Global Quantitative Structure-activity Relationship Research Articles

Random forest algorithm-based classification model of pesticide aquatic toxicity to fishes

Aquatic toxicity of pesticides can result in poisoning of many non-target organisms, of which various fishes are the most prominent one. It is a challenge to predict the toxicity (LC50) classes of organic pesticides to various fish species from global QSAR models with a larger applicability domain. In this paper, by applying the random forest (RF) algorithm for a two-class problem, only eight molecular descriptors were used to develop a quantitative structure–activity relationship (QSAR) model for 1106 toxicity data (96 h, LC50) of organic pesticides to various fish species including Oncorhynchus mykiss, Lepomis macrochirus, Pimephales promelas, Brachydanio rerio, Cyprinodon, Cyprinus carpio, etc. By the prediction of the optimal RF Model I (ntree =280, mtry = 3 and nodesize = 5), the training set (885 organic pesticides) has the prediction accuracies of 99.6% for Class 1 (LC50 ≤ 10) and 96.7% for Class 2 (LC50 > 10); the test set (221 organic pesticides) has the accuracies being 90.8% for Class 1 and 91.2% for Class 2. The optimal RF Model I is satisfactory compared with other QSAR model reported in the literature, although its descriptor subset is small.

Prediction of acute toxicity to Daphnia magna and interspecific correlation: a global QSAR model and a Daphnia-minnow QTTR model

ABSTRACT Acute toxicity is an important basis for the assessment of hazardous chemicals, but currently there is a huge data gap in chemical toxicity information. The in silico Quantitative Structure Activity Relationship (QSAR) models can use the existing experimental data information to predict the missing chemical toxicity information data and thus reduce animal testing. In the present study, a global QSAR model for the prediction of acute Daphnia magna toxicity has been developed based on the five principles proposed by the Organization for Economic Co-operation and Development (OECD). Moreover, a Daphnia-minnow (referring specifically to the fathead minnow) Quantitative Toxicity–Toxicity Relationship (QTTR) prediction model has been developed based on the present study and our previous work on fathead minnow (Pimephales promelas). Both the QSAR and QTTR prediction models have good goodness-of-fit, robustness, and predictive ability. Finally, the acute toxicity mode of action (MOA) for fathead minnow and Daphnia magna was compared by toxicity ratio based on interspecies toxicity data. By comparison, Daphnia magna was found more sensitive to anilines and phosphorothioates than fathead minnow. The present models can fill the acute toxicity data gap and contribute to the chemicals risk assessment and priority setting.

Predicting oxidative stress induced by organic chemicals by using quantitative Structure-Activity relationship methods.

Cellular exposure to xenobiotic human-made products will lead to oxidative stress that gives rise to DNA damage, as well as chemical or mechanical damage. Distinguishing the chemicals that will induce oxidative stress and predicting their toxicity is necessary. In the present study, 4270 compounds in the ARE-bla assay were investigated to predict active and inactive compounds by using simple algorithms, namely, recursive partitioning (RP) and binomial logistic regression, and to develop the quantitative structure-activity relationship (QSAR) models of chemicals that activate the ARE pathway to induce oxidative stress and exert toxic effects on cells. A decision tree based on scaffold-based fragments obtained through RP analysis showed the best identification accuracy. However, the overall identification accuracy of this model for active compounds was unsatisfactory due to limited fragments. Furthermore, a binomial logistic regression model was developed from 638 active compounds and 3632 inactive chemicals. The model with a cutoff of 0.15 could predict chemicals that were active or inactive with the prediction accuracy of 69.1%. Its area under the receiver operating characteristic (ROC) curve metric (AUROC) was 0.762, which indicated the acceptable predictive ability of this model. The parameters nBM (number of multiple bonds) and H% (percentage of H atom) played dominant roles in the prediction of the activity (inactive or active) of chemicals. A global QSAR model was developed to predict the toxicity of active chemicals. However, the model displayed an unsatisfactory result with R2=0.316 and R2ext=0.090. Active chemicals were then classified on the basis of structure. A total of 79 compounds with carbon chains could be predicted with acceptable performance by using a QSAR model with six descriptors (R2=0.722, R2ext=0.798, Q2Loo=0.654, Q2Boot=0.755, Q2ext=0.721). The simple models established here contribute to efforts on identification compounds inducing oxidative stress and provide the scientific basis for risk assessment to organisms in the environment.

