative approach to assimilation of geophysical and geodetic data, which allows for incorporating observations and unknown initial conditions for mantle temperature and flow into a three-dimensional dynamic model in order to determine the initial conditions in the geological past. Once the conditions are determined the evolution of mantle structures can be restore backward in time. We apply data assimilation techniques to model the evolution of mantle plumes and lithospheric slabs. We show that the geometry of the mantle structures changes with time diminishing the degree of surface curvature of the structures, because the heat conduction smoothes the complex thermal surfaces of mantle bodies with time. Present seismic tomography images of mantle structures do not allow definition of the sharp shapes of these structures. Assimilation of mantle temperature and flow to the geological past instead provides a quantitative tool to restore thermal shapes of prominent structures in the past from their diffusive shapes at present.
Jeon, Ju-Hyun; Lee, Sang-Guei; Lee, Hoi-Seon
2015-08-01
Isolates from essential oil extracted from the flowers and leaves of Ruta graveolens and commercial phenolic analogs were evaluated using fumigant and contact toxicity bioassays against adults of the stored-food pests Sitophilus zeamais, Sitophilus oryzae, and Lasioderma serricorne. The insecticidal activity of these compounds was then compared with that of the synthetic insecticide dichlorvos. To investigate the structure-activity relationships, the activity of 2-isopropyl-5-methylphenol and its analogs was examined against these stored-food pests. Based on the 50% lethal dose, the most toxic compound against S. zeamais was 3-isopropylephenol, followed by 2-isopropylphenol, 4-isopropylphenol, 5-isopropyl-2-methylphenol, 2-isopropyl-5-methylphenol, 3-methylphenol, and 2-methylphenol. Similar results were observed with phenolic compounds against S. oryzae. However, when 2-isopropyl-5-methylphenol isolated from R. graveolens oil and its structurally related analogs were used against L. serricorne, little or no insecticidal activity was found regardless of bioassay. These results indicate that introducing and changing the positions of functional groups in the phenol skeleton have an important effect on insecticidal activity of these compounds against stored-food pests.
Toxic Hazards Research Unit Annual Report: 1987
1988-03-01
Low Density Lipoproteins and in Model Membranes 14 Sep Is Cigarette Smoking Neurotoric? Mr. Steven Goden Dr. R. Kutzman 4> ’October 1986 through September 1987 2I ...based pharmcokinetic model phosphoniteo,o-diethylmethyl quantitative structure activity relationship respiratory epithelium Salmonella sensitization j...in Cultured Respiratory Epithelial Cells ----------------------- ------------ 73 6 PHARMACOKINETIC AND PHARMACODYNAMIC MODELING .------------------ 78
[Validation of a Japanese version of the Experience in Close Relationship- Relationship Structure].
Komura, Kentaro; Murakami, Tatsuya; Toda, Koji
2016-08-01
The purpose of this study was to translate the Experience of Close Relationship-Relationship Structure (ECRRS) and evaluate its validity. In study 1 (N = 982), evidence based internal structure (factor structure, internal consistency, and correlation among sub-scales) and evidence based relations to other variables (depression, reassurance seeking and self-esteem) were confirmed. In study 2 (N = 563), evidence based on internal structure was reconfirmed, and evidence based relations to other variables (IWMS, RQ, and ECR-GO) were confirmed. In study 3 (N = 342), evidence based internal structure (test-retest reliability) was confirmed. Based on these results, we concluded that ECR-RS was valid for measuring adult attachment style.
Learning the Structure of Biomedical Relationships from Unstructured Text
Percha, Bethany; Altman, Russ B.
2015-01-01
The published biomedical research literature encompasses most of our understanding of how drugs interact with gene products to produce physiological responses (phenotypes). Unfortunately, this information is distributed throughout the unstructured text of over 23 million articles. The creation of structured resources that catalog the relationships between drugs and genes would accelerate the translation of basic molecular knowledge into discoveries of genomic biomarkers for drug response and prediction of unexpected drug-drug interactions. Extracting these relationships from natural language sentences on such a large scale, however, requires text mining algorithms that can recognize when different-looking statements are expressing similar ideas. Here we describe a novel algorithm, Ensemble Biclustering for Classification (EBC), that learns the structure of biomedical relationships automatically from text, overcoming differences in word choice and sentence structure. We validate EBC's performance against manually-curated sets of (1) pharmacogenomic relationships from PharmGKB and (2) drug-target relationships from DrugBank, and use it to discover new drug-gene relationships for both knowledge bases. We then apply EBC to map the complete universe of drug-gene relationships based on their descriptions in Medline, revealing unexpected structure that challenges current notions about how these relationships are expressed in text. For instance, we learn that newer experimental findings are described in consistently different ways than established knowledge, and that seemingly pure classes of relationships can exhibit interesting chimeric structure. The EBC algorithm is flexible and adaptable to a wide range of problems in biomedical text mining. PMID:26219079
Huang, Ruili; Xia, Menghang; Sakamuru, Srilatha; Zhao, Jinghua; Shahane, Sampada A.; Attene-Ramos, Matias; Zhao, Tongan; Austin, Christopher P.; Simeonov, Anton
2016-01-01
Target-specific, mechanism-oriented in vitro assays post a promising alternative to traditional animal toxicology studies. Here we report the first comprehensive analysis of the Tox21 effort, a large-scale in vitro toxicity screening of chemicals. We test ∼10,000 chemicals in triplicates at 15 concentrations against a panel of nuclear receptor and stress response pathway assays, producing more than 50 million data points. Compound clustering by structure similarity and activity profile similarity across the assays reveals structure–activity relationships that are useful for the generation of mechanistic hypotheses. We apply structural information and activity data to build predictive models for 72 in vivo toxicity end points using a cluster-based approach. Models based on in vitro assay data perform better in predicting human toxicity end points than animal toxicity, while a combination of structural and activity data results in better models than using structure or activity data alone. Our results suggest that in vitro activity profiles can be applied as signatures of compound mechanism of toxicity and used in prioritization for more in-depth toxicological testing. PMID:26811972
Toxicity challenges in environmental chemicals: Prediction of ...
