Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

An ultra-fast ultra-high-performance liquid chromatography-tandem mass spectrometry method for estimating the in vitro metabolic stability of palbociclib in human liver microsomes: In silico study for metabolic lability, absorption, distribution, metabolism, and excretion features, and DEREK alerts screening

Palbociclib (Ibrance; Pfizer) was approved for the management of metastatic breast cancer characterized by hormone receptor-positive/human epidermal growth factor receptor 2 negative status. The objective of this study was to create a fast, precise, environmentally friendly, and highly sensitive ultra-high-performance liquid chromatography-tandem mass spectrometry approach for quantifying palbociclib (PAB) in human liver microsomes with the application for assessing metabolic stability. The validation features were performed in agreement with the bioanalytical method validation standards outlined by the US Food and Drug Administration. The StarDrop software (WhichP450 and DEREK modules) was used in screening the metabolic lability and structural alerts of PAB. The separation of PAB and encorafenib (as an internal standard) was achieved on a C8 column, employing an isocratic mobile phase. The inter-day and intra-day accuracy and precision ranged from -6.00% to 4.64% and from -2.33% to 3.13%, respectively. The constructed calibration curve displayed a linearity in the range of 1-3000 ng/mL. The sensitivity of the established approach was proven by the lower limit of quantification of 0.73 ng/mL. The Analytical GREEness calculator results revealed the high level of greenness of the developed method. The PAB's metabolic stability (t1/2 of 18.5 min and a moderate clearance (Clint) of 44.8 mL/min/kg) suggests a high extraction ratio medication that matched the WhichP450 software results.

Characterization of the in vitro metabolic profile of nazartinib in HLMs using UPLC-MS/MS method: In silico metabolic lability and DEREK structural alerts screening using StarDrop software

The orally given, irreversible, third-generation inhibitor of the epidermal growth factor receptor (EGFR), known as Nazartinib (EGF816), is now undergoing investigation in Phase II clinical trials conducted by Novartis for Non-Small Cell Lung Cancer. The primary aim of the current research was to establish a rapid, specific, environmentally friendly, and highly versatile UPLC-MS/MS methodology for the determination of nazartinib (NZT) levels in human liver microsomes (HLMs). Subsequently, same approach was used to examine the metabolic stability of NZT. The UPLC-MS/MS method employed in HLMs was validated as stated in the bioanalytical method validation criteria outlined by the US- FDA. The evaluation of the metabolic stability of NZT and the identification of potentially structural alarms were performed using the StarDrop software package that includes the P450 and DEREK software. The calibration curve for NZT showed a linearity in the range from 1 to 3000 ng/mL. The inter-day accuracy and precision exhibited a range of values between -4.33 % and 4.43 %, whereas the intra-day accuracy and precision shown a range of values between -2.78 % and 7.10 %. The sensitivity of the developed approach was verified through the determination of a LLOQ of 0.39 ng/mL. The intrinsic clearance and in vitro half-life of NZT were assessed to be 46.48 mL/min/kg and 17.44 min, respectively. In our preceding inquiry, we have effectively discerned the bioactivation center, denoted by the carbon atom between the unsaturated conjugated system and aliphatic linear tertiary amine. In the context of computational software, making minor adjustments or substituting the dimethylamino-butenoyl moiety throughout the drug design process may increase the metabolic stability and safety properties of new synthesized derivatives. The efficiency of utilizing different in silico software approaches to conserve resources and reduce effort was proved by the outcomes attained from in vitro incubation experiments and the use of NZT in silico software.

In Silico Investigation against Inhibitors of Alpha-Amylase Using Structure-based Screening, Molecular Docking, and Molecular Simulations Studies

Type-II diabetes mellitus is a chronic disorder that results from fluctuations in the glucose level leading to hyperglycemia with severe adverse effects increasing worldwide. Alpha-Amylase is the key enzyme involved in the mechanism of glucose formation therefore Alpha-Amylase inhibitors have become a therapeutic target in the development of new leads as they have the potential to suppress glucose levels. Existing drugs targeting Alpha-Amylase highlight major drawbacks in terms of poor absorption rate that causes several gastrointestinal issues. So, this research is aimed to develop novel inhibitors interacting with Alpha-Amylase's active site using structural-based screening, binding pattern analysis, and molecular dynamic simulation. Hence, to search for a potential lead, we analyzed a total of 133 valiolamine derivatives and 535 desoxynojirimycin derivatives that exhibited drug-like properties screened through Lipinski filters. Virtual screening followed by binding interaction analysis we identified ten compounds that exhibited better binding energy scores compared to the standard drugs voglibose and miglitol, used in our study. The docking analysis, ADMET and metabolic site prediction estimated the best top two compounds with good drug profiles. Further, top compounds VG9 and VG15 were promoted to simulation study using the Biovia Discovery study to access the stability at a time interval of 100 ns. MD simulation results revealed that our compound VG9 possesses better conformational stability in the complex to the active site residues of Alpha-Amylase target protein than standard drug voglibose. Thus, our investigation revealed that compound VG9 also exhibits the best pharmacokinetic as well as binding affinity results and could act as a potential lead compound targeting Alpha-Amylase for Type II diabetes.

Ultra-fast UPLC-MS/MS approach for estimating X-376 in human liver microsomes: Evaluation of metabolic stability via in silico software and in vitro analysis

X-376 is a novel anaplastic lymphoma kinase (ALK) inhibitor that is capable of penetrating the blood brain barrier. This makes it suitable for use in patients with ALK-positive non-small cell lung cancer (NSCLC) who have metastases in the central nervous system. This study developed a highly sensitive, fast, eco-friendly, and reliable UPLC-MS/MS approach to quantify X-376 in human liver microsomes (HLMs). This approach was used to evaluate X-376's metabolic stability in HLMs in vitro. The UPLC-MS/MS analytical technique validation followed US-FDA bio-analytical method validation guidelines. StarDrop software, containing P450 metabolic and DEREK modules, was utilized to scan X-376's chemical structure for metabolic lability and hazardous warnings. X-376 and Encorafenib (ENF as internal standard) were resoluted on the Eclipse Plus C18 column utilizing an isocratic mobile phase method. The X-376 calibration curve was linear from 1 to 3000 ng/mL. The precision and accuracy of this study's UPLC-MS/MS approach were tested for intra- and inter-day measurements. Inter-day accuracy was -1.32% to 9.36% while intra-day accuracy was -1.5% to 10.00%. The intrinsic clearance (Clint) and in vitro half-life (t1/2) of X-376 were 59.77 mL/min/kg and 13.56 min. The high extraction ratio of X-376 supports the 50 mg twice-daily dose for ALK-positive NSCLC and CNS metastases patients. In silico software suggests that simple structural changes to the piperazine ring or group substitution in drug design may improve metabolic stability and safety compared to X-376.

Novel Acyl Thiourea-Based Hydrophobic Tagging Degraders Exert Potent Anti-Influenza Activity through Two Distinct Endonuclease Polymerase Acidic-Targeted Degradation Pathways

The spread of the influenza virus has caused devastating pandemics and huge economic losses worldwide. Antiviral drugs with diverse action modes are urgently required to overcome the challenges of viral mutation and drug resistance, and targeted protein degradation strategies constitute excellent candidates for this purpose. Herein, the first degradation of the influenza virus polymerase acidic (PA) protein using small-molecule degraders developed by hydrophobic tagging (HyT) technology to effectively combat the influenza virus was reported. The SAR results revealed that compound 19b with Boc2-(L)-Lys demonstrated excellent inhibitory activity against A/WSN/33/H1N1 (EC50 = 0.015 μM) and amantadine-resistant strain (A/PR/8/H1N1), low cytotoxicity, high selectivity, substantial degradation ability, and good drug-like properties. Mechanistic studies demonstrated that the proteasome system and autophagic lysosome pathway were the potential drivers of these HyT degraders. Thus, this study provides a powerful tool for investigating the targeted degradation of influenza virus proteins and for antiviral drug development.