A novel strategy for prediction of human plasma protein binding using machine learning techniques

Plasma protein binding (PPB) is a key player of drug ADME (absorption, distribution, metabolism, elimination) behaviors, enabling PPB to have significant impact on drug efficacy and toxicity. As drug discovery enters the era of rational drug design, it is desirable to use in silico model to predict PPB so as to achieve rapid initial screening for potential candidate compounds prior to further time-consuming and costly in vitro and in vivo experimental assay. In this study, a global quantitative structure-activity relationship (QSAR) model of PPB was built on the basis of a large training set comprising more than 5000 compounds to represent large structural diversity. The uneven distribution of PPB was often rectified by two mathematical transformations of PPB but this led to a decrease in prediction accuracy at the lower binding level. To resolve this problem, we proposed a novel strategy to build models for different binding levels. The best model yielded much lower mean absolute error (MAE) of 0.076 on the test set than published models and the MAE was further reduced to 0.041 ​at the high level of binding (0.8–1). The models also performed excellent in the validation set containing some compounds from traditional Chinese medicine. In addition, the applicability domain was determined to identify new compounds which are appropriate for prediction using our built models. In conclusion, this study developed a novel strategy to construct robust QSAR model for PPB prediction which could be used by chemists to predict the PPB of candidate compounds efficiently and make structural modification in the early stage of drug development.

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase.

A prototype of a family of at least nine members, cellular Src tyrosine kinase is a therapeutically interesting target because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We explored the use of global quantitative structure-activity relationship (QSAR) models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a dataset of 1038 compounds from ChEMBL database, we developed over 350 QSAR classification models. A total of 49 models with reasonably good performance were selected and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over 100,000 compounds. A total of 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using AutoDock Vina and LeDock, and 89 were predicted to be active based on the energy of binding.

Ecotoxicological QSAR modeling of organic compounds against fish: Application of fragment based descriptors in feature analysis

Organic compounds (OCs) constitute an enormously large class of highly persistent and toxic chemicals widely used for various purposes throughout the world. Their increased detection in water bodies, mainly sewage treatment plants via industrial discharge, has rendered them to become a cause for ecological concern. The limited availability of experimental toxicological data has necessitated development of models that can help us identify the most hazardous and potentially toxic compounds thus prioritizing the experiments on the selected chemicals. Computational tools such as quantitative structure-activity relationship (QSAR) can be used to predict the missing data and classify the chemicals based on their acute predicted responses for existing as well as not yet synthesized chemicals. In the current study, novel, externally validated, highly robust local QSAR models for different chemical classes and moderately robust global QSAR models were developed using partial least squares (PLS) regression technique using a large dataset of 1121 OCs for the fish mortality endpoint. For feature selection, genetic algorithm along with stepwise regression was used while model validation was performed using various stringent validation criteria following the strict rules of OECD guidelines of QSAR validation. The variables included in the models were obtained from simplex representation of molecular structures (SiRMS) (Version 4.1.2.270), Dragon (Version 7.0) and PaDEL-descriptor software (Version 2.20). The final developed models were robust, externally predictive and characterized by a large chemical as well as biological domain. The predictive efficiency of the developed models was then compared with the ECOSAR tool in order to justify the applicability of the developed models in ecotoxicological predictions for organic chemicals. Better predictive efficiency of the developed QSAR models compared to the ECOSAR derived predictions signifies their applicability in early risk assessment of known as well as untested chemicals in order to design safer alternatives to the environment.

Quantitative Structure-Activity Relationships of Aquatic Narcosis: A Review.

