Background: While establishing efficacy in translational models and humans through clinically-relevant
endpoints for disease is of great interest, assessing the potential toxicity of a putative therapeutic drug is critical.
Toxicological assessments in the pre-clinical discovery phase help to avoid future failure in the clinical phases of
drug development. Many in vitro assays exist to aid in modular toxicological assessment, such as hepatotoxicity and
genotoxicity. While these methods have provided tremendous insight into human toxicity by investigational new
drugs, they are expensive, require substantial resources, and do not account for pharmacogenomics as well as critical
ADME properties. Computational tools can fill this niche in toxicology if in silico models are accurate in relating
drug molecular properties to toxicological endpoints as well as reliable in predicting important drug-target interactions
that mediate known adverse events or adverse outcome pathways (AOPs).
Methods: We undertook an unstructured search of multiple bibliographic databases for peer-reviewed literature regarding
computational methods in predictive toxicology for in silico drug discovery. As this review paper is meant to serve
as a survey of available methods for the interested reader, no focused criteria were applied. Literature chosen was based
on the writers’ expertise and intent in communicating important aspects of in silico toxicology to the interested reader.
Conclusion: This review provides a purview of computational methods of pre-clinical toxicologic assessments for
novel small molecule drugs that may be of use for novice and experienced investigators as well as academic and
commercial drug discovery entities.