Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein

Human P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter that confers resistance to a wide range of chemotherapeutic agents in cancer cells by active efflux of the drugs from cells. P-gp also plays a key role in limiting oral absorption and brain penetration and in facilitating b...

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Bibliographic Details
Published in:PloS one 2011-10, Vol.6 (10), p.e25815
Main Authors: Bikadi, Zsolt, Hazai, Istvan, Malik, David, Jemnitz, Katalin, Veres, Zsuzsa, Hari, Peter, Ni, Zhanglin, Loo, Tip W, Clarke, David M, Hazai, Eszter, Mao, Qingcheng
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Animal models
Antibiotics
ATP-Binding Cassette, Sub-Family B, Member 1 - chemistry
ATP-Binding Cassette, Sub-Family B, Member 1 - metabolism
Biochemistry
Biological Transport
Biology
Brain
Brain research
Cancer
Chemotherapy
Classification
Computer Science
Coordination compounds
Crystal structure
Crystallography, X-Ray
Cystic fibrosis
Databases as Topic
Drug development
Drugs
Efflux
Glycoproteins
Homology
Humans
Hypotheses
Internet
Kidneys
Ligands
Medicine
Methods
Models, Molecular
Molecular docking
Mutagenesis
Nervous system
Neural networks
P-Glycoprotein
Permeability
Pharmaceutical Preparations - chemistry
Pharmaceutical Preparations - metabolism
Pharmaceutical sciences
Pharmacokinetics
Pharmacology
Pharmacy
Predictions
Proteins
R&D
Reproducibility of Results
Research & development
Rhodamines - chemistry
Servers
Substrates
Support Vector Machine
Support vector machines
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Summary:Human P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter that confers resistance to a wide range of chemotherapeutic agents in cancer cells by active efflux of the drugs from cells. P-gp also plays a key role in limiting oral absorption and brain penetration and in facilitating biliary and renal elimination of structurally diverse drugs. Thus, identification of drugs or new molecular entities to be P-gp substrates is of vital importance for predicting the pharmacokinetics, efficacy, safety, or tissue levels of drugs or drug candidates. At present, publicly available, reliable in silico models predicting P-gp substrates are scarce. In this study, a support vector machine (SVM) method was developed to predict P-gp substrates and P-gp-substrate interactions, based on a training data set of 197 known P-gp substrates and non-substrates collected from the literature. We showed that the SVM method had a prediction accuracy of approximately 80% on an independent external validation data set of 32 compounds. A homology model of human P-gp based on the X-ray structure of mouse P-gp as a template has been constructed. We showed that molecular docking to the P-gp structures successfully predicted the geometry of P-gp-ligand complexes. Our SVM prediction and the molecular docking methods have been integrated into a free web server (http://pgp.althotas.com), which allows the users to predict whether a given compound is a P-gp substrate and how it binds to and interacts with P-gp. Utilization of such a web server may prove valuable for both rational drug design and screening.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0025815