Exploring the selectivity of cytochrome P450 for enhanced novel anticancer agent synthesis

Cytochrome P450 monooxygenases are an extensive and unique class of enzymes, which can regio- and stereo-selectively functionalise hydrocarbons by way of oxidation reactions. These enzymes are naturally occurring but have also been extensively applied in a synthesis context, where they are used as efficient biocatalysts. Recently, a biosynthetic pathway where a cytochrome P450 monooxygenase catalyses a critical step of the pathway was uncovered, leading to the production of a number of products that display high antitumour potency. In this work, we use computational techniques to gain insight into the factors that determine the relative yields of the different products. We use conformational search algorithms to understand the substrate stereochemistry. On a machine-learned 3D protein structure, we use molecular docking to obtain a library of favourable poses for substrate-protein interaction. With molecular dynamics, we investigate the most favourable poses for reactivity on a molecular level, allowing us to investigate which protein-substrate interactions favour a given product and thus gain insight into the product selectivity.

An in silico investigation of the toxicological effects and biological activities of 3-phenoxybenzoic acid and its metabolite products

We aimed to elucidate the toxic effects and biological activities of 3-phenoxybenzoic acid (3PBA) and its metabolite products.

Numerous in silico methods were used to identify the toxic effects and biological activities of 3PBA, including PASS online, molecular docking, ADMETlab 2.0, ADMESWISS, MetaTox, and molecular dynamic simulation.

Ten metabolite products were identified via Phase II reactions (O-glucuronidation, O-sulfation, and methylation).

All of the investigated compounds were followed by Lipinski's rule, indicating that they were stimulants or inducers of hazardous processes.

Because of their high gastrointestinal absorption and ability to reach the blood-brain barrier, the studied compounds' physicochemical and pharmacokinetic properties matched existing evidence of harmful effects, including haematemesis, reproductive dysfunction, allergic dermatitis, toxic respiration, and neurotoxicity.

The studied compounds have been linked to the apoptotic pathway, the reproductivity system, neuroendocrine disruptors, phospholipid-translocating ATPase inhibitors, and JAK2 expression.

An O-glucuronidation metabolite product demonstrated higher binding affinity and interaction with CYP2C9, CYP3A4, caspase 3, and caspase 8 than 3PBA and other metabolite products, whereas metabolite products from methylation were predominant and more toxic.

Our in silico findings partly meet the 3Rs principle by minimizing animal testing before more study is needed to identify the detrimental effects of 3PBA on other organs (liver, kidneys).

Future research directions may involve experimental validation of in silico predictions, elucidation of molecular mechanisms, and exploration of therapeutic interventions.

These findings contribute to our understanding of the toxicological profile of 3PBA and its metabolites, which has implications for risk assessment and regulatory decisions.

MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Simulators

Xenobiotic metabolism is a key consideration in evaluating the hazards and risks posed by environmental chemicals. A number of software tools exist that are capable of simulating metabolites, but each reports its predictions in a different format and with varying levels of detail. This makes comparing the performance and coverage of the tools a practical challenge. To address this shortcoming, we developed a metabolic simulation framework called MetSim, which comprises three main components. A graph-based schema was developed to allow metabolism information to be harmonized. The schema was implemented in MongoDB to store and retrieve metabolic graphs for subsequent analysis. MetSim currently includes an application programming interface for four metabolic simulators: BioTransformer, the OECD Toolbox, EPA's chemical transformation simulator (CTS), and tissue metabolism simulator (TIMES). Lastly, MetSim provides functions to help evaluate simulator performance for specific data sets. In this study, a set of 112 drugs with 432 reported metabolites were compiled, and predictions were made using the 4 simulators. Fifty-nine of the 112 drugs were taken from the Small Molecule Pathway Database, with the remainder sourced from the literature. The human models within BioTransformer and CTS (Phase I only) and the rat models within TIMES and the OECD Toolbox (Phase I only) were used to make predictions for the chemicals in the data set. The recall and precision (recall, precision) ranked in order of highest recall for each individual tool were CTS (0.54, 0.017), BioTransformer (0.50, 0.008), Toolbox in vitro (0.40, 0.144), TIMES in vivo (0.40, 0.133), Toolbox in vivo (0.40, 0.118), and TIMES in vitro (0.39, 0.128). Combining all of the model predictions together increased the overall recall (0.73, 0.008). MetSim enabled insights into the performance and coverage of in silico metabolic simulators to be more efficiently derived, which in turn should aid future efforts to evaluate other data sets.

Prediction of cytochrome P450-mediated bioactivation using machine learning models and in vitro validation

Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3-6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities.

In Silico and Chromatographic Methods for Analysis of Biotransformation of Prospective Neuroprotective Pyrrole-Based Hydrazone in Isolated Rat Hepatocytes

In the current study, chromatographic and in silico techniques were applied to investigate the biotransformation of ethyl 5-(4-bromophenyl)-1-(2-(2-(2-hydroxybenzylidene) hydrazinyl)-2-oxoethyl)-2-methyl-1H-pyrrole-3-carboxylate (11b) in hepatocytic media. The initial chromatographic procedure was based on the employment of the conventional octadecyl stationary phase method for estimation of the chemical stability. Subsequently, a novel and rapid chromatographic approach based on a phenyl-hexyl column was developed, aiming to separate the possible metabolites. Both methods were performed on a Dionex 3000 ThermoScientific (ACM 2, Sofia, Bulgaria) device equipped with a diode array detector set up at 272 and 279 nm for analytes detection. An acetonitrile: phosphate buffer of pH 3.5: methanol (17:30:53 v/v/v) was eluted isocratically as a mobile phase with a 1 mL/min flow rate. A preliminary purification from the biological media was achieved by protein precipitation with methanol. A validation procedure was carried out, where the method was found to correspond to all ICH (Q2) and M10 set criteria. Additionally, an in silico-based approach with the online server BioTransformer 3.0 was applied in an attempt to predict the possible metabolites of the title compound 11b. It was hypothesized that four CYP450 isoforms (1A2, 2C9, 3A4, and 2C8) were involved in the phase I metabolism, resulting in the formation of 12 metabolites. Moreover, docking studies were conducted to evaluate the formation of stable complexes between 11b and the aforementioned isoforms. The obtained data indicated three metabolites as the most probable products, two of which (M9_11b and M10_11b) were synthesized by a classical approach for verification. Finally, liquid chromatography with a mass detector was implemented for comprehensive and summarized analysis, and the obtained results revealed that the metabolism of the 11b proceeds possibly with the formation of glucuronide and glycine conjugate of M11_11b.

Human biomonitoring and toxicokinetics as key building blocks for next generation risk assessment

Human health risk assessment is historically built upon animal testing, often following Organisation for Economic Co-operation and Development (OECD) test guidelines and exposure assessments. Using combinations of human relevant in vitro models, chemical analysis and computational (in silico) approaches bring advantages compared to animal studies. These include a greater focus on the human species and on molecular mechanisms and kinetics, identification of Adverse Outcome Pathways and downstream Key Events as well as the possibility of addressing susceptible populations and additional endpoints. Much of the advancement and progress made in the Next Generation Risk Assessment (NGRA) have been primarily focused on new approach methodologies (NAMs) and physiologically based kinetic (PBK) modelling without incorporating human biomonitoring (HBM). The integration of toxicokinetics (TK) and PBK modelling is an essential component of NGRA. PBK models are essential for describing in quantitative terms the TK processes with a focus on the effective dose at the expected target site. Furthermore, the need for PBK models is amplified by the increasing scientific and regulatory interest in aggregate and cumulative exposure as well as interactions of chemicals in mixtures. Since incorporating HBM data strengthens approaches and reduces uncertainties in risk assessment, here we elaborate on the integrated use of TK, PBK modelling and HBM in chemical risk assessment highlighting opportunities as well as challenges and limitations. Examples are provided where HBM and TK/PBK modelling can be used in both exposure assessment and hazard characterization shifting from external exposure and animal dose/response assays to animal-free, internal exposure-based NGRA.