Physiologically based pharmacokinetic (PBPK) models bridge the gap between in vitro assays and in vivo effects by accounting for the adsorption, distribution, metabolism, and excretion of xenobiotics, which is especially useful in the assessment of human toxicity. Quantitative structure-activity relationships (QSAR) serve as a vital tool for the high-throughput prediction of chemical-specific PBPK parameters, such as the fraction of a chemical unbound by plasma protein (Fub). The presented work explores the merit of utilizing experimental pharmaceutical Fub data for the construction of a universal QSAR model, in order to compensate for the limited range of high-quality experimental Fub data for environmentally relevant chemicals, such as pollutants, pesticides, and consumer products. Independent QSAR models were constructed with three machine-learning algorithms, k nearest neighbors (kNN), random forest (RF), and support vector machine (SVM) regression, from a large pharmaceutical training set (~1000) and assessed with independent test sets of pharmaceuticals (~200) and environmentally relevant chemicals in the ToxCast program (~400). Small descriptor sets yielded the optimal balance of model complexity and performance, providing insight into the biochemical factors of plasma protein binding, while preventing over fitting to the training set. Overlaps in chemical space between pharmaceutical and environmental compounds were considered through applicability of do
Animal Toxicity of Phytopathogenic Fungi
Main, C. E.; Hamilton, P. B.
1972-01-01
Twelve genera of phytopathogenic fungi comprising 27 species previously reported to produce phytotoxins were tested concurrently for animal and plant toxicity. There appeared to be no direct relationship between plant and animal toxicity. PMID:5059620
Guerrero-Muñoz, Marcos J; Castillo-Carranza, Diana L; Kayed, Rakez
2014-04-15
Impaired proteostasis is one of the main features of all amyloid diseases, which are associated with the formation of insoluble aggregates from amyloidogenic proteins. The aggregation process can be caused by overproduction or poor clearance of these proteins. However, numerous reports suggest that amyloid oligomers are the most toxic species, rather than insoluble fibrillar material, in Alzheimer's, Parkinson's, and Prion diseases, among others. Although the exact protein that aggregates varies between amyloid disorders, they all share common structural features that can be used as therapeutic targets. In this review, we focus on therapeutic approaches against shared features of toxic oligomeric structures and future directions. Copyright © 2014 Elsevier Inc. All rights reserved.
Thirty years and over a billion of today’s dollars worth of pesticide registration toxicity studies, historically stored as hardcopy and scanned documents, have been digitized into highly standardized and structured toxicity data, within the U.S. Environmental Protection Agency’s...
Ion toxicity and the development of a salinity toxicity relationship (STR) model for marine species
DOE Office of Scientific and Technical Information (OSTI.GOV)
Tietge, J.E.; Mount, D.R.
1994-12-31
Salinity in effluents can cause acute toxicity to marine organisms. The toxicity of the water can be due to an excess or deficiency of common ions, which usually are not thought of as toxicants. In order to develop an understanding of this phenomenon, laboratory toxicity tests were conducted to determine the effects of single ion deficiency, single ion excess, multiple ion deficiency, multiple ion excess, and total salinity on survival of three common marine test organisms (Mysidopsis bahia, Cyprinidon variegatus, and Menidia beryllina). The ions which were manipulated in these studies were Na{sup +}, K{sup +}, Ca{sup ++}, Mg{sup ++},more » Sr{sup ++}, Cl{sup {minus}}, Br{sup {minus}}, SO{sub 4}{sup {minus}{minus}}, HCO{sub 3}{sup {minus}}, and B{sub 4}O{sub 7}{sup {minus}{minus}}. Results indicate that Ca{sup ++} and K{sup +} are essential ions at normal salinities, since the deficiency of these two ions causes mortality. In contrast, the complete deficiency of Mg{sup ++}, Sr{sup ++}, B{sub 4}O{sub 7}{sup {minus}{minus}}, and HCO{sub 3}{sup {minus}} did not affect survival. The single ion excess studies demonstrated that K{sup +}, Ca{sup ++}, Mg{sup ++}, and B{sub 4}O{sub 7}{sup {minus}} were acutely toxic in excess at normal salinities. Total salinity studies determined the salinity tolerance range for each species, with upper and lower LC{sub 50}s for Mysidopsis bahia at 44 g/L and 8 g/L, for Cyprinidon variegatus at 73 g/L and < 0 g/L, and for Menidia beryllina at 45 g/L and < 0 g/L. These data will be used to develop a model to predict toxicity due to common ions.« less
Park, Soo Hyun; Talebi, Mohammad; Amos, Ruth I J; Tyteca, Eva; Haddad, Paul R; Szucs, Roman; Pohl, Christopher A; Dolan, John W
2017-11-10
Quantitative Structure-Retention Relationships (QSRR) are used to predict retention times of compounds based only on their chemical structures encoded by molecular descriptors. The main concern in QSRR modelling is to build models with high predictive power, allowing reliable retention prediction for the unknown compounds across the chromatographic space. With the aim of enhancing the prediction power of the models, in this work, our previously proposed QSRR modelling approach called "federation of local models" is extended in ion chromatography to predict retention times of unknown ions, where a local model for each target ion (unknown) is created using only structurally similar ions from the dataset. A Tanimoto similarity (TS) score was utilised as a measure of structural similarity and training sets were developed by including ions that were similar to the target ion, as defined by a threshold value. The prediction of retention parameters (a- and b-values) in the linear solvent strength (LSS) model in ion chromatography, log k=a - blog[eluent], allows the prediction of retention times under all eluent concentrations. The QSRR models for a- and b-values were developed by a genetic algorithm-partial least squares method using the retention data of inorganic and small organic anions and larger organic cations (molecular mass up to 507) on four Thermo Fisher Scientific columns (AS20, AS19, AS11HC and CS17). The corresponding predicted retention times were calculated by fitting the predicted a- and b-values of the models into the LSS model equation. The predicted retention times were also plotted against the experimental values to evaluate the goodness of fit and the predictive power of the models. The application of a TS threshold of 0.6 was found to successfully produce predictive and reliable QSRR models (Q ext(F2) 2 >0.8 and Mean Absolute Error<0.1), and hence accurate retention time predictions with an average Mean Absolute Error of 0.2min. Crown Copyright
Antonucci, Francesca; Costa, Corrado; Aguzzi, Jacopo; Cataudella, Stefano
2009-07-01
In many fish species, morphological similarity can be considered as a proxy for similarities in habitat use. The Sparidae family includes species that are recognized for common morphological features such as structure and positioning of the fins and specialized dentition. The aim of this study was to quantitatively describe the relationship of body shape morphology with habitat use, trophic level, and systematics in the majority of known Sparidae species (N = 92). This ecomorphological comparison was performed with a geometric morphometric approach considering as variables the Trophic Index (TROPH), the habitat (i.e., classified as demersal, benthopelagic and reef associated) and the phylogenetic relationship of species at the subfamily level. The analysis by the TROPH variable showed a positive relation with shape because the morphological features of all the species are strongly correlated with their trophic behavior (e.g., herbivore species have a smaller mouth gap that make them able to feed upon sessile resources). The morphological analysis according to the Habitat variable was used to classify species according to a feeding-habitat niche in terms of portion of the water column and seabed space where species mostly perform their behavioral activities. We described three kinds of morphological designs in relation to a benthopelagic, demersal and reef-associated habit. The six subfamily groups were morphologically well distinguishable and the cladogram relative to Mahalanobis' morphological distances was compared with those proposed by other authors. We also quantified the phylogenetic relationship among the different subfamilies based on the analysis of shape in relation to trophic ecology, confirming the observations of the authors. (c) 2009 Wiley-Liss, Inc.
Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu
2016-01-01
We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1–10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633–0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926–0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation. PMID:27586851
NASA Astrophysics Data System (ADS)
Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu
2016-09-01
We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1-10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633-0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926-0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation.
Watkins, Marquita; Sizochenko, Natalia; Moore, Quentarius; Golebiowski, Marek; Leszczynska, Danuta; Leszczynski, Jerzy
2017-02-01
The presence of chlorophenols in drinking water can be hazardous to human health. Understanding the mechanisms of adsorption under specific experimental conditions would be beneficial when developing methods to remove toxic substances from drinking water during water treatment in order to limit human exposure to these contaminants. In this study, we investigated the sorption of chlorophenols on multi-walled carbon nanotubes using a density functional theory (DFT) approach. This was applied to study selected interactions between six solvents, five types of nanotubes, and six chlorophenols. Experimental data were used to construct structure-adsorption relationship (SAR) models that describe the recovery process. Specific interactions between solvents and chlorophenols were taken into account in the calculations by using novel specific mixture descriptors.
Analytical methods for toxic gases from thermal degradation of polymers
NASA Technical Reports Server (NTRS)
Hsu, M.-T. S.
1977-01-01
Toxic gases evolved from the thermal oxidative degradation of synthetic or natural polymers in small laboratory chambers or in large scale fire tests are measured by several different analytical methods. Gas detector tubes are used for fast on-site detection of suspect toxic gases. The infrared spectroscopic method is an excellent qualitative and quantitative analysis for some toxic gases. Permanent gases such as carbon monoxide, carbon dioxide, methane and ethylene, can be quantitatively determined by gas chromatography. Highly toxic and corrosive gases such as nitrogen oxides, hydrogen cyanide, hydrogen fluoride, hydrogen chloride and sulfur dioxide should be passed into a scrubbing solution for subsequent analysis by either specific ion electrodes or spectrophotometric methods. Low-concentration toxic organic vapors can be concentrated in a cold trap and then analyzed by gas chromatography and mass spectrometry. The limitations of different methods are discussed.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Seligman, P.E.; Zirino, A.
1998-11-01
This document details issues addressed at a June 1997 workshop attended by Navy and regulatory representatives and scientific experts. Objectives discussed were: (1) define the current status and future direction of copper (Cu) regulations, (2) define problems and issues associated with the introduction of copper into the estuarine environment, and (3) discuss and evaluate the relationship between copper speciation, bio-availability, and toxicity.