Prior estimation of toxicity of each and every, existing and yet to be synthesized chemicals is a must to elude their adverse effect on the environment. Experimental determination of such parameters is time consuming, cost effective and above all, it demands the sacrifice of many vertebrates. At this end, the REACH regulations advocate for the use of non-testing predictive methods such as read-across, weight-of-evidence and QSAR (quantitative structure-activity relationship) techniques. Among these methods, QSAR is found to be the best as it is based on molecular structure only. The descriptors used in deriving the model in QSAR vary according to the nature of the narcotics as well as the species used for. The success of a model in predicting the toxicity of a narcotic purely depends on the type of descriptors selected that explains the structural features closely related to the property under study. In this review, we have focused on the different types of descriptors and QSAR models used to explain the narcosis phenomenon. Literature was scanned for acute toxicity of chemicals on species like tadpoles, protozoa, planktonic crustaceans, and small fishes like million fish, rainbow fish etc. from different sources. The toxicity and toxicants were classified considering their polarity and specific interactions of the compounds. Due to complex nature of the substrate, the mechanism of action of toxicant is uncertain. However, the overall results obtained from the biological study have been subjected to QSAR studies to obtain various models, which can provide some ideas on the mode of toxicological action. Different types of molecular descriptors derived both experimentally and theoretically have been used in the QSAR studies. Mostly biochemicals have a specific signature on oil/water partition (Ko/w, P), which is the crux in biological activity. Accordingly, the toxicological activities have good correlations with log P. Addition of some more structural descriptors improves the structure-toxicity relationship. Among these, electronic descriptors like EHOMO, ELUMO and ΔE derived from molecular orbitals have been used in the QSAR. ELUMO describing the energy of excited species of the molecule is found to be the most suitable one. Other molecular descriptors used in the QSAR include constitutional, topological and Abraham's solute descriptors. The models derived from the QSAR studies were found to be highly significant to predict the toxicology as well as to throw light on the mechanism. The best descriptor for aquatic narcosis is the KO/W or P. Addition of an electronic parameter (ELUMO) improves the QSAR to some extent. However, substitution of ELUMO by other class of molecular descriptors has also some statistical significance. To have a global QSAR model, in addition to P, some more appropriate descriptors are to be derived either experimentally or theoretically, latter being the more cost effective and easy in derivation.

New TTC dataset compilation to support thresholds of toxicological concern in the risk assessment of antimicrobials beyond Cramer Classes

Thyroperoxidase (TPO) is the enzyme that synthesizes thyroid hormones (THs). TPO inhibition by chemicals can result in decreased TH levels and developmental neurotoxicity, and therefore identification of TPO inhibition is of high relevance in safety evaluation of chemicals. In the present study, we developed two global quantitative structure–activity relationship (QSAR) models for TPO inhibition in vitro. Rigorous cross- and blinded external validations demonstrated that the first model, QSAR1, built from a training set of 877 chemicals, was robust and highly predictive with balanced accuracies of 80.6% (SD = 4.6%) and 85.3%, respectively. The external validation test set was subsequently merged with the training set to constitute a larger training set totaling 1519 chemicals for a second model, QSAR2, which underwent robust cross-validation with a balanced accuracy of 82.7% (SD = 2.2%). An analysis of QSAR2 identified the ten most discriminating structural features for TPO inhibition and non-inhibition, respectively. Both models were used to screen 72,524 REACH substances and 32,197 U.S. EPA substances, and QSAR2 with the expanded training set had an approximately 10% larger coverages compared to QSAR1. Of the substances predicted within QSAR2’s applicability domain, 8,790 (19.3%) REACH substances and 7,166 (19.0%) U.S. EPA substances, respectively, were predicted to be TPO inhibitors. A case study on butyl hydroxyanisole (BHA), which is extensively used as an antioxidant, was included to exemplify how predictions from the developed QSAR2 model may aid in elucidating the modes of action in adverse outcomes of chemicals. Overall, predictions from QSAR2 can for example be used in priority setting of chemicals and in read-across cases or weight-of-evidence assessments.

QSAR models for thyroperoxidase inhibition and screening of U.S. and EU chemical inventories

Thyroperoxidase (TPO) is the enzyme that synthesizes thyroid hormones (THs). TPO inhibition by chemicals can result in decreased TH levels and developmental neurotoxicity, and therefore identification of TPO inhibition is of high relevance in safety evaluation of chemicals. In the present study, we developed two global quantitative structure–activity relationship (QSAR) models for TPO inhibition in vitro. Rigorous cross- and blinded external validations demonstrated that the first model, QSAR1, built from a training set of 877 chemicals, was robust and highly predictive with balanced accuracies of 80.6% (SD=4.6%) and 85.3%, respectively. The external validation test set was subsequently merged with the training set to constitute a larger training set totaling 1519 chemicals for a second model, QSAR2, which underwent robust cross-validation with a balanced accuracy of 82.7% (SD=2.2%). An analysis of QSAR2 identified the ten most discriminating structural features for TPO inhibition and non-inhibition, respectively. Both models were used to screen 72,524 REACH substances and 32,197 U.S. EPA substances, and QSAR2 with the expanded training set had an approximately 10% larger coverages compared to QSAR1. Of the substances predicted within QSAR2’s applicability domain, 8,790 (19.3%) REACH substances and 7,166 (19.0%) U.S. EPA substances, respectively, were predicted to be TPO inhibitors. A case study on butyl hydroxyanisole (BHA), which is extensively used as an antioxidant, was included to exemplify how predictions from the developed QSAR2 model may aid in elucidating the modes of action in adverse outcomes of chemicals. Overall, predictions from QSAR2 can for example be used in priority setting of chemicals and in read-across cases or weight-of-evidence assessments.