Chemical Catalysis Guides Structural Identification for the Major In Vivo Metabolite of the BET Inhibitor JQ1

The bromodomain inhibitor (+)-JQ1 is a highly validated chemical probe; however, it exhibits poor in vivo pharmacokinetics. To guide efforts toward improving its pharmacological properties, we identified the (+)-JQ1 primary metabolite using chemical catalysis methods. Treatment of (+)-JQ1 with tetrabutylammonium decatungstate under photochemical conditions resulted in selective formation of an aldehyde at the 2-position of the thiophene ring [(+)-JQ1-CHO], which was further reduced to the 2-hydroxymethyl analog [(+)-JQ1-OH]. Comparative LC/MS analysis of (+)-JQ1-OH to the product obtained from liver microsomes suggested (+)-JQ1-OH as the major metabolite of (+)-JQ1. The 2-thienyl position was then substituted to generate a trideuterated (-CD3, (+)-JQ1-D) analog having half-lives that were 1.8- and 2.8-fold longer in mouse and human liver microsomes, respectively. This result unambiguously confirmed (+)-JQ1-OH as the major metabolite of (+)-JQ1. These studies demonstrate an efficient process for studying drug metabolism and identifying the metabolic soft spots of bioactive compounds.

Strategies for translating proteomics discoveries into drug discovery for dementia

Tauopathies, diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of frontotemporal dementia, make up the vast majority of dementia cases. Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments, ongoing progress is required to ensure these are effective, economical, and accessible for the globally ageing population. As such, continued identification of new potential drug targets and biomarkers is critical. "Big data" studies, such as proteomics, can generate information on thousands of possible new targets for dementia diagnostics and therapeutics, but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development. In this review, we discuss current tauopathy biomarkers and therapeutics, and highlight areas in need of improvement, particularly when addressing the needs of frail, comorbid and cognitively impaired populations. We highlight biomarkers which have been developed from proteomic data, and outline possible future directions in this field. We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development, and demonstrate its application to our group's recent tau interactome dataset as an example.

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.

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.

DNA hypomethylation characterizes genes encoding tissue-dominant functional proteins in liver and skeletal muscle

Each tissue has a dominant set of functional proteins required to mediate tissue-specific functions. Epigenetic modifications, transcription, and translational efficiency control tissue-dominant protein production. However, the coordination of these regulatory mechanisms to achieve such tissue-specific protein production remains unclear. Here, we analyzed the DNA methylome, transcriptome, and proteome in mouse liver and skeletal muscle. We found that DNA hypomethylation at promoter regions is globally associated with liver-dominant or skeletal muscle-dominant functional protein production within each tissue, as well as with genes encoding proteins involved in ubiquitous functions in both tissues. Thus, genes encoding liver-dominant proteins, such as those involved in glycolysis or gluconeogenesis, the urea cycle, complement and coagulation systems, enzymes of tryptophan metabolism, and cytochrome P450-related metabolism, were hypomethylated in the liver, whereas those encoding-skeletal muscle-dominant proteins, such as those involved in sarcomere organization, were hypomethylated in the skeletal muscle. Thus, DNA hypomethylation characterizes genes encoding tissue-dominant functional proteins.

Optimization of 3-Cyano-7-cyclopropylamino-pyrazolo[1,5-a]pyrimidines toward the Development of an In Vivo Chemical Probe for CSNK2A

3-Cyano-7-cyclopropylamino-pyrazolo[1,5-a]pyrimidines, including the chemical probe SGC-CK2-1, are potent and selective inhibitors of CSNK2A in cells but have limited utility in animal models due to their poor pharmacokinetic properties. While developing analogues with reduced intrinsic clearance and the potential for sustained exposure in mice, we discovered that phase II conjugation by GST enzymes was a major metabolic transformation in hepatocytes. A protocol for codosing with ethacrynic acid, a covalent reversible GST inhibitor, was developed to improve the exposure of analogue 2h in mice. A double codosing protocol, using a combination of ethacrynic acid and irreversible P450 inhibitor 1-aminobenzotriazole, increased the blood level of 2h by 40-fold at a 5 h time point.

An Ultrafast UPLC-MS/MS Method for Characterizing the In Vitro Metabolic Stability of Acalabrutinib

Acalabrutinib, commercially known as Calquence®, is a pharmacological molecule that has robust inhibitory activity against Bruton tyrosine kinase. The medicine in question was carefully developed by the esteemed pharmaceutical company AstraZeneca. The FDA granted authorization on 21 November 2019 for the utilization of acalabrutinib (ACB) in the treatment of small lymphocytic lymphoma (SLL) or chronic lymphocytic leukemia (CLL) in adult patients. The aim of this study was to develop a UPLC-MS/MS method that is effective, accurate, environmentally sustainable, and has a high degree of sensitivity. The methodology was specifically developed with the intention of quantifying ACB in human liver microsomes (HLMs). The methodology described above was subsequently utilized to assess the metabolic stability of ACB in HLMs in an in vitro environment. The validation procedures for the UPLC-MS/MS method in the HLMs were conducted in accordance with the bioanalytical method validation criteria established by the U.S.- DA. The utilization of the StarDrop software (version 6.6), which integrates the P450 metabolic module and DEREK software (KB 2018 1.1), was employed for the purpose of evaluating the metabolic stability and identifying potential hazardous alarms associated with the chemical structure of ACB. The calibration curve, as established by the ACB, demonstrated a linear correlation across the concentration range of 1 to 3000 ng/mL in the matrix of HLMs. The present study conducted an assessment of the accuracy and precision of the UPLC-MS/MS method in quantifying inter-day and intra-day fluctuations. The inter-day accuracy demonstrated a spectrum of values ranging from -1.00% to 8.36%, whilst the intra-day accuracy presented a range of values spanning from -2.87% to 4.11%. The t1/2 and intrinsic clearance (Clint) of ACB were determined through in vitro testing to be 20.45 min and 39.65 mL/min/kg, respectively. The analysis concluded that the extraction ratio of ACB demonstrated a moderate level, thus supporting the recommended dosage of ACB (100 mg) to be administered twice daily for the therapeutic treatment of persons suffering from B-cell malignancies. Several computational tools have suggested that introducing minor structural alterations to the butynoyl group, particularly the alpha, beta-unsaturated amide moiety, or substituting this group during the drug design procedure, could potentially enhance the metabolic stability and safety properties of novel derivatives in comparison to ACB.

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.

