SuperCYPsPred-a web server for the prediction of cytochrome activity

Investigation of in silico studies for cytochrome P450 isoforms specificity

Cytochrome P450 (CYP450) enzymes comprise a highly diverse superfamily of heme-thiolate proteins that responsible for catalyzing over 90 % of enzymatic reactions associated with xenobiotic metabolism in humans. Accurately predicting whether chemicals are substrates or inhibitors of different CYP450 isoforms can aid in pre-selecting hit compounds for the drug discovery process, chemical toxicology studies, and patients treatment planning. In this work, we investigated in silico studies on CYP450s specificity over past twenty years, categorizing these studies into structure-based and ligand-based approaches. Subsequently, we utilized 100 of the most frequently prescribed drugs to test eleven machine learning-based prediction models which were published between 2015 and 2024. We analyzed various aspects of the evaluated models, such as their datasets, algorithms, and performance. This will give readers with a comprehensive overview of these prediction models and help them choose the most suitable one to do prediction. We also provide our insights for future research trend in both structure-based and ligand-based approaches in this field.

3MTox: A motif-level graph-based multi-view chemical language model for toxicity identification with deep interpretation

Toxicity identification plays a key role in maintaining human health, as it can alert humans to the potential hazards caused by long-term exposure to a wide variety of chemical compounds. Experimental methods for determining toxicity are time-consuming, and costly, while computational methods offer an alternative for the early identification of toxicity. For example, some classical ML and DL methods, which demonstrate excellent performance in toxicity prediction. However, these methods also have some defects, such as over-reliance on artificial features and easy overfitting, etc. Proposing novel models with superior prediction performance is still an urgent task. In this study, we propose a motifs-level graph-based multi-view pretraining language model, called 3MTox, for toxicity identification. The 3MTox model uses Bidirectional Encoder Representations from Transformers (BERT) as the backbone framework, and a motif graph as input. The results of extensive experiments showed that our 3MTox model achieved state-of-the-art performance on toxicity benchmark datasets and outperformed the baseline models considered. In addition, the interpretability of the model ensures that the it can quickly and accurately identify toxicity sites in a given molecule, thereby contributing to the determination of the status of toxicity and associated analyses. We think that the 3MTox model is among the most promising tools that are currently available for toxicity identification.

Sex-specific effects of subchronic NMDA receptor antagonist MK-801 treatment on hippocampal gamma oscillations

N-methyl-D-aspartate (NMDA) receptor antagonists are widely used to pharmacologically model schizophrenia and have been recently established in the treatment of treatment-resistant major depression demonstrating that the pharmacology of this substance class is complex. Cortical gamma oscillations, a rhythmic neuronal activity associated with cognitive processes, are increased in schizophrenia and deteriorated in depressive disorders and are increasingly used as biomarker in these neuropsychiatric diseases. The opposite use of NMDA receptor antagonists in schizophrenia and depression raises the question how their effects are in accordance with the observed disease pathophysiology and if these effects show a consequent sex-specificity. In this study in rats, we investigated the effects of subchronic (14 days) intraperitoneal injections of the NMDA receptor antagonist MK-801 at a subanesthetic daily dose of 0.2 mg/kg on the behavioral phenotype of adult female and male rats and on pharmacologically induced gamma oscillations measured ex vivo from the hippocampus. We found that MK-801 treatment leads to impaired recognition memory in the novel object recognition test, increased stereotypic behavior and reduced grooming, predominantly in female rats. MK-801 also increased the peak power of hippocampal gamma oscillations induced by kainate or acetylcholine only in female rats, without affecting the peak frequency of the oscillations. The findings indicate that blockade of NMDA receptors enhances gamma oscillations predominantly in female rats and this effect is associated with behavioral changes in females. The results are in accordance with clinical electrophysiological findings and highlight the importance of hippocampal gamma oscillations as a biomarker in schizophrenia and depression.

Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties

Cytochrome P450 enzymes are a superfamily of enzymes responsible for the metabolism of a variety of medicines and xenobiotics. Among the Cytochrome P450 family, five isozymes that include 1A2, 2C9, 2C19, 2D6, and 3A4 are most important for the metabolism of xenobiotics. Inhibition of any of these five CYP isozymes causes drug-drug interactions with high pharmacological and toxicological effects. So, the inhibition or non-inhibition prediction of these isozymes is of great importance. Many techniques based on machine learning and deep learning algorithms are currently being used to predict whether these isozymes will be inhibited or not. In this study, three different molecular or substructural properties that include Morgan, MACCS and Morgan (combined) and RDKit of the various molecules are used to train a distinct SVM model against each isozyme (1A2, 2C9, 2C19, 2D6, and 3A4). On the independent dataset, Morgan fingerprints provided the best results, while MACCS and Morgan (combined) achieved comparable results in terms of balanced accuracy (BA), sensitivity (Sn), and Mathews correlation coefficient (MCC). For the Morgan fingerprints, balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4 on an independent dataset ranged between 0.81 and 0.85, 0.61 and 0.70, 0.72 and 0.83, respectively. Similarly, on the independent dataset, MACCS and Morgan (combined) fingerprints achieved competitive results in terms of balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4, which ranged between 0.79 and 0.85, 0.59 and 0.69, 0.69 and 0.82, respectively.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

