Dataset Information

A multiparametric organ toxicity predictor for drug discovery.

ABSTRACT: The assessment of major organ toxicities through in silico predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of in silico predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, clinical adverse impacts and metabolism of pharmaceutically active substances has gained widespread acceptance in academia and pharmaceutical industries. With unrestricted accessibility to powerful biomarkers, researchers have an opportunity to contemplate the most accurate predictive scores to evaluate drug's adverse impact on various organs.A multiparametric model involving physico-chemical properties, quantitative structure-activity relationship predictions and docking score was found to be a more reliable predictor for estimating chemical toxicities with potential to reflect atomic-level insights. These in silico models provide informed decisions to carry out in vitro and in vivo studies and subsequently confirms the molecules clues deciphering the cytotoxicity, pharmacokinetics, and pharmacodynamics and organ toxicity properties of compounds. Even though the drugs withdrawn by USFDA at later phases of drug discovery which should have passed all the state-of-the-art experimental approaches and currently acceptable toxicity filters, there is a dire need to interconnect all these molecular key properties to enhance our knowledge and guide in the identification of leads to drug optimization phases. Current computational tools can predict ADMET and organ toxicities based on pharmacophore fingerprint, toxicophores and advanced machine-learning techniques.

SUBMITTER: Patel CN

PROVIDER: S-EPMC7383222 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A multiparametric organ toxicity predictor for drug discovery.

Patel Chirag N CN Kumar Sivakumar Prasanth SP Rawal Rakesh M RM Patel Daxesh P DP Gonzalez Frank J FJ Pandya Himanshu A HA Pandya Himanshu A HA

Toxicology mechanisms and methods 20191029 3

The assessment of major organ toxicities through <i>in silico</i> predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of <i>in silico</i> predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, ...[more]

PMID: 31618094

Similar Datasets

Project description:BackgroundIn practice, some drugs produce a number of negative biological effects that can mitigate their effectiveness as a remedy. To address this issue, several studies have been performed for the prediction of drug-induced toxicity from gene-expression data, and a significant amount of work has been done on predicting limited drug-induced symptoms or single-organ toxicity. Since drugs often lead to some injuries in several organs like liver or kidney, however, it would be very useful to forecast the drug-induced injuries for multiple organs. Therefore, in this work, our aim was to develop a multi-organ toxicity prediction model using an integrative model of gene-expression data.ResultsTo train our integrative model, we used 3708 in-vivo samples of gene-expression profiles exposed to one of 41 drugs related to 21 distinct physiological changes divided between liver and kidney (liver 11, kidney 10). Specifically, we used the gene-expression profiles to learn an ensemble classifier for each of 21 pathology prediction models. Subsequently, these classifiers were combined with weights to generate an integrative model for each pathological finding. The integrative model outputs the likeliness of presenting the trained pathology in a given test sample of gene-expression profile, called an integrative prediction score (IPS). For the evaluation of an integrative model, we estimated the prediction performance with the k-fold cross-validation. Our results demonstrate that the proposed integrative model is superior to individual pathology prediction models in predicting multi-organ drug-induced toxicities over all the targeted pathological findings. On average, the AUC of the integrative models was 88% while the AUC of individual pathology prediction models was 68%.ConclusionsNot only does this integrative model produce comparable prediction performance to existing approaches, but also it produces very stable performance overall. In addition, our approach is easily expandable to a variety of other multi-organ toxicology applications.

Project description:In this study, we aim to identify candidate biomarkers which may be useful as surrogate indicators of toxicity for pre-clinical development of panPPAR-agonist drug candidates. Gene expression microarray, histopathology and clinical chemistry data were generated from liver, heart, kidney and skeletal muscles of three groups of mice administered with three different dosages of an experimental pan-peroxisome proliferator-activated receptor (pan-PPAR) agonist, PPM-201, for 14 days. The histopathology and clinical chemistry data were compared with the gene expression analysis and candidate biomarker genes were identified. Nine wild type mice (strain: C57BL/6J) were randomly divided into three groups - Group-I, II and III. PPM-201 in the vehicle base was administered daily for 14 days at 6 mg/kg body weight dose rate to each mouse in Group-II and at 20mg/kg body weight dose rate to each mouse in Group-III while the mice in Group-I received only the vehicle base. On 15th day, the mice were sacrificed to harvest blood, heart, skeletal muscle, liver and kidney tissues for clinical chemistry, microarray and histopathology analysis. In the clinical chemistry analysis, alanine aminotransferase (ALT, U/L), aspartate aminotransferase (AST, U/L), creatinine kinase (CK, U/L), blood urea nitrogen (BUN, mmol/L), creatinine (umol/L) and lactate dehydrogenase (LDH, U/L) were measured from the blood of each mouse. Two sections of liver, two sections of kidney, one or two sections of skeletal muscle, and one section of heart were prepared from each mouse, stained with hematoxylin and eosin (H&E), and examined by a veterinary pathologist. RNA was extracted and processed as per the established protocol from heart, skeletal muscle, liver and kidney tissue samples of all the 9 mice for profiling with Affymetrix Mouse Genome 430 2.0 Array and in total 36 chips were prepared. Using various quality control measures, the data was analysed for its quality. As it was found to be good in quality, the data from all the 36 chips were used for further analysis. The results from histopathology and clinical chemistry analysis were compared with the gene expression to determine if the dosages selected for the study were associated with findings in target organs.

Dataset Information

A multiparametric organ toxicity predictor for drug discovery.

Publications

A multiparametric organ toxicity predictor for drug discovery.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets