title: Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme.
abstract: Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2-3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D ¹³C-NMR and 1D ¹⁵N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.
authors.affiliation: Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA.
authors.forename: Yunfeng
authors.initials: Y
authors.lastname: Tie
authors.forename: Brooks
authors.initials: B
authors.lastname: McPhail
authors.forename: Huixiao
authors.initials: H
authors.lastname: Hong
authors.forename: Bruce A
authors.initials: BA
authors.lastname: Pearce
authors.forename: Laura K
authors.initials: LK
authors.lastname: Schnackenberg
authors.forename: Weigong
authors.initials: W
authors.lastname: Ge
authors.forename: Dan A
authors.initials: DA
authors.lastname: Buzatu
authors.forename: Jon G
authors.initials: JG
authors.lastname: Wilkes
authors.forename: James C
authors.initials: JC
authors.lastname: Fuscoe
authors.forename: Weida
authors.initials: W
authors.lastname: Tong
authors.forename: Bruce A
authors.initials: BA
authors.lastname: Fowler
authors.forename: Richard D
authors.initials: RD
authors.lastname: Beger
authors.forename: Eugene
authors.initials: E
authors.lastname: Demchuk
mesh_terms: D051544:Cytochrome P-450 CYP3A / Q000378:metabolism; D065692:Cytochrome P-450 CYP3A Inhibitors; D065607:Cytochrome P-450 Enzyme Inhibitors; D003577:Cytochrome P-450 Enzyme System / Q000378:metabolism*; D004785:Environmental Pollutants / Q000633:toxicity; D004791:Enzyme Inhibitors / Q000633:toxicity; D006801:Humans; D007527:Isoenzymes / Q000037:antagonists & inhibitors* / Q000378:metabolism*; D013329:Structure-Activity Relationship
chemical_list: D065692:Cytochrome P-450 CYP3A Inhibitors; D065607:Cytochrome P-450 Enzyme Inhibitors; D004785:Environmental Pollutants; D004791:Enzyme Inhibitors; D007527:Isoenzymes; D003577:Cytochrome P-450 Enzyme System; D051544:Cytochrome P-450 CYP3A