GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics

Pharmaceutical metabolite identification in lettuce (Lactuca sativa) and earthworms (Eisenia fetida) using liquid chromatography coupled to high-resolution mass spectrometry and in silico spectral library

Pharmaceuticals released into the aquatic and soil environments can be absorbed by plants and soil organisms, potentially leading to the formation of unknown metabolites that may negatively affect these organisms or contaminate the food chain. The aim of this study was to identify pharmaceutical metabolites through a triplet approach for metabolite structure prediction (software-based predictions, literature review, and known common metabolic pathways), followed by generating in silico mass spectral libraries and applying various mass spectrometry modes for untargeted LC-qTOF analysis. Therefore, Eisenia fetida and Lactuca sativa were exposed to a pharmaceutical mixture (atenolol, enrofloxacin, erythromycin, ketoprofen, sulfametoxazole, tetracycline) under hydroponic and soil conditions at environmentally relevant concentrations. Samples collected at different time points were extracted using QuEChERS and analyzed with LC-qTOF in data-dependent (DDA) and data-independent (DIA) acquisition modes, applying both positive and negative electrospray ionization. The triplet approach for metabolite structure prediction yielded a total of 3762 pharmaceutical metabolites, and an in silico mass spectral library was created based on these predicted metabolites. This approach resulted in the identification of 26 statistically significant metabolites (p < 0.05), with DDA + and DDA - outperforming DIA modes by successfully detecting 56/67 sample type:metabolite combinations. Lettuce roots had the highest metabolite count (26), followed by leaves (6) and earthworms (2). Despite the lower metabolite count, earthworms showed the highest peak intensities, closely followed by roots, with leaves displaying the lowest intensities. Common metabolic reactions observed included hydroxylation, decarboxylation, acetylation, and glucosidation, with ketoprofen-related metabolites being the most prevalent, totaling 12 distinct metabolites. In conclusion, we developed a high-throughput workflow combining open-source software with LC-HRMS for identifying unknown metabolites across various sample types.

Identification and quantification of acetyl tributyl citrate (ATBC) metabolites using human liver microsomes and human urine

Plasticizers are chemicals that make plastics flexible, and phthalates are commonly used. Due to the toxic effects of phthalates, there is increasing use of non-phthalate plasticizers like acetyl tributyl citrate (ATBC). ATBC has emerged as a safer alternative, yet concerns about its long-term safety persist due to its high leachability and potential endocrine-disrupting effects. This study aims to identify ATBC metabolites using human liver microsomes and suspect screening methods, and to explore potential urinary biomarkers for ATBC exposure. Using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry, we identified ATBC metabolites, including acetyl dibutyl citrate (ADBC), tributyl citrate (TBC), and dibutyl citrate (DBC). Urine samples from 15 participants revealed the presence of ADBC in 5, TBC in 11, and DBC in all samples, with DBC concentrations pointedly higher than the other metabolites. These metabolites show promise as biomarkers for ATBC exposure, though further validation with human data is required. Our results underscore the need for comprehensive studies on ATBC metabolism, exposure pathways, and urinary excretion to accurately assess human exposure levels.

Use of Structural Alerts for Reactive Metabolites in the Application SpotRM

Reactive metabolite (RM) formation is widely accepted as playing a crucial role in causing idiosyncratic adverse drug reactions (IADRs), where the liver is most affected. An important goal of drug design is to avoid selection of drug candidates giving rise to RMs and therefore risk causing problems later on involving IADRs. The simplest, initial approach is to avoid test structures that have substructures known or strongly suspected to be associated with IADRs. However, as is evident from the many case reports of IADRs, in most cases a clear association with any (bio)chemical mechanism is lacking, which makes it hard to establish any structure-toxicity relationship. Separate studies of RM formation, in vitro and in vivo, have led to likely evidence and to establishing many structural alerts (SAs) that can be used for fast selection/deselection of planned test compounds. As a background to a discussion of the concept, 25 kinase inhibitor drugs with known problems of hepatotoxicity were probed against a set of SAs contained in the application SpotRM. A clear majority of the probed drugs show liabilities as evident by being flagged by more than one of the fairly established types of SAs. At the same time, no clear SAs were found in three drugs, which is discussed in the broader context of usefulness and selection tactics of SAs in drug design.

Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

In vitro, in vivo metabolism and quantification of the novel synthetic opioid N-piperidinyl etonitazene (etonitazepipne)

OBJECTIVES

N-piperidinyl etonitazene (etonitazepipne) is a newly synthesized opioid related to the 2-benzylbenzimidazole analog class. Etonitazepipne has been formally notified and placed under intensive monitoring in Europe in January 2022. Nitazenes have high affinity at µ-opioid receptor (MOR). Etonitazepipne, specifically shows a EC50 of 2.49 nM, suggesting about 50 times higher potency combined with higher efficacy compared to morphine. Antinociceptive potency l ('hot plate test' with rats) was 192-fold greater than that of morphine.

