In silicooptimization of pharmacokinetic properties and receptor binding affinity simultaneously: a ‘parallel progression approach to drug design’ applied to β-blockers

Abstract

The present work exploits the potential of in silico approaches for minimizing attrition of leads in the later stages of drug development. We propose a theoretical approach, wherein 'parallel' information is generated to simultaneously optimize the pharmacokinetics (PK) and pharmacodynamics (PD) of lead candidates. β-blockers, though in use for many years, have suboptimal PKs; hence are an ideal test series for the 'parallel progression approach'. This approach utilizes molecular modeling tools viz. hologram quantitative structure activity relationships, homology modeling, docking, predictive metabolism, and toxicity models. Validated models have been developed for PK parameters such as volume of distribution (log Vd) and clearance (log Cl), which together influence the half-life (t1/2) of a drug. Simultaneously, models for PD in terms of inhibition constant pKi have been developed. Thus, PK and PD properties of β-blockers were concurrently analyzed and after iterative cycling, modifications were proposed that lead to compounds with optimized PK and PD. We report some of the resultant re-engineered β-blockers with improved half-lives and pKi values comparable with marketed β-blockers. These were further analyzed by the docking studies to evaluate their binding poses. Finally, metabolic and toxicological assessment of these molecules was done through in silico methods. The strategy proposed herein has potential universal applicability, and can be used in any drug discovery scenario; provided that the data used is consistent in terms of experimental conditions, endpoints, and methods employed. Thus the 'parallel progression approach' helps to simultaneously fine-tune various properties of the drug and would be an invaluable tool during the drug development process.

GPCR-SSFE 2.0—a fragment-based molecular modeling web tool for Class A G-protein coupled receptors

“…Template selection is based on the structure–sequence relationship and uses our published workflow whereby the template for each TMH is selected individually thus allowing either a single template or a fragment-based approach to be used for model building (10). GPCR-SSFE's models have been frequently cited and used, for example, to help generate new hypotheses on the enteroendocrine fat sensor GPR119 (11), rationalize the design of potent CB1 antagonists (12) and the server is linked as a partner tool on the GPCRdb website (13). …”