Global versus local QSAR models for predicting ionic liquids toxicity against IPC-81 leukemia rat cell line: The predictive ability

Abstract The unlimited possibility of modifying the structure of the cation and anion of a given ionic liquid (IL) delivers a countless number of potential derivatives having different toxicological activities. The experimental measurements for such huge set of compounds are impossible due to time and costs limitations. In the absence of experimental data, computational methods such as quantitative structure–activity relationship (QSAR) method can be applied to characterize those species for which experimental data is not available. However, two different ways of QSAR modeling could be applied: local modeling - dedicating to one specific group and more complex global models. The aim of this study was to investigate how local/global QSAR modeling works for the ionic liquids. Which of these two approaches is more suitable in ILs' modeling? We have developed six local models and a global one for predicting the toxicity against the leukemia rat cell line IPC-81. Based on the obtained results, we conclude that local models are useful to solve specific mechanistic problems among one group of ILs, but in case of predicting the toxicity for a new designed compound the global model, whenever fulfills all quality recommendations, is recommended.

Development of quantitative structure–activity relationship (QSAR) models to predict the carcinogenic potency of chemicals. II. Using oral slope factor as a measure of carcinogenic potency

The overall risk associated with exposure to a chemical is determined by combining quantitative estimates of exposure to the chemical with their known health effects. For chemicals that cause carcinogenicity, oral slope factors (OSFs) and inhalation unit risks are used to quantitatively estimate the carcinogenic potency or the risk associated with exposure to the chemical by oral or inhalation route, respectively. Frequently, there is a lack of animal or human studies in the literature to determine OSFs. This study aims to circumvent this problem by developing quantitative structure–activity relationship (QSAR) models to predict the OSFs of chemicals. The OSFs of 70 chemicals based on male/female human, rat, and mouse bioassay data were obtained from the United States Environmental Protection Agency’s Integrated Risk Information System (IRIS) database. A global QSAR model that considered all 70 chemicals as well as species and/or sex-specific QSARs were developed in this study. Study results indicate that the species and sex-specific QSARs (r2>0.8, q2>0.7) had a better predictive abilities than the global QSAR developed using data from all species and sexes (r2=0.77, q2=0.73). The QSARs developed in this study were externally validated, and demonstrated reasonable predictive abilities.

Introduction of Jumping Fragments in Combination with QSARs for the Assessment of Classification in Ecotoxicology

Starting from a random set of structures taken from the European Chemical Bureau (ECB) Web site, an estimation of the classification by acute category in ecotoxicology was carried out. This estimation was based on two approaches. One approach consists in starting with global quantitative structure-activity relationship (QSAR) equations, analyzing the results and defining an interpretation in terms of overall results and mode of action. The other starts with the notion of emerging fragments and more specifically with the introduction of a particular concept: the jumping fragments. This publication studies the scopes and limitations of each approach for the classification of the derivatives. A promising combination of the two methods is proposed for the classification and also for bringing new information about the importance, for the ecotoxicity, of specific chemical fragments considered alone or in association with others.

Testing computational toxicology models with phytochemicals

Computational toxicology employing quantitative structure-activity relationship (QSAR) modeling is an evidence-based predictive method being evaluated by regulatory agencies for risk assessment and scientific decision support for toxicological endpoints of interest such as rodent carcinogenicity. Computational toxicology is being tested for its usefulness to support the safety assessment of drug-related substances (e.g. active pharmaceutical ingredients, metabolites, impurities), indirect food additives, and other applied uses of value for protecting public health including safety assessment of environmental chemicals. The specific use of QSAR as a chemoinformatic tool for estimating the rodent carcinogenic potential of phytochemicals present in botanicals, herbs, and natural dietary sources is investigated here by an external validation study, which is the most stringent scientific method of measuring predictive performance. The external validation statistics for predicting rodent carcinogenicity of 43 phytochemicals, using two computational software programs evaluated at the FDA, are discussed. One software program showed very good performance for predicting non-carcinogens (high specificity), but both exhibited poor performance in predicting carcinogens (sensitivity), which is consistent with the design of the models. When predictions were considered in combination with each other rather than based on any one software, the performance for sensitivity was enhanced, However, Chi-square values indicated that the overall predictive performance decreases when using the two computational programs with this particular data set. This study suggests that complementary multiple computational toxicology software need to be carefully selected to improve global QSAR predictions for this complex toxicological endpoint.

