Computational prediction of cytochrome P450 inhibition and induction

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.

Prescription Patterns of Inducers and Inhibitors of Cytochrome P450 and Their Potential Drug Interactions in the Real World: A Cross-Sectional Study

INTRODUCTION

Both the induction and inhibition of cytochrome P450 are associated with multiple pharmacological interactions, which can lead to loss of efficacy or increase the risk of adverse drug reactions.

OBJECTIVE

The aim was to determine the prescription patterns of cytochrome P450-inducing and -inhibiting drugs and their contraindicated and major pharmacological interactions in a group of patients from Colombia.

METHODS

This cross-sectional observational study included patients who received drugs that induce or inhibit metabolism and examined their contraindicated and major pharmacological interactions. The patients were identified from a population-based database of drug dispensing. Patients were included between December 1 and December 31, 2021. Inhibitors and inducers of cytochrome P450 were classified based on FDA (Food and Drug Administration) guidelines. Drug interactions were identified using the Micromedex® database. Descriptive, bivariate and multivariable analysis was performed.

RESULTS

A total of 63,433 patients were analyzed. Antiseizure medications (35.9%) and antifungals (27.6%) were the most used inducers and inhibitors. A total of 30.1% of patients had potential contraindicated or greater interactions. The following factors were associated with a higher probability of presenting a potential pharmacological interaction: being male (OR 1.14; 95% CI 1.10-1.19), aged 18-39 years (OR 1.77; 95% CI 1.67-1.89) or 40-64 years (OR 1.64; 95% CI 1.56-1.72), having neurological diseases (OR 1.28; 95% CI 1.21-1.35), having psychiatric diseases (OR 3.84; 95% CI 3.58-4.13), having rheumatologic diseases (OR 1.32; 95% CI 1.23-1.41), receiving comedications with statins (OR 1.14; 95% CI 1.08-1.19), receiving comedications with analgesics (OR 1.33; 95% CI 1.27-1.38), receiving comedications with antiparasitics (OR 2.88; 95% CI 2.66-3.11) and an increase in the number of medications (OR 1.24; 95% CI 1.23-1.25).

CONCLUSION

Among the users of cytochrome P450 inhibitors and inducers, potential contraindications and greater interactions are very common, especially in men under 65 years of age with comorbidities and polypharmacy.

Design and Implementation of a Dashboard for Drug Interactions Mediated by Cytochromes Using a Health Care Data Warehouse in a University Hospital Center: Development Study

Background

The enzymatic system of cytochrome P450 (CYP450) is a group of enzymes involved in the metabolism of drugs present in the liver. Literature records instances of underdosing of drugs due to the concurrent administration of another drug that strongly induces the same cytochrome for which the first drug is a substrate and overdosing due to strong inhibition. IT solutions have been proposed to raise awareness among prescribers to mitigate these interactions.

Objective

This study aimed to develop a drug interaction dashboard for Cytochrome-mediated drug interactions (DIDC) using a health care data warehouse to display results that are easily readable and interpretable by clinical experts.

Methods

The initial step involved defining requirements with expert pharmacologists. An existing model of interactions involving the (CYP450) was used. A program for the automatic detection of cytochrome-mediated drug interactions (DI) was developed. Finally, the development and visualization of the DIDC were carried out by an IT engineer. An evaluation of the tool was carried out.

Results

The development of the DIDC was successfully completed. It automatically compiled cytochrome-mediated DIs in a comprehensive table and provided a dedicated dashboard for each potential DI. The most frequent interaction involved paracetamol and carbamazepine with CYP450 3A4 (n=50 patients). The prescription of tacrolimus with CYP3A5 genotyping pertained to 675 patients. Two experts qualitatively evaluated the tool, resulting in overall satisfaction scores of 6 and 5 out of 7, respectively.

Conclusions

At our hospital, measurements of molecules that could have altered concentrations due to cytochrome-mediated DIs are not systematic. These DIs can lead to serious clinical consequences. The purpose of this DIDC is to provide an overall view and raise awareness among prescribers about the importance of measuring concentrations of specific drugs and metabolites. Ultimately, the tool could lead to an individualized approach and become a prescription support tool if integrated into prescription assistance software.

Cinnamaldehyde as a Potential Cathepsin-B Inhibitor: A Comparative Investigation with some Commercial Anticancer Drugs

Cancer is a leading cause of death worldwide, surpassed only by heart disease. Despite improved diagnosis and treatment, cancer cells still evade normal physiological processes such as apoptosis, metabolism, angiogenesis, cell cycle, and epigenetics. To mitigate the numerous side effects linked to chemotherapy, leveraging natural products emerged as a promising alternative, either alone or in tandem with traditional agents. Cinnamaldehyde, an active ingredient of Cinnamomum cassia's stem bark has emerged as a molecule of research with diverse pharmacological properties. In the present study, we report an in silico potential of cinnamaldehyde (CM) potential as an anticancer agent across thirteen anti-cancer targets in comparison with chlorambucil (CB), docetaxel (DOC), melphalan (MP). Computational tools such as DFT, CHEM3D, molinspiration, vNNADMET, SWISS ADME, admetSAR, galaxyrefine, iGEMDOCK, and DS-Visualizer were employed. Additionally, anti-cathepsin B activity was assessed for cinnamaldehyde and the commercial drugs CB, DOC, MP and the results showed 52.76, 62.41, 72.48 and 65.52 % inhibition respectively which is comparable. The results supported molecular docking using iGEMDOCK. Both in silico and experimental findings substantiate cinnamaldehyde as a promising drug for cancer treatment including metastasis and invasion where cathepsin B involvement is indicated.