BioPrint meets the AI age: development of artificial intelligence-based ADMET models for the drug-discovery platform SAFIRE

Background: To prioritize compounds with a higher likelihood of success, artificial intelligence models can be used to predict absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of molecules quickly and efficiently. Methods: Models were trained with BioPrint database proprietary data along with public datasets to predict various ADMET end points for the SAFIRE platform. Results: SAFIRE models performed at or above 75% accuracy and 0.4 Matthew's correlation coefficient with validation sets. Training with both proprietary and public data improved model performance and expanded the chemical space on which the models were trained. The platform features scoring functionality to guide user decision-making. Conclusion: High-quality datasets along with chemical space considerations yielded ADMET models performing favorably with utility in the drug discovery process.

A Multicenter Retrospective Observational Study Analyzing the Effect of Polypharmacy on Oxycodone Tolerability

Polypharmacy is becoming increasingly troublesome in the treatment of cancer. The aim of this study was to explore the effects of concomitant polypharmacy comprising drugs that inhibit CYP3A4 and/or CYP2D6 on the oxycodone tolerability in patients with cancer. We conducted a multicenter retrospective study encompassing 20 hospitals. The data used for the study were obtained during the first 2 wk of oxycodone administration. The incidence of oxycodone discontinuation or dose reductions due to side effects and oxycodone-induced nausea and vomiting (OINV) were compared between patients not treated with either inhibitor and those treated with concomitant CYP3A4 or CYP2D6 inhibitors. The incidence of oxycodone discontinuation or dose reductions in patients treated with ≥3 concomitant CYP2D6 inhibitors (18.2%) tended to be higher than that in patients without this treatment (8.2%; p = 0.09). Moreover, the incidence of OINV in patients treated with 2 concomitant CYP3A4 inhibitors (29.8%) was significantly higher than that in patients without this treatment (15.5%; p = 0.049). Multivariate analysis showed that more than two concomitant CYP3A4 inhibitors and no concomitant use of naldemedine were independent risk factors for OINV. Concomitant polypharmacy involving CYP3A4 inhibitors increases the risk of OINV. Therefore, medications concomitantly used with oxycodone should be optimized.

Nicotine Metabolite Ratio Decreases After Switching Off Efavirenz-Based Therapy in People With HIV Who Smoke

Rates of cigarette smoking in people with HIV (PWH) are two to three times higher than in people without HIV. Nicotine is metabolized by CYP2A6 and the nicotine metabolite ratio (NMR; 3-hydroxycotinine/cotinine) is a measure of nicotine clearance. Higher NMR has been observed in PWH and is associated with lower quit rates. Efavirenz, a mainstay antiretroviral therapy (ART) globally, partially upregulates its own metabolism through CYP2A6. We hypothesized that efavirenz also upregulates nicotine metabolism by CYP2A6, resulting in a higher NMR, and switching to non-efavirenz ART would decrease the NMR, potentially leading to improved quit rates. We compared the NMR during and after efavirenz use among PWH in a longitudinal, multisite cohort. Eligibility criteria included: (i) active cigarette smoking, (ii) ART switched from efavirenz-based to non-efavirenz-based regimen, (iii) plasma available at pre- and post-ART switch, and (iv) viral suppression during study period. Plasma cotinine and 3-hydroxycotinine were measured by liquid chromatography-tandem mass spectrometry. T-tests compared the NMR on and off efavirenz. Samples were collected between 2010 and 2019 in 72 PWH. The mean NMR difference after switching to a non-efavirenz-based regimen was -0.24 (SD: 0.37, P < 0.001); 44 PWH had at least a 0.1 decrease in NMR. Effect modification by race was present; Black PWH had a larger mean decrease. Our findings suggest that previously observed higher NMR among PWH may be due to direct pharmacologic effects of ART. Assessing the effect of ART on the NMR suggests that avoiding nicotine metabolism inducers could potentially increase quit rates.

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.

