From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions

Evaluation of the nitrosamine impurities of ACE inhibitors using computational, in vitro, and in vivo methods demonstrate no genotoxic potential

Evaluation and mitigation of the potential carcinogenic risks associated with nitrosamines in marketed pharmaceutical products are areas of interest for pharmaceutical companies and health authorities alike. Significant progress has been made to establish acceptable intake (AI) levels for N-nitrosamine drug substance-related impurities (NDSRIs) using SAR, however some compounds require experimental data to support derivation of a recommended AI. Many angiotensin-converting enzyme inhibitors, identified by the suffix "pril," have secondary amines that can potentially react to form nitrosamines. Here we consider a structural assessment and metabolism data, coupled with comprehensive in vitro and in vivo (mouse) genotoxicity testing to evaluate this particular class of nitrosamines. N-nitroso ramipril and N-nitroso quinapril, both of which are predicted to have inhibited nitrosamine bioactivation due to steric hinderance and branching at the α-position were non-genotoxic in the in vivo liver comet assay and non-mutagenic in the in vivo Big Blue® mutation and duplex sequencing assays. Predicted metabolism along with in vitro metabolism data and quantum chemical calculations related to DNA interactions offer a molecular basis for the negative results observed in both in vitro and in vivo testing. These nitrosamines are concluded to be non-mutagenic and non-carcinogenic; therefore, they should be controlled according to ICH Q3B guidance. Furthermore, these results for N-nitroso ramipril and N-nitroso quinapril should be considered when evaluating the appropriate AI and control strategy for other structurally similar "pril" NDSRIs.

Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction

Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.

Large Computational Survey of Intrinsic Reactivity of Aromatic Carbon Atoms with Respect to a Model Aldehyde Oxidase

Aldehyde oxidase (AOX) and other related molybdenum-containing enzymes are known to oxidize the C-H bonds of aromatic rings. This process contributes to the metabolism of pharmaceutical compounds and, therefore, is of vital importance to drug pharmacokinetics. The present work describes an automated computational workflow and its use for the prediction of intrinsic reactivity of small aromatic molecules toward a minimal model of the active site of AOX. The workflow is based on quantum chemical transition state searches for the underlying single-step oxidation reaction, where the automated protocol includes identification of unique aromatic C-H bonds, creation of three-dimensional reactant and product complex geometries via a templating approach, search for a transition state, and validation of reaction end points. Conformational search on the reactants, products, and the transition states is performed. The automated procedure has been validated on previously reported transition state barriers and was used to evaluate the intrinsic reactivity of nearly three hundred heterocycles commonly found in approved drug molecules. The intrinsic reactivity of more than 1000 individual aromatic carbon sites is reported. Stereochemical and conformational aspects of the oxidation reaction, which have not been discussed in previous studies, are shown to play important roles in accurate modeling of the oxidation reaction. Observations on structural trends that determine the reactivity are provided and rationalized.

In Silico Prediction of Metabolic Reaction Catalyzed by Human Aldehyde Oxidase

Aldehyde oxidase (AOX) plays an important role in drug metabolism. Human AOX (hAOX) is widely distributed in the body, and there are some differences between species. Currently, animal models cannot accurately predict the metabolism of hAOX. Therefore, more and more in silico models have been constructed for the prediction of the hAOX metabolism. These models are based on molecular docking and quantum chemistry theory, which are time-consuming and difficult to automate. Therefore, in this study, we compared traditional machine learning methods, graph convolutional neural network methods, and sequence-based methods with limited data, and proposed a ligand-based model for the metabolism prediction catalyzed by hAOX. Compared with the published models, our model achieved better performance (ACC = 0.91, F1 = 0.77). What's more, we built a web server to predict the sites of metabolism (SOMs) for hAOX. In summary, this study provides a convenient and automatable model and builds a web server named Meta-hAOX for accelerating the drug design and optimization stage.

Computer-Assisted Discovery of Alkaloids with Schistosomicidal Activity

Schistosomiasis is a chronic parasitic disease caused by trematodes of the genus Schistosoma; it is commonly caused by Schistosoma mansoni, which is transmitted by Bioamphalaria snails. Studies show that more than 200 million people are infected and that more than 90% of them live in Africa. Treatment with praziquantel has the best cost–benefit result on the market. However, hypersensitivity, allergy, and drug resistance are frequently presented after administration. From this perspective, ligand-based and structure-based virtual screening (VS) techniques were combined to select potentially active alkaloids against S. mansoni from an internal dataset (SistematX). A set of molecules with known activity against S. mansoni was selected from the ChEMBL database to create two different models with accuracy greater than 84%, enabling ligand-based VS of the alkaloid bank. Subsequently, structure-based VS was performed through molecular docking using four targets of the parasite. Finally, five consensus hits (i.e., five alkaloids with schistosomicidal potential), were selected. In addition, in silico evaluations of the metabolism, toxicity, and drug-like profile of these five selected alkaloids were carried out. Two of them, namely, 11,12-methylethylenedioxypropoxy and methyl-3-oxo-12-methoxy-n(1)-decarbomethoxy-14,15-didehydrochanofruticosinate, had plausible toxicity, metabolomics, and toxicity profiles. These two alkaloids could serve as starting points for the development of new schistosomicidal compounds based on natural products.