Evaluation of Alectinib Metabolic Stability in HLMs Using Fast LC-MS/MS Method: In Silico ADME Profile, P450 Metabolic Lability, and Toxic Alerts Screening

Alectinib, also known as Alecensa®, is prescribed for the therapeutic treatment of individuals diagnosed with metastatic non-small cell lung cancer (NSCLC) who have a specific genetic mutation referred to as anaplastic lymphoma kinase (ALK) positivity. The Food and Drug Administration granted regular approval to alectinib, a drug developed by Hoffmann-La Roche, Inc. (Basel, Switzerland)/Genentech, Inc. (South San Francisco, CA, USA), on 6 November 2017. The screening of the metabolic stability and identification of hazardous alarms within the chemical structure of ALC was conducted using the StarDrop software package (version 6.6), which incorporates the P450 metabolic module and DEREK software (KB 2018 1.1). The primary aim of this investigation was to develop a high-throughput and accurate LC-MS/MS technique for the quantification of ALC in the metabolic matrix (human liver microsomes; HLMs). The aforementioned methodology was subsequently employed to assess the metabolic stability of ALC in HLMs through in vitro tests, with the obtained results further validated using in silico software. The calibration curve of the ALC showed a linear correlation that exists within the concentration range from 1 to 3000 ng/mL. The LC-MS/MS approach that was recommended exhibited accuracy and precision levels for both inter-day and intra-day measurements. Specifically, the accuracy values ranged from -2.56% to 3.45%, while the precision values ranged from -3.78% to 4.33%. The sensitivity of the established approach was proved by its ability to adhere to an LLOQ of 0.82 ng/mL. The half-life (t1/2) and intrinsic clearance (Clint) of ALC were estimated to be 22.28 min and 36.37 mL/min/kg, correspondingly, using in vitro experiments. The ALC exhibited a moderate extraction ratio. The metabolic stability and safety properties of newly created derivatives can be enhanced by making modest adjustments to the morpholine and piperidine rings or by substituting the substituent, as per computational software. In in silico ADME prediction, ALC was shown to have poor water solubility and high gastrointestinal absorption along with inhibition of some cytochrome P450s (CYP2C19 and CYP2C9) without inhibition of others (CYP1A2, CYP3A4, and CYP2D6) and P-glycoprotein substrate. The study design that involves using both laboratory experiments and different in silico software demonstrates a novel and groundbreaking approach in the establishment and uniformization of LC-MS/MS techniques for the estimation of ALC concentrations, identifying structural alerts and the assessment of its metabolic stability. The utilization of this study strategy has the potential to be employed in the screening and optimization of prospective compounds during the drug creation process. This strategy may also facilitate the development of novel derivatives of the medicine that maintain the same biological action by targeted structural modifications, based on an understanding of the structural alerts included within the chemical structure of ALC.

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.

A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing

Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.

MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database

The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program.

Interaction between Changan Granule and its main components in the plasma and CYP450 enzymes

ETHNOPHARMACOLOGICAL RELEVANCE

Changan Granule (CAG) is a Chinese patent drug developed based on an empirical prescription in accordance with the formulation theory of Traditional Chinese Medicine. The prescription is composed of eight herbal drugs which have been traditionally used by Chinese people for a long history. It has effects of invigorating spleen and supplementing qi, as well as regulating liver and ceasing diarrhea, and is indicated for the treatment of irritable bowel syndrome (IBS).

AIM OF THE STUDY

This study was aimed to investigate the interaction between CAG and its main components and cytochrome P450 (CYP450) enzymes so as to characterize the major metabolites and metabolic enzymes and evaluate the safety concerns to its clinical use.

MATERIALS AND METHODS

Both in vivo and in vitro experiments using such as diarrhea-predominant IBS (IBS-D) rat model, HepG2 cells, and human liver microsomes (HLM) were carried out to investigate the interaction between CAG and its main components and CYP450 enzymes. Real-time quantitative PCR (qPCR), ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and cocktail probes were employed to qualitatively or quantitatively measure the metabolites and metabolic enzymes.

RESULTS

CAG inhibited the enzyme activities of CYP1A2, CYP2E1, CYP2D6, CYP2C9, and CYP3A4 and the mRNA expressions of CYP2E1, CYP2C9, CYP3A4, and CYP2D6 in vitro. CAG down-regulated the increased expression of CYP1A2 and up-regulated the decreased expression of CYP3A1 in vivo. Twenty-two metabolites were characterized from the main components of CAG after incubation with HLM in vitro. CYP2D6, CYP2E1, CYP3A4 and CYP2C9 were identified as the characteristic metabolic enzymes.

CONCLUSIONS

This study provides a reference for clinical application of CAG in safety. CAG and CYP450 enzymes are interacted. CAG is mainly metabolized by CYP2E1 and CYP2D6. The expression of CYP2E1 and CYP2D6 are more susceptible to be influenced by CAG in comparison with that of CYP3A4, CYP2C9 and CYP1A2. It implies the potential risk of interaction when CAG is taken together with the drugs metabolized by CYP2E1 and CYP2D6.

Discovery and design of dual inhibitors targeting Sphk1 and Sirt1

CONTEXT

Leukaemia has become a serious threat to human health. Although tyrosine kinase inhibitors (TKIs) have been developed as targets for the remedy of leukaemia, drug resistance occurs. Research demonstrated that the simultaneous targeting of sphingosine kinase 1 (Sphk1) and Sirtuin 1 (Sirt1) can downregulate myeloid cell leukaemia-1 (MCL-1), overcome the resistance of tyrosine kinase inhibitors, and play a synergistic inhibitory impact on leukaemia treatment.

METHODS

In this study, virtual screening of 7.06 million small molecules was done by sphingosine kinase 1 and Sirtuin 1 pharmacophore models using Schrödinger version 2019; after that, ADME and Toxicity molecule properties were predicted using Discovery Studio. Molecular docking using Schrödinger selected five molecules, which have the best binding affinity with sphingosine kinase 1 and Sirtuin 1. The five molecules and reference inhibitors were constructed with a total of 12 systems with GROMACS that carried out 100 ns molecular dynamics simulation and molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculation. Due to compound 3 has the lowest binding energy, its structure was modified. A series of compounds docked with sphingosine kinase 1 and Sirtuin 1, respectively. Among them, QST-LC03, QST-LD05, QST-LE03, and QST-LE04 have the better binding affinity than reference inhibitors. Moreover, the SwissADME and PASS platforms predict that 1, 3, QST-LC03, and QST-LE04 have further study value.

Unusual Biotransformation Reactions of Drugs and Drug Candidates

Detailed assessment of the fate of drugs in nonclinical test species and humans is essential to ensure the safety and efficacy of medicines in patients. In this context, biotransformation of drugs and drug candidates has been an area of keen interest over many decades in the pharmaceutical industry as well as academia. Although many of the enzymes and biotransformation pathways involved in the metabolism of xenobiotics and more specifically drugs have been well characterized, each drug molecule is unique and constitutes specific challenges for the biotransformation scientist. In this mini-review written for the special issue on the occasion of the 50th Anniversary celebration of Drug Metabolism and Disposition and to celebrate contributions of F. Peter Guengerich, one of the pioneers of the drug metabolism field, recently reported "unusual" biotransformation reactions are presented. Scientific and technological advances in the "toolbox" of the biotransformation scientists are summarized. As the pharmaceutical industry continues to explore therapeutic modalities different from the traditional small molecule drugs, the new challenges confronting the biotransformation scientist as well as future opportunities are discussed. SIGNIFICANCE STATEMENT: For the biotransformation scientists, it is essential to share and be aware of unexpected biotransformation reactions so that they can increase their confidence in predicting metabolites of drugs in humans to ensure the safety and efficacy of these metabolites before the medicines reach large numbers of patients. The purpose of this review is to highlight recent observations of "unusual" metabolites so that the scientists working in the area of drug metabolism can strengthen their readiness in expecting the unexpected.