An Uncertainty-Guided Deep Learning Method Facilitates Rapid Screening of CYP3A4 Inhibitors

Cytochrome P450 3A4 (CYP3A4), a prominent member of the P450 enzyme superfamily, plays a crucial role in metabolizing various xenobiotics, including over 50% of clinically significant drugs. Evaluating CYP3A4 inhibition before drug approval is essential to avoiding potentially harmful pharmacokinetic drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Despite the development of several CYP inhibitor prediction models, the primary approach for screening CYP inhibitors still relies on experimental methods. This might stem from the limitations of existing models, which only provide deterministic classification outcomes instead of precise inhibition intensity (e.g., IC50) and often suffer from inadequate prediction reliability. To address this challenge, we propose an uncertainty-guided regression model to accurately predict the IC50 values of anti-CYP3A4 activities. First, a comprehensive data set of CYP3A4 inhibitors was compiled, consisting of 27,045 compounds with classification labels, including 4395 compounds with explicit IC50 values. Second, by integrating the predictions of the classification model trained on a larger data set and introducing an evidential uncertainty method to rank prediction confidence, we obtained a high-precision and reliable regression model. Finally, we use the evidential uncertainty values as a trustworthy indicator to perform a virtual screening of an in-house compound set. The in vitro experiment results revealed that this new indicator significantly improved the hit ratio and reduced false positives among the top-ranked compounds. Specifically, among the top 20 compounds ranked with uncertainty, 15 compounds were identified as novel CYP3A4 inhibitors, and three of them exhibited activities less than 1 μM. In summary, our findings highlight the effectiveness of incorporating uncertainty in compound screening, providing a promising strategy for drug discovery and development.

Synthesis, crystal structure and molecular docking study of novel isoxazole derivatives as CYP450 inhibitors in search of anticancer agents

Synthesis of some novel isoxazole derivatives and their molecular docking with enzymes from CYP450 family carried out using erlotinib, gemcitabine and ketoconazole as reference drugs are reported in this work. Eight isoxazole derivatives of 3,4-substituted phenyl 3-chloroacrylaldehyde and one isoxazole derivative of cinnamaldehyde were synthesized. A molecular docking study of all nine compounds shows good docking score compared to standard drugs erlotinib, gemcitabine and ketoconazole. 4-OH and 4-F derivatives were found to have strong affinity for all six CYP450 proteins under study in the present work. 4-F and 3-NO2 derivatives could be a suitable lead compound inhibitor to CYP1A2 followed by 4-OH derivatives. 4-OH derivative with significant binding affinity showed encouraging inhibition of CYP1A2, CYP2C9, CYP2C8, CYP2C19 and CYP2D6. The current predictions over these nine isoxazole derivatives of 3,4-substituted phenyl 3-chloroacrylaldehyde will be needed to be further investigated in vivo and in vitro conditions to identify the optimum therapeutic efficacy. Synthesis of the isoxazole derivatives is the first known report of the Knoevenagal condensation of acrylaldehyde derivatives to form isoxazole derivatives as per the literature survey. A detailed crystal structure study of five analogues gives insight into the solid-state structural features of this new framework with isoxazole moieties.Communicated by Ramaswamy H. Sarma.

DPP-IV Inhibitory Peptides from Coix Seed Prolamins: Release, Identification, and Analysis of the Interaction between Key Residues and Enzyme Domains

Dipeptidyl peptidase IV (DPP-IV) inhibitory peptides can regulate type 2 diabetes by inhibiting the cleavage of glucagon-like peptide-1 and prolonging its half-life. The development of DPP-IV inhibitory peptides is still a hot topic. The primary structure of coix seed prolamins contains peptide sequence fragments that potentially inhibit DPP-IV; however, limited information is available regarding the extraction of peptides from coix seeds and the analysis of their conformational relationships. In this study, novel coix seed prolamin-derived peptides were obtained through single hydrolysis and double-enzyme stepwise hydrolysis. The inhibitory activity of these peptides against DPP-IV was evaluated to explore new functional properties of coix seeds. The results evidenced that the step-by-step enzymolysis (papain and alcalase) compared to single enzymolysis promoted the secondary structure disruption of the hydrolysates, enhanced the β-turn structure, significantly increased the content of peptides below 1 kDa, and exhibited a substantial increase in DPP-IV inhibitory activity (97% inhibition). Three nontoxic DPP-IV inhibitory peptides, namely, LPFYPN, TFFPQ, and ATFFPQ (IC50 = 70.24, 176.87, 268.31 μM), were isolated and identified. All three peptides exhibited strong interactions with DPP-IV (all KA values >103). LPFYPN exhibited competitive inhibition, while TFFPQ and ATFFPQ demonstrated mixed competitive-noncompetitive inhibition. Hydrogen bonding and hydrophobic interactions were the main contributors to the coix seed prolamin peptides binding to DPP-IV. The central residue was a key amino acid in the parent peptide sequence, forming a more stable π-π stacking with residues in the active pocket, which may facilitate peptide activity. This study provides theoretical support for the development of coix seed-derived hypoglycemic peptides.

Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism

The cytochrome P450 (CYP450) enzyme system is responsible for the metabolism of more than two-thirds of xenobiotics. This review summarizes reports of a series of in silico tools for CYP450 enzyme-drug interaction predictions, including the prediction of sites of metabolism (SOM) of a drug and the identification of inhibitor/substrates for CYP subtypes. We also evaluated four prediction tools to identify CYP inhibitors utilizing 52 of the most frequently prescribed drugs. ADMET Predictor and CYPlebrity demonstrated the best performance. We hope that this review provides guidance for choosing appropriate enzyme prediction tools from a variety of in silico platforms to meet individual needs. Such predictions are useful for medicinal chemists to prioritize their designed compounds for further drug discovery.

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.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

Opportunities and challenges in application of artificial intelligence in pharmacology

Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.

Modeling and integrating interactions involving the CYP450 enzyme system in a multi-terminology server: Contribution to information extraction from a clinical data warehouse

INTRODUCTION

The cytochrome P450 (CYP450) enzyme system is involved in the metabolism of certain drugs and is responsible for most drug interactions. These interactions result in either an enzymatic inhibition or an enzymatic induction mechanism that has an impact on the therapeutic management of patients. Detecting these drug interactions will allow for better predictability in therapeutic response. Therefore, computerized solutions can represent a valuable help for clinicians in their tasks of detection.

OBJECTIVE

The objective of this study is to provide a structured data-source of interactions involving the CYP450 enzyme system. These interactions are aimed to be integrated in the cross-lingual multi-terminology server HeTOP (Health Terminologies and Ontologies Portal), to support the query processing of the clinical data warehouse (CDW) EDSaN (Entrepôt de Données de Santé Normand).

MATERIAL AND METHODS

A selection and curation of drug components (DCs) that share a relationship with the CYP450 system was performed from several international data sources. The DCs were linked according to the type of relationship which can be substrate, inhibitor, or inducer. These relationships were then integrated into the HeTOP server. To validate the CYP450 relationships, a semantic query was performed on the CDW, whose search engine is founded on HeTOP data (concepts, terms, and relations).

RESULTS

A total of 776 DCs are associated by a new interaction relationship, integrated in HeTOP, by 14 enzymes. These are CYP450 1A2, 2A6, 2B6, 2C8, 2C9, 2C18, 2C19, 2D6, 2E1, 3A4, 3A7, 11B1,11B2 mitochondrial and P-glycoprotein, constituting a total of 2,088 relationships. A general modelling of cytochromic interactions was performed. From this model, 233,006 queries were processed in less than two hours, demonstrating the usefulness and performance of our CDW implementation. Moreover, they showed that in our university hospital, the concurrent prescription that could cause a cytochromic interaction is Bisoprolol with Amiodarone by enzymatic inhibition for 2,493 patients.

DISCUSSION

The queries submitted to the CDW EDSaN allowed to highlight the most prescribed molecules simultaneously and potentially responsible for cytochromic interactions. In a second step, it would be interesting to evaluate the real clinical impact by looking for possible adverse effects of these interactions in the patients' files. Other computational solutions for cytochromic interactions exist. The impact of CYP450 is particularly important for drugs with narrow therapeutic window (NTW) as they can lead to increased toxicity or therapeutic failure. It is also important to define which drug component is a pro-drug and to considerate the many genetic polymorphisms of patients.

CONCLUSION

The HeTOP server contains a non-negligible number of relationships between drug components and CYP450 from multiple reference sources. These data allow us to query our Clinical Data Warehouse to highlight these cytochromic interactions. It would be interesting in the future to assess the actual clinical impact in hospital reports.

In silico ADMET analysis of the A-, B- and D-modified androstane derivatives with potential anticancer effects

The major challenge in the fight against cancer is to design new drugs that will be more selective for cancer cells, with fewer side effects. Synthetic steroids such as cyproterone, fulvestrant, exemestane and abiraterone are approved powerful drugs for the treatment of hormone-dependent diseases such as breast and prostate cancers. Therefore, androstane derivatives in 17-substituted, 17a-homo lactone and 16,17-seco series, with potent anticancer activity, were selected for pharmacokinetic and druglike predictions from the absorption, distribution, metabolism and excretion (ADME) models. In silico determination of physico-chemical and ADMET properties was performed using SwissADME and ProTox-II web tools. The possibility of gastrointestinal absorption and brain penetration was analyzed using the BOILED-Egg model, while the in silico evaluation of the similarities between selected steroid derivatives and FDA-approved drugs was carried out using the SwissSimilarity tool. Of all tested, two compounds that showed good in silico ADMET results, in addition to promising cytotoxicity and molecular docking results, could potentially be evaluated in in vivo tests.