Non-cytochrome P450 enzymes involved in the oxidative metabolism of xenobiotics: Focus on the regulation of gene expression and enzyme activity

Oxidative metabolism is one of the major biotransformation reactions that regulates the exposure of xenobiotics and their metabolites in the circulatory system and local tissues and organs, and influences their efficacy and toxicity. Although cytochrome (CY)P450s play critical roles in the oxidative reaction, extensive CYP450-independent oxidative metabolism also occurs in some xenobiotics, such as aldehyde oxidase, xanthine oxidoreductase, flavin-containing monooxygenase, monoamine oxidase, alcohol dehydrogenase, or aldehyde dehydrogenase-dependent oxidative metabolism. Drugs form a large portion of xenobiotics and are the primary target of this review. The common reaction mechanisms and roles of non-CYP450 enzymes in metabolism, factors affecting the expression and activity of non-CYP450 enzymes in terms of inhibition, induction, regulation, and species differences in pharmaceutical research and development have been summarized. These non-CYP450 enzymes are detoxifying enzymes, although sometimes they mediate severe toxicity. Synthetic or natural chemicals serve as inhibitors for these non-CYP450 enzymes. However, pharmacokinetic-based drug interactions through these inhibitors have rarely been reported in vivo. Although multiple mechanisms participate in the basal expression and regulation of non-CYP450 enzymes, only a limited number of inducers upregulate their expression. Therefore, these enzymes are considered non-inducible or less inducible. Overall, this review focuses on the potential xenobiotic factors that contribute to variations in gene expression levels and the activities of non-CYP450 enzymes.

Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA

Background

The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}αC).

Results

We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures.

Conclusions

Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04363-6.

Computer-aided understanding and engineering of enzymatic selectivity

Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.

A comprehensive review of in silico approaches for the prediction and modulation of aldehyde oxidase-mediated drug metabolism: The current features, challenges and future perspectives

The importance of aldehyde oxidase (AOX) in drug metabolism necessitates the development and application of the in silico rational drug design methods as an integral part of drug discovery projects for the early prediction and modulation of AOX-mediated metabolism. The current study represents an up-to-date and thorough review of in silico studies of AOX-mediated metabolism and modulation methods. In addition, the challenges and the knowledge gap that should be covered have been discussed. The importance of aldehyde oxidase (AOX) in drug metabolism is a hot topic in drug discovery. Different strategies are available for the modulation of the AOX-mediated metabolism of drugs. Application of the rational drug design methods as an integral part of drug discovery projects is necessary for the early prediction of AOX-mediated metabolism. The current study represents a comprehensive review of AOX molecular structure, AOX-mediated reactions, AOX substrates, AOX inhibition, approaches to modify AOX-mediated metabolism, prediction of AOX metabolism/substrates/inhibitors, and the AOX related structure-activity relationship (SAR) studies. Furthermore, an up-to-date and thorough review of in silico studies of AOX metabolism has been carried out. In addition, the challenges and the knowledge gap that should be covered in the scientific literature have been discussed in the current review.

Recent developments in predicting CYP-independent metabolism

As lead optimization efforts have successfully reduced metabolic liabilities due to cytochrome P450 (CYP)-mediated metabolism, there has been an increase in the frequency of involvement of non-CYP enzymes in the metabolism of investigational compounds. Although there have been numerous notable advancements in the characterization of non-CYP enzymes with respect to their localization, reaction mechanisms, species differences and identification of typical substrates, accurate prediction of non-CYP-mediated clearance, with a particular emphasis with the difficulties in accounting for any extrahepatic contributions, remains a challenge. The current manuscript comprehensively summarizes the recent advancements in the prediction of drug metabolism and the in vitro to in vitro extrapolation of clearance for substrates of non-CYP drug metabolizing enzymes.

Understanding the Metabolism of Proteolysis Targeting Chimeras (PROTACs): The Next Step toward Pharmaceutical Applications

Hetero-bifunctional PROteolysis TArgeting Chimeras (PROTACs) represent a new emerging class of small molecules designed to induce polyubiquitylation and proteasomal-dependent degradation of a target protein. Despite the increasing number of publications about the synthesis, biological evaluation, and mechanism of action of PROTACs, the characterization of the pharmacokinetic properties of this class of compounds is still minimal. Here, we report a study on the metabolism of a series of 40 PROTACs in cryopreserved human hepatocytes at multiple time points. Our results indicated that the metabolism of PROTACs could not be predicted from that of their constituent ligands. Their linkers' chemical nature and length resulted in playing a major role in the PROTACs' liability. A subset of compounds was also tested for metabolism by human cytochrome P450 3A4 (CYP3A4) and human aldehyde oxidase (hAOX) for more in-depth data interpretation, and both enzymes resulted in active PROTAC metabolism.

