The metabolism of drugs and other xenobiotics by cytochrome P450 enzymes (CYP) is an essential detoxification and drug clearance mechanism in humans. Despite the publication of several X-ray structures since 2000, reliable computational prediction of drug metabolism remains a huge challenge. In this grant application, we propose an integrative high-throughput approach that combines electronic properties of a ligand with structural properties of the protein. Our main aim is the development and application of innovative structure-based design techniques that address serious shortcomings of current approaches. In particular, we have extended our novel docking concept to incorporate all observed forms of protein flexibility relevant for ligand-CYP interactions, entropic contributions influencing the prediction of binding poses, and we will add on- the-fly solvation to ligand-CYP complexes. To improve the docking quality we will optimize the parameters of a scoring function tailor-made for each CYP enzyme studied. In combination with an initial focus on efficient calculation of hydrogen-abstraction energies we will predict regioselective metabolism of drugs or drug candidates binding to CYPs. Based on the resulting docking poses, multidimensional QSAR simulations will be performed for accurate quantification of binding affinity as a measure CYP-inhibition and better ranking of binding modes. The new computational methods will be applied to two CYP enzymes (CYP2C9, 3A4) important in drug metabolism. The generated computational models will be stored in a database and made publicly available. Other researchers will be invited to screen compounds against our CYP database via a secure Web protocol to predict drug metabolism and inhibition. The submission of data by other researchers will provide valuable feedback on the performance and applicability of the models.

Public Health Relevance

Cytochrome P450 mediated drug metabolism plays a critical role for the efficacy of administered drugs. This project is aimed toward developing and applying innovative computational methods to efficiently predict drug metabolism as well as inhibition of the drug metabolizing enzymes. The resulting computational models allow for the estimation of drug efficiency and the potential of adverse reactions early in drug discovery and thus have a strong impact on drug development.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM092855-03
Application #
8304931
Study Section
Special Emphasis Panel (ZRG1-DKUS-F (02))
Program Officer
Okita, Richard T
Project Start
2010-09-01
Project End
2015-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
3
Fiscal Year
2012
Total Cost
$202,003
Indirect Cost
$64,943
Name
Purdue University
Department
Pharmacology
Type
Schools of Pharmacy
DUNS #
072051394
City
West Lafayette
State
IN
Country
United States
Zip Code
47907
Kingsley, Laura J; Wilson, Gregory L; Essex, Morgan E et al. (2015) Combining structure- and ligand-based approaches to improve site of metabolism prediction in CYP2C9 substrates. Pharm Res 32:986-1001
Kingsley, Laura J; Lill, Markus A (2014) Including ligand-induced protein flexibility into protein tunnel prediction. J Comput Chem 35:1748-56
Yang, Ying; Hu, Bingjie; Lill, Markus A (2014) Analysis of factors influencing hydration site prediction based on molecular dynamics simulations. J Chem Inf Model 54:2987-95
Kingsley, Laura J; Lill, Markus A (2014) Ensemble generation and the influence of protein flexibility on geometric tunnel prediction in cytochrome P450 enzymes. PLoS One 9:e99408
Hu, Bingjie; Lill, Markus A (2014) WATsite: hydration site prediction program with PyMOL interface. J Comput Chem 35:1255-60
Hu, Bingjie; Lill, Markus A (2014) PharmDock: a pharmacophore-based docking program. J Cheminform 6:14
Hu, Bingjie; Lill, Markus A (2013) Exploring the potential of protein-based pharmacophore models in ligand pose prediction and ranking. J Chem Inf Model 53:1179-90
Xu, Mengang; Lill, Markus A (2013) Induced fit docking, and the use of QM/MM methods in docking. Drug Discov Today Technol 10:e411-8
Danielson, Matthew L; Lill, Markus A (2012) Predicting flexible loop regions that interact with ligands: the challenge of accurate scoring. Proteins 80:246-60
Xu, Mengang; Lill, Markus A (2012) Utilizing experimental data for reducing ensemble size in flexible-protein docking. J Chem Inf Model 52:187-98

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