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Quantitative Structure‐Activity Relationships for PPAR‐γ Binding and Gene Transactivation of Tyrosine‐Based Agonists Using Multivariate Statistics
Author(s) -
Giaginis Costas,
Theocharis Stamatios,
TsantiliKakoulidou Anna
Publication year - 2008
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.1747-0285.2008.00701.x
Subject(s) - transactivation , quantitative structure–activity relationship , computational biology , molecular descriptor , peroxisome proliferator activated receptor , chemistry , pharmacology , gene , biology , biochemistry , stereochemistry , gene expression
Peroxisome proliferator‐activated receptor‐γ offers a molecular target for drugs aimed to treat type II diabetes mellitus, while its therapeutic potency against cancer disease is currently being explored in preclinical studies. Tyrosine derivatives constitute a major class of peroxisome proliferator‐activated receptor‐γ agonists attracting considerable research interest in drug discovery. Thus, the establishment of adequate QSAR models would serve as a guide for further molecular design. In the present study, multivariate data analysis was applied on a large set of tyrosine‐based peroxisome proliferator‐activated receptor‐γ agonists for modelling binding affinity, expressed as pKi and gene transactivation, expressed as pEC 50 . A pool of descriptors based on physicochemical and molecular properties as well as on specific structural characteristics was used and two PLS models with satisfactory statistics were produced for binding data. According to them, molecular weight, rotatable bonds and lipophilicity were found to exert a considerable positive influence, while excess negative and positive charge created by additional acidic or basic groups in the molecules was unfavourable. With gene transactivation data, an adequate model was obtained only for the highly active compounds if considered separately. The higher complexity incorporated in gene transactivation data was further investigated by establishing a PLS model, which improved the inter‐relationship between pEC 50 and pKi.