The method for QSAR modelling of rat acute toxicity based on the combination of QNA (Quantitative Neighbourhoods of Atoms) descriptors, PASS (Prediction of Activity Spectra for Substances) predictions and self-consistent regression (SCR) is presented. PASS predicted biological activity profiles are used as independent input variables for QSAR modelling with SCR. QSAR models were developed using LD50 values for compounds tested on rats with four types of administration (oral, intravenous, intraperitoneal, subcutaneous). The proposed method was evaluated on the set of compounds tested for acute rat toxicity with oral administration (7286 compounds) used for testing the known QSAR methods in T.E.S.T. 3.0 program (U.S. EPA). The several other sets of compounds tested for acute rat toxicity by different routes of administration selected from SYMYX MDL Toxicity Database were used too. The method was compared with the results of prediction of acute rodent toxicity for noncongeneric sets obtained by ACD/Labs Inc. The test sets were predicted with regards to the applicability domain. Comparison of accuracy for QSAR models obtained separately using QNA descriptors, PASS predictions, nearest neighbours' assessment with consensus models clearly demonstrated the benefits of consensus prediction. Free available web-service for prediction of LD50 values of rat acute toxicity was developed: http://www.pharmaexpert.ru/GUSAR/AcuToxPredict/.

QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction

Quantitative structure-activity relationship (QSAR) models have long been used for making predictions and data gap filling in diverse fields including medicinal chemistry, predictive toxicology, environmental fate modeling, materials science, agricultural science, nanoscience, food science, and so forth. Usually a QSAR model is developed based on chemical information of a properly designed training set and corresponding experimental response data while the model is validated using one or more test set(s) for which the experimental response data are available. However, it is interesting to estimate the reliability of predictions when the model is applied to a completely new data set (true external set) even when the new data points are within applicability domain (AD) of the developed model. In the present study, we have categorized the quality of predictions for the test set or true external set into three groups (good, moderate, and bad) based on absolute prediction errors. Then, we have used three criteria [(a) mean absolute error of leave-one-out predictions for 10 most close training compounds for each query molecule; (b) AD in terms of similarity based on the standardization approach; and (c) proximity of the predicted value of the query compound to the mean training response] in different weighting schemes for making a composite score of predictions. It was found that using the most frequently appearing weighting scheme 0.5-0-0.5, the composite score-based categorization showed concordance with absolute prediction error-based categorization for more than 80% test data points while working with 5 different datasets with 15 models for each set derived in three different splitting techniques. These observations were also confirmed with true external sets for another four endpoints suggesting applicability of the scheme to judge the reliability of predictions for new datasets. The scheme has been implemented in a tool "Prediction Reliability Indicator" available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/, and the tool is presently valid for multiple linear regression models only.

How Precise Are Our Quantitative Structure–Activity Relationship Derived Predictions for New Query Chemicals?

Drug-induced liver injury, although infrequent, is an important safety concern that can lead to fatality in patients and failure in drug developments. In this study, we have used an ensemble of mixed learning algorithms and mixed features for the development of a model to predict hepatic effects. This robust method is based on the premise that no single learning algorithm is optimum for all modelling problems. An ensemble model of 617 base classifiers was built from a diverse set of 1,087 compounds. The ensemble model was validated internally with five-fold cross-validation and 25 rounds of y-randomization. In the external validation of 120 compounds, the ensemble model had achieved an accuracy of 75.0%, sensitivity of 81.9% and specificity of 64.6%. The model was also able to identify 22 of 23 withdrawn drugs or drugs with black box warning against hepatotoxicity. Dronedarone which is associated with severe liver injuries, announced in a recent FDA drug safety communication, was predicted as hepatotoxic by the ensemble model. It was found that the ensemble model was capable of classifying positive compounds (with hepatic effects) well, but less so on negatives compounds when they were structurally similar. The ensemble model built in this study is made available for public use.

Mixed learning algorithms and features ensemble in hepatotoxicity prediction

https://hal-univ-rennes1.archives-ouvertes.fr/hal-01220889/document

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction.

The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure–activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency’s National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA’s Chemistry Dashboard and made freely available to the scientific community.

/pdf/sar-and-qsar-modeling-of-a-large-collection-of-ld50-rat-3t35c18r31.pdf

SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

This study presents a new type of acute toxicity (LD(50)) prediction that enables automated assessment of the reliability of predictions (which is synonymous with the assessment of the Model Applicability Domain as defined by the Organization for Economic Cooperation and Development). Analysis involved nearly 75,000 compounds from six animal systems (acute rat toxicity after oral and intraperitoneal administration; acute mouse toxicity after oral, intraperitoneal, intravenous, and subcutaneous administration). Fragmental Partial Least Squares (PLS) with 100 bootstraps yielded baseline predictions that were automatically corrected for non-linear effects in local chemical spaces--a combination called Global, Adjusted Locally According to Similarity (GALAS) modelling methodology. Each prediction obtained in this manner is provided with a reliability index value that depends on both compound's similarity to the training set (that accounts for similar trends in LD(50) variations within multiple bootstraps) and consistency of experimental results with regard to the baseline model in the local chemical environment. The actual performance of the Reliability Index (RI) was proven by its good (and uniform) correlations with Root Mean Square Error (RMSE) in all validation sets, thus providing quantitative assessment of the Model Applicability Domain. The obtained models can be used for compound screening in the early stages of drug development and prioritization for experimental in vitro testing or later in vivo animal acute toxicity studies.

P Japertas

Papers

Estimation of reliability of predictions and model applicability domain evaluation in the analysis of acute toxicity (LD50).