Improving drug-drug interactions prediction with interpretability via meta-path-based information fusion

Drug-drug interactions (DDIs) are compound effects when patients take two or more drugs at the same time, which may weaken the efficacy of drugs or cause unexpected side effects. Thus, accurately predicting DDIs is of great significance for the drug development and the drug safety surveillance. Although many methods have been proposed for the task, the biological knowledge related to DDIs is not fully utilized and the complex semantics among drug-related biological entities are not effectively captured in existing methods, leading to suboptimal performance. Moreover, the lack of interpretability for the predicted results also limits the wide application of existing methods for DDIs prediction. In this study, we propose a novel framework for predicting DDIs with interpretability. Specifically, we construct a heterogeneous information network (HIN) by explicitly utilizing the biological knowledge related to the procedure of inducing DDIs. To capture the complex semantics in HIN, a meta-path-based information fusion mechanism is proposed to learn high-quality representations of drugs. In addition, an attention mechanism is designed to combine semantic information obtained from meta-paths with different lengths to obtain final representations of drugs for DDIs prediction. Comprehensive experiments are conducted on 2410 approved drugs, and the results of predictive performance comparison show that our proposed framework outperforms selected representative baselines on the task of DDIs prediction. The results of ablation study and cold-start scenario indicate that the meta-path-based information fusion mechanism red is beneficial for capturing the complex semantics among drug-related biological entities. Moreover, the results of case study demonstrate that the designed attention mechanism is able to provide partial interpretability for the predicted DDIs. Therefore, the proposed method will be a feasible solution to the task of predicting DDIs.

Assessment of In Silico and In Vitro Selpercatinib Metabolic Stability in Human Liver Microsomes Using a Validated LC-MS/MS Method

Selpercatinib (SLP; brand name Retevmo®) is a selective and potent RE arranged during transfection (RET) inhibitor. On 21 September 2022, the FDA granted regular approval to SLP (Retevmo, Eli Lilly, and Company). It is considered the only and first RET inhibitor for adults with metastatic or locally advanced solid tumors with RET gene fusion. In the current experiment, a highly specific, sensitive, and fast liquid chromatography tandem mass spectrometry (LC-MS/MS) method for quantifying SLP in human liver microsomes (HLMs) was developed and applied to the metabolic stability evaluation of SLP. The LC-MS/MS method was validated following the bioanalytical methodology validation guidelines outlined by the FDA (linearity, selectivity, matrix effect, accuracy, precision, carryover, and extraction recovery). SLP was detected by a triple quadrupole detector (TQD) using a positive ESI source and multiple reaction monitoring (MRM) mode for mass spectrometric analysis and estimation of analytes ions. The IS-normalized matrix effect and extraction recovery were acceptable according to the FDA guidelines for the bioanalysis of SLP. The SLP calibration standards were linear from 1 to 3000 ng/mL HLMs matrix, with a regression equation (y = 1.7298x + 3.62941) and coefficient of variation (r2 = 0.9949). The intra-batch and inter-batch precision and accuracy of the developed LC-MS/MS method were −6.56–5.22% and 5.08–3.15%, respectively. SLP and filgotinib (FLG) (internal standard; IS) were chromatographically separated using a Luna 3 µm PFP (2) stationary phase (150 × 4.6 mm) with an isocratic mobile phase at 23 ± 1 °C. The limit of quantification (LOQ) was 0.78 ng/mL, revealing the LC-MS/MS method sensitivity. The intrinsic clearance and in vitro t1/2 (metabolic stability) of SLP in the HLMs matrix were 34 mL/min/kg and 23.82 min, respectively, which proposed an intermediate metabolic clearance rate of SLP, confirming the great value of this type of kinetic experiment for more accurate metabolic stability predictions. The literature review approved that the established LC-MS/MS method is the first developed and reported method for quantifying SLP metabolic stability.

Development and Validation of a Rapid LC-MS/MS Method for Quantifying Alvocidib: In Silico and In Vitro Metabolic Stability Estimation in Human Liver Microsomes

Alvocidib (AVC; flavopiridol) is a potent cyclin-dependent kinase inhibitor used in patients with acute myeloid leukemia (AML). The FDA has approved orphan drug designation to AVC for treating patients with AML. In the current work, the in silico calculation of AVC metabolic lability was done using the P450 metabolism module of the StarDrop software package, that is expressed as a composite site lability (CSL). This was followed by establishing an LC-MS/MS analytical method for AVC estimation in human liver microsomes (HLMs) to assess metabolic stability. AVC and glasdegib (GSB), used as internal standards (IS), were separated utilizing a C18 column (reversed chromatography) with an isocratic mobile phase. The lower limit of quantification (LLOQ) was 5.0 ng/mL, revealing the sensitivity of the established LC-MS/MS analytical method that exhibited a linearity in the range 5-500 ng/mL in the HLMs matrix with correlation coefficient (R² = 0.9995). The interday and intraday accuracy and precision of the established LC-MS/MS analytical method were -1.4% to 6.7% and -0.8% to 6.4%, respectively, confirming the reproducibility of the LC-MS/MS analytical method. The calculated metabolic stability parameters were intrinsic clearance (CL_int) and in vitro half-life (t_1/2) of AVC at 26.9 µL/min/mg and 25.8 min, respectively. The in silico results from the P450 metabolism model matched the results generated from in vitro metabolic incubations; therefore, the in silico software can be used to predict the metabolic stability of the drugs, saving time and resources. AVC exhibits a moderate extraction ratio, indicating reasonable in vivo bioavailability. The established chromatographic methodology was the first LC-MS/MS method designed for AVC estimation in HLMs matrix that was applied for AVC metabolic stability estimation.

Development of an LC-MS/MS Method for Quantification of Sapitinib in Human Liver Microsomes: In Silico and In Vitro Metabolic Stability Evaluation

Sapitinib (AZD8931, SPT) is a tyrosine kinase inhibitor of the epidermal growth factor receptor (EGFR) family (pan-erbB). In multiple tumor cell lines, STP has been shown to be a much more potent inhibitor of EGF-driven cellular proliferation than gefitinib. In the current study, a highly sensitive, rapid, and specific LC-MS/MS analytical method for the estimation of SPT in human liver microsomes (HLMs) was established with application to metabolic stability assessment. The LC-MS/MS analytical method was validated in terms of linearity, selectivity, precision, accuracy, matrix effect, extraction recovery, carryover, and stability following the FDA guidelines for bioanalytical method validation. SPT was detected using electrospray ionization (ESI) as an ionization source under multiple reaction monitoring (MRM) in the positive ion mode. The IS-normalized matrix factor and extraction recovery were acceptable for the bioanalysis of SPT. The SPT calibration curve was linear, from 1 ng/mL to 3000 ng/mL HLM matrix samples, with a linear regression equation of y = 1.7298x + 3.62941 (r² = 0.9949). The intraday and interday accuracy and precision values of the LC-MS/MS method were -1.45-7.25% and 0.29-6.31%, respectively. SPT and filgotinib (FGT) (internal standard; IS) were separated through the use of an isocratic mobile phase system with a Luna 3 µm PFP(2) column (150 × 4.6 mm) stationary phase column. The limit of quantification (LOQ) was 0.88 ng/mL, confirming the LC-MS/MS method sensitivity. The intrinsic clearance and in vitro half-life of STP were 38.48 mL/min/kg and 21.07 min, respectively. STP exhibited a moderate extraction ratio that revealed good bioavailability. The literature review demonstrated that the current analytical method is the first developed LC-MS/MS method for the quantification of SPT in an HLM matrix with application to SPT metabolic stability evaluation.

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products

MOTIVATION

While traditionally utilized for identifying site-specific metabolic activity within a compound to alter its interaction with a metabolizing enzyme, predicting the site-of-metabolism (SOM) is essential in analyzing the promiscuity of enzymes on substrates. The successful prediction of SOMs and the relevant promiscuous products has a wide range of applications that include creating extended metabolic models (EMMs) that account for enzyme promiscuity and the construction of novel heterologous synthesis pathways. There is therefore a need to develop generalized methods that can predict molecular SOMs for a wide range of metabolizing enzymes.

