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|Translating Favorable ADME Profile of a Lead Compound into Virtual Analogs in Restricted||Pranas Japertas, Rytis Kubilius, Andrius Sazonovas, Kiril Lanevskij||Download Poster|
Designing lead molecules with appropriate physicochemical properties is the ideal starting point in drug discovery, and is the reason for extensive in silico and in vitro compound screening. The structure design application in ACD/Percepta is a software tool that aids chemical structure optimization based on pre-defined physicochemical, ADME, and toxicity requirements. Customize your level of prediction detail, use various tools to filter and sort results, and apply experimental data to improve prediction accuracy. Meet us at the PhysChem Forum to discuss how this and other solutions can help accelerate your drug discovery projects.
Title: Translating Favorable ADME Profile of a Lead Compound into Virtual Analogs in Restricted Physicochemical Space
Authors: Pranas Japertas, Rytis Kubilius, Andrius Sazonovas, Kiril Lanevskij
Date: Thursday, Apr. 19, 2012
Abstract: View Abstract
The efforts of lead optimization projects are directed towards analogs that have favorable ADME profiles and are devoid of safety concerns whilst retaining target activity. In this work we present a novel computational platform to aid such projects by generating virtual analog libraries in physicochemical space regions compatible with the desired biological characteristics.
The main idea lying behind our approach is that many considered properties are governed by basic physicochemical parameters, such as ionization, lipophilicity, or molecular size. We have devised simple, yet accurate physicochemical models of intestinal absorption and passive permeation across the BBB, as well as general physicochemical rules that hold, even for protein-ligand interactions (P-gp, hERG inhibitor specificity). Changing parameter values may have distinct, even opposite effects on different ADME properties, and the impact of a particular parameter may depend on the allowed variation ranges of other parameters. Using the cumulative output of available predictive models enables us to account for the multitude of possible effects and identify the regions in physicochemical space that are most likely occupied by analogs with the needed combination of ADME properties. Advanced techniques are also applied to improve selection of substituents fitting within these regions, including custom Hammett equations for estimating the mutual effects of the core molecule and the modified substituent on the analog's pKa.
The presented methods coupled with automatic analog generation in accordance with the imposed physicochemical restrictions make our software platform a valuable tool to guide drug discovery projects towards the most promising candidates.