title: Computational models for predicting substrates or inhibitors of P-glycoprotein.
abstract: The impact of P-glycoprotein (P-gp) on the multidrug resistance and pharmacokinetics of clinically important drugs has been widely recognized. Here, we review in silico approaches and computational models for identifying substrates or inhibitors of P-gp. The advances in the datasets for model building and available computational models are summarized and the advantages and drawbacks of these models are outlined. We also discuss the impact of the recently reported crystal structures of P-gp on potential breakthroughs in the computational modeling of P-gp substrates. Finally, the challenges of developing reliable prediction models for P-gp inhibitors or substrates, as well as the strategies to surmount these challenges, are reviewed.
authors: Institute of Functional Nano & Soft Materials and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
authors: Lei
authors: L
authors: Chen
authors: Youyong
authors: Y
authors: Li
authors: Huidong
authors: H
authors: Yu
authors: Liling
authors: L
authors: Zhang
authors: Tingjun
authors: T
authors: Hou
mesh_terms: D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1 / Q000037:antagonists & inhibitors* / Q000737:chemistry*; D003198:Computer Simulation; D018432:Drug Resistance, Multiple; D006801:Humans; D008956:Models, Chemical; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000737:chemistry*
chemical_list: D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1; D004364:Pharmaceutical Preparations