Actionable Predictions of Human Pharmacokinetics at the Drug Design Stage

Evaluating AutoGrow4 - an open-source toolkit for semi-automated computer-aided drug discovery

INTRODUCTION

Drug discovery is a long and expensive process characterized by a high failure rate. To make this process more rational and efficient, scientists always look for new and better ways to design novel ligands for a target of interest. Among different approaches, de novo ones gained popularity in the last decade, thanks to their ability to efficiently explore the chemical space and their increasing reliability in generating high-quality compounds. Autogrow4 is open-source software for de novo drug design that generates ligands for a given target by exploiting a combination of a genetic algorithm and molecular docking calculations.

AREAS COVERED

In the present paper, the authors dissect this program's usefulness and limitations in generating new compounds from a pharmacodynamic and pharmacokinetic perspective. Specifically, this article examines all reported applications of the Autogrow code in the literature (as retrieved from the Scopus database) from the release of its first version in 2009 to the present.

EXPERT OPINION

In the hands of an expert molecular modeler, Autogrow4 is a useful tool for de novo ligand design. Its modular and open-source codebase offers many protocol customization features. The main downsides are limited control over the pharmacokinetic features of generated ligands and the bias toward high molecular weight compounds.

Integrating Pharmacokinetics and Quantitative Systems Pharmacology Approaches in Generative Drug Design

Integrated understanding of pharmacokinetics (PK) and pharmacodynamics (PD) is a key aspect of successful drug discovery. Yet in generative computational drug design, the focus often lies on optimizing potency. Here we integrate PK property predictions in DrugEx, a generative drug design framework and we explore the generated compounds' PD through simulations with a quantitative systems pharmacology (QSP) model. Quantitative structure-property relationship models were developed to predict molecule PK (clearance, volume of distribution and unbound fraction) and affinity for the Adenosine A2AR receptor (A2AR), a drug target in immuno-oncology. These models were used to score compounds in a reinforcement learning framework to generate molecules with a specific PK profile and high affinity for the A2AR. We predicted the expected tumor growth inhibition profiles using the QSP model for selected candidate molecules with varying PK and affinity profiles. We show that optimizing affinity to the A2AR, while minimizing or maximizing a PK property, shifts the type of molecular scaffolds that are generated. The difference in physicochemical properties of the compounds with different predicted PK parameters was found to correspond with the differences observed in the PK data set. We demonstrated the use of the QSP model by simulating the effect of a broad range of compound properties on the predicted tumor volume. In conclusion, our proposed integrated workflow incorporating affinity predictions with PKPD may provide a template for the next generation of advanced generative computational drug design.