Concern about potential susceptibilities of infants and children to chemicals has led to the consideration of immature rodents as potential test surrogates. Maturation of some hepatic microsomal cytochrome P450s (CYPs), that participate in metabolic activation of organic solvents and polycyclic aromatic hydrocarbons (PAHs), may differ significantly between humans and rodents. The present investigation was undertaken to delineate the ontogeny of selected hepatic CYPs in male and female Sprague-Dawley (S-D) rats, and to contrast them with developmental profiles in humans. Microsomes were prepared from the liver of sexed and unsexed postnatal day (PND) 1-90 rats, and total CYP450 levels, as well as CYP1A1/2, CYP2E1 and CYP2B1/2 activities and protein, were quantified. CYP1A1/2 and CYP2E1 activity and expression rose rapidly after birth, peaked from PND 21-40/50, and declined substantially to adult values by PND 90. The same ontogenic profiles were manifested when the enzyme activities were expressed per entire liver or liver normalized to body weight. CYP1A1/2 and CYP2E1 activity and protein expression were well correlated. CYP2B1/2 activity peaked abruptly on PND 21 and declined irregularly to adult values. These patterns are in contrast to human CYP1A2 and CYP2E1, which are reported to progressively increase in liver during the first few months to years of life. The three CYP protein developmental profiles were largely gender independent in rats. The immature rat does not appear to be a suitable model for assessing risks posed to infants and children by chemicals metabolically activated by CYP2E1, based on the findings of greater carbon tetrachloride hepatotoxicity in preweanlings and weanlings than in adult animals. Additional studies are required to determine whether immature S-D rats may be used as an animal model for substrates of other CYPs, as total CYP450 levels in the liver progressively rose during maturation, similarly to humans.

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 (13)C-NMR and 1D (15)N-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.

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 (13)C and 1D (15)N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D (13)C-NMR and (15)N-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 Mold(2) 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.