GTransCYPs: an improved graph transformer neural network with attention pooling for reliably predicting CYP450 inhibitors

State‑of‑the‑art medical studies proved that predicting CYP450 enzyme inhibitors is beneficial in the early stage of drug discovery. However, accurate machine learning-based (ML) in silico methods for predicting CYP450 inhibitors remains challenging. Here, we introduce GTransCYPs, an improved graph neural network (GNN) with a transformer mechanism for predicting CYP450 inhibitors. This model significantly enhances the discrimination between inhibitors and non-inhibitors for five major CYP450 isozymes: 1A2, 2C9, 2C19, 2D6, and 3A4. GTransCYPs learns information patterns from molecular graphs by aggregating node and edge representations using a transformer. The GTransCYPs model utilizes transformer convolution layers to process features, followed by a global attention-pooling technique to synthesize the graph-level information. This information is then fed through successive linear layers for final output generation. Experimental results demonstrate that the GTransCYPs model achieved high performance, outperforming other state-of-the-art methods in CYP450 prediction.Scientific contributionThe prediction of CYP450 inhibition via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we presented a deep learning (DL) architecture based on GNN with transformer mechanism and attention pooling (GTransCYPs) to predict CYP450 inhibitors. Four GTransCYPs of different pooling technique were tested on an experimental tasks on the CYP450 prediction problem for the first time. Graph transformer with attention pooling algorithm achieved the best performances. Comparative and ablation experiments provide evidence of the efficacy of our proposed method in predicting CYP450 inhibitors. The source code is publicly available at https://github.com/zonwoo/GTransCYPs .

GCLmf: A Novel Molecular Graph Contrastive Learning Framework Based on Hard Negatives and Application in Toxicity Prediction

In silico methods for prediction of chemical toxicity can decrease the cost and increase the efficiency in the early stage of drug discovery. However, due to low accessibility of sufficient and reliable toxicity data, constructing robust and accurate prediction models is challenging. Contrastive learning, a type of self-supervised learning, leverages large unlabeled data to obtain more expressive molecular representations, which can boost the prediction performance on downstream tasks. While molecular graph contrastive learning has gathered growing attentions, current models neglect the quality of negative data set. Here, we proposed a self-supervised pretraining deep learning framework named GCLmf. We first utilized molecular fragments that meet specific conditions as hard negative samples to boost the quality of the negative set and thus increase the difficulty of the proxy tasks during pre-training to learn informative representations. GCLmf has shown excellent predictive power on various molecular property benchmarks and demonstrates high performance in 33 toxicity tasks in comparison with multiple baselines. In addition, we further investigated the necessity of introducing hard negatives in model building and the impact of the proportion of hard negatives on the model.

Probe substrates assay estimates the effect of polyphyllin H on the activity of cytochrome P450 enzymes in human liver microsomes

Cytochrome P450 enzymes (CYPs) play a crucial role in phase I metabolic reactions. The activity of CYPs would affect therapeutic efficacy and may even induce toxicity. Given the complex components of traditional Chinese medicine, it is important to understand the effect of active ingredients on CYPs activity to guide their prescription. This study aimed to evaluate the effect of polyphyllin H on the activity of CYPs major isoforms providing a reference for the clinical prescription of polyphyllin H and its source herbs. The effects of polyphyllin H were evaluated in pooled human liver microsomes using probe substrates of CYP1A2, 2A6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4 to determine their activities. The Lineweaver-Burk was used to model the inhibition, and a time-dependent inhibition experiment was performed to understand the characteristics of the inhibition. Polyphyllin H significantly suppressed the activity of CYP1A2, 2D6, and 3A4 with IC50 values of 6.44, 13.88, and 4.52 μM, respectively. The inhibition of CYP1A2 and 2D6 was best fitted with a competitive model, yielding the inhibition constant (Ki) values of 3.18 and 6.77 μM, respectively. The inhibition of CYP3A4 was fitted with the non-competitive model with the Ki value of 2.38 μM. Moreover, the inhibition of CYP3A4 was revealed to be time-dependent with the inhibition parameters inhibition constant (KI) and inactivation rate constant (Kinact) values of 2.26 μM-1 and 0.045 min-1. Polyphyllin H acted as a competitive inhibitor of CYP1A2 and 2D6 and a non-competitive and time-dependent inhibitor of CYP3A4.

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.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Tinospora cordifolia Extract Enhances Dextromethorphan Bioavailability: Implications for Alzheimer's Disease

Tinospora cordifolia (Willd.) Miers (Menispermaceae) is a traditional rejuvenator and a conventional medicine used to manage oxidative stress-related diseases, including those associated with the central nervous system. Decreased dextromethorphan (DEM) metabolism is necessary for high bioavailability and application against Alzheimer's disease (AD). Since T. cordifolia stem extract (TCE) can potentially inhibit several metabolic enzymes, it can also enhance dextromethorphan bioavailability. This study investigates the potential of TCE to improve DEM's bioavailability and efficacy for the management of AD. In silico analysis was carried out to find the inhibition potential of phytocomponents of T. cordifolia for CYP2D6 and CYP3A4. The LC-MS method was revalidated for the analysis of DEM and metabolite dextrorphan (DEX) in the presence of quinidine (QN). The ratio of DEM to DEX was estimated with varying doses of TCE following pharmacokinetic analysis. Network pharmacology analysis was carried out to understand the complementary potential of phytocomponents. This was further validated in the scopolamine-induced dementia model through behavioral and histopathological analyses. TCE (100 mg/kg) for 14 days increased the DEM to DEX ratio by 2.8-fold compared to QN treatment. While T max was comparable to that of QN treatment at this dose (100 mg/kg) of TCE, it increased significantly at the higher dose (400 mg/kg) of TCE pretreatment. All other pharmacokinetic parameters were also enhanced at this dose with a 4.7-fold increase in DEM/DEX compared with QN. Network pharmacology analysis indicated the ability of TCE to target multiple factors associated with AD. Furthermore, it improved spatial memory and reduced hyperactivity in rodents better than the combination of QN and DEM.

ADMET-PrInt: Evaluation of ADMET Properties: Prediction and Interpretation

Great progress in the development of computational strategies for drug design applications has revolutionized the process of searching for new drugs. Although the focus of in silico strategies is still put on the provision of the desired activity of a compound to the considered target, characterization of a compound in terms of its physicochemical and ADMET properties becomes an indispensable element of computer-aided drug design protocols. In the study, an online application ADMET-PrInt for in silico assessment of selected compound features: cardiotoxicity, solubility, genotoxicity, membrane permeability, and plasma protein binding was prepared. In addition to the prediction of particular property, ADMET-PrInt enables also the identification of compound features influencing this property thanks to the application of two explainability approaches: local interpretabile model-agnostic explanations and counterfactual analysis. It is an important factor for medicinal chemists, as it greatly facilitates the process of optimization of the compound structure in terms of the evaluated properties. The intuitive webpage, available at admet.if-pan.krakow.pl, allows making use of all predictive and interpretability models also by nonexperts and nonprogrammers.

