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 Applying this model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil,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/ .

MetScore: Site of Metabolism Prediction Beyond Cytochrome P450 Enzymes

“…report on six oxidation reactions, namely hydroxylation and N‐dealkylation with 0.91 (0.91) and 0.99 (0.99), two alcohol oxidation models with 0.96 (0.98) and 0.99 (0.98), as well as C−C oxidation and deamination with AUCs of 0.99 and 1.00 (0.99). Similarly well with 0.96 (0.99) performs the general N‐dealkylation model of Dang et al . as well as the isoform selective models.…”

“…He et al [29] report on six oxidationr eactions, namely hydroxylation and N-dealkylation with 0.91 (0.91) and 0.99 (0.99), two alcohol oxidation modelsw ith 0.96 (0.98) and 0.99 (0.98),a sw ell as CÀCo xidation and deaminationw ith AUCs of 0.99 and 1.00 (0.99). Similarly well with 0.96 (0.99) performs the general N-dealkylation [30] as well as the isoform selective models. Other single transformation models like the epoxidation model of Hughes,M iller and Swamidass, [27] that have no counterpart in our sub-modelp redictions,a lwaysg ive AUCs beyond0 .9.…”

Prediction of the Antioxidant Response Elements' Response of Compound by Deep Learning

“…Recently, deep learning (Lecun et al, 2015), as a promising machine learning method, has been applied in a wide range of fields, such as physics, life science and medical science (Gulshan et al, 2016). There were also some researches in biology (Mamoshina et al, 2016; Dang et al, 2018; Hou et al, 2018) and drug design areas (Gawehn et al, 2016; Hughes and Swamidass, 2017). Furthermore, deep learning methods have been also applied in small molecule toxicity assessment (Blomme and Will, 2016).…”

Riboflavin Directly Mediates the Dealkylation by Microbial Cytochrome P450 Monooxygeneses

“…In the present study, given the valuable applications of N-dealkylation of P450s, especially in the drug metabolism, 18 we chose this reaction based on the unusual finding as a model to uncover the roles of riboflavin in microbial P450s. After confirming riboflavinmediated catalysis by representative microbial P450 monooxygenases, we were able to identify a new electron transfer pathway in addition to previously proposed catalytic mechanisms of P450s.…”