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Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.

ABSTRACT: Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals in both development and postmarketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the quantitative structure-activity relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity, and balanced (40/60) active/inactive ratios were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k nearest neighbor method and validated using external data sets. Models with high sensitivity (>73%) and specificity (>94%) for the prediction of liver AEDs in external validation sets were developed. To test applicability of the models, three chemical databases (World Drug Index, Prestwick Chemical Library, and Biowisdom Liver Intelligence Module) were screened in silico, and the validity of predictions was determined, where possible, by comparing model-based classification with assertions in publicly available literature. Validated QSAR models of liver AEDs based on the data from the FDA spontaneous reporting system can be employed as sensitive and specific predictors of AEDs in preclinical screening of drug candidates for potential hepatotoxicity in humans.

SUBMITTER: Rodgers AD

PROVIDER: S-EPMC2965736 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.

Rodgers Amie D AD Zhu Hao H Fourches Denis D Rusyn Ivan I Tropsha Alexander A

Chemical research in toxicology 20100401 4

Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals in both development and postmarketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the quantitative structure-activity relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs inactive) of drugs for liver AEDs ...[more]

PMID: 20192250

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Project description:Xanthone derivatives have a wide range of pharmacological activities, such as those involving antibacterial, antiviral, antimalarial, anthelmintic, anti-inflammatory, antiprotozoal, and anticancer properties. Among these, we investigated the anticancer properties of xanthone. This research aimed to analyze the biological activity of ten novel xanthone derivatives, to investigate the most contributing-descriptors for their cytotoxic activities, and to examine the possible mechanism of actions of xanthone compound through molecular docking.The cytotoxic tests were carried out on WiDR and Vero cell lines, by a 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) assay method. The structural features required for xanthone's anticancer activity were conducted by using the semi-empirical Austin Model-1 method, and continued with quantitative structure-activity relationship (QSAR) analysis using BuildQSAR program. The study of the possible mechanism of actions of the selected xanthone compound was done through molecular docking with PLANTS.The three novel xanthone derivatives (compounds 5, 7, and 8) exhibited cytotoxic activity with compound 5 showed the highest degree of cytotoxicity at concentration 9.23 µg/mL (37.8 µM). The following best equation model was obtained from the BuildQSAR calculation: log 1/IC50 = -8.124 qC1 -35.088 qC2 -6.008 qC3 + 1.831 u + 0.540 logP -9.115 (n = 10, r = 0.976, s = 0.144, F = 15.920, Q2 = 0.651, SPRESS = 0.390). This equation model generated 15 proposed new xanthone compounds with better-predicted anticancer activities. A molecular docking study of compound 5 showed that xanthone formed binding interactions with some receptors involved in cancer pathology, including telomerase, tumor-promoting inflammation (COX-2), and cyclin-dependent kinase-2 (CDK2) inhibitor.The results suggested that compound 5 showed the best cytotoxic activity among the xanthone derivatives tested. QSAR analysis showed that the descriptors contributed to xanthone's cytotoxic activity were the net atomic charge at qC1, qC2, and qC3 positions, also dipole moment and logP. Compound 5 was suspected to be cytotoxic by its inhibition of telomerase, COX-2, and CDK2 receptors.

Dataset Information

Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.

Publications

Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.

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