Zhu, Jing-Jing; Jiang, Jian-Guo
2018-05-11
Coumarins are fused benzene and pyrone ring systems with a wide spectrum of bioactivities including anti-tumor, anti-inflammation, antiviral and antibacterial effects. In this paper, the current development of coumarins-based drugs is introduced, and their structure-activity relationship is discussed by reviewing the relevant literatures published in the past twenty years. Coumarin molecules can be customized by the target site to prevent systemic side effects by virtue of structural modification. The ortho-phenolic hydroxyl on the benzene ring had remarkable antioxidant and anti-tumor activities. Coumarins with aryl groups at the C-4 position have good activities in anti-HIV, anti-tumor, anti-inflammation and analgesia. C-3 phenylcoumarins have strong anti-HIV and antioxidant effects. Tetracycline pyranocoumarins can significantly inhibit the HIV, osthol structural analogues have antimicrobial activity. Praeruptorin C and its derivatives play an important role in lowering blood pressure and dilating coronary arteries, and khellactone derivatives have significant inhibitory effects on AIDS, cancer and cardiovascular diseases. It is concluded that the specific site on the core structure of coumarin exhibits one or more activities due to the electronic or steric effects of the substituents. This review is designed to be conducive to rational design and development of more active and less toxic agents with a coumarin scaffold. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Sangwan, Smriti; Zhao, Anni; Adams, Katrina L.; Jayson, Christina K.; Sawaya, Michael R.; Guenther, Elizabeth L.; Pan, Albert C.; Ngo, Jennifer; Moore, Destaye M.; Soriaga, Angela B.; Do, Thanh D.; Goldschmidt, Lukasz; Nelson, Rebecca; Bowers, Michael T.; Koehler, Carla M.; Shaw, David E.; Novitch, Bennett G.; Eisenberg, David S.
2017-01-01
Fibrils and oligomers are the aggregated protein agents of neuronal dysfunction in ALS diseases. Whereas we now know much about fibril architecture, atomic structures of disease-related oligomers have eluded determination. Here, we determine the corkscrew-like structure of a cytotoxic segment of superoxide dismutase 1 (SOD1) in its oligomeric state. Mutations that prevent formation of this structure eliminate cytotoxicity of the segment in isolation as well as cytotoxicity of the ALS-linked mutants of SOD1 in primary motor neurons and in a Danio rerio (zebrafish) model of ALS. Cytotoxicity assays suggest that toxicity is a property of soluble oligomers, and not large insoluble aggregates. Our work adds to evidence that the toxic oligomeric entities in protein aggregation diseases contain antiparallel, out-of-register β-sheet structures and identifies a target for structure-based therapeutics in ALS. PMID:28760994
Structure-Activity Relationship of the Antimalarial Ozonide Artefenomel (OZ439).
Dong, Yuxiang; Wang, Xiaofang; Kamaraj, Sriraghavan; Bulbule, Vivek J; Chiu, Francis C K; Chollet, Jacques; Dhanasekaran, Manickam; Hein, Christopher D; Papastogiannidis, Petros; Morizzi, Julia; Shackleford, David M; Barker, Helena; Ryan, Eileen; Scheurer, Christian; Tang, Yuanqing; Zhao, Qingjie; Zhou, Lin; White, Karen L; Urwyler, Heinrich; Charman, William N; Matile, Hugues; Wittlin, Sergio; Charman, Susan A; Vennerstrom, Jonathan L
2017-04-13
Building on insights gained from the discovery of the antimalarial ozonide arterolane (OZ277), we now describe the structure-activity relationship (SAR) of the antimalarial ozonide artefenomel (OZ439). Primary and secondary amino ozonides had higher metabolic stabilities than tertiary amino ozonides, consistent with their higher pK a and lower log D 7.4 values. For primary amino ozonides, addition of polar functional groups decreased in vivo antimalarial efficacy. For secondary amino ozonides, additional functional groups had variable effects on metabolic stability and efficacy, but the most effective members of this series also had the highest log D 7.4 values. For tertiary amino ozonides, addition of polar functional groups with H-bond donors increased metabolic stability but decreased in vivo antimalarial efficacy. Primary and tertiary amino ozonides with cycloalkyl and heterocycle substructures were superior to their acyclic counterparts. The high curative efficacy of these ozonides was most often associated with high and prolonged plasma exposure, but exposure on its own did not explain the presence or absence of either curative efficacy or in vivo toxicity.
Westlund, Paul; Nasuhoglu, Deniz; Isazadeh, Siavash; Yargeau, Viviane
2018-05-01
High-throughput acute and chronic toxicity tests using Vibrio fischeri were used to assess the toxicity of a variety of fungicides, herbicides, and neonicotinoids. The use of time points beyond the traditional 30 min of an acute test highlighted the sensitivity and applicability of the chronic toxicity test and indicated that for some compounds toxicity is underestimated using only the acute test. The comparison of EC 50 values obtained from acute and chronic tests provided insight regarding the toxicity mode of action, either being direct or indirect. Using a structure-activity relationship approach similar to the one used in hazard assessments, the relationship between toxicity and key physicochemical properties of pesticides was investigated and trends were identified. This study not only provides new information regarding acute toxicity of some pesticides but also is one of the first studies to investigate the chronic toxicity of pesticides using the test organism V. fischeri. The findings demonstrated that the initial bioluminescence has a large effect on the calculated effective concentrations for target compounds in both acute and chronic tests, providing a way to improve and standardize the test protocol. In addition, the findings emphasize the need for additional investigation regarding the relationship between a toxicant's physicochemical properties and mode of action in nontarget organisms.