Desirability-Based Methods of Multiobjective Optimization and Ranking for Global QSAR Studies. Filtering Safe and Potent Drug Candidates from Combinatorial Libraries

Up to now, very few applications of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies have been reported in the literature. However, none of them report the optimization of objectives related directly to the final pharmaceutical profile of a drug. In this paper, a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies, simultaneously considering the potency, bioavailability, and safety of a set of drug candidates, is introduced. The results of the desirability-based MOOP (the levels of the predictor variables concurrently producing the best possible compromise between the properties determining an optimal drug candidate) are used for the implementation of a ranking method that is also based on the application of desirability functions. This method allows ranking drug candidates with unknown pharmaceutical properties from combinatorial libraries according to the degree of similarity with the previously determined optimal candidate. Application of this method will make it possible to filter the most promising drug candidates of a library (the best-ranked candidates), which should have the best pharmaceutical profile (the best compromise between potency, safety and bioavailability). In addition, a validation method of the ranking process, as well as a quantitative measure of the quality of a ranking, the ranking quality index (Psi), is proposed. The usefulness of the desirability-based methods of MOOP and ranking is demonstrated by its application to a library of 95 fluoroquinolones, reporting their gram-negative antibacterial activity and mammalian cell cytotoxicity. Finally, the combined use of the desirability-based methods of MOOP and ranking proposed here seems to be a valuable tool for rational drug discovery and development.

Desirability‐based multiobjective optimization for global QSAR studies: Application to the design of novel NSAIDs with improved analgesic, antiinflammatory, and ulcerogenic profiles

Up to now, very few reports have been published concerning the application of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies. However, none reports the optimization of objectives related directly to the desired pharmaceutical profile of the drug. In this work, for the first time, it is proposed a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies considering simultaneously the pharmacological, pharmacokinetic and toxicological profile of a set of molecule candidates. The usefulness of the method is demonstrated by applying it to the simultaneous optimization of the analgesic, antiinflammatory, and ulcerogenic properties of a library of fifteen 3-(3-methylphenyl)-2-substituted amino-3H-quinazolin-4-one compounds. The levels of the predictor variables producing concurrently the best possible compromise between these properties is found and used to design a set of new optimized drug candidates. Our results also suggest the relevant role of the bulkiness of alkyl substituents on the C-2 position of the quinazoline ring over the ulcerogenic properties for this family of compounds. Finally, and most importantly, the desirability-based MOOP method proposed is a valuable tool and shall aid in the future rational design of novel successful drugs.

Screening of 397 Chemicals and Development of a Quantitative Structure−Activity Relationship Model for Androgen Receptor Antagonism

We have screened 397 chemicals for human androgen receptor (AR) antagonism by a sensitive reporter gene assay to generate data for the development of a quantitative structure-activity relationship (QSAR) model. A total of 523 chemicals comprising data on 292 chemicals from our laboratory and data on 231 chemicals from the literature constituted the training set for the model. The chemicals were selected with the purpose of representing a wide range of chemical structures (e.g., organochlorines and polycyclic aromatic hydrocarbons) and various functions (e.g., natural hormones, pesticides, plastizicers, plastic additives, brominated flame retardants, and roast mutagens). In addition, the intention was to obtain an equal number of positive and negative chemicals. Among our own data for the training set, 45.7% exhibited inhibitory activity against the transcriptional activity induced by the synthetic androgen R1881. The MultiCASE expert system was used to construct a QSAR model for AR antagonizing potential. A "5 Times, 2-Fold 50% Cross Validation" of the model showed a sensitivity of 64%, a specificity of 84%, and a concordance of 76%. Data for 102 chemicals were generated for an external validation of the model resulting in a sensitivity of 57%, a specificity of 98%, and a concordance of 92% of the model. The model was run on a set of 176103 chemicals, and 47% were within the domain of the model. Approximately 8% of chemicals was predicted active for AR antagonism. We conclude that the predictability of the global QSAR model for this end point is good. This most comprehensive QSAR model may become a valuable tool for screening large numbers of chemicals for AR antagonism.