METHODS

Here we report on a post-mortem case involving etonitazepipne and its quantification using a standard addition method (SAM) through liquid chromatography tandem mass spectrometry (LC-MS/MS). In addition, characterization and identification of phase I human metabolites using in vitro assay based on pooled human liver microsomes (pHLM) was performed along with the analysis of authentic urine samples by means of high-performance liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS).

RESULTS

The concentration of etonitazepipne in post-mortem blood and urine was 8.3 and 11 ng/mL, respectively. SAM was validated by assessing the following parameters: intraday and interday repeatability, matrix effect and recovery rate in post-mortem blood. A total of 20 and 14 metabolites were identified after pHLM incubation and urine analysis, respectively. Most pronounced in vitro and in vivo transformations were O-deethylation, hydroxylation, ketone reduction, and combinations thereof.

CONCLUSIONS

Considering small traces of the parent drug often found in real cases, the identification of metabolic biomarkers is crucial to identify exposure to this drug. O-deethylated, oxidated metabolites, and combination thereof are proposed as urinary biomarkers along with the parent compound.

MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering

Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational methods have been developed to predict drug metabolites, but most of them suffer from two main obstacles: the lack of model generalization due to restrictions on metabolic transformation rules or specific enzyme families, and high rate of false-positive predictions. Here, we presented MetaPredictor, a rule-free, end-to-end and prompt-based method to predict possible human metabolites of small molecules including drugs as a sequence translation problem. We innovatively introduced prompt engineering into deep language models to enrich domain knowledge and guide decision-making. The results showed that using prompts that specify the sites of metabolism (SoMs) can steer the model to propose more accurate metabolite predictions, achieving a 30.4% increase in recall and a 16.8% reduction in false positives over the baseline model. The transfer learning strategy was also utilized to tackle the limited availability of metabolic data. For the adaptation to automatic or non-expert prediction, MetaPredictor was designed as a two-stage schema consisting of automatic identification of SoMs followed by metabolite prediction. Compared to four available drug metabolite prediction tools, our method showed comparable performance on the major enzyme families and better generalization that could additionally identify metabolites catalyzed by less common enzymes. The results indicated that MetaPredictor could provide a more comprehensive and accurate prediction of drug metabolism through the effective combination of transfer learning and prompt-based learning strategies.

In Vitro and In Vivo Human Metabolism of Ostarine, a Selective Androgen Receptor Modulator and Doping Agent

Ostarine (enobasarm) is a selective androgen receptor modulator with great therapeutic potential. However, it is also used by athletes to promote muscle growth and enhance performances without the typical adverse effects of anabolic steroids. Ostarine popularity increased in recent years, and it is currently the most abused "other anabolic agent" (subclass S1.2. of the "anabolic agents" class S1) from the World Anti-Doping Agency's (WADA) prohibited list. Several cases of liver toxicity were recently reported in regular users. Detecting ostarine or markers of intake in biological matrices is essential to document ostarine use in doping. Therefore, we sought to investigate ostarine metabolism to identify optimal markers of consumption. The substance was incubated with human hepatocytes, and urine samples from six ostarine-positive cases were screened. Analyses were performed via liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS) and software-assisted data mining, with in silico metabolite predictions. Ten metabolites were identified with hydroxylation, ether cleavage, dealkylation, O-glucuronidation, and/or sulfation. The production of cyanophenol-sulfate might participate in the mechanism of ostarine liver toxicity. We suggest ostarine-glucuronide (C25H22O9N3F3, diagnostic fragments at m/z 118, 185, and 269) and hydroxybenzonitrile-ostarine-glucuronide (C25H22O10N3F3, diagnostic fragments at m/z 134, 185, and 269) in non-hydrolyzed urine and ostarine and hydroxybenzonitrile-ostarine (C19H14O4N3F3, diagnostic fragments at m/z 134, 185, and 269) in hydrolyzed urine as markers to document ostarine intake in doping.

Visualization of renal rotenone accumulation after oral administration and in situ detection of kidney injury biomarkers via MALDI mass spectrometry imaging

The examination of drug accumulation within complex biological systems offers valuable insights into the molecular aspects of drug metabolism and toxicity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an innovative methodology that enables the spatial visualization and quantification of biomolecules as well as drug and its metabolites in complex biological system. Hence, this method provides valuable insights into the metabolic profile and any molecular changes that may occur as a result of drug treatment. The renal system is particularly vulnerable to adverