title: Prediction of promiscuous p-glycoprotein inhibition using a novel machine learning scheme.
abstract: BACKGROUND
  P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development.
  
  
  RESULTS
  An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set (n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88, r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model.
  
  
  CONCLUSIONS
  This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile.
authors.affiliation: Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien, Taiwan. leong@mail.ndhu.edu.tw
authors.forename: Max K
authors.initials: MK
authors.lastname: Leong
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Chen
authors.forename: Yu-Hsuan
authors.initials: YH
authors.lastname: Shih
mesh_terms: D018435:ATP Binding Cassette Transporter, Subfamily B; D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1 / Q000037:antagonists & inhibitors* / Q000737:chemistry*; D000818:Animals; D003198:Computer Simulation; D015195:Drug Design; D055808:Drug Discovery; D006801:Humans; D051379:Mice; D008958:Models, Molecular; D011487:Protein Conformation; D060388:Support Vector Machine
chemical_list: C513055:ABCB1 protein, human; D018435:ATP Binding Cassette Transporter, Subfamily B; D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1