title: Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Georgetown-Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Washington DC, 20057, United States.
authors.forename: Naiem T
authors.initials: NT
authors.lastname: Issa
authors.affiliation: Georgetown-Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Washington DC, 20057, United States.
authors.forename: Henri
authors.initials: H
authors.lastname: Wathieu
authors.affiliation: College of Pharmacy, Howard University, Washington, DC 20059, United States.
authors.forename: Abiola
authors.initials: A
authors.lastname: Ojo
authors.affiliation: Georgetown-Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Washington DC, 20057, United States.
authors.forename: Stephen W
authors.initials: SW
authors.lastname: Byers
authors.affiliation: Georgetown-Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Washington DC, 20057, United States.
authors.forename: Sivanesan
authors.initials: S
authors.lastname: Dakshanamurthy
mesh_terms: D000818:Animals; D055808:Drug Discovery; D006801:Humans; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D014116:Toxicology
chemical_list: D004364:Pharmaceutical Preparations
keywords: Preclinical drug development; computational tools; drug-target signatures; in silico prediction; toxicology