RESULTS

This article develops a Graph Neural Network (GNN) model for the classification of an atom (or a bond) being an SOM. Our model, GNN-SOM, is trained on enzymatic interactions, available in the KEGG database, that span all enzyme commission numbers. We demonstrate that GNN-SOM consistently outperforms baseline machine learning models, when trained on all enzymes, on Cytochrome P450 (CYP) enzymes, or on non-CYP enzymes. We showcase the utility of GNN-SOM in prioritizing predicted enzymatic products due to enzyme promiscuity for two biological applications: the construction of EMMs and the construction of synthesis pathways.

AVAILABILITY AND IMPLEMENTATION

A python implementation of the trained SOM predictor model can be found at https://github.com/HassounLab/GNN-SOM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structural basis of the activation of PPARγ by the plasticizer metabolites MEHP and MINCH

Di-2-ethylhexyl phthalate (DEHP) and its substitute 1,2-cyclohexane dicarboxylic acid diisononyl ester (DINCH) are widely used as plasticizers but may have adverse health effects. Via hydrolysis of one of the two ester bonds in the human body, DEHP and DINCH form the monoesters MEHP and MINCH, respectively. Previous studies demonstrated binding of these metabolites to PPARγ and the induction of adipogenesis via this pathway. Detailed structural understanding of how these metabolites interact with PPARγ and thereby affect human health is lacking until now. We therefore characterized the binding modes of MINCH and MEHP to the ligand binding domain of PPARγ by X-ray crystallography and molecular dynamics (MD) simulations. Both compounds bind to the activating function-2 (AF-2) binding site via an interaction of the free carboxylates with the histidines 323 and 449, tyrosine 473 and serine 289. The alkyl chains form hydrophobic interactions with the tunnel next to cysteine 285. These binding modes are generally stable as demonstrated by the MD simulations and they resemble the complexation of fatty acids and their metabolites to the AF-2 site of PPARγ. Similar to the situation for these natural PPARγ agonists, the interaction of the free carboxylate groups of MEHP and MINCH with tyrosine 473 and surrounding residues stabilizes the AF-2 helix in the upward conformation. This state promotes binding of coactivator proteins and thus formation of the active complex for transcription of the specific target genes. Moreover, a comparison of the residues involved in binding of the plasticizer metabolites in vertebrate PPARγ orthologs shows that these compounds likely have similar effects in other species.

Use of innovative, cross-disciplinary in vitro, in silico and in vivo approaches to characterize the metabolism of chloro-alpha-pyrrolidinovalerophenone (4-Cl-PVP)

Synthetic cathinones constitute a family of new psychoactive substances, the consumption of which is increasingly worldwide. A lack of metabolic knowledge limits the detection of these compounds in cases of intoxication. Here, we used an innovative cross-disciplinary approach to study the metabolism of the newly emerging cathinone chloro-alpha-pyrrolidinovalerophenone (4-Cl-PVP). Three complementary approaches (in silico, in vitro, and in vivo) were used to identify putative 4-Cl-PVP metabolites that could be used as additional consumption markers. The in silico approach used predictive software packages. Molecular networking was used as an innovative bioinformatics approach for re-processing high-resolution tandem mass spectrometry data acquired with both in vitro and in vivo samples. In vitro experiments were performed by incubating 4-Cl-PVP (20 µM) for four different durations with a metabolically competent human hepatic cell model (differentiated HepaRG cells). In vivo samples (blood and urine) were obtained from a patient known to have consumed 4-Cl-PVP. The in silico software predicted 17 putative metabolites, and molecular networking identified 10 metabolites in vitro. On admission to the intensive care unit, the patient's plasma and urine 4-Cl-PVP concentrations were, respectively, 34.4 and 1018.6 µg/L. An in vivo analysis identified the presence of five additional glucuronoconjugated 4-Cl-PVP derivatives in the urine. Our combination of a cross-disciplinary approach with molecular networking enabled the detection of 15 4-Cl-PVP metabolites, 10 of them had not previously been reported in the literature. Two metabolites appeared to be particular relevant candidate as 4-Cl-PVP consumption markers in cases of intoxication: hydroxy-4-Cl-PVP (m/z 282.1254) and dihydroxy-4-Cl-PVP (m/z 298.1204).

First Insight into the Formation of In Vivo Transformation Products of 2-Ethylhexyl diphenyl phosphate in Zebrafish and Prediction of Their Potential Toxicities

As a frequently detected organophosphorus flame retardant in the environment, 2-ethylhexyl diphenyl phosphate (EHDPHP) is vulnerable to biotransformation, while the transformation mechanisms and potential toxicities of its transformation products remain unclear. In the present study, in vivo transformation products of EHDPHP in exposed zebrafish for 21d were analyzed by suspect screening and identified by mass spectrometry. Fifteen metabolites were identified, including 10 phase I and 5 phase II products with monohydroxylated products being primary, among which 5-OH-EHDPHP was the most predominant. Two sulfation products and one terminal desaturation metabolite of EHDPHP were reported for the first time. A density functional calculation coupled with molecular docking disclosed that the specific conformation of EHDPHP docked in the protein pockets favored the primary formation of 5-OH-EHDPHP, which was fortified to be a more suitable biomarker of EHDPHP exposure. The in vitro tests suggested that EHDPHP transformation took place not only in liver but also in intestine, where gut microbes played an important role. Due to lack of standards, in silico toxicity prediction combined with molecular docking indicated that several metabolites potentially cause higher toxicities than EHDPHP. The results provide deep insight into the potential health risks due to specific in vivo transformation of EHDPHP.

Liver immune and lipid metabolism disorders in mice induced by triphenyl phosphate with or without high fructose and high fat diet

Organophosphorus flame retardants (OPFRs) are frequently detected in food and human samples, and epidemiological studies have found that human exposure to aryl-OPFRs (triphenyl phosphate, TPP) is associated with lipid metabolism. Although toxicity studies suggest a potential obesity risk from TPP exposure, the molecular mechanism remains unclear. This study investigated the subchronic dietary effects on mouse liver significantly changed proteins (SCPs) and elucidated the underlying molecular mechanisms of TPP with or without a high-fructose and high-fat (HFF) diet. Male C57BL/6J mice were exposed to low-dose TPP (corresponding to the oral reference dose, 10 μg/kg body weight (bw)/day) and high-dose TPP (1000 μg/kg bw/day) for 12 weeks. The results showed that exposure to TPP generated changes of liver function and organelle damage as well as increases in total cholesterol and triglyceride levels. TPP exposure at a low dose damaged the liver immune system via major histocompatibility complex-related proteins involved in antigen processing and presentation. TPP exposure at a high dose caused disorders of the biosynthesis of unsaturated fatty acids and steroid hormones, thereby inducing lipid accumulation in the liver. Although 10 μg/kg TPP did not cause serious lipid metabolism disorders in the liver, significant overexpression of fatty acid-binding protein 5, malic enzyme 1, and other related SCPs was observed, which led to disorders of cholesterol metabolism and lipogenesis to activate the proliferator-activated receptor signaling pathway and thus induced potential obesity risks. In addition, lipid metabolism disorders related to TPP were aggravated under the HFF diet, impairing liver mitochondrial and endoplasmic reticulum function in mice by altering the activity of cytochrome P450 enzyme subfamilies. These findings provide an in-depth understanding of the molecular toxicity mechanisms and health risks associated with subchronic exposure to TPP under different dietary regimes.