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.

Target Prediction of 5,10,15,20-Tetrakis(4'-Sulfonatophenyl)-Porphyrin Using Molecular Docking

Photodynamic therapy has the potential to be a new and effective cancer treatment. Even if in vitro and in vivo research show promise, the molecular mechanism remains unclear. In this study, molecular docking simulations predict the binding affinity of the 5,10,15,20-tetrakis(4'-sulfonatophenyl)-porphyrin tetraammonium photosensitizer on several potential targets in photodynamic treatment. Our results indicate that this photosensitizer binds to several receptor targets, including B-cell lymphoma 2 (BCL-2) and other related proteins BCL-xL, MCL-1, or A1. The binding affinity of the porphyrin derivative with human serum albumin was determined using UV-vis absorption spectroscopy and predicted using molecular docking. We conclude that the studied porphyrin photosensitizer binds to human serum albumin and may inhibit the cancer cell line through its interactions with HIS and MET AA residues from BCL-2, MCL-1, and β-catenin receptors or through its low estimated free energy of binding when interacting with A1 and BCL-B receptors.

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.

Computational Prediction of Inhibitors and Inducers of the Major Isoforms of Cytochrome P450

Human cytochrome P450 enzymes (CYPs) are heme-containing monooxygenases. This superfamily of drug-metabolizing enzymes is responsible for the metabolism of most drugs and other xenobiotics. The inhibition of CYPs may lead to drug–drug interactions and impair the biotransformation of drugs. CYP inducers may decrease the bioavailability and increase the clearance of drugs. Based on the freely available databases ChEMBL and PubChem, we have collected over 70,000 records containing the structures of inhibitors and inducers together with the IC50 values for the inhibitors of the five major human CYPs: 1A2, 3A4, 2D6, 2C9, and 2C19. Based on the collected data, we developed (Q)SAR models for predicting inhibitors and inducers of these CYPs using GUSAR and PASS software. The developed (Q)SAR models could be applied for assessment of the interaction of novel drug-like substances with the major human CYPs. The created (Q)SAR models demonstrated reasonable accuracy of prediction. They have been implemented in the web application P450-Analyzer that is freely available via the Internet.

Comprehensive Assessment of Indian Variations in the Druggable Kinome Landscape Highlights Distinct Insights at the Sequence, Structure and Pharmacogenomic Stratum

India confines more than 17% of the world’s population and has a diverse genetic makeup with several clinically relevant rare mutations belonging to many sub-group which are undervalued in global sequencing datasets like the 1000 Genome data (1KG) containing limited samples for Indian ethnicity. Such databases are critical for the pharmaceutical and drug development industry where diversity plays a crucial role in identifying genetic disposition towards adverse drug reactions. A qualitative and comparative sequence and structural study utilizing variant information present in the recently published, largest curated Indian genome database (IndiGen) and the 1000 Genome data was performed for variants belonging to the kinase coding genes, the second most targeted group of drug targets. The sequence-level analysis identified similarities and differences among different populations based on the nsSNVs and amino acid exchange frequencies whereas a comparative structural analysis of IndiGen variants was performed with pathogenic variants reported in UniProtKB Humsavar data. The influence of these variations on structural features of the protein, such as structural stability, solvent accessibility, hydrophobicity, and the hydrogen-bond network was investigated. In-silico screening of the known drugs to these Indian variation-containing proteins reveals critical differences imparted in the strength of binding due to the variations present in the Indian population. In conclusion, this study constitutes a comprehensive investigation into the understanding of common variations present in the second largest population in the world and investigating its implications in the sequence, structural and pharmacogenomic landscape. The preliminary investigation reported in this paper, supporting the screening and detection of ADRs specific to the Indian population could aid in the development of techniques for pre-clinical and post-market screening of drug-related adverse events in the Indian population.

Kidney injury and oxidative damage alleviation by Zingiber officinale: pharmacokinetics and protective approach in a combined murine model of osteoporosis

Ginger (Zingiber officinale) is considered as a nutraceutical spice, which possesses several health promotion and benefits. This study was carried out to investigate the phyto-chemical composition, the antioxidant capacities, the drug-likeness, and pharmacokinetic properties of ginger extract on kidney injury-associated osteoporosis in rats. Phenolic and flavonoid contents were assessed by standard chemical analysis methods and HPLC. In vivo protective effect was based on the use of female rats to evaluate the effect on renal injury as a result of combined osteoporosis using biochemical markers, oxidative status, and histological analyses. Results showed that ZO contained appreciable amounts of phenolics and flavonoids and it exhibited high scavenging activity. Ovariectomy-associated corticotherapy induced severe renal injury marked by altered biochemical markers (creatinine, urea, and uric acid), reduced GFR, significative oxidative damage signs, and disrupted antioxidant status in the combined osteoporotic rats. The histopathological examination revealed structural modifications of kidney tissues. However, all these changes were reversed following the use of ZO. These results confirm the renoprotective and antioxidant potential of ginger against renal injuries in osteoporotic rats.

