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1. Molecules. 2012 Mar 15;17(3):3407-60.

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.

Tie Y, McPhail B, Hong H, Pearce BA, Schnackenberg LK, Ge W, Buzatu DA, Wilkes
JG, Fuscoe JC, Tong W, Fowler BA, Beger RD, Demchuk E.

Division of Toxicology and Environmental Medicine, Agency for Toxic Substances
and Disease Registry, Atlanta, GA 30333, USA. EDemchuk@cdc.gov.

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.

PMID: 22421793 [PubMed - in process]