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.

Machine Learning in Drug Metabolism Study

Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resource-demanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-to-activity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.

In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network

UDP-glucuronosyltransferases (UGTs) have gained increasing attention as they play important roles in the phase II metabolism of drugs. Due to the time-consuming process and high cost of experimental approaches to identify the metabolic fate of UGT enzymes, in silico methods have been developed to predict the UGT-mediated metabolism of drug-like molecules. We developed consensus models with the combination of machine learning (ML) and graph neural network (GNN) methods to predict if a drug-like molecule is a potential UGT substrate, and then we applied the Weisfeiler-Lehman Network (WLN) model to identify the sites of metabolism (SOMs) of UGT-catalyzed substrates. For the substrate model, the accuracy of the single substrate prediction model on the test set could reach to 0.835. Compared with the single estimators, the consensus models are more stable and have better generalization ability, and the accuracy on the test set reached to 0.851. For the SOM model, the top-1 accuracy of the SOM model on the test set reached to 0.898, outperforming existing works. Thus, in this study, we proposed a computational framework, named Meta-UGT, which would provide a useful tool for the prediction and optimization of metabolic profiles and drug design.

Computational insight into biotransformation of halophenols by cytochrome P450: Mechanism and reactivity for epoxidation

Halophenols (XPs) have aroused great interests due to their high toxicity and low biodegradability. Previous experimental studies have shown that XPs can be catalytically transformed into epoxides and haloquinones by cytochrome P450 enzymes (CYPs). However, these metabolites have never been detected directly. Moreover, the effects of the reaction site and the type and number of halogen substituents on the biotransformation reactivity of halophenols still remain unknown. In this work, we performed density functional theory (DFT) calculations to simulate the CYP-mediated biotransformation of 36 XPs with mono-, di-, and tri-halogen (F, Cl, and Br) substitutions to unravel the mechanism and relevant kinetics of XPs epoxidation. The whole epoxidation process consists of initial rate-determining O-addition and subsequent ring-closure steps. The simulation results show that the epoxidation in low-spin (LS) state is kinetically preferred over that in high-spin (HS) state, and the formation of epoxide metabolite is strongly exothermic. For all XPs, the epoxidation reactivity follows the order of ortho/para O-addition > meta O-addition. Moreover, the O-addition with higher energy barriers roughly corresponds to chlorophenols and fluorophenols with more halogen atoms. Compared with dichlorophenols, the additional ortho-Cl substitution on trichlorophenols can slightly increase the energy barriers of meta O-addition. By contrast, the additional inclusion of an ortho-Cl to monochlorophenols enhances the meta O-addition reactivity of dichlorophenols. Overall, the present work clarifies the biotransformation routes of XPs to produce epoxides, and identifies the key factors affecting the epoxidation reactivity, which are beneficial in understanding comprehensively the metabolic fate and toxicity of XPs.