Inhibitory effects of the main metabolites of Apatinib on CYP450 isozymes in human and rat liver microsomes

PURPOSE

The inhibitory effect of Apatinib on cytochrome P450 (CYP450) enzymes has been studied. However, it is unknown whether the inhibition is related to the major metabolites, M1-1, M1-2 and M1-6.

METHODS

A 5-in-1 cocktail system composed of CYP2B6/Cyp2b1, CYP2C9/Cyp2c11, CYP2E1/Cyp2e1, CYP2D6/Cyp2d1 and CYP3A/Cyp3a2 was used in this study. Firstly, the effects of APA and its main metabolites on the activities of HLMs, RLMs and recombinant isoforms were examined. The reaction mixture included HLMs, RLMs or recombinant isoforms (CYP3A4.1, CYP2D6.1, CYP2D6.10 or CYP2C9.1), analyte (APA, M1-1, M1-2 or M1-6), probe substrates. The reactions were pre-incubated for 5 min at 37 °C, followed by the addition of NAPDH to initiate the reactions, which continued for 40 min. Secondly, IC50 experiments were conducted to determine if the inhibitions were reversible. The reaction mixture of the "+ NADPH Group" included HLMs or RLMs, 0 to 100 of μM M1-1 or M1-2, probe substrates. The reactions were pre-incubated for 5 min at 37 °C, and then NAPDH was added to initiate reactions, which proceeded for 40 min. The reaction mixture of the "- NADPH Group" included HLMs or RLMs, probe substrates, NAPDH. The reactions were pre-incubated for 30 min at 37 °C, and then 0 to 100 μM of M1-1 or M1-2 was added to initiate the reactions, which proceeded for 40 min. Finally, the reversible inhibition of M1-1 and M1-2 on isozymes was determined. The reaction mixture included HLMs or RLMs, 0 to 10 μM of M1-1 or M1-2, probe substrates with concentrations ranging from 0.25Km to 2Km.

RESULTS

Under the influence of M1-6, the activity of CYP2B6, 2C9, 2E1 and 3A4/5 was increased to 193.92%, 210.82%, 235.67% and 380.12% respectively; the activity of CYP2D6 was reduced to 92.61%. The inhibitory effects of M1-1 on CYP3A4/5 in HLMs and on Cyp2d1 in RLMs, as well as the effect of M1-2 on CYP3A in HLMs, were determined to be noncompetitive inhibition, with the Ki values equal to 1.340 μM, 1.151 μM and 1.829 μM, respectively. The inhibitory effect of M1-1 on CYP2B6 and CYP2D6 in HLMs, as well as the effect of M1-2 on CYP2C9 and CYP2D6 in HLMs, were determined to be competitive inhibition, with the Ki values equal to 12.280 μM, 2.046 μM, 0.560 μM and 4.377 μM, respectively. The inhibitory effects of M1-1 on CYP2C9 in HLMs and M1-2 on Cyp2d1 in RLMs were determined to be mixed-type, with the Ki values equal to 0.998 μM and 0.884 μM. The parameters could not be obtained due to the atypical kinetics of CYP2E1 in HLMs under the impact of M1-2.

CONCLUSIONS

M1-1 and M1-2 exhibited inhibition for several CYP450 isozymes, especially CYP2B6, 2C9, 2D6 and 3A4/5. This observation may uncover potential drug-drug interactions and provide valuable insights for the clinical application of APA.

Chelerythrine Chloride is an Affinity-Labeling Inactivator of CYP3A4 by Modification of Cysteine239

Chelerythrine chloride (CHE) is a quaternary benzo[c]phenanthridine alkaloid with an iminium group that was found to cause time- and concentration-dependent inhibition of CYP3A4. The loss of CYP3A4 activity was independent of NADPH. CYP3A4 competitive inhibitor ketoconazole and nucleophile N-acetylcysteine (NAC) slowed the inactivation. No recovery of CYP3A4 activity was observed after dialysis. Dihydrochelerythrine hardly inhibited CYP3A4, suggesting that the iminium group was primarily responsible for the inactivation. UV spectral analysis revealed that the maximal absorbance of CHE produced a significant red-shift after being mixed with NAC, suggesting that 1,2-addition possibly took place between the sulfhydryl group of NAC and iminium group of CHE. Molecular dynamics simulation and site-direct mutagenesis studies demonstrated that modification of Cys239 by the iminium group of CHE attributed to the inactivation. In conclusion, CHE is an affinity-labeling inactivator of CYP3A4. The observed enzyme inactivation resulted from the modification of Cys239 of CYP3A4 by the iminium group of CHE.

Predicting the unpredictable: the regulatory nature and promiscuity of herbicide cross resistance

The emergence of herbicide-resistant weeds is a significant threat to modern agriculture. Cross resistance, a phenomenon where resistance to one herbicide confers resistance to another, is a particular concern owing to its unpredictability. Nontarget-site (NTS) cross resistance is especially challenging to predict, as it arises from genes that encode enzymes that do not directly involve the herbicide target site and can affect multiple herbicides. Recent advancements in genomic and structural biology techniques could provide new venues for predicting NTS resistance in weed species. In this review, we present an overview of the latest approaches that could be used. We discuss the use of genomic and epigenomics techniques such as ATAC-seq and DAP-seq to identify transcription factors and cis-regulatory elements associated with resistance traits. Enzyme/protein structure prediction and docking analysis are discussed as an initial step for predicting herbicide binding affinities with key enzymes to identify candidates for subsequent in vitro validation. We also provide example analyses that can be deployed toward elucidating cross resistance and its regulatory patterns. Ultimately, our review provides important insights into the latest scientific advancements and potential directions for predicting and managing herbicide cross resistance in weeds. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Water Exchange from the Buried Binding Sites of Cytochrome P450 Enzymes 1A2, 2D6, and 3A4 Correlates with Conformational Fluctuations

Human cytochrome P450 enzymes (CYPs) are critical for the metabolism of small-molecule pharmaceuticals (drugs). As such, the prediction of drug metabolism by and drug inhibition of CYP activity is an important component of the drug discovery and design process. Relative to the availability of a wide range of experimental atomic-resolution CYP structures, the development of structure-based CYP activity models has been limited. To better characterize the role of CYP conformational fluctuations in CYP activity, we perform multiple microsecond-scale all-atom explicit-solvent molecular dynamics (MD) simulations on three CYP isoforms, 1A2, 2D6, and 3A4, which together account for the majority of CYP-mediated drug metabolism. The MD simulations employ a variety of positional restraints, ranging from keeping all CYP atoms close to their experimentally determined coordinates to allowing full flexibility. We find that, with full flexibility, large fluctuations in the CYP binding sites correlate with efficient water exchange from these buried binding sites. This is especially true for 1A2, which, when restrained to its crystallographic conformation, is unable to exchange water between the binding site and bulk solvent. These findings imply that, in addition to crystal structures, a representative ensemble of conformational states ought to be included when developing structure-based CYP activity models.

