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.

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.

Keywords: Preclinical drug development; computational tools; drug-target signatures; in silico prediction; toxicology.

Fig. 1. Major sites of drug metabolism and associated xenobiotic-metabolizing Cytochrome P450 (CYP) isoforms when considering oral drug administration

CYPs expressed in the gut wall [116], liver [16], lung [117], tumor cell [118], and kidney [119] guide the phase 1 preclinical assessment of tissue-specific drug metabolism. Tissue-specific CYPs are not exhaustive but are presented to demonstrate inter-tissue differences and similarities in predominant CYP expression. Other xenobiotic metabolizing enzymes originating from both membraneous and cytosolic media are not included.

Fig. 2

Computational approaches utilized in PKPD in silico studies during the pre-discovery phase.

Fig. 3. Computational optimization workflow for a drug known to bind a protein target of interest in a disease state that can serve as a clinical therapeutic candidate

This workflow is intended for drug discovery instances where investigators have a drug with potential efficacy but would like to optimize the drug for ADMET. (1) Virtual modification of R groups to create a virtual library of chemical congeners for the drug. (2) Computational experiments of congener library against the protein target of interest to determine potential binding and potency. (3) Computational experiments of congeners against curated virtual library of all known metabolism-associated enzymes to predict all potential metabolites. (4) Prediction of downstream pathway perturbations of all potential metabolites for each congener that may be related to toxicity. (5) Computational experiments to determine interaction of drug with important proteins implicated in absorption, distribution and excretion such that optimal bioavailability may be determined. (6) Algorithm to rank congeners based on greatest binding potential to given therapeutic protein target, least potential for metabolism into toxic metabolites and greatest potential bioavailability depending on interested route of administration.