Šegan, Sandra; Trifković, Jelena; Verbić, Tatjana; Opsenica, Dejan; Zlatović, Mario; Burnett, James; Šolaja, Bogdan; Milojković-Opsenica, Dušanka
2013-01-01
The physicochemical properties, retention parameters (R(M)(0)), partition coefficients (logP(OW)), and pK(a) values for a series of thirteen 1,7-bis(aminoalkyl) diazachrysene (1,7-DAAC) derivatives were determined in order to reveal the characteristics responsible for their biological behavior. The investigated compounds inhibit three unrelated pathogens (the Botulinum neurotoxin serotype A light chain (BoNT/A LC), Plasmodium falciparum malaria, and Ebola filovirus) via three different mechanisms of action. To determine the most influential factors governing the retention and activities of the investigated diazachrysenes, R(M)(0), logP(OW), and biological activity values were correlated with 2D and 3D molecular descriptors, using a partial least squares regression. The resulting quantitative structure-retention (property) relationships indicate the importance of descriptors related to the hydrophobicity of the molecules (e.g., predicted partition coefficients and hydrophobic surface area). Quantitative structure-activity relationship models for describing biological activity against the BoNT/A LC and malarial strains also include overall compound polarity, electron density distribution, and proton donor/acceptor potential. Furthermore, models for Ebola filovirus inhibition are presented qualitatively to provide insights into parameters that may contribute to the compounds' antiviral activities. Overall, the models form the basis for selecting structural features that significantly affect the compound's absorption, distribution, metabolism, excretion, and toxicity profiles. Copyright © 2012 Elsevier B.V. All rights reserved.
Cristale, Joyce; García Vázquez, Alejandro; Barata, Carlos; Lacorte, Silvia
2013-09-01
The occurrence, partitioning and risk of eight polybrominated diphenyl ethers (PBDEs), nine new brominated (NBFRs) and ten organophosphorus flame retardants (OPFRs) were evaluated in three Spanish rivers suffering different anthropogenic pressures (Nalón, Arga and Besòs). OPFRs were ubiquitous contaminants in water (ΣOPFRs ranging from 0.0076 to 7.2μgL(-1)) and sediments (ΣOPFRs ranging 3.8 to 824μgkg(-1)). Brominated flame retardants were not detected in waters, whereas ΣPBDEs ranged from 88 to 812μgkg(-1) and decabromodiphenyl ethane (DBDPE) reached 435μgkg(-1) in sediments from the River Besòs, the most impacted river. The occurrence of flame retardants in river water and sediment was clearly associated with human activities, since the highest levels occurred near urban and industrial zones and after wastewater treatment plants discharge. Daphnia magna toxicity was carried out for OPFRs, the most ubiquitous flame retardants, considering individual compounds and mixtures. Toxicity of nine tested OPFRs differed largely among compounds, with EC50 values ranging over three magnitude orders (0.31-381mgL(-1)). Results evidenced that these compounds act by non-polar narcosis, since their toxicity was proportional to their lipophilicity (Kow). Furthermore, their joint toxicity was additive, which means that single and joint toxicity can be predicted knowing their concentration levels in water using quantitative structure activity relationships (QSARs) and predictive mixture models. Based on these results, a risk assessment considering joint effect was performed calculating and summing risk quotients (RQs) for the water and sediment samples. No significant risk to D. magna (ΣRQs <1) was observed for any of the monitored rivers. © 2013.
Burns, Darren K; Jones, Andrew P; Suhrcke, Marc
2016-03-01
Markets throughout the world have been reducing barriers to international trade and investment in recent years. The resulting increases in levels of international trade and investment have subsequently generated research interest into the potential population health impact. We present a systematic review of quantitative studies investigating the relationship between international trade, foreign direct investment and non-nutritional health outcomes. Articles were systematically collected from the SCOPUS, PubMed, EconLit and Web of Science databases. Due to the heterogeneous nature of the evidence considered, the 16 included articles were subdivided into individual level data analyses, selected country analyses and international panel analyses. Articles were then quality assessed using a tool developed as part of the project. Nine of the studies were assessed to be high quality, six as medium quality, and one as low quality. The evidence from the quantitative literature suggests that overall, there appears to be a beneficial association between international trade and population health. There was also evidence of the importance of foreign direct investment, yet a lack of research considering the direction of causality. Taken together, quantitative research into the relationship between trade and non-nutritional health indicates trade to be beneficial, yet this body of research is still in its infancy. Future quantitative studies based on this foundation will provide a stronger basis on which to inform relevant national and international institutions about the health consequences of trade policies. Copyright © 2016 Elsevier Ltd. All rights reserved.
Dyhrman, Sonya T.; Haley, Sheean T.; Borchert, Jerry A.; Lona, Bob; Kollars, Nicole; Erdner, Deana L.