A Review of CYP-Mediated Drug Interactions: Mechanisms and In Vitro Drug-Drug Interaction Assessment

Drug metabolism is a major determinant of drug concentrations in the body. Drug-drug interactions (DDIs) caused by the co-administration of multiple drugs can lead to alteration in the exposure of the victim drug, raising safety or effectiveness concerns. Assessment of the DDI potential starts with in vitro experiments to determine kinetic parameters and identify risks associated with the use of comedication that can inform future clinical studies. The diverse range of experimental models and techniques has significantly contributed to the examination of potential DDIs. Cytochrome P450 (CYP) enzymes are responsible for the biotransformation of many drugs on the market, making them frequently implicated in drug metabolism and DDIs. Consequently, there has been a growing focus on the assessment of DDI risk for CYPs. This review article provides mechanistic insights underlying CYP inhibition/induction and an overview of the in vitro assessment of CYP-mediated DDIs.

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.

Screening of phytochemicals from Clerodendrum inerme (L.) Gaertn as potential anti-breast cancer compounds targeting EGFR: an in-silico approach

Breast cancer (BC) is the most prevalent malignancy among women around the world. The epidermal growth factor receptor (EGFR) is a tyrosine kinase receptor (RTK) of the ErbB/HER family. It is essential for triggering the cellular signaling cascades that control cell growth and survival. However, perturbations in EGFR signaling lead to cancer development and progression. Hence, EGFR is regarded as a prominent therapeutic target for breast cancer. Therefore, in the current investigation, EGFR was targeted with phytochemicals from Clerodendrum inerme (L.) Gaertn (C. inerme). A total of 121 phytochemicals identified by gas chromatography-mass spectrometry (GC-MS) analysis were screened against EGFR through molecular docking, ADMET analyses (Absorption, Distribution, Metabolism, Excretion, and Toxicity), PASS predictions, and molecular dynamics simulation, which revealed three potential hit compounds with CIDs 10586 [i.e. alpha-bisabolol (-6.4 kcal/mol)], 550281 [i.e. 2,(4,4-Trimethyl-3-hydroxymethyl-5a-(3-methyl-but-2-enyl)-cyclohexene) (-6.5 kcal/mol)], and 161271 [i.e. salvigenin (-7.4 kcal/mol)]. The FDA-approved drug gefitinib was used to compare the inhibitory effects of the phytochemicals. The top selected compounds exhibited good ADMET properties and obeyed Lipinski's rule of five (ROF). The molecular docking analysis showed that salvigenin was the best among the three compounds and formed bonds with the key residue Met 793. Furthermore, the molecular mechanics generalized born surface area (MMGBSA) calculations, molecular dynamics simulation, and normal mode analysis validated the binding affinity of the compounds and also revealed the strong stability and compactness of phytochemicals at the docked site. Additionally, DFT and DOS analyses were done to study the reactivity of the compounds and to further validate the selected phytochemicals. These results suggest that the identified phytochemicals possess high inhibitory potential against the target EGFR and can treat breast cancer. However, further in vitro and in vivo investigations are warranted towards the development of these constituents into novel anti-cancer drugs.Communicated by Ramaswamy H. Sarma.

Assessments of CYP‑inhibition‑based drug-drug interaction between vonoprazan and poziotinib in vitro and in vivo

CONTEXT

Poziotinib and vonoprazan are two drugs mainly metabolized by CYP3A4. However, the drug-drug interaction between them is unknown.

OBJECTIVE

To study the interaction mechanism and pharmacokinetics of poziotinib on vonoprazan.

MATERIALS AND METHODS

In vitro experiments were performed with rat liver microsomes (RLMs) and the contents of vonoprazan and its metabolite were then determined with UPLC-MS/MS after incubation of RLMs with vonoprazan and gradient concentrations of poziotinib. For the in vivo experiment, rats in the poziotinib treated group were given 5 mg/kg poziotinib by gavage once daily for 7 days, and the control group was only given 0.5% CMC-Na. On Day 8, tail venous blood was collected at different time points after the gavage administration of 10 mg/kg vonoprazan, and used for the quantification of vonoprazan and its metabolite. DAS and SPSS software were used for the pharmacokinetic and statistical analyses.

RESULTS

In vitro experimental data indicated that poziotinib inhibited the metabolism of vonoprazan (IC₅₀ = 10.6 μM) in a mixed model of noncompetitive and uncompetitive inhibition. The inhibitory constant K_i was 0.574 μM and the binding constant αK_i was 2.77 μM. In vivo experiments revealed that the AUC_(0-T) (15.05 vs. 90.95 μg/mL·h) and AUC_(0-∞) (15.05 vs. 91.99 μg/mL·h) of vonoprazan increased significantly with poziotinib pretreatment. The MRT_(0-∞) of vonoprazan increased from 2.29 to 5.51 h, while the CLz/F value decreased from 162.67 to 25.84 L/kg·h after pretreatment with poziotinib.

CONCLUSIONS

Poziotinib could significantly inhibit the metabolism of vonoprazan and more care may be taken when co-administered in the clinic.

