Dataset Information

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation.

ABSTRACT: Cytochromes P450 (CYPs) oxidize alkylated amines commonly found in drugs and other biologically active molecules, cleaving them into an amine and an aldehyde. Metabolic studies usually neglect to report or investigate aldehydes, even though they can be toxic. It is assumed that they are efficiently detoxified into carboxylic acids and alcohols. Nevertheless, some aldehydes are reactive and escape detoxification pathways to cause adverse events by forming DNA and protein adducts. Herein, we modeled N-dealkylations that produce both amine and aldehyde metabolites and then predicted the reactivity of the aldehyde. This model used a deep learning approach previously developed by our group to predict other types of drug metabolism. In this study, we trained the model to predict N-dealkylation by human liver microsomes (HLM), finding that including isozyme-specific metabolism data alongside HLM data significantly improved results. The final HLM model accurately predicted the site of N-dealkylation within metabolized substrates (97% top-two and 94% area under the ROC curve). Next, we combined the metabolism, metabolite structure prediction, and previously published reactivity models into a bioactivation model. This combined model predicted the structure of the most likely reactive metabolite of a small validation set of drug-like molecules known to be bioactivated by N-dealkylation. Applying this model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil, and ziprasidone. Our results suggest that N-dealkylation may be an under-appreciated bioactivation pathway, especially in clinical contexts where aldehyde detoxification pathways are inhibited. Moreover, this is the first report of a bioactivation model constructed by combining a metabolism and reactivity model. These results raise hope that more comprehensive models of bioactivation are possible. The model developed in this study is available at http://swami.wustl.edu/xenosite/ .

SUBMITTER: Dang NL

PROVIDER: S-EPMC5871345 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Computationally Assessing the Bioactivation of Drugs by N-Dealkylation.

Dang Na Le NL Hughes Tyler B TB Miller Grover P GP Swamidass S Joshua SJ

Chemical research in toxicology 20180206 2

Cytochromes P450 (CYPs) oxidize alkylated amines commonly found in drugs and other biologically active molecules, cleaving them into an amine and an aldehyde. Metabolic studies usually neglect to report or investigate aldehydes, even though they can be toxic. It is assumed that they are efficiently detoxified into carboxylic acids and alcohols. Nevertheless, some aldehydes are reactive and escape detoxification pathways to cause adverse events by forming DNA and protein adducts. Herein, we model ...[more]

PMID: 29355304

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Project description:Electrophilically reactive drug metabolites are implicated in many adverse drug reactions. In this mechanism-termed bioactivation-metabolic enzymes convert drugs into reactive metabolites that often conjugate to nucleophilic sites within biological macromolecules like proteins. Toxic metabolite-product adducts induce severe immune responses that can cause sometimes fatal disorders, most commonly in the form of liver injury, blood dyscrasia, or the dermatologic conditions toxic epidermal necrolysis and Stevens-Johnson syndrome. This study models four of the most common metabolic transformations that result in bioactivation: quinone formation, epoxidation, thiophene sulfur-oxidation, and nitroaromatic reduction, by synthesizing models of metabolism and reactivity. First, the metabolism models predict the formation probabilities of all possible metabolites among the pathways studied. Second, the exact structures of these metabolites are enumerated. Third, using these structures, the reactivity model predicts the reactivity of each metabolite. Finally, a feedfoward neural network converts the metabolism and reactivity predictions to a bioactivation prediction for each possible metabolite. These bioactivation predictions represent the joint probability that a metabolite forms and that this metabolite subsequently conjugates to protein or glutathione. Among molecules bioactivated by these pathways, we predicted the correct pathway with an AUC accuracy of 89.98%. Furthermore, the model predicts whether molecules will be bioactivated, distinguishing bioactivated and nonbioactivated molecules with 81.06% AUC. We applied this algorithm to withdrawn drugs. The known bioactivation pathways of alclofenac and benzbromarone were identified by the algorithm, and high probability bioactivation pathways not yet confirmed were identified for safrazine, zimelidine, and astemizole. This bioactivation model-the first of its kind that jointly considers both metabolism and reactivity-enables drug candidates to be quickly evaluated for a toxicity risk that often evades detection during preclinical trials. The XenoSite bioactivation model is available at http://swami.wustl.edu/xenosite/p/bioactivation.

Project description:Cytochrome P450 enzymes are often responsible for the toxic and carcinogenic effects of toxicants, such as aflatoxin B1 (AFB1). The human hepatic CYP2A6 enzyme mediates the oxidative metabolism of several procarcinogens. In this study, we characterized a partial sequence of CYP2A6 gene from Arbor Acres (AA) broiler and studied its role in AFB1 bioactivation. Moreover, the effect of curcumin on CYP2A6 is illustrated. Six groups of AA broiler were treated for 28 days including the control group (fed only basal diet), curcumin alone-treated group (450 mg/kg feed), the group fed AFB1-contaminated feed (5 mg/kg feed) plus the low (150 mg), medium (300 mg) or high (450 mg) of curcumin, and the group fed AFB1-contaminated diet alone (5 mg/kg feed). After the end of treatment period, liver samples were collected for different analyses. The results revealed that the histopathological examination showed clear signs of liver toxicity in AA broliers in AFB1-fed group, but curcumin-supplementation in feed prevented partially AFB1-induced liver toxicity. Liver and body weights were recorded to study the AFB1 harmful effects. We noted an obvious increase in liver weight and decrease in body weight in AFB1-fed group. But, the administration of curcumin partially ameliorated the increase in liver weight and decrease in body weight in a dose-dependent manner. The results (RT-PCR and Elisa) revealed that mRNA and protein expression level enhanced in AFB1-fed group. Consistently, CYP2A6 enzyme activity also increased in AFB1-fed group, suggesting that AA broiler CYP2A6 actively involved in bioactivation of AFB1. However, curcumin treatment inhibited CYP2A6 at mRNA and protein levels in AFB1 treated AA broiler in a dose-dependent manner. Maximum inhibition of liver CYP2A6 enzyme activity in AA broiler has been achieved at a dose of 450 mg/kg curcumin. This is the first study identifying and confirming the role of CYP2A6 enzyme in AFB1 bioactivation in AA broiler liver (in vivo), and the hepatoprotective role of curcumin via inhibiting CYP2A6 expression and enzyme activity. The study contributed to identify an important CYP enzyme involved in AFB1 bioactivation in broilers and thus could pave the way for the prevention of the harmful effects of AFB1 in broilers.

Dataset Information

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation.

Publications

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation.

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