In Silico Model for Chemical-Induced Chromosomal Damages Elucidates Mode of Action and Irrelevant Positives.

Yurika Fujita,Hiroshi Honda,Osamu Morita

doi:10.3390/genes11101181

Abstract

In silico tools to predict genotoxicity have become important for high-throughput screening of chemical substances. However, current in silico tools to evaluate chromosomal damage do not discriminate in vitro-specific positives that can be followed by in vivo tests. Herein, we establish an in silico model for chromosomal damages with the following approaches: (1) re-categorizing a previous data set into three groups (positives, negatives, and misleading positives) according to current reports that use weight-of-evidence approaches and expert judgments; (2) utilizing a generalized linear model (Elastic Net) that uses partial structures of chemicals (organic functional groups) as explanatory variables of the statistical model; and (3) interpreting mode of action in terms of chemical structures identified. The accuracy of our model was 85.6%, 80.3%, and 87.9% for positive, negative, and misleading positive predictions, respectively. Selected organic functional groups in the models for positive prediction were reported to induce genotoxicity via various modes of actions (e.g., DNA adduct formation), whereas those for misleading positives were not clearly related to genotoxicity (e.g., low pH, cytotoxicity induction). Therefore, the present model may contribute to high-throughput screening in material design or drug discovery to verify the relevance of estimated positives considering their mechanisms of action.

Highlights

In silico prediction tools for toxicological evaluations have become increasingly important owing to the demand for high-throughput evaluation in drug discovery and chemical substance design without animal testing
Bacterial reverse mutation assays and in vitro mammalian cell tests that were developed to evaluate gene mutations and chromosomal damages are commonly used in a battery evaluation to achieve high sensitivity for carcinogenicity predictions [5]
After synthetic minority oversampling technique (SMOTE) treatment, 150 positive, 144 misleading positive, and 150 negative chemicals were used for model development

Summary

Introduction

In silico prediction tools for toxicological evaluations have become increasingly important owing to the demand for high-throughput evaluation in drug discovery and chemical substance design without animal testing. Bacterial reverse mutation assays (especially the Ames test) and in vitro mammalian cell tests that were developed to evaluate gene mutations and chromosomal damages are commonly used in a battery evaluation to achieve high sensitivity for carcinogenicity predictions [5]. In vivo studies, such as in vivo micronucleus tests, have been used to follow up misleading positives. They are low-throughput and have been restricted in terms of animal welfare.

Methods

Results

Conclusion