Synthesis and evaluation of dual fatty acid amide hydrolase-monoacylglycerol lipase inhibition and antinociceptive activities of 4-methylsulfonylaniline-derived semicarbazones

Fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL) are promising targets for neuropathic pain and other CNS disorders. Based on our previous lead compound SIH 3, we designed and synthesized a series of 4-methylsulfonylphenyl semicarbazones and evaluated for FAAH and MAGL inhibition properties. Most of the compounds showed potency towards both enzymes with leading FAAH selectivity. Compound (Z)-2-(2,6-dichlorobenzylidene)-N-(4-(methylsulfonyl)phenyl)hydrazine-1-carboxamide emerged as the lead inhibitor against both FAAH (IC₅₀ = 11 nM) and MAGL (IC₅₀ = 36 nM). The lead inhibitor inhibited FAAH by non-competitive mode, but showed a mixed-type inhibition against MAGL. Molecular docking study unveiled that the docked ligands bind favorably to the active sites of FAAH and MAGL. The lead inhibitor interacted with FAAH and MAGL via π-π stacking via phenyl ring and hydrogen bonding through sulfonyl oxygen atoms or amide NH. Moreover, the stability of docked complexes was rationalized by molecular simulation studies. PAMPA assay revealed that the lead compound is suitable for blood-brain penetration. The lead compound showed better cell viability in lipopolysaccharide-induced neurotoxicity assay in SH-SY5Y cell lines. Further, in-vivo experiments unveiled that dual inhibitor was safe up to 2000 mg/kg with no hepatotoxicity. The dual FAAH-MAGL inhibitor produced significant anti-nociceptive effect in the CCI model of neuropathic pain without altering locomotion activity. Lastly, the lead compound exhibited promising ex-vivo FAAH/MAGL inhibition activity at the dose of 10 mg/kg and 20 mg/kg. Thus, these findings suggest that the semicarbazone-based lead compound can be a potential template for the development of agents for neuropathic pain.

Combinative ex vivo studies and in silico models ProTox-II for investigating the toxicity of chemicals used mainly in cosmetic products

Human data on remains sparse and of varying quality and reproducibility. Ex vivo experiments and animal experiments currently is the most preferred way to predict the skin sensitization approved by the regulatory agencies across the world. However, there is a constant need and demand to reduce animal experiments and provide the scope of alternative methods to animal testing. In this study, we have compared the predictive performance of the published computational tools such as ProTox-II, SuperCYPsPred with the data obtained from ex-vivo experiments. From the results of the retrospective analysis, it can be observed that the computational predictions are in agreement with the experimental results. The computational models used here are generative models based on molecular structures and machine learning algorithms and can be applied also for the prediction of skin sensitization. Besides prediction of the toxicity endpoints, the models can also provide deeper insights into the molecular mechanisms and adverse outcome pathways (AOPs) associated with the chemicals used in cosmetic products.

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

Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug-drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.

Evaluation of anti-cancer and anti-covid-19 properties of cationic pentapeptide Glu-Gln-Arg-Pro-Arg, from rice bran protein and its d-isomer analogs through molecular docking simulations

Bioactive peptides derived from food proteins are becoming increasingly popular due to the growing awareness of their health-promoting properties. The structure and mechanism of anti-cancer action of pentapeptide Glu-Gln-Arg-Pro-Arg (EQRPR) derived from a rice bran protein are not known. Theoretical and experimental methods were employed to fill this gap. The conformation analysis of the EQRPR pentapeptide was performed first and the obtained lowest energy conformer was optimized. The experimental structural data obtained by FTIR and CD spectroscopies agree well with the theoretical results. d-isomer introduced one-by-one to each position and all D-isomers of the peptide were also examined for its possible anti-proteolytic and activity enhancement properties. The molecular docking revealed avid binding of the pentapeptide to the integrins α5β1 and αIIbβ3, with Kd values of 90 nM and 180 nM, respectively. Moreover, the EQRPR and its D-isomers showed strong binding affinities to apo- and holo-forms of Mpro, spike glycoprotein, ACE2, and dACE2. The predicted results indicate that the pentapeptide may significantly inhibit SARS-CoV-2 infection. Thus, the peptide has the potential to be the leading molecule in the drug discovery process as having multifunctional with diverse biological activities.

