Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action

Abstract

Small-molecule drug design is a complex and iterative decision-making process relying on pre-existing knowledge and driven by experimental data. Low-molecular-weight chemicals represent an attractive therapeutic option, as they are readily accessible to organic synthesis and can easily be characterized.1 Their potency as well as pharmacokinetic and pharmacodynamic properties can be systematically and rationally investigated and ultimately optimized via expert science behind medicinal chemistry and methods of computer-aided drug design (CADD). In recent years, significant advances in molecular modeling techniques have afforded a variety of tools to effectively identify potential binding pockets on prospective targets, to map key interactions between ligands and their binding sites, to construct and assess energetics of the resulting complexes, to predict ADMET properties of candidate compounds, and to systematically analyze experimental and computational data to derive meaningful structure-activity relationships leading to the creation of a drug candidate. This Perspective describes a real case of a drug discovery campaign accomplished in a relatively short time with limited resources. The study integrated an arsenal of available molecular modeling techniques with an array of experimental tools to successfully develop a novel class of potent and selective androgen receptor inhibitors with a novel mode of action. It resulted in the largest academic licensing deal in Canadian history, totaling $142M. This project exemplifies the importance of team science, an integrative approach to drug discovery, and the use of best practices in CADD. We posit that the lessons learned and best practices for executing an effective CADD project can be applied, with similar success, to many drug discovery projects in both academia and industry.