CORAL Models for Drug-Induced Nephrotoxicity

Journal of computer-aided molecular design

It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity prediction are discussed. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints. However, QSARs also exist for the prediction of toxicity of very diverse libraries, although often such QSARs are of the classification type; that is, they predict simply whether or not a compound is toxic, and do not give an indication of the level of toxicity. Examples are given of all of...

Toxicological Sciences, 2009

Journal of Cheminformatics, 2016

Background: With a constant increase in the number of new chemicals synthesized every year, it becomes important to employ the most reliable and fast in silico screening methods to predict their safety and activity profiles. In recent years, in silico prediction methods received great attention in an attempt to reduce animal experiments for the evaluation of various toxicological endpoints, complementing the theme of replace, reduce and refine. Various computational approaches have been proposed for the prediction of compound toxicity ranging from quantitative structure activity relationship modeling to molecular similarity-based methods and machine learning. Within the "Toxicology in the 21st Century" screening initiative, a crowd-sourcing platform was established for the development and validation of computational models to predict the interference of chemical compounds with nuclear receptor and stress response pathways based on a training set containing more than 10,000 compounds tested in high-throughput screening assays. Results: Here, we present the results of various molecular similarity-based and machine-learning based methods over an independent evaluation set containing 647 compounds as provided by the Tox21 Data Challenge 2014. It was observed that the Random Forest approach based on MACCS molecular fingerprints and a subset of 13 molecular descriptors selected based on statistical and literature analysis performed best in terms of the area under the receiver operating characteristic curve values. Further, we compared the individual and combined performance of different methods. In retrospect, we also discuss the reasons behind the superior performance of an ensemble approach, combining a similarity search method with the Random Forest algorithm, compared to individual methods while explaining the intrinsic limitations of the latter. Conclusions: Our results suggest that, although prediction methods were optimized individually for each modelled target, an ensemble of similarity and machine-learning approaches provides promising performance indicating its broad applicability in toxicity prediction.

Human & Experimental Toxicology, 2015

Predictive toxicology plays a critical role in reducing the failure rate of new drugs in pharmaceutical research and development. Despite recent gains in our understanding of drug-induced toxicity, however, it is urgent that the utility and limitations of our current predictive tools be determined in order to identify gaps in our understanding of mechanistic and chemical toxicology. Using recently published computational regression analyses of in vitro and in vivo toxicology data, it will be demonstrated that significant gaps remain in early safety screening paradigms. More strategic analyses of these data sets will allow for a better understanding of their domain of applicability and help identify those compounds that cause significant in vivo toxicity but which are currently mis-predicted by in silico and in vitro models. These 'outliers' and falsely predicted compounds are metaphorical lighthouses that shine light on existing toxicological knowledge gaps, and it is essential that these compounds are investigated if attrition is to be reduced significantly in the future. As such, the modern computational toxicologist is more productively engaged in understanding these gaps and driving investigative toxicology towards addressing them.

Expert Opinion on Drug Metabolism & Toxicology

Introduction: The kidney is a major target for toxicity elicited by pharmaceuticals and environmental pollutants. Standard testing which often does not investigate underlying mechanisms has proven not to be an adequate hazard assessment approach. As such, there is an opportunity for the application of computational approaches that utilise multi-scale data based on the Adverse Outcome Pathway (AOP) paradigm, coupled with an understanding of the chemistry underpinning the molecular initiating event (MIE) to provide a deep understanding of how structural fragments of molecules relate to specific mechanisms of nephrotoxicity. The aim of this investigation was to review the current scientific landscape related to computational methods, including mechanistic data, AOPs, publicly available knowledge bases and current in silico models, for the assessment of pharmaceuticals and other chemicals with regard to their potential to elicit nephrotoxicity. A list of over 250 nephrotoxicants enriched with, where possible, mechanistic and AOP-derived understanding was compiled. Expert opinion: Whilst little mechanistic evidence has been translated into AOPs, this review identified a number of data sources of in vitro, in vivo and human data that may assist in the development of in silico models which in turn may shed light on the interrelationships between nephrotoxicity mechanisms.