Metabolism by Aldehyde Oxidase: Drug Design and Complementary Approaches to Challenges in Drug Discovery

Aldehyde oxidase (AO) catalyzes oxidations of azaheterocycles and aldehydes, amide hydrolysis, and diverse reductions. AO substrates are rare among marketed drugs, and many candidates failed due to poor pharmacokinetics, interspecies differences, and adverse effects. As most issues arise from complex and poorly understood AO biology, an effective solution is to stop or decrease AO metabolism. This perspective focuses on rational drug design approaches to modulate AO-mediated metabolism in drug discovery. AO biological aspects are also covered, as they are complementary to chemical design and important when selecting the experimental system for risk assessment. The authors' recommendation is an early consideration of AO-mediated metabolism supported by computational and in vitro experimental methods but not an automatic avoidance of AO structural flags, many of which are versatile and valuable building blocks. Preferably, consideration of AO-mediated metabolism should be part of the multiparametric drug optimization process, with the goal to improve overall drug-like properties.

Scaffold-hopping as a strategy to address metabolic liabilities of aromatic compounds

Understanding and minimizing oxidative metabolism of aromatic compounds is a key hurdle in lead optimization. Metabolic processes not only clear compounds from the body, but they can also transform parent compounds into reactive metabolites. One particularly useful strategy when addressing metabolically labile or oxidation-prone structures is scaffold-hopping. Replacement of an aromatic system with a more electron-deficient ring system can often increase robustness towards cytochrome P450-mediated oxidation while conserving the structural requirements of the pharmacophore. The most common example of this substitution strategy, replacement of a phenyl ring with a pyridyl substituent, is prevalent throughout the literature; however scaffold-hopping encompasses a much wider scope of heterocycle replacement. This review will showcase recent examples where different scaffold-hopping approaches were used to reduce metabolic clearance or block the formation of reactive metabolites. Additionally, we will highlight considerations that should be made to garner the most benefit from a scaffold-hopping strategy for lead optimization.

Aldehyde oxidase and its role as a drug metabolizing enzyme

Aldehyde oxidase (AO) is a cytosolic enzyme that belongs to the family of structurally related molybdoflavoproteins like xanthine oxidase (XO). The enzyme is characterized by broad substrate specificity and marked species differences. It catalyzes the oxidation of aromatic and aliphatic aldehydes and various heteroaromatic rings as well as reduction of several functional groups. The references to AO and its role in metabolism date back to the 1950s, but the importance of this enzyme in the metabolism of drugs has emerged in the past fifteen years. Several reviews on the role of AO in drug metabolism have been published in the past decade indicative of the growing interest in the enzyme and its influence in drug metabolism. Here, we present a comprehensive monograph of AO as a drug metabolizing enzyme with emphasis on marketed drugs as well as other xenobiotics, as substrates and inhibitors. Although the number of drugs that are primarily metabolized by AO are few, the impact of AO on drug development has been extensive. We also discuss the effect of AO on the systemic exposure and clearance these clinical candidates. The review provides a comprehensive analysis of drug discovery compounds involving AO with the focus on developmental candidates that were reported in the past five years with regards to pharmacokinetics and toxicity. While there is only one known report of AO-mediated clinically relevant drug-drug interaction (DDI), a detailed description of inhibitors and inducers of AO known to date has been presented here and the potential risks associated with DDI. The increasing recognition of the importance of AO has led to significant progress in predicting the site of AO-mediated metabolism using computational methods. Additionally, marked species difference in expression of AO makes it is difficult to predict human clearance with high confidence. The progress made towards developing in vivo, in vitro and in silico approaches for predicting AO metabolism and estimating human clearance of compounds that are metabolized by AO have also been discussed.

Metabolism of c-Met Kinase Inhibitors Containing Quinoline by Aldehyde Oxidase, Electron Donating, and Steric Hindrance Effect

Some quinoline-containing c-Met kinase inhibitors are aldehyde oxidase (AO) substrates. 3-Substituted quinoline triazolopyridine analogs were synthesized to understand the electron-donating and steric hindrance effects on AO-mediated metabolism. Metabolic stability studies for these quinoline analogs were carried out in liver cytosol from mice, rats, cynomolgus monkeys, and humans. Several 3-N-substituted analogs were found to be unstable in monkey liver cytosolic incubations (half-life, <10 minutes), and five of them (63, 53, 51, 11, and 71) were chosen for additional mechanistic studies. Mono-oxygenation on the quinoline ring was identified by liquid chromatography tandem mass spectrometry. Metabolite formation was inhibited by the AO inhibitors menadione and raloxifene, but not by the xanthine oxidase inhibitor allopurinol. It was found that small electron-donating groups at the 3-quinoline moiety made the analogs more susceptible to AO metabolism, whereas large 3-substituents could reverse the trend. Although species differences were observed, this trend was applicable to all species tested. Small electron-donating substituents at the 3-quinoline moiety increased both affinity (decreased Michaelis constant) and V _max maximum velocity toward AO in kinetic studies, whereas large substituents decreased both parameters probably as a result of steric hindrance. Based on our analysis, a common structural feature with high AO liability was proposed. Our finding could provide useful information for chemists to minimize potential AO liability when designing quinoline analogs.