title: Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models.
abstract: In-silico models were generated to predict the extent of inhibition of cytochrome P450 isoenzymes using a set of relatively interpretable descriptors in conjunction with partial least squares (PLS) and regression trees (RT). The former was chosen due to the conservative nature of the resultant models built and the latter to more effectively account for any non-linearity between dependent and independent variables. All models are statistically significant and agree with the known SAR and they could be used as a guide to P450 liability through a classification based on the continuous pIC50 prediction given by the model. A compound is classified as having either a high or low P450 liability if the predicted pIC(50) is at least one root mean square error (RMSE) from the high/low pIC(50) cut-off of 5. If predicted within an RMSE of the cut-off we cannot be confident a compound will be experimentally low or high so an indeterminate classification is given. Hybrid models using bulk descriptors and fragmental descriptors do significantly better in modeling CYP450 inhibition, than bulk property QSAR descriptors alone.
authors.affiliation: Department of Physical & Metabolic Sciences, AstraZeneca R&D Charnwood, Bakewell Road, Loughborough, Leicestershire LE11 5RH, UK. paul.x.gleeson@gsk.com
authors.forename: M Paul
authors.initials: MP
authors.lastname: Gleeson
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Davis
authors.forename: Kamaldeep K
authors.initials: KK
authors.lastname: Chohan
authors.forename: Stuart W
authors.initials: SW
authors.lastname: Paine
authors.forename: Scott
authors.initials: S
authors.lastname: Boyer
authors.forename: Claire L
authors.initials: CL
authors.lastname: Gavaghan
authors.forename: Catrin Hasselgren
authors.initials: CH
authors.lastname: Arnby
authors.forename: Cecilia
authors.initials: C
authors.lastname: Kankkonen
authors.forename: Nan
authors.initials: N
authors.lastname: Albertson
mesh_terms: D000465:Algorithms; D001189:Aryl Hydrocarbon Hydroxylases / Q000037:antagonists & inhibitors; D003198:Computer Simulation; D065609:Cytochrome P-450 CYP1A2 Inhibitors; D065731:Cytochrome P-450 CYP2C19; D065729:Cytochrome P-450 CYP2C9; D065690:Cytochrome P-450 CYP2D6 Inhibitors; D051544:Cytochrome P-450 CYP3A; D065607:Cytochrome P-450 Enzyme Inhibitors; D015195:Drug Design; D004353:Drug Evaluation, Preclinical; D006801:Humans; D016018:Least-Squares Analysis; D006899:Mixed Function Oxygenases / Q000037:antagonists & inhibitors; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
chemical_list: D065609:Cytochrome P-450 CYP1A2 Inhibitors; D065690:Cytochrome P-450 CYP2D6 Inhibitors; D065607:Cytochrome P-450 Enzyme Inhibitors; D006899:Mixed Function Oxygenases; C450260:CYP2C9 protein, human; D065729:Cytochrome P-450 CYP2C9; D001189:Aryl Hydrocarbon Hydroxylases; C045793:CYP2C19 protein, human; D065731:Cytochrome P-450 CYP2C19; D051544:Cytochrome P-450 CYP3A; C510163:CYP3A4 protein, human