Water biocatalytic effect attenuates cytochrome P450-mediated carcinogenicity of diethylnitrosamine: A computational insight

The mechanism-based mutagenicity and carcinogenicity of diethylnitrosamine (DEN) are believed to act through interactions with cytochrome P450 (P450) enzymes. DFT calculations to explore the conceivable mechanisms underlying the reaction of P450 with DEN with and without water as a biocatalyst were performed. The results shed light on the biocatalytic role of water in lowering the H-abstraction energy barriers because of the electrostatic effect driven by hydrogen bonding. Our DFT analysis revealed how metabolites are formed in the dealkylation (toxification) and denitrosation (detoxification) pathways. Also, our findings uncovered the active position of DEN vulnerable to P450 interactions. Two factors control the toxification and detoxification rates: the stability of denitrosation products and the HS rebound barrier of the α-pathway. Thus, water biocatalytic attenuation of DEN carcinogenicity was attained by stabilizing denitrosation products and slowing the α-HS rebound process. Docking and MD simulations were performed to assess the binding modes of DEN to P450's active site and to inspect the denitrosation and dealkylation processes, respectively.

iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding

The human cytochrome P450 (CYP) superfamily holds responsibilities for the metabolism of both endogenous and exogenous compounds such as drugs, cellular metabolites, and toxins. The inhibition exerted on the CYP enzymes is closely associated with adverse drug reactions encompassing metabolic failures and induced side effects. In modern drug discovery, identification of potential CYP inhibitors is, therefore, highly essential. Alongside experimental approaches, numerous computational models have been proposed to address this biochemical issue. In this study, we introduce iCYP-MFE, a computational framework for virtual screening on CYP inhibitors toward 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms. iCYP-MFE contains a set of five robust, stable, and effective prediction models developed using multitask learning incorporated with molecular fingerprint-embedded features. The results show that multitask learning can remarkably leverage useful information from related tasks to promote global performance. Comparative analysis indicates that iCYP-MFE achieves three predominant tasks, one equivalent task, and one less effective task compared to state-of-the-art methods. The area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR) were two decisive metrics used for model evaluation. The prediction task for CYP2D6-inhibition achieves the highest AUC-ROC value of 0.93 while the prediction task for CYP1A2-inhibition obtains the highest AUC-PR value of 0.92. The substructural analysis preliminarily explains the nature of the CYP-inhibitory activity of compounds. An online web server for iCYP-MFE with a user-friendly interface was also deployed to support scientific communities in identifying CYP inhibitors.

DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety

Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Despite the continuous information accumulation of clinically significant DDIs, there are few open-access knowledge systems dedicated to the curation of DDI associations. To facilitate the clinicians to screen for dangerous drug combinations and improve health systems, we present DDInter, a curated DDI database with comprehensive data, practical medication guidance, intuitive function interface, and powerful visualization to the scientific community. Currently, DDInter contains about 0.24M DDI associations connecting 1833 approved drugs (1972 entities). Each drug is annotated with basic chemical and pharmacological information and its interaction network. For DDI associations, abundant and professional annotations are provided, including severity, mechanism description, strategies for managing potential side effects, alternative medications, etc. The drug entities and interaction entities are efficiently cross-linked. In addition to basic query and browsing, the prescription checking function is developed to facilitate clinicians to decide whether drugs combinations can be used safely. It can also be used for informatics-based DDI investigation and evaluation of other prediction frameworks. We hope that DDInter will prove useful in improving clinical decision-making and patient safety. DDInter is freely available, without registration, at http://ddinter.scbdd.com/.

MetaClass, a Comprehensive Classification System for Predicting the Occurrence of Metabolic Reactions Based on the MetaQSAR Database

(1) Background: Machine learning algorithms are finding fruitful applications in predicting the ADME profile of new molecules, with a particular focus on metabolism predictions. However, the development of comprehensive metabolism predictors is hampered by the lack of highly accurate metabolic resources. Hence, we recently proposed a manually curated metabolic database (MetaQSAR), the level of accuracy of which is well suited to the development of predictive models. (2) Methods: MetaQSAR was used to extract datasets to predict the metabolic reactions subdivided into major classes, classes and subclasses. The collected datasets comprised a total of 3788 first-generation metabolic reactions. Predictive models were developed by using standard random forest algorithms and sets of physicochemical, stereo-electronic and constitutional descriptors. (3) Results: The developed models showed satisfactory performance, especially for hydrolyses and conjugations, while redox reactions were predicted with greater difficulty, which was reasonable as they depend on many complex features that are not properly encoded by the included descriptors. (4) Conclusions: The generated models allowed a precise comparison of the propensity of each metabolic reaction to be predicted and the factors affecting their predictability were discussed in detail. Overall, the study led to the development of a freely downloadable global predictor, MetaClass, which correctly predicts 80% of the reported reactions, as assessed by an explorative validation analysis on an external dataset, with an overall MCC = 0.44.

CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes

The vast majority of approved drugs are metabolized by the five major cytochrome P450 (CYP) isozymes, 1A2, 2C9, 2C19, 2D6 and 3A4. Inhibition of CYP isozymes can cause drug-drug interactions with severe pharmacological and toxicological consequences. Computational methods for the fast and reliable prediction of the inhibition of CYP isozymes by small molecules are therefore of high interest and relevance to pharmaceutical companies and a host of other industries, including the cosmetics and agrochemical industries. Today, a large number of machine learning models for predicting the inhibition of the major CYP isozymes by small molecules are available. With this work we aim to go beyond the coverage of existing models, by combining data from several major public and proprietary sources. More specifically, we used up to 18815 compounds with measured bioactivities to train random forest classification models for the individual CYP isozymes. A major advantage of the new data collection over existing ones is the better representation of the minority class, the CYP inhibitors. With the new data collection we achieved inhibitor-to-non-inhibitor ratios in the order of 1:1 (CYP1A2) to 1:3 (CYP2D6). We show that our models reach competitive performance on external data, with Matthews correlation coefficients (MCCs) ranging from 0.62 (CYP2C19) to 0.70 (CYP2D6), and areas under the receiver operating characteristic curve (AUCs) between 0.89 (CYP2C19) and 0.92 (CYPs 2D6 and 3A4). Importantly, the models show a high level of robustness, reflected in a good predictivity also for compounds that are structurally dissimilar to the compounds represented in the training data. The best models presented in this work are freely accessible for academic research via a web service.

ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties

Because undesirable pharmacokinetics and toxicity of candidate compounds are the main reasons for the failure of drug development, it has been widely recognized that absorption, distribution, metabolism, excretion and toxicity (ADMET) should be evaluated as early as possible. In silico ADMET evaluation models have been developed as an additional tool to assist medicinal chemists in the design and optimization of leads. Here, we announced the release of ADMETlab 2.0, a completely redesigned version of the widely used AMDETlab web server for the predictions of pharmacokinetics and toxicity properties of chemicals, of which the supported ADMET-related endpoints are approximately twice the number of the endpoints in the previous version, including 17 physicochemical properties, 13 medicinal chemistry properties, 23 ADME properties, 27 toxicity endpoints and 8 toxicophore rules (751 substructures). A multi-task graph attention framework was employed to develop the robust and accurate models in ADMETlab 2.0. The batch computation module was provided in response to numerous requests from users, and the representation of the results was further optimized. The ADMETlab 2.0 server is freely available, without registration, at https://admetmesh.scbdd.com/.

Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug-drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

In Silico Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 Isoforms

Drug–drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated by inhibition or induction of the most important drug-metabolizing cytochrome P450 isoforms. Our study was dedicated to creating a computer model for prediction of the DDIs mediated by the seven most important P450 cytochromes: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, and CYP3A4. For the creation of structure–activity relationship (SAR) models that predict metabolism-mediated DDIs for pairs of molecules, we applied the Prediction of Activity Spectra for Substances (PASS) software and Pairs of Substances Multilevel Neighborhoods of Atoms (PoSMNA) descriptors calculated based on structural formulas. About 2500 records on DDIs mediated by these cytochromes were used as a training set. Prediction can be carried out both for known drugs and for new, not-yet-synthesized substances. The average accuracy of the prediction of DDIs mediated by various isoforms of cytochrome P450 estimated by leave-one-out cross-validation (LOO CV) procedures was about 0.92. The SAR models created are publicly available as a web resource and provide predictions of DDIs mediated by the most important cytochromes P450.

Screening of Human CYP1A2 and CYP3A4 Inhibitors from Seaweed In Silico and In Vitro

Phenolic compounds and carotenoids are potential inhibitors of cytochrome P450s. Sixteen known compounds, phenolic compounds and carotenoids from seaweed were examined for potential inhibitory capacity against CYP1A2 and CYP3A4 in silico and in vitro. Morin, quercetin, and fucoxanthin inhibited the enzyme activity of CYP1A2 and CYP3A4 in a dose-dependent manner. The IC50 values of morin, quercetin, and fucoxanthin were 41.8, 22.5, and 30.3 μM for CYP1A2 and 86.6, 16.1, and 24.4 μM for CYP3A4, respectively. Siphonaxanthin and hesperidin did not show any significant effect on CYP1A2, but they slightly inhibited CYP3A4 activity at high concentrations. In silico modeling of CYP's binding site revealed that the potential inhibitors bound in the cavity located above the distal surface of the heme prosthetic group through the 2a or 2f channel of CYPs. This study presents an approach for quickly predicting CYP inhibitory activity and shows the potential interactions of compounds and CYPs through in silico modeling.