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

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.

Necessity for retrospective evaluation of past-positive chemicals in in vitro chromosomal aberration tests using recommended cytotoxicity indices

We have demonstrated that retrospective evaluation of existing data of in vitro chromosomal aberration test using the new cytotoxicity indices RICC (relative increase in cell count) or RPD (relative population doubling) reduces the false-positive rate. We have constructed an algorithm to predict the likelihood that past-positive results would differ when retested accordingly. Here, we emphasize the importance of reviewing existing in vitro chromosomal aberration test results. The present Letter not only supports the rediscovery of potentially useful chemicals excluded from further development as a result of misclassification due to in vitro false-positive results, but also contributes to the development of a precise Quantitative Structure-Activity Relationship (QSAR) model by providing an appropriate training data-set. Furthermore, re-evaluation is expected to provide novel insights into underlying mechanisms and/or key structures involved in the development of chromosomal aberrations.