2010-01-01
Alexandrium catenella is widespread in western North America and produces a suite of potent neurotoxins that cause paralytic shellfish poisoning (PSP) in humans and have deleterious impacts on public health and economic resources. There are seasonal PSP-related closures of recreational and commercial shellfisheries in the Puget Sound, but the factors that influence cell distribution, abundance, and relationship to paralytic shellfish toxins (PSTs) in this system are poorly described. Here, a quantitative PCR assay was used to detect A. catenella cells in parallel with state shellfish toxicity testing during the 2006 bloom season at 41 sites from April through October. Over 500,000 A. catenella cells liter−1 were detected at several stations, with two main pulses of cells driving cell distribution, one in June and the other in August. PSTs over the closure limit of 80 μg of PST 100 per g of shellfish tissue were detected at 26 of the 41 sites. Comparison of cell numbers and PST data shows that shellfish toxicity is preceded by an increase in A. catenella cells in 71% of cases. However, cells were also observed in the absence of PSTs in shellfish, highlighting the complex relationship between A. catenella and the resulting shellfish toxicity. These data provide important information on the dynamics of A. catenella cells in the Puget Sound and are a first step toward assessing the utility of plankton monitoring to augment shellfish toxicity testing in this system. PMID:20495054
Lin, Kai; Zhang, Lanwei; Han, Xue; Meng, Zhaoxu; Zhang, Jianming; Wu, Yifan; Cheng, Dayou
2018-03-28
In this study, Qula casein derived from yak milk casein was hydrolyzed using a two-enzyme combination approach, and high angiotensin I-converting enzyme (ACE) inhibitory activity peptides were screened by quantitative structure-activity relationship (QSAR) modeling integrated with molecular docking analysis. Hydrolysates (<3 kDa) derived from combinations of thermolysin + alcalase and thermolysin + proteinase K demonstrated high ACE inhibitory activities. Peptide sequences in hydrolysates derived from these two combinations were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). On the basis of the QSAR modeling prediction, a total of 16 peptides were selected for molecular docking analysis. The docking study revealed that four of the peptides (KFPQY, MPFPKYP, MFPPQ, and QWQVL) bound the active site of ACE. These four novel peptides were chemically synthesized, and their IC 50 was determined. Among these peptides, KFPQY showed the highest ACE inhibitory activity (IC 50 = 12.37 ± 0.43 μM). Our study indicated that Qula casein presents an excellent source to produce ACE inhibitory peptides.
Food plant toxicants and safety Risk assessment and regulation of inherent toxicants in plant foods.
Essers, A J; Alink, G M; Speijers, G J; Alexander, J; Bouwmeister, P J; van den Brandt, P A; Ciere, S; Gry, J; Herrman, J; Kuiper, H A; Mortby, E; Renwick, A G; Shrimpton, D H; Vainio, H; Vittozzi, L; Koeman, J H
1998-05-01
The ADI as a tool for risk management and regulation of food additives and pesticide residues is not readily applicable to inherent food plant toxicants: The margin between actual intake and potentially toxic levels is often small; application of the default uncertainty factors used to derive ADI values, particularly when extrapolating from animal data, would prohibit the utilisation of the food, which may have an overall beneficial health effect. Levels of inherent toxicants are difficult to control; their complete removal is not always wanted, due to their function for the plant or for human health. The health impact of the inherent toxicant is often modified by factors in the food, e.g. the bioavailability from the matrix and interaction with other inherent constituents. Risk-benefit analysis should be made for different consumption scenarios, without the use of uncertainty factors. Crucial in this approach is analysis of the toxicity of the whole foodstuff. The relationship between the whole foodstuff and the pure toxicant is expressed in the `product correction factor' (PCF). Investigations in humans are essential so that biomarkers of exposure and for effect can be used to analyse the difference between animals and humans and between the food and the pure toxicant. A grid of the variables characterising toxicity is proposed, showing their inter-relationships. A flow diagram for risk estimate is provided, using both toxicological and epidemiological studies.
ERIC Educational Resources Information Center
Szafranski, Sandra L.
2009-01-01
The purpose of this quantitative correlational study was to assess the relationship between the level of technology use of administrators and the level of technology use of their teachers. The target sample was principals and teachers in nine schools in three school districts in south central Wisconsin. Participants were from one elementary…
Ranking the in vivo toxicity of nanomaterials in Drosophila melanogaster
NASA Astrophysics Data System (ADS)
Vecchio, G.; Galeone, A.; Malvindi, M. A.; Cingolani, R.; Pompa, P. P.
2013-09-01
In this work, we propose a quantitative assessment of nanoparticles toxicity in vivo. We show a quantitative ranking of several types of nanoparticles (AuNPs, AgNPs, cadmium-based QDs, cadmium-free QDs, and iron oxide NPs, with different coating and/or surface chemistries), providing a categorization of their toxicity outcomes. This strategy may offer an innovative high-throughput screening tool of nanomaterials, of potential and broad interest to the nanoscience community.
Analysis of quantitative data obtained from toxicity studies showing non-normal distribution.
Kobayashi, Katsumi
2005-05-01
The data obtained from toxicity studies are examined for homogeneity of variance, but, usually, they are not examined for normal distribution. In this study I examined the measured items of a carcinogenicity/chronic toxicity study with rats for both homogeneity of variance and normal distribution. It was observed that a lot of hematology and biochemistry items showed non-normal distribution. For testing normal distribution of the data obtained from toxicity studies, the data of the concurrent control group may be examined, and for the data that show a non-normal distribution, non-parametric tests with robustness may be applied.
Integrated Proteomic Approaches for Understanding Toxicity of Environmental Chemicals
To apply quantitative proteomic analysis to the evaluation of toxicity of environmental chemicals, we have developed an integrated proteomic technology platform. This platform has been applied to the analysis of the toxic effects and pathways of many important environmental chemi...
Neale, Peta A; Leusch, Frederic D L; Escher, Beate I
2017-04-01
Pharmaceuticals and antibiotics co-occur in the aquatic environment but mixture studies to date have mainly focused on pharmaceuticals alone or antibiotics alone, although differences in mode of action may lead to different effects in mixtures. In this study we used the Bacterial Luminescence Toxicity Screen (BLT-Screen) after acute (0.5 h) and chronic (16 h) exposure to evaluate how non-specifically acting pharmaceuticals and specifically acting antibiotics act together in mixtures. Three models were applied to predict mixture toxicity including concentration addition, independent action and the two-step prediction (TSP) model, which groups similarly acting chemicals together using concentration addition, followed by independent action to combine the two groups. All non-antibiotic pharmaceuticals had similar EC 50 values at both 0.5 and 16 h, indicating together with a QSAR (Quantitative Structure-Activity Relationship) analysis that they act as baseline toxicants. In contrast, the antibiotics' EC 50 values decreased by up to three orders of magnitude after 16 h, which can be explained by their specific effect on bacteria. Equipotent mixtures of non-antibiotic pharmaceuticals only, antibiotics only and both non-antibiotic pharmaceuticals and antibiotics were prepared based on the single chemical results. The mixture toxicity models were all in close agreement with the experimental results, with predicted EC 50 values within a factor of two of the experimental results. This suggests that concentration addition can be applied to bacterial assays to model the mixture effects of environmental samples containing both specifically and non-specifically acting chemicals. Copyright © 2017 Elsevier Ltd. All rights reserved.
Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali
2013-09-01
The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds. Copyright © 2013 Elsevier Inc. All rights reserved.
Different structural stability and toxicity of PrP(ARR) and PrP(ARQ) sheep prion protein variants.
Paludi, Domenico; Thellung, Stefano; Chiovitti, Katia; Corsaro, Alessandro; Villa, Valentina; Russo, Claudio; Ianieri, Adriana; Bertsch, Uwe; Kretzschmar, Hans A; Aceto, Antonio; Florio, Tullio
2007-12-01
The polymorphisms at amino acid residues 136, 154, and 171 in ovine prion protein (PrP) have been associated with different susceptibility to scrapie: animals expressing PrP(ARQ) [PrP(Ala136/Arg154/Gln171)] show vulnerability, whereas those that express PrP(ARR) [PrP(Ala136/Arg154/Arg171)] are resistant to scrapie. The aim of this study was to evaluate the in vitro toxic effects of PrP(ARR) and PrP(ARQ) variants in relation with their structural characteristics. We show that both peptides cause cell death inducing apoptosis but, unexpectedly, the scrapie resistant PrP(ARR) form was more toxic than the scrapie susceptible PrP(ARQ) variant. Moreover, the alpha-helical conformation of PrP(ARR) was less stable than that of PrP(ARQ) and the structural determinants responsible of these different conformational stabilities were characterized by spectroscopic analysis. We observed that PrP toxicity was inversely related to protein structural stability, being the unfolded conformation more toxic than the native one. However, the PrP(ARQ) variant displays a higher propensity to form large aggregates than PrP(ARR). Interestingly, in the presence of small amounts of PrP(ARR), PrP(ARQ) aggregability was reduced to levels similar to that of PrP(ARR). Thus, in contrast to PrP(ARR) toxicity, scrapie transmissibility seems to reside in the more stable conformation of PrP(ARQ) that allows the formation of large amyloid fibrils.
Zhu, Xiaolin; Zhang, Kexin; Wang, Chengzhi; Guan, Jiunian; Yuan, Xing; Li, Baikun
2016-01-01
This study aimed at developing simple, sensitive and rapid electrochemical approach to quantitatively determine and assess the toxicity of 2,4-dichlorophenol (2,4-DCP), a priority pollutant and has potential risk to public health through a novel poly(eosin Y, EY)/hydroxylated multi-walled carbon nanotubes composite modified electrode (PEY/MWNTs-OH/GCE). The distinct feature of this easy-fabricated electrode was the synergistic coupling effect between EY and MWNTs-OH that enabled a high electrocatalytic activity to 2,4-DCP. Under optimum conditions, the oxidation peak current enhanced linearly with concentration increasing from 0.005 to 0.1 μM and 0.2 to 40.0 μM, and revealed the detection limit of 1.5 nM. Moreover, the PEY/MWNTs-OH/GCE exhibited excellent electrocatalytic activity toward intracellular electroactive species. Two sensitive electrochemical signals ascribed to guanine/xanthine and adenine/hypoxanthine in human hepatoma (HepG2) cells were detected simultaneously. The sensor was successfully applied to evaluate the toxicity of 2,4-DCP to HepG2 cells. The IC50 values based on the two electrochemical signals are 201.07 and 252.83 μM, respectively. This study established a sensitive platform for the comprehensive evaluation of 2,4-DCP and posed a great potential to simplify environmental toxicity monitoring. PMID:27941912
ERIC Educational Resources Information Center
Castillo, Alan F.
2014-01-01
The purpose of this quantitative correlational cross-sectional research study was to examine a theoretical model consisting of leadership practice, attitudes of business process outsourcing, and strategic intentions of leaders to use cloud computing and to examine the relationships between each of the variables respectively. This study…
Devillers, J; Pandard, P; Richard, B
2013-01-01
Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.
The NRC has examined the availability of toxicity endpoints for industrial chemicals and concluded that many of these chemicals lack even minimum testing. One way of carrying out risk assessments of chemicals having insufficient experimental data is by using Quantitative Structur...
Quantitative structural MRI for early detection of Alzheimer’s disease
McEvoy, Linda K; Brewer, James B
2011-01-01
Alzheimer’s disease (AD) is a common progressive neurodegenerative disorder that is not currently diagnosed until a patient reaches the stage of dementia. There is a pressing need to identify AD at an earlier stage, so that treatment, when available, can begin early. Quantitative structural MRI is sensitive to the neurodegeneration that occurs in mild and preclinical AD, and is predictive of decline to dementia in individuals with mild cognitive impairment. Objective evidence of ongoing brain atrophy will be critical for risk/benefit decisions once potentially aggressive, disease-modifying treatments become available. Recent advances have paved the way for the use of quantitative structural MRI in clinical practice, and initial clinical use has been promising. However, further experience with these measures in the relatively unselected patient populations seen in clinical practice is needed to complete translation of the recent enormous advances in scientific knowledge of AD into the clinical realm. PMID:20977326
The EPA DSSTox website (http://www/epa.gov/nheerl/dsstox) publishes standardized, structure-annotated toxicity databases, covering a broad range of toxicity disciplines. Each DSSTox database features documentation written in collaboration with the source authors and toxicity expe...
ERIC Educational Resources Information Center
Webster, Katina F.
2012-01-01
General educators and special educators in Title I elementary schools perceive the relationships between principles of RTI and their state RTI framework, the implementation of RTI, and professional development received in RTI differently. A quantitative survey-based research methodology was employed including the use of Cronbach's alpha to…
Structural and quantitative expression analyses of HERV gene family in human tissues.
Ahn, Kung; Kim, Heui-Soo
2009-08-31
Human endogenous retroviruses (HERVs) have been implicated in the pathogenesis of several human diseases as multi-copy members in the human genome. Their gene expression profiling could provide us with important insights into the pathogenic relationship between HERVs and cancer. In this study, we have evaluated the genomic structure and quantitatively determined the expression patterns in the env gene of a variety of HERV family members located on six specific loci by the RetroTector 10 program, as well as real-time RT-PCR amplification. The env gene transcripts evidenced significant differences in the human tumor/normal adjacent tissues (colon, liver, uterus, lung and testis). As compared to the adjacent normal tissues, high levels of expression were noted in testis tumor tissues for HERV-K, in liver and lung tumor tissues for HERV-R, in liver, lung, and testis tumor tissues for HERV-H, and in colon and liver tumor tissues for HERV-P. These data warrant further studies with larger groups of patients to develop biomarkers for specific human cancers.
Schober, Eva; Werndl, Michael; Laakso, Kati; Korschineck, Irina; Sivonen, Kaarina; Kurmayer, Rainer
2011-01-01
Summary The application of quantitative real time PCR has been proposed for the quantification of toxic genotypes of cyanobacteria. We have compared the Taq Nuclease Assay (TNA) in quantifying the toxic cyanobacteria Microcystis sp. via the intergenic spacer region of the phycocyanin operon (PC) and mcyB indicative of the production of the toxic heptapeptide microcystin between three research groups employing three instruments (ABI7300, GeneAmp5700, ABI7500). The estimates of mcyB genotypes were compared using (i) DNA of a mcyB containing strain and a non-mcyB containing strain supplied in different mixtures across a low range of variation (0-10% of mcyB) and across a high range of variation (20-100%), and (ii) DNA from field samples containing Microcystis sp. For all three instruments highly significant linear regression curves between the proportion of the mcyB containing strain and the percentage of mcyB genotypes both within the low range and within the high range of mcyB variation were obtained. The regression curves derived from the three instruments differed in slope and within the high range of mcyB variation mcyB proportions were either underestimated (0-50%) or overestimated (0-72%). For field samples cell numbers estimated via both TNAs as well as mcyB proportions showed significant linear relationships between the instruments. For all instruments a linear relationship between the cell numbers estimated as PC genotypes and the cell numbers estimated as mcyB genotypes was observed. The proportions of mcyB varied from 2-28% and did not differ between the instruments. It is concluded that the TNA is able to provide quantitative estimates on mcyB genotype numbers that are reproducible between research groups and is useful to follow variation in mcyB genotype proportion occurring within weeks to months. PMID:17258828
Lam, In Kei; Alex, Deepa; Wang, You-Hua; Liu, Ping; Liu, Ai-Lin; Du, Guan-Hua; Lee, Simon Ming Yuen
2012-06-01
Polymethoxylated flavonoids are present in citrus fruit in a range of chemical structures and abundance. These compounds have potential for anticarcinogenesis, antitumor, and cardiovascular protective activity, but the effect on angiogenesis has not been well studied. Human umbilical vein endothelial cells (HUVECs) in vitro and zebrafish (Danio rerio) in vivo models were used to screen and identify the antiangiogenesis activity of seven polymethoxylated flavonoids; namely, hesperetin, naringin, neohesperidin, nobiletin, scutellarein, scutellarein tetramethylether, and sinensetin. Five, excluding naringin and neohesperidin, showed different degrees of potency of antiangiogenesis activity. Sinensetin, which had the most potent antiangiogenesis activity and the lowest toxicity, inhibited angiogenesis by inducing cell cycle arrest in the G0/G1 phase in HUVEC culture and downregulating the mRNA expressions of angiogenesis genes flt1, kdrl, and hras in zebrafish. The in vivo structure-activity relationship (SAR) analysis indicated that a flavonoid with a methoxylated group at the C3' position offers a stronger antiangiogenesis activity, whereas the absence of a methoxylated group at the C8 position offers lower lethal toxicity in addition to enhancing the antiangiogenesis activity. This study provides new insight into how modification of the chemical structure of polymethoxylated flavonoids affects this newly identified antiangiogenesis activity. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
The Family Relationships Grid: Measuring Family Structure.
ERIC Educational Resources Information Center
Copeland, Anne P.; And Others
This study examined the Family Relationships Grid (FRG), a new measure of family structure that evaluates alliances, identification, isolation, and the relative strength of sibling and marital relationships. Subjects were 52 female and 35 male adolescents who were recruited through a university course and who each had at least one sibling.…
The EPA document, Relationships Among Exceedances of Chemical Criteria or Guidelines, the Results of Ambient Toxicity Tests, and Community Metrics in Aquatic Ecosystems, presents two studies where the three general approaches for the ecological assessment of contaminant ex...