This poster was presented by Jinju Byeon, Professor Young Shin and co-authors from Chungnam National University at the 2014 Pharmaceutical Society of Korea Meeting.

Introduction

Lead optimization is one of the most critical stages of drug discovery. The conventional lead optimization process normally starts with the identification of hit compounds which show decent Ki or IC50 for target proteins. Once identified, hundreds or thousands of derivatives are further synthesized to improve ADME(Absorption Distribution Metabolism Excretion)/PK(Pharmacokinetics) properties without compromising potency. However, this requires significant amounts of DMPK(Drug metabolism and Pharmacokinetics) resources and time due to the various in vitro ADME assays/in vivo PK studies that must be evaluated. Therefore, several in silico approaches have been recently introduced to predict physicochemical properties and ADME properties in a high throughput manner for quick ranking-ordering of compounds by several pharmaceutical scientists using in-house models and global models supplied by commercial software. In this study, we introduce an innovative in silico-based high throughput lead optimization strategy with QSAR and PBPK modelings using StarDrop™, ADMET predictor® and GastroPlus®.

You can download a PDF of this poster.