Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

Marine Prostanoids with Cytotoxic Activity from Octocoral Clavularia spp

Octocoral of the genus Clavularia is a kind of marine invertebrate possessing abundant cytotoxic secondary metabolites, such as prostanoids and dolabellanes. In our continuous natural product study of C. spp., two previously undescribed prostanoids [clavulone I-15-one (1) and 12-O-deacetylclavulone I (2)] and eleven known analogs (3-13) were identified. The structures of these new compounds were elucidated based on analysis of their 1D and 2D NMR, HRESIMS, and IR data. Additionally, all tested prostanoids (1 and 3-13) showed potent cytotoxic activities against the human oral cancer cell line (Ca9-22). The major compound 3 showed cytotoxic activity against the Ca9-22 cells with the IC50 value of 2.11 ± 0.03 μg/mL, which echoes the cytotoxic effect of the coral extract. In addition, in silico tools were used to predict the possible effects of isolated compounds on human tumor cell lines and nitric oxide production, as well as the pharmacological potentials.

Targeting PIM kinases in cancer therapy: An update on pharmacological small-molecule inhibitors

PIM kinases, a serine/threonine kinase family with three isoforms, has been well-known to participate in multiple physiological processes by phosphorylating various downstream targets. Accumulating evidence has recently unveiled that aberrant upregulation of PIM kinases (PIM1, PIM2, and PIM3) are closely associated with tumor cell proliferation, migration, survival, and even resistance. Inhibiting or silencing of PIM kinases has been reported have remarkable antitumor effects, such as anti-proliferation, pro-apoptosis and resensitivity, indicating the therapeutic potential of PIM kinases as potential druggable targets in many types of human cancers. More recently, several pharmacological small-molecule inhibitors have been preclinically and clinically evaluated and showed their therapeutic potential; however, none of them has been approved for clinical application so far. Thus, in this perspective, we focus on summarizing the oncogenic roles of PIM kinases, key signaling network, and pharmacological small-molecule inhibitors, which will provide a new clue on discovering more candidate antitumor drugs targeting PIM kinases in the future.

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.

Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties.

An Insight into the Metabolism of 2,5-Disubstituted Monotetrazole Bearing Bisphenol Structures: Emerging Bisphenol A Structural Congeners

The non-estrogenic 2,5-disubstituted tetrazole core-bearing bisphenol structures (TbB) are being researched as emerging structural congeners of Bisphenol A, an established industrial endocrine disruptor. However, there is no understanding of TbB's adverse effects elicited via metabolic activation. Therefore, the current study aimed to investigate the metabolism of TbB ligands, with in silico results serving as a guide for in vitro studies. The Cytochrome P450 enzymes (CYP) inhibitory assay of TbB ligands on the seven human liver CYP isoforms (i.e., 1A2, 2A6, 2D6, 2C9, 2C8, 2C19, and 3A4) using human liver microsomes (HLM) revealed TbB ligand 223-3 to have a 50% inhibitory effect on all the CYP isoforms at a 10 μM concentration, except 1A2. The TbB ligand 223-10 inhibited 2B6 and 2C8, whereas the TbB ligand 223-2 inhibited only 2C9. The first-order inactivity rate constant (Kobs) studies indicated TbB ligands 223-3, 223-10 to be time-dependent (TD) inhibitors, whereas the TbB 223-2 ligand did not show such a significant effect. The 223-3 exhibited a TD inhibition for 2C9, 2C19, and 1A2 with Kobs values of 0.0748, 0.0306, and 0.0333 min-1, respectively. On the other hand, the TbB ligand 223-10 inhibited 2C9 in a TD inhibition manner with Kobs value 0.0748 min-1. However, the TbB ligand 223-2 showed no significant TD inhibition effect on the CYPs. The 223-2 ligand biotransformation pathway by in vitro studies in cryopreserved human hepatocytes suggested the clearance via glucuronidation with the predominant detection of only 223-2 derived mono glucuronide as a potential inactive metabolite. The present study demonstrated that the 223-2 ligand did not elicit any significant adverse effect via metabolic activation, thus paving the way for its in vivo drug-drug interactions (DDI) studies.

DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction

Cytochrome P450 (CYP) is a superfamily of heme-containing oxidizing enzymes involved in the metabolism of a wide range of medicines, xenobiotics, and endogenous compounds. Five of the CYPs (1A2, 2C9, 2C19, 2D6, and 3A4) are responsible for metabolizing the vast majority of approved drugs. Adverse drug-drug interactions, many of which are mediated by CYPs, are one of the important causes for the premature termination of drug development and drug withdrawal from the market. In this work, we reported in silicon classification models to predict the inhibitory activity of molecules against these five CYP isoforms using our recently developed FP-GNN deep learning method. The evaluation results showed that, to the best of our knowledge, the multi-task FP-GNN model achieved the best predictive performance with the highest average AUC (0.905), F1 (0.779), BA (0.819), and MCC (0.647) values for the test sets, even compared to advanced machine learning, deep learning, and existing models. Y-scrambling testing confirmed that the results of the multi-task FP-GNN model were not attributed to chance correlation. Furthermore, the interpretability of the multi-task FP-GNN model enables the discovery of critical structural fragments associated with CYPs inhibition. Finally, an online webserver called DEEPCYPs and its local version software were created based on the optimal multi-task FP-GNN model to detect whether compounds bear potential inhibitory activity against CYPs, thereby promoting the prediction of drug-drug interactions in clinical practice and could be used to rule out inappropriate compounds in the early stages of drug discovery and/or identify new CYPs inhibitors.

Machine learning and structure-based modeling for the prediction of UDP-glucuronosyltransferase inhibition

UDP-glucuronosyltransferases (UGTs) are responsible for 35% of the phase II drug metabolism. In this study, we focused on UGT1A1, which is a key UGT isoform. Strong inhibition of UGT1A1 may trigger adverse drug/herb-drug interactions, or result in disorders of endobiotic metabolism. Most of the current machine learning methods predicting the inhibition of drug metabolizing enzymes neglect protein structure and dynamics, both being essential for the recognition of various substrates and inhibitors. We performed molecular dynamics simulations on a homology model of the human UGT1A1 structure containing both the cofactor- (UDP-glucuronic acid) and substrate-binding domains to explore UGT conformational changes. Then, we created models for the prediction of UGT1A1 inhibitors by integrating information on UGT1A1 structure and dynamics, interactions with diverse ligands, and machine learning. These models can be helpful for further prediction of drug-drug interactions of drug candidates and safety treatments.

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UGTs are responsible for 35% of the phase II drug metabolism reactions

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We created machine learning models for prediction of UGT1A1 inhibitors

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Our simulations suggested key residues of UGT1A1 involved in the substrate binding

A unified GCNN model for predicting CYP450 inhibitors by using graph convolutional neural networks with attention mechanism

Undesirable drug-drug interactions (DDIs) may lead to serious adverse side effects when more than two drugs are administered to a patient simultaneously. One of the most common DDIs is caused by unexpected inhibition of a specific human cytochrome P450 (CYP450), which plays a dominant role in the metabolism of the co-administered drugs. Therefore, a unified and reliable method for predicting the potential inhibitors of CYP450 family is extremely important in drug development. In this work, graph convolutional neural network (GCN) with attention mechanism and 1-D convolutional neural network (CNN) were used to extract the features of CYP ligands and the binding sites of CYP450 respectively, which were then combined to establish a unified GCN-CNN (GCNN) model for predicting the inhibitors of 5 dominant CYP isoforms, i.e., 1A2, 2C9, 2C19, 2D6, and 3A4. Overall, the established GCNN model showed good performances on the test samples and achieved better performances than the recently proposed iCYP-MFE model by using the same datasets. Based on the heat-map analysis of the resulting molecular graphs, the key structural determinants of the CYP inhibitors were further explored.

ABCG2/BCRP transport mechanism revealed through kinetically excited targeted molecular dynamics simulations

ABCG2/BCRP is an ABC transporter that plays an important role in tissue protection by exporting endogenous substrates and xenobiotics. ABCG2 is of major interest due to its involvement in multidrug resistance (MDR), and understanding its complex efflux mechanism is essential to preventing MDR and drug-drug interactions (DDI). ABCG2 export is characterized by two major conformational transitions between inward- and outward-facing states, the structures of which have been resolved. Yet, the entire transport cycle has not been characterized to date. Our study bridges the gap between the two extreme conformations by studying connecting pathways. We developed an innovative approach to enhance molecular dynamics simulations, ‘kinetically excited targeted molecular dynamics’, and successfully simulated the transitions between inward- and outward-facing states in both directions and the transport of the endogenous substrate estrone 3-sulfate. We discovered an additional pocket between the two substrate-binding cavities and found that the presence of the substrate in the first cavity is essential to couple the movements between the nucleotide-binding and transmembrane domains. Our study shed new light on the complex efflux mechanism, and we provided transition pathways that can help to identify novel substrates and inhibitors of ABCG2 and probe new drug candidates for MDR and DDI.