A Transversal Approach Combining In Silico, In Vitro and In Vivo Models to Describe the Metabolism of the Receptor Interacting Protein 1 Kinase Inhibitor Sibiriline

Sibiriline is a novel drug inhibiting receptor-interacting protein 1 kinase (RIPK1) and necroptosis, a regulated form of cell death involved in several disease models. In this study, we aimed to investigate the metabolic fate of sibiriline in a cross-sectional manner using an in silico prediction, coupled with in vitro and in vivo experiments. In silico predictions were performed using GLORYx and Biotransformer 3.0 freeware; in vitro incubation was performed on differentiated human HepaRG cells, and in vivo experiments including a pharmacokinetic study were performed on mice treated with sibiriline. HepaRG culture supernatants and mice plasma samples were analyzed with ultra-high-performance liquid chromatography, coupled with tandem mass spectrometry (LC-HRMS/MS). The molecular networking bioinformatics tool applied to LC-HRMS/MS data allowed us to visualize the sibiriline metabolism kinetics. Overall, 14 metabolites, mostly produced by Phase II transformations (glucuronidation and sulfation) were identified. These data provide initial reassurance regarding the toxicology of this new RIPK1 inhibitor, although further studies are required.

Developmental and Reproductive Impacts of Four Bisphenols in Daphnia magna

Bisphenol A (BPA) is a typical endocrine-disrupting chemical (EDC) used worldwide. Considering its adverse effects, BPA has been banned or strictly restricted in some nations, and many analogs have been introduced to the market. In this study, we selected three representative substitutes, BPS, BPF, and BPAF, along with BPA, to assess the developmental and reproductive effects on Daphnia magna. The F0 generation was exposed to bisphenols (BPs) at an environmentally relevant concentration (100 μg/L) for 21 d; then the embryo spawn at day 21 was collected. Behavior traits, the activity of antioxidant enzymes, and gene transcription were evaluated at three developmental stages (days 7, 14, and 21). Notably, body length, heart rate, and thoracic limb beating were significantly decreased, and D. magna behaved more sluggishly in the exposed group. Moreover, exposure to BPs significantly increased the antioxidant enzymatic activities, which indicated that BPs activated the antioxidant defense system. Additionally, gene expression indicated intergenerational effects in larvae, particularly in the BPAF group. In conclusion, BPA analogs such as BPF and BPAF showed similar or stronger reproductive and developmental toxicity than BPA in D. magna. These findings collectively deepen our understanding of the toxicity of BPA analogs and provide empirical evidence for screening safe alternatives to BPA.

3,4,3'-Tri-O-methylellagic acid as an anticancer agent: in vitro and in silico studies

We report a natural product compound isolated from Syzygium polycephalum known as 3,4,3'-tri-O-methylellagic acid (T-EA) as a candidate drug for cancer treatment. The characterization of the isolated T-EA compound was carried out using various spectroscopic methods. The in vitro evaluation showcased the inhibition activity of T-EA towards the T47D and HeLa cell lines with EC₅₀ values of 55.35 ± 6.28 μg mL^-1 and 12.57 ± 2.22 μg mL^-1, respectively. Meanwhile, the in silico evaluation aimed to understand the interaction of T-EA with enzymes responsible for cancer regulation at the molecular level by targeting the hindrance of cyclin-dependent kinase 9 (CDK9) and sirtuin 1 (SIRT1) enzymes. T-EA showed a binding free energy towards the SIRT1 protein of ΔG _{bind (MM-GBSA)}: -30.98 ± 0.25 kcal mol^-1 and ΔG _{bind (MM-PBSA)}: -24.07 ± 0.30 kcal mol^-1, while that of CDK9 was ΔG _{bind (MM-GBSA)}: -29.50 ± 0.22 kcal mol^-1 and ΔG _{bind (MM-PBSA)}: -25.87 ± 0.40 kcal mol^-1. The obtained results from this research could be considered as important information on 3,4,3'-tri-O-methylellagic acid as a drug to treat cervical and breast cancers.

A Discovery Biotransformation Strategy: Combining In Silico Tools with High-Resolution Mass Spectrometry and Software-Assisted Data Analysis for High-Throughput Metabolism

Understanding compound metabolism in early drug discovery aids medicinal chemistry in designing molecules with improved safety and ADME properties. While advancements in metabolite prediction brings increasedconfidence, structural decisions require experimental data. In vitro metabolism studies using liquid chromatography and high-resolution mass spectrometry (LC-MS) are generally resource intensive and performed on very few compounds, limiting the chemical space that can be examined.Here, we describe a novel metabolism strategy increasing compound throughput using residual in vitro clearance samples conducted at drug concentrations of 0.5 µM. Analysis by robust UHPLC separation and accurate-mass MS detection ensures major metabolites are identified from a single injection. In silico prediction (parent cLogD) tailors chromatographic conditions, with data-dependent MS/MS targeting predicted metabolites. Software-assisted data mining, structure elucidation and automatic reporting are used.Confidence in the globally-aligned workflow is demonstrated with sixteen marketed drugs. The approach is now implemented routinely across our laboratories. To date, the success rate for identification of at least one major metabolite is 85%. The utility of these data has been demonstrated across multiple projects, allowing earlier medicinal chemistry decisions to increase efficiency and impact of the design-make-test cycle; thus improving the translatability of early in vitro metabolism data.

Impact of Established and Emerging Software Tools on the Metabolite Identification Landscape

Scientists’ ability to detect drug-related metabolites at trace concentrations has improved over recent decades. High-resolution instruments enable collection of large amounts of raw experimental data. In fact, the quantity of data produced has become a challenge due to effort required to convert raw data into useful insights. Various cheminformatics tools have been developed to address these metabolite identification challenges. This article describes the current state of these tools. They can be split into two categories: Pre-experimental metabolite generation and post-experimental data analysis. The former can be subdivided into rule-based, machine learning-based, and docking-based approaches. Post-experimental tools help scientists automatically perform chromatographic deconvolution of LC/MS data and identify metabolites. They can use pre-experimental predictions to improve metabolite identification, but they are not limited to these predictions: unexpected metabolites can also be discovered through fractional mass filtering. In addition to a review of available software tools, we present a description of pre-experimental and post-experimental metabolite structure generation using MetaSense. These software tools improve upon manual techniques, increasing scientist productivity and enabling efficient handling of large datasets. However, the trend of increasingly large datasets and highly data-driven workflows requires a more sophisticated informatics transition in metabolite identification labs. Experimental work has traditionally been separated from the information technology tools that handle our data. We argue that these IT tools can help scientists draw connections via data visualizations and preserve and share results via searchable centralized databases. In addition, data marshalling and homogenization techniques enable future data mining and machine learning.

Hesperidin abrogates bisphenol A endocrine disruption through binding with fibroblast growth factor 21 (FGF-21), α-amylase and α-glucosidase: an in silico molecular study

Background

Fibroblast growth factor 21 (FGF-21), alpha-amylase, and alpha-glucosidase are key proteins implicated in metabolic dysregulations. Bisphenol A (BPA) is an environmental toxicant known to cause endocrine dysregulations. Hesperidin from citrus is an emerging flavonoid for metabolic diseases management. Through computational approach, we investigated the potentials of hesperidin in abrogating BPA interference in metabolism. The 3D crystal structure of the proteins (FGF-21, α-amylase, and α-glucosidase) and the ligands (BPA and hesperidin) were retrieved from the PDB and PubChem database respectively. Using Autodock plugin Pyrx, molecular docking of the ligands and individual proteins were performed to ascertain their binding affinities and their potentials to compete for the same binding site. Validation of the docking study was considered as the ability of the ligands to bind at the same site of each proteins. The docking poses were visualized using UCSF Chimera and Discovery Studio 2020, respectively to reveal each of the protein-ligands interactions within the binding pockets. Using SwissAdme and AdmeSar servers, we further investigated hesperidin’s ADMET profile. Hesperidin used was purchased commercially.