Toxicology Letters, 2003

Ecotoxicology and Environmental Safety, 2019

In the present work, quantitative structure-activity relationship (QSAR) models have been developed for ecotoxicity of pharmaceuticals on four different aquatic species namely Pseudokirchneriella subcapitata, Daphnia magna, Oncorhynchus mykiss and Pimephales promelas using genetic algorithm (GA) for feature selection followed by Partial Least Squares regression technique according to the Organization for Economic Cooperation and Development (OECD) guidelines. Double cross-validation methodology was employed for selecting suitable models. Only 2D descriptors were used for capturing chemical information and model building, whereas validation of the models was performed by considering various stringent internal and external validation metrics. Interestingly, models could be developed even without using any LogP terms in contrary to the usual dependence of toxicity on lipophilicity. However, the current manuscript proposes highly robust and more predictive models employing computed logP descriptors. The applicability domain study was performed in order to set a predefined chemical zone of applicability for the obtained QSAR models, and the test compounds falling outside the domain were not taken for further analysis while making a prioritized list. An additional comparison was made with ECOSAR, an online expert system for toxicity prediction of organic pollutants, in order to prove predictability of the obtained models. The obtained robust consensus models were utilized to predict the toxicity of a large dataset of approximately 9300 drug-like molecules in order to prioritize the existing drug-like substances in accordance to their acute predicted aquatic toxicities following a scaling technique. Finally, prioritized lists of 500 most toxic chemicals obtained by respective consensus models and those predicted from ECOSAR tool have been reported. Cleuvers, 2003; Sanderson et al., 2003). The main concern regarding ecotoxicity of pharmaceuticals lies in their chronic accumulation in water bodies mainly in sweet and saline waters. The progressive accumulation in water bodies are attributed mainly due to irrational use, over-exploitation, improper pretreatment before releasing into the water bodies by waste water treatment plants (WWTPs), etc. In the last few decades, an increased interest has been shown by ecotoxicologists, chemists and environmentalists in analyzing content of different sewage water effluents, to find out relative concentration of various pharmaceuticals, agrochemicals and their transformed metabolites. The results can help to quantify the amount of toxic chemicals present in the particular water bodies. However, it is next to impossible to analyze all of the contents of aqueous effluents because some of their concentrations may be present in nanomolar range.

A predefined number of features were selected using Pearson correlation, training and prediction was done using Support Vector Regression (SVR; radial basis Bidirectional search was used to select features, training and prediction was done using a support vector machine (SVM; radial basis).

Lecture Notes in Computer Science, 2021

Toxicity in drug design is a very important step prior to human or animal evaluation phases. Establishing drug toxicity involves the modification or redesign of the drug into an analog to suppress or reduce the toxicity. In this work, two different deep neural networks architectures and a proposed model to classify drug toxicity were evaluated. Three datasets of molecular descriptors were build based on SMILES from the Tox21 database and the AhR protein to test the accuracy prediction of the models. All models were tested with different sets of hyperparameters. The proposed model showed higher accuracy and lower loss compared to the other architectures. The number of descriptors played a key roll in the accuracy of the proposed model along with the Adam optimizer.

Pure and Applied Chemistry, 1997

Chemometrics is the application of statistics to the analysis of chemical data (from organic, analytical or medicinal chemistry) and design of chemical experiments and simulations. CLOGP values CLOGP values are calculated 1-octanol/water partition coefficients, frequently used in structure-property correlation or quantitative structure-activity relationship (SPUQSAR) studies (Leo, 1993) (see Structure-property correlations (SPC) and Quantitative structure-activity relationships (QSAR)) Cluster analysis Cluster analysis is the clustering, or grouping, of large data sets (e.g., chemical and/or pharmacological data sets) on the basis of similarity criteria for appropriately scaled variables that represent the data of interest. Similarity criteria (distance based, associative, correlative, probabilistic) among the several clusters facilitate the recognition of patterns and reveal otherwise hidden structures (Rouvray, 1990; Willett, 1987, 1991). CNDO/2 calculations CND0/2 calculations are semi-empirical molecular orbital (MO) calculations using complete neglect of differential overlap. (see Molecular orbital (MU) calculations) Comparative molecular field analysis (CoMFA) * Comparative Molecular Field Analysis (CoMFA) is a 3D-QSAR method that uses statistical correlation techniques for the analysis of the quantitative relationship between the biological activity of a set of compounds with a specified alignment, and their three-dimensional electronic and steric properties. Other 0 1997 IUPAC, Pure andApplied Chernistry69, 1137-1 152 Terms in computational drug design 1141 properties, such as hydrophobicity and H-bonding can also be incorporated into the analysis (Cramer et al., 1988; Kubinyi, 1993b).