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1. Molecules. 2012 Mar 15;17(3):3383-406.

Modeling Chemical Interaction Profiles: I. Spectral Data-Activity Relationship
and Structure-Activity Relationship Models for Inhibitors and Non-inhibitors of
Cytochrome P450 CYP3A4 and CYP2D6 Isozymes.

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

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

An interagency collaboration was established to model chemical interactions that
may cause adverse health effects when an exposure to a mixture of chemicals
occurs. Many of these chemicals-drugs, pesticides, and environmental
pollutants-interact at the level of metabolic biotransformations mediated by
cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity
relationship (SDAR) and structure-activity relationship (SAR) approaches were
used to develop machine-learning classifiers of inhibitors and non-inhibitors of
the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference
pharmaceutical compounds whose interactions have been deduced from clinical data,
and 100 additional chemicals that were used to evaluate model performance in an
external validation (EV) test. SDAR is an innovative modeling approach that
relies on discriminant analysis applied to binned nuclear magnetic resonance
(NMR) spectral descriptors. In the present work, both 1D 13C and 1D 15N-NMR
spectra were used together in a novel implementation of the SDAR technique. It
was found that increasing the binning size of 1D 13C-NMR and 15N-NMR spectra
caused an increase in the tenfold cross-validation (CV) performance in terms of
both the rate of correct classification and sensitivity. The results of SDAR
modeling were verified using SAR. For SAR modeling, a decision forest approach
involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of
correct classification of SDAR and SAR models in a hundred CV tests were 60% and
61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct
classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4,
and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods
demonstrated a comparable performance in modeling a large set of structurally
diverse data. Based on unique NMR structural descriptors, the new SDAR modeling
method complements the existing SAR techniques, providing an independent
estimator that can increase confidence in a structure-activity assessment. When
modeling was applied to hazardous environmental chemicals, it was found that up
to 20% of them may be substrates and up to 10% of them may be inhibitors of the
CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for
the environmental health branch of the public health service to extrapolate to
hazardous chemicals directly from human clinical data. Therefore, the
pharmacological and environmental health branches are both expected to benefit
from these reported models.

PMID: 22421792 [PubMed - in process]