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|Translating the Favorable ADME Profile of a Lead Compound Into Virtual Analogs in Restricted Physicochemical Space||P. Japertas, et al.||Download Poster|
Structure optimization of compounds for physicochemical and ADME properties continues to be a challenge in drug discovery. The new ACD/Structure Design Engine aids lead optimization efforts by allowing scientists to explore substituent effects, and design analogs with desired property profiles. Using the design engine with either predictors or drug profiling tools can help to focus synthetic efforts on compounds that meet physicochemical and ADME requirements. Learn more about the ACD/Structure Design Engine, and discuss your needs with our staff.
Booth # 1009
Title: Translating favorable ADME profile of a lead compound into virtual analogs in restricted physicochemical space
Authors: Pranas Japertas
Date: Aug. 22, 2012
Time: 7:00–10:00 PM
Location: Pennsylvania Convention Center, Room: Grand Ballroom PCC
Poster ID: 21052
Division: Division of Medicinal Chemistry Session: General Poster Session 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 compatible with the desired biological characteristics.
The main idea behind our approach is that many considered properties are governed by basic physicochemical parameters, such as ionization, lipophilicity, and molecular size. We have devised simple, yet accurate physicochemical models of intestinal absorption and passive permeation across the BBB, and general physicochemical rules that hold even for protein-ligand interactions (P-gp, hERG inhibitor specificity). Changing parameter values may have distinct, even opposing effects on different ADME properties, and the impact of a particular parameter may depend on the allowed variation ranges of others. Using the cumulative output of available predictive models enables us to account for the multitude of possible effects and identify regions in physicochemical space that are most likely occupied by analogs with the desired combination of ADME properties. Advanced techniques are also applied to improve the selection of substituents fitting these regions, including custom Hammett equations for estimating 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 toimposed physicochemical restrictions, make our software platform a valuable tool to guide drug discovery projects towards the most promising candidates.