Results

Hesperidin and BPA competitively bound to the same site on each protein. Interestingly, hesperidin had greater binding affinities (Kcal/mol) − 5.80, − 9.60, and − 9.60 than BPA (Kcal/mol) − 4.40, − 7.20, − 7.10 for FGF-21, α-amylase, and α-glucosidase respectively. Visualizations of the binding poses showed that hesperidin interacted with stronger bonds than BPA within the proteins’ pockets. Although hesperidin violated Lipinski rule of five, this however can be optimized through structural modifications.

Conclusions

Hesperidin may be an emerging natural product with promising therapeutic potentials against metabolic and endocrine derangement.

Boost-RS: boosted embeddings for recommender systems and its application to enzyme-substrate interaction prediction

MOTIVATION

Despite experimental and curation efforts, the extent of enzyme promiscuity on substrates continues to be largely unexplored and under documented. Providing computational tools for the exploration of the enzyme-substrate interaction space can expedite experimentation and benefit applications such as constructing synthesis pathways for novel biomolecules, identifying products of metabolism on ingested compounds, and elucidating xenobiotic metabolism. Recommender systems (RS), which are currently unexplored for the enzyme-substrate interaction prediction problem, can be utilized to provide enzyme recommendations for substrates, and vice versa. The performance of Collaborative-Filtering (CF) RSs; however, hinges on the quality of embedding vectors of users and items (enzymes and substrates in our case). Importantly, enhancing CF embeddings with heterogeneous auxiliary data, specially relational data (e.g. hierarchical, pairwise or groupings), remains a challenge.

RESULTS

We propose an innovative general RS framework, termed Boost-RS that enhances RS performance by 'boosting' embedding vectors through auxiliary data. Specifically, Boost-RS is trained and dynamically tuned on multiple relevant auxiliary learning tasks Boost-RS utilizes contrastive learning tasks to exploit relational data. To show the efficacy of Boost-RS for the enzyme-substrate prediction interaction problem, we apply the Boost-RS framework to several baseline CF models. We show that each of our auxiliary tasks boosts learning of the embedding vectors, and that contrastive learning using Boost-RS outperforms attribute concatenation and multi-label learning. We also show that Boost-RS outperforms similarity-based models. Ablation studies and visualization of learned representations highlight the importance of using contrastive learning on some of the auxiliary data in boosting the embedding vectors.

AVAILABILITY AND IMPLEMENTATION

A Python implementation for Boost-RS is provided at https://github.com/HassounLab/Boost-RS. The enzyme-substrate interaction data is available from the KEGG database (https://www.genome.jp/kegg/).

The Therapeutic Potential of 2-{[4-(2-methoxyphenyl)piperazin-1-yl]alkyl}-1H-benzo[d]imidazoles as Ligands for Alpha1-Adrenergic Receptor - Comparative In Silico and In Vitro Study

Adrenergic receptors are among the most studied G protein-coupled receptors. Activation or blockade of these receptors is a major therapeutic approach for the treatment of numerous disorders such as cardiac hypertrophy, congestive heart failure, hypertension, angina pectoris, cardiac arrhythmias, depression, benign prostate hyperplasia, anaphylaxis, asthma, and hyperthyroidism. Among all nine cloned adrenoceptor subtypes and the subsequent development of animal models, a significant target for various neurological conditions treatment is alpha1-adrenergic receptors. 2-{[4-(2-Methoxyphenyl)piperazin-1-yl]alkyl}-1H-benzo[d]imidazoles, their 5 substituted derivatives, and structurally similar, arylpiperazine based alpha1-adrenergic receptors antagonists (trazodone, naftopidil, and urapidil) have been subjects of comparative analysis. Most of the novel compounds showed alpha1-adrenergic affinity in the range from 22 nM to 250 nM. The in silico docking and molecular dynamics simulations, binding data together with absorption, distribution, metabolism, and excretion (ADME) calculations identified the promising lead compounds. The results brought out the conclusions which allowed us to propose a rationale for the activity of these molecules and to highlight six compounds (2-5, 8, and 12) that exhibited an acceptable pharmacokinetic profile to the advanced investigation as the potential alpha1-adrenergic receptor antagonists.

Using Physical Organic Chemistry Knowledge to Predict Unusual Metabolites of Synthetic Phenolic Antioxidants by Cytochrome P450

Biotransformation, especially by human CYP450 enzymes, plays a crucial role in regulating the toxicity of organic compounds in organisms, but is poorly understood for most emerging pollutants, as their numerous "unusual" biotransformation reactions cannot retrieve examples from the textbooks. Therefore, in order to predict the unknown metabolites with altering toxicological profiles, there is a realistic need to develop efficient methods to reveal the "unusual" metabolic mechanism of emerging pollutants. Combining experimental work with computational predictions has been widely accepted as an effective approach in studying complex metabolic reactions; however, the full quantum chemical computations may not be easily accessible for most environmentalists. Alternatively, this work practiced using the concepts from physical organic chemistry for studying the interrelationships between structure and reactivity of organic molecules, to reveal the "unusual" metabolic mechanism of synthetic phenolic antioxidants catalyzed by CYP450, for which the simple pencil-and-paper and property-computation methods based on physical organic chemistry were performed. The phenol-coupling product of butylated hydroxyanisole (BHA) (based on spin aromatic delocalization) and ipso-addition quinol metabolite of butylated hydroxytoluene (BHT) (based on hyperconjugative effect) were predicted as two "unusual" metabolites, which were further confirmed by our in vitro analysis. We hope this easily handled approach will promote environmentalists to attach importance to physical organic chemistry, with an eye to being able to use the knowledge gained to efficiently predict the fates of substantial unknown synthesized organic compounds in the future.

Comparing the performance and coverage of selected in silico (liver) metabolism tools relative to reported studies in the literature to inform analogue selection in read-across: A case study

Changes in the regulatory landscape of chemical safety assessment call for the use of New Approach Methodologies (NAMs) including read-across to fill data gaps. One critical aspect of analogue evaluation is the extent to which target and source analogues are metabolically similar. In this study, a set of 37 structurally diverse chemicals were compiled from the EPA ToxCast inventory to compare and contrast a selection of metabolism in silico tools, in terms of their coverage and performance relative to metabolism information reported in the literature. The aim was to build understanding of the scope and capabilities of these tools and how they could be utilised in a read-across assessment. The tools were Systematic Generation of Metabolites (SyGMa), Meteor Nexus, BioTransformer, Tissue Metabolism Simulator (TIMES), OECD Toolbox, and Chemical Transformation Simulator (CTS). Performance was characterised by sensitivity and precision determined by comparing predictions against literature reported metabolites (from 44 publications). A coverage score was derived to provide a relative quantitative comparison between the tools. Meteor, TIMES, Toolbox, and CTS predictions were run in batch mode, using default settings. SyGMa and BioTransformer were run with user-defined settings, (two passes of phase I and one pass of phase II). Hierarchical clustering revealed high similarity between TIMES and Toolbox. SyGMa had the highest coverage, matching an average of 38.63% of predictions generated by the other tools though was prone to significant overprediction. It generated 5,125 metabolites, which represented 54.67% of all predictions. Precision and sensitivity values ranged from 1.1-29% and 14.7-28.3% respectively. The Toolbox had the highest performance overall. A case study was presented for 3,4-Toluenediamine (3,4-TDA), assessed for the derivation of screening-level Provisional Peer Reviewed Toxicity Values (PPRTVs), was used to demonstrate the practical role in silico metabolism information can play in analogue evaluation as part of a read-across approach.

Machine Learning in Drug Metabolism Study

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