Computational prediction of the metabolites of agrochemicals formed in rats

Today, computational tools for the prediction of the metabolite structures of xenobiotics are widely available and employed in small-molecule research. Reflecting the availability of measured data, these in silico tools are trained and validated primarily on drug metabolism data. In this work, we assessed the capacity of five leading metabolite structure predictors to represent the metabolism of agrochemicals observed in rats. More specifically, we tested the ability of SyGMa, GLORY, GLORYx, BioTransformer 3.0, and MetaTrans to correctly predict and rank the experimentally observed metabolites of a set of 85 parent compounds. We found that the models were able to recover about one to two-thirds of the experimentally observed first-generation, second-generation and third-generation metabolites, confirming their value in applications such as metabolite identification. However, precision was low for all investigated tools and did not exceed approximately 18 % for the pool of first-generation metabolites and 2 % for the pool of compounds representing the first three generations of metabolites. The variance in prediction success rates was high across the individual metabolic maps, meaning that outcomes depend strongly on the specific compound under investigation. We also found that the predictions for individual parent compounds differed strongly between the tools, particularly between those built on orthogonal technologies (e.g., rule-based and end-to-end machine learning approaches). This renders ensemble model strategies promising for improving success rates. Overall, the results of this benchmark study show that there is still considerable room for the improvement of metabolite structure predictors left. Our discussion points out several avenues to progress. The bottleneck in method development certainly has been, and will remain, for the foreseeable future, the limited quantity and quality of available measured data on small-molecule metabolism.

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.

Artificial intelligence-driven prediction of multiple drug interactions

When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug-drug, drug-food (excipients) and drug-microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug-drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions.

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

High-altitude Hypoxia Influences the Activities of the Drug-Metabolizing Enzyme CYP3A1 and the Pharmacokinetics of Four Cardiovascular System Drugs

(1) Background: High-altitude hypoxia has been shown to affect the pharmacokinetic properties of drugs. Although there is a high incidence of cardiovascular disease among individuals living in high-altitude areas, studies on the effect of high-altitude hypoxia on the pharmacokinetic properties of cardiovascular drugs are limited. (2) Methods: The aim of this study was to evaluate the pharmacokinetics of nifedipine, bosentan, simvastatin, sildenafil, and their respective main metabolites, dehydronifedipine, hydroxybosentan, simvastatin hydroxy acid, and N-desmethyl sildenafil, in rats exposed to high-altitude hypoxia. Additionally, the protein and mRNA expression of cytochrome P450 3A1 (CYP3A1), a drug-metabolizing enzyme, were examined. (3) Results: There were significant changes in the pharmacokinetic properties of the drugs in rats exposed to high-altitude hypoxia, as evidenced by an increase in the area under the curve (AUC) and the half-life (t_1/2z) and a decrease in total plasma clearance (CL_z/F). However, most of these changes were reversed when the rats returned to a normoxic environment. Additionally, there was a significant decrease in CYP3A1 expression in rats exposed to high-altitude hypoxia at both the protein and mRNA levels. (4) Conclusions: High-altitude hypoxia suppressed the metabolism of the drugs, indicating that the pharmacokinetics of the drugs should be re-examined, and the optimal dose should be reassessed in patients living in high-altitude areas.

Assessment of the CYP1A2 Inhibition-Mediated Drug Interaction Potential for Pinocembrin Using In Silico, In Vitro, and In Vivo Approaches

Pinocembrin, a bioflavonoid, is extensively used in complementary/alternative medicine. It turns out as a promising candidate against neurodegenerative diseases because of its multifaceted pharmacological action toward neuroprotection. However, literature evidence is still lacking for its inhibitory action on CYP1A2, which is responsible for xenobiotic metabolism leading to the generation of toxic metabolites and bioactivation of procarcinogens. In the present study, our aim was to evaluate the CYP1A2 inhibitory potential of pinocembrin via in silico, in vitro, and in vivo investigations. From the results of in vitro studies, pinocembrin is found to be a potent and competitive inhibitor of CYP1A2. In vitro-in vivo extrapolation results indicate the potential of pinocembrin to interact with CYP1A2 substrate drugs clinically. Molecular docking-based in silico studies demonstrate the strong interaction of pinocembrin with human CYP1A2. In in vivo investigations using a rat model, pinocembrin displayed a marked alteration in the plasma exposure of CYP1A2 substrate drugs, namely, caffeine and tacrine. In conclusion, pinocembrin has a potent CYP1A2 inhibitory action to cause drug interactions, and further confirmatory study is warranted at the clinical level.

Meta-Hybrid Density Functional Theory Prediction of the Reactivity, Stability, and IGM of Azepane, Oxepane, Thiepane, and Halogenated Cycloheptane

The application of plain cycloalkanes and heterocyclic derivatives in the synthesis of valuable natural products and pharmacologically active intermediates has increased tremendously in recent times with much attention being paid to the lower cycloalkane members. The structural and molecular properties of higher seven-membered and nonaromatic heterocyclic derivatives are less known despite their stable nature and vast application; thus, an insight into their structural and electronic properties is still needed. Appropriate quantum chemical calculations utilizing the ab initio (MP2) method, meta-hybrid (M06-2X) functional, and long-range-separated functionals (ωB97XD) have been utilized in this work to investigate the structural reactivity, stability, and behavior of substituents on cycloheptane (CHP) and its derivatives: azepane, oxepane, thiepane, fluorocycloheptane (FCHP), bromocycloheptane (BrCHP), and chlorocycloheptane (ClCHP). Molecular global reactivity descriptors such as Fukui function, frontier molecular orbitals (FMOs), and molecular electrostatic potential were computed and compared with lower members. The results of two population methods CHELPG and Atomic Dipole Corrected Hirshfeld Charges (ADCH) were equally compared to scrutinize the charge distribution in the molecules. The susceptibility of intramolecular interactions between the substituents and cycloalkane ring is revealed by natural bond orbital analysis and intramolecular weak interactions by the independent gradient model (IGM). Other properties such as atomic density of states, intrinsic bond strength index (IBSI), and dipole moments are considered. It is acclaimed that the strain effect is a major determinant effect in the energy balance of cyclic molecules; thus, the ring strain energies and validation of spectroscopic specificities with reference to the X-ray crystallographic data are also considered.

ZY12201, A Potent TGR5 Agonist: Identification of a Novel Pan CYP450 Inhibitor Tool Compound for In-Vitro Assessment

BACKGROUND

Identification of clinical drug-drug interaction (DDI) risk is an important aspect of drug discovery and development owing to poly-pharmacy in present-day clinical therapy. Drug metabolizing enzymes (DME) plays important role in the efficacy and safety of drug candidates. Hence evaluation of a New Chemical Entity (NCE) as a victim or perpetrator is very crucial for DDI risk mitigation. ZY12201 (2-((2-(4-(1H-imidazol-1-yl) phenoxy) ethyl) thio)-5-(2-(3, 4- dimethoxy phenyl) propane-2-yl)-1-(4-fluorophenyl)-1H-imidazole) is a novel and potent Takeda-G-protein-receptor-5 (TGR-5) agonist. ZY12201 was evaluated in-vitro to investigate the DDI liabilities.

OBJECTIVE

The key objective was to evaluate the CYP inhibition potential of ZY12201 for an opportunity to use it as a tool compound for pan CYP inhibition activities.

METHOD

In-vitro drug metabolizing enzymes (DME) inhibition potential of ZY12201 was evaluated against major CYP isoforms (1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4/5), aldehyde oxidase (AO), monoamine oxidase (MAO), and flavin-containing monooxygenase (FMO in human liver cytosol/mitochondrial preparation/ microsomes using probe substrates and Liquid Chromatography with tandem mass spectrometry (LC-MS-MS) method.

RESULTS

The study conducted on ZY12201 at 100 µM ZY12201 was found to reduce the metabolism of vanillin (AO probe substrate), tryptamine (MAO probe substrate), and benzydamine (FMO probe substrate) by 49.2%, 14.7%, and 34.9%, respectively. ZY12201 Ki values were 0.38, 0.25, 0.07, 0.01, 0.06, 0.02, 7.13, 0.03 and 0.003 μM for CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4/5 (substrate: testosterone) and CYP3A4/5 (substrate: midazolam), respectively. Time-dependant CYP inhibition potential of ZY12201 was assessed against CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4/5 and no apparent IC50 shift was observed.

CONCLUSIONS

ZY12201, at 100 µM concentration showed low inhibition potential of AO, MAO, and FMO. ZY12201 was found as a potent inhibitor of CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, and 3A4/5 while moderately inhibits to CYP2E1. Inhibition of CYP1A2, CYP2B6, CYP2C19, and CYP2E1 by ZY12201 was competitive, while inhibition of CYP2C8, CYP2C9, CYP2D6, and CYP3A4/5 was of mixed-mode. ZY12201 is a non-time-dependent inhibitor of CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4/5. In summary, the reported Ki values unequivocally support that ZY12201 has a high potential to inhibit all major CYP isoforms. ZY12201 can be effectively used as a tool compound for in-vitro evaluation of CYP-based metabolic contribution to total drug clearance in the lead optimization stage of Drug Discovery Research.

Effects of CYP2D6 Genetic Polymorphism and Drug Interaction on the Metabolism of Dacomitinib

We aim to study the effects of CYP2D6 variants and drug-drug interaction on the metabolism of dacomitinib. CYP2D6 variants were incubated with 25-1000 μM dacomitinib for 40 min at 37 °C, and the reaction was terminated by cooling to -80 °C immediately. For an in vivo experiment, 18 male Sprague-Dawley rats were randomly divided into three groups (n = 6): a single dose of 5 mg/kg dacomitinib (group A), a single dose of 6 mg/kg trazodone (group B), and a combined group (group C). Processed samples were analyzed by ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS.) The relative clearance of dacomitinib was reduced for most of the variants. Moreover, the inhibitory potency of classic CYP inhibitors on dacomitinib metabolism was significantly different among the main subtypes of CYP2D6. Interestingly, compared with gefitinib, even the same CYP2D6 variants showed significant differences in metabolic activity, suggesting that the activity of CYP2D6 has strong variability. In addition, the interaction between trazodone and dacomitinib was determined both in vitro and in vivo. When dacomitinib was given in combination with trazodone, the blood exposure to these two drugs increased remarkably. The mechanistic study revealed that the interaction followed the noncompetitive inhibition. We demonstrated that the activity of CYP2D6 variants to metabolize dacomitinib was significantly reduced. In combination with the CYP2D6 inhibitor, the degree of activity inhibition of different variants obviously differed. When trazodone and dacomitinib were used in combination, the body exposure to the two drugs increased significantly. This study provides data for the precise use of dacomitinib in clinical settings.

Influence of Inflammation on Cytochromes P450 Activity in Adults: A Systematic Review of the Literature

Background: Available in-vitro and animal studies indicate that inflammation impacts cytochromes P450 (CYP) activity via multiple and complex transcriptional and post-transcriptional mechanisms, depending on the specific CYP isoforms and the nature of inflammation mediators. It is essential to review the current published data on the impact of inflammation on CYP activities in adults to support drug individualization based on comorbidities and diseases in clinical practice. Methods: This systematic review was conducted in PubMed through 7th January 2021 looking for articles that investigated the consequences of inflammation on CYP activities in adults. Information on the source of inflammation, victim drugs (and CYPs involved), effect of disease-drug interaction, number of subjects, and study design were extracted. Results: The search strategy identified 218 studies and case reports that met our inclusion criteria. These articles were divided into fourteen different sources of inflammation (such as infection, autoimmune diseases, cancer, therapies with immunomodulator…). The impact of inflammation on CYP activities appeared to be isoform-specific and dependent on the nature and severity of the underlying disease causing the inflammation. Some of these drug-disease interactions had a significant influence on drug pharmacokinetic parameters and on clinical management. For example, clozapine levels doubled with signs of toxicity during infections and the concentration ratio between clopidogrel's active metabolite and clopidogrel is 48-fold lower in critically ill patients. Infection and CYP3A were the most cited perpetrator of inflammation and the most studied CYP, respectively. Moreover, some data suggest that resolution of inflammation results in a return to baseline CYP activities. Conclusion: Convincing evidence shows that inflammation is a major factor to be taken into account in drug development and in clinical practice to avoid any efficacy or safety issues because inflammation modulates CYP activities and thus drug pharmacokinetics. The impact is different depending on the CYP isoform and the inflammatory disease considered. Moreover, resolution of inflammation appears to result in a normalization of CYP activity. However, some results are still equivocal and further investigations are thus needed.

Predicting drug-drug interactions by electrochemically driven cytochrome P450 3A4 reactions

OBJECTIVES

Human cytochrome P450 3A4 is the most abundant hepatic and intestinal Phase I enzyme that metabolizes approximately 60% marketed drugs. Simultaneous administration of several drugs may result in appearance of drug-drug interaction. Due to the great interest in the combination therapy, the exploration of the role of drug as "perpetrator" or "victim" is important task in pharmacology. In this work the model systems based on electrochemically driven cytochrome P450 3A4 for the analysis of drug combinations was used. We have shown that the analysis of electrochemical parameters of cytochrome P450 3A4 and especially, potential of the start of catalysis, Eonset, possess predictive properties in the determination of the leading ("perpetrator") properties of drug. Based on these experimental data, we concluded, that the more positive potential of the start of catalysis, Eonset, the more pronounced the role of drug as leading medication.

METHODS

Electrochemically driven cytochrome P450 3A4 was used as probe and measuring tool for the estimation of the role of interacting drugs.

RESULTS

It is shown that the electrochemical non-invasive model systems for monitoring the catalytic activity of cytochrome P450 3A4 can be used as prognostic devise in assessment of drug/drug interacting medications.

CONCLUSIONS

Cytochrome P450 3A4 activity was studied in electrochemically driven system. Method was implemented to monitor drug/drug interactions. Based on the obtained experimental data, we can conclude that electrochemical parameter such as potential of onset of catalysis, Eonset, has predictive efficiency in assessment of drug/drug interacting medications in the case of the co-administration.

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.

Cytochrome P450 enzymes: a review on drug metabolizing enzyme inhibition studies in drug discovery and development

Assessment of drug candidate's potential to inhibit cytochrome P450 (CYP) enzymes remains crucial in pharmaceutical drug discovery and development. Both direct and time-dependent inhibition of drug metabolizing CYP enzymes by the concomitant administered drug is the leading cause of drug-drug interactions (DDIs), resulting in the increased toxicity of the victim drug. In this context, pharmaceutical companies have grown increasingly diligent in limiting CYP inhibition liabilities of drug candidates in the early stages and examining risk assessments throughout the drug development process. This review discusses different strategies and decision-making processes for assessing the drug-drug interaction risks by enzyme inhibition and lays particular emphasis on in vitro study designs and interpretation of CYP inhibition data in a stage-appropriate context.

In vitro study on the effect of leonurine hydrochloride on the enzyme activity of cytochrome P450 enzymes in human liver microsomes

Leonurine hydrochloride (LH) is derived from an ingredient of Leonurus japonicus Houtt which is widely used for diseases in women.The influence of LH on the activity of cytochrome P450 (CYPs) enzymes was investigated in this study.The effect of LH on CYPs enzyme activities were studied using the enzyme-selective substrates phenacetin (1A2), coumarin (2A6), diclofenac (2C9), S-mephenytoin (2C19), paclitaxel (2C8), dextromethorphan (2D6), chlorzoxazone (2E1) and testosterone (3A4). The IC₅₀ value was calculated to express the strength of inhibition. The inhibition of CYPs was fitted with competitive or non-competitive inhibition models and corresponding parameters were also obtained.LH exerted inhibitory effects on the activity of CYP1A2, 2D6, and 3A4 with the IC₅₀ values of 18.05, 15.13, and 20.09 μM, respectively. The obtained results showed that LH inhibited the activity of CYP1A2 and CYP2D6 via competitive manners (K_i = 8.667 μM and K_i = 7.805 μM, respectively), while LH attenuated the CYP3A4 activity via a non-competitive manner (K_i = 9.507 μM). Moreover, LH showed time-dependent inhibition on CYP3A4 with the K_I/K_inact value of 4.31/0.044 min^-1·μM^-1.The inhibition of CYP1A2, CYP2D6, and CYP3A4 by LH, demonstrated in vitro, indicated the potential herb-drug interaction. Therefore, pharmacokinetic interactions involving LH and CYP1A2 or CYP2D6 or CYP1A2 substrates are likely to occur.

CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates

The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacological efficacy and toxicity. Therefore, efficient means of predicting the interactions of small organic molecules with CYPs are of high importance to a host of different industries. In this work, we present a new set of machine learning models for the classification of xenobiotics into substrates and non-substrates of nine human CYP isozymes: CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4. The models are trained on an extended, high-quality collection of known substrates and non-substrates and have been subjected to thorough validation. Our results show that the models yield competitive performance and are favorable for the detection of CYP substrates. In particular, a new consensus model reached high performance, with Matthews correlation coefficients (MCCs) between 0.45 (CYP2C8) and 0.85 (CYP3A4), although at the cost of coverage. The best models presented in this work are accessible free of charge via the "CYPstrate" module of the New E-Resource for Drug Discovery (NERDD).

Machine learning models for classification tasks related to drug safety

In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts.

Semisynthesis, Antiplasmodial Activity, and Mechanism of Action Studies of Isocoumarin Derivatives

In this study, eight natural isocoumarins (1-8) were isolated from a marine-derived Exserohilum sp. fungus. To explore their structure-activity relationship and discover potent antimalarial leads, a small library of 22 new derivatives (1a-1n, 2a, 3a-3c, 4a-4c, and 7a) were semisynthesized by varying the substituents of the aromatic ring and the aliphatic side chains. The natural compound (1) and three semisynthetic derivatives (1d, 1n, and 2a), possessing an all-cis stereochemistry, exhibited strong antiplasmodial activity with IC₅₀ values of 1.1, 0.8, 0.4, and 2.6 μM, respectively. Mechanism studies show that 1n inhibits hemozoin polymerization and decreases the mitochondrial membrane potential but also inhibits P. falciparum DNA gyrase. 1n not only combines different mechanisms of action but also exhibits a high therapeutic index (CC₅₀/IC₅₀ = 675), high selectivity, and a notable drug-like profile.

Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design

The objective of this study was to obtain the indicators of physicochemical parameters and structurally active sites to design new chemical entities with desirable pharmacokinetic profiles by investigating the process by which machine learning prediction models arrive at their decisions, which are called explainable artificial intelligence. First, we developed the prediction models for metabolic stability, CYP inhibition, and P-gp and BCRP substrate recognition using 265 physicochemical parameters for designing the molecular structures. Four important parameters, including the well-known indicator h_logD, are common in some in vitro studies; as such, these can be used to optimize compounds simultaneously to address multiple pharmacokinetic concerns. Next, we developed machine learning models that had been programmed to show structurally active sites. Many types of machine learning models were developed using the results of in vitro metabolic stability study of around 30000 in-house compounds. The metabolic sites of in-house compounds predicted using some prediction models matched experimentally identified metabolically active sites, with a ratio of number of metabolic sites (predicted/actual) of over 90%. These models can be applied to several screening projects. These two approaches can be employed for obtaining lead compounds with desirable pharmacokinetic profiles efficiently.

Predicting drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model

We constructed machine learning-based pharmacokinetic prediction models with very high performance. The models were trained on 26138 and 16613 compounds involved in metabolic stability and cytochrome P450 inhibition, respectively. Because the compound features largely differed between the publicly available and in-house compounds, the models learned on the public data could not predict the in-house compounds, suggesting that outside of a certain applicability domain (AD), the prediction results are unreliable and can mislead the design of novel compounds. To exclude the uncertain prediction results, we constructed another machine learning model that determines whether the newly designed compound is inside or outside the AD. The AD was evaluated multi-dimensionally with some explanatory variables: The structural similarities and the probability obtained from the pharmacokinetic prediction model. The accuracy of predicting metabolic stability was 79.9% on the test set, increasing significantly to 93.6% after excluding the low-reliability compounds. The model properly classified the reliability of the compounds. After learning on the in-house compounds, the reliability model classified almost all (90%) of the public compounds as low reliability, indicating that the AD was properly determined by the model.

Effects of saikosaponin-d on CYP3A4 in HepaRG cell and protein-ligand docking study

Saikosaponin-d (SSd) is a major bioactive triterpenoid saponin extracted from Bupleurum, which has anti-inflammatory, anticancer, antioxidative and anti-hepatic fibrosis effects. Due to the effects of Bupleurum-related formulations on cytochrome P450 (CYPs) expression still remain unclear, the combination therapies involved formulations containing Bupleurum may sometimes lead to unexpected drug-drug interactions in clinical practice. These interactions can limit the clinical applications of related formulations. In this study, we tried to explore the effects of SSd on CYP3A4 mRNA, protein expression and the enzyme activity in HepaRG cells by real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR), Western blot (WB) and HPLC method, respectively. The interaction between SSd and CYP3A4 was analysed by molecular docking. HepaRG cells were cultured with different concentrations of SSd (0.5, 1, 5 and 10 μmol/L) for 72 hours. It is revealed that SSd can inhibit CYP3A4 mRNA and its protein expression, and also the enzyme activity. Molecular docking study demonstrated that SSd can bind to several key active sites of amino acid residues of CYP3A4 protein with hydrogen bonds and hydrophobic interactions. Thus, drug-drug interactions resulted by SSd inhibiting CYP3A4 need attention when formulations containing SSd or Bupleurum are co-administrated with drugs metabolized by CYP3A4.

SuperCYPsPred-a web server for the prediction of cytochrome activity

Cytochrome P450 enzymes (CYPs)-mediated drug metabolism influences drug pharmacokinetics and results in adverse outcomes in patients through drug–drug interactions (DDIs). Absorption, distribution, metabolism, excretion and toxicity (ADMET) issues are the leading causes for the failure of a drug in the clinical trials. As details on their metabolism are known for just half of the approved drugs, a tool for reliable prediction of CYPs specificity is needed. The SuperCYPsPred web server is currently focused on five major CYPs isoenzymes, which includes CYP1A2, CYP2C19, CYP2D6, CYP2C9 and CYP3A4 that are responsible for more than 80% of the metabolism of clinical drugs. The prediction models for classification of the CYPs inhibition are based on well-established machine learning methods. The models were validated both on cross-validation and external validation sets and achieved good performance. The web server takes a 2D chemical structure as input and reports the CYP inhibition profile of the chemical for 10 models using different molecular fingerprints, along with confidence scores, similar compounds, known CYPs information of drugs—published in literature, detailed interaction profile of individual cytochromes including a DDIs table and an overall CYPs prediction radar chart (http://insilico-cyp.charite.de/SuperCYPsPred/). The web server does not require log in or registration and is free to use.

Inhibition and induction of CYP enzymes in humans: an update

The cytochrome P450 (CYP) enzyme family is the most important enzyme system catalyzing the phase 1 metabolism of pharmaceuticals and other xenobiotics such as herbal remedies and toxic compounds in the environment. The inhibition and induction of CYPs are major mechanisms causing pharmacokinetic drug–drug interactions. This review presents a comprehensive update on the inhibitors and inducers of the specific CYP enzymes in humans. The focus is on the more recent human in vitro and in vivo findings since the publication of our previous review on this topic in 2008. In addition to the general presentation of inhibitory drugs and inducers of human CYP enzymes by drugs, herbal remedies, and toxic compounds, an in-depth view on tyrosine-kinase inhibitors and antiretroviral HIV medications as victims and perpetrators of drug–drug interactions is provided as examples of the current trends in the field. Also, a concise overview of the mechanisms of CYP induction is presented to aid the understanding of the induction phenomena.

Large-scale evaluation of cytochrome P450 2C9 mediated drug interaction potential with machine learning-based consensus modeling

Cytochrome P450 (CYP) enzymes play an important role in the metabolism of xenobiotics. Since they are connected to drug interactions, screening for potential inhibitors is of utmost importance in drug discovery settings. Our study provides an extensive classification model for P450-drug interactions with one of the most prominent members, the 2C9 isoenzyme. Our model involved the largest set of 45,000 molecules ever used for developing prediction models. The models are based on three different types of descriptors, (a) typical one, two and three dimensional molecular descriptors, (b) chemical and pharmacophore fingerprints and (c) interaction fingerprints with docking scores. Two machine learning algorithms, the boosted tree and the multilayer feedforward of resilient backpropagation network were used and compared based on their performances. The models were validated both internally and using external validation sets. The results showed that the consensus voting technique with custom probability thresholds could provide promising results even in large-scale cases without any restrictions on the applicability domain. Our best model was capable to predict the 2C9 inhibitory activity with the area under the receiver operating characteristic curve (AUC) of 0.85 and 0.84 for the internal and the external test sets, respectively. The chemical space covered with the largest available dataset has reached its limit encompassing publicly available bioactivity data for the 2C9 isoenzyme.