Predicting drug metabolism by CYP1A1, CYP1A2, and CYP1B1: insights from MetaSite, molecular docking and quantum chemical calculations

Recently, CYP1 enzymes are documented for selective metabolism of anticancer leads in cancer prevention and/or progression. Elucidation of specificity of substrates/inhibitors of CYP1 isoforms plays a vital role in design of more selective and potent anticancer leads. However, an area of concern is...

Full description

Saved in:
Bibliographic Details
Published in:Molecular diversity 2014-11, Vol.18 (4), p.865-878
Main Authors: Pragyan, Preeti, Kesharwani, Siddharth S., Nandekar, Prajwal P., Rathod, Vijay, Sangamwar, Abhay T.
Format: Article
Language:English
Subjects:
Binding Sites
Biochemistry
Biomedical and Life Sciences
Cytochrome P-450 CYP1A1 - chemistry
Cytochrome P-450 CYP1A2 - chemistry
Cytochrome P-450 CYP1B1 - chemistry
Enzymes
Full-Length Paper
Life Sciences
Metabolism
Models, Chemical
Models, Molecular
Molecular Docking Simulation
Molecular Dynamics Simulation
Molecular structure
Organic Chemistry
Pharmacology
Pharmacy
Polymer Sciences
Quantum Theory
Reproducibility of Results
Substrate Specificity
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recently, CYP1 enzymes are documented for selective metabolism of anticancer leads in cancer prevention and/or progression. Elucidation of specificity of substrates/inhibitors of CYP1 isoforms plays a vital role in design of more selective and potent anticancer leads. However, an area of concern is the broad range of substrate specificities and planar nature of substrates with limited dataset which makes it difficult to predict their site of metabolism (SOM) accurately. In the present study, various models for prediction of site of metabolism in case of CYP1A1, CYP1A2, and CYP1B1 substrates were developed using MetaSite, molecular docking, and quantum chemical descriptors. The predictive accuracy of MetaSite, molecular docking, and quantum chemical descriptors in identifying experimental site of metabolism was analyzed at three levels; top rank, top three ranks, and top five ranks. Two quantum chemical descriptors, chemical hardness and local nucleophilicity are proposed for the prediction of CYP-mediated SOM for the first time. The predictive accuracy shown by chemical hardness at top three ranks was 83.3, 85.7, and 84.6 % for CYP1A1, CYP1A2 and CYP1B1, respectively, whereas local nucleophilicity gave poor predictions of 50, 42.8, and 46.2 %, respectively. The predictability of chemical hardness descriptor outperformed at all three levels of ranks for CYP1A1, CYP1A2, and CYP1B1. Hence, we propose chemical hardness as an useful quantum chemical descriptor for prediction of metabolically vulnerable prints in CYP1A1, CYP1A2, and CYP1B1 mediated metabolism and support the optimization efforts in drug discovery and development programs
ISSN:1381-1991
1573-501X
DOI:10.1007/s11030-014-9534-6