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A Novel Methodology for Multicomponent Drug Design and Its Application in Optimizing the Combination of Active Components from Chinese Medicinal Formula Shenmai
Author(s) -
Wang Yi,
Yu Lingyan,
Zhang Ling,
Qu Haibin,
Cheng Yiyu
Publication year - 2010
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/j.1747-0285.2009.00934.x
Subject(s) - computer science , drug , multivariate statistics , mathematical optimization , biochemical engineering , mathematics , machine learning , engineering , medicine , pharmacology
Traditional Chinese Medicine has become an important resource for searching the effective drug combinations in multicomponent drug designs. In this article, we investigate the methodology on how to efficiently optimize the combination of several active components from traditional Chinese formula. A new method based upon lattice experimental design and multivariate regression was applied to model the quantitative composition‐activity relationship (QCAR) in this study. As a result, multi‐objective optimization was achieved by Derringer function using extensive search algorithm. This newly proposed QCAR‐based strategy for multicomponent drug design was then successfully applied on search optimal combination of three components from Chinese medicinal formula Shenmai . The result validated the effectiveness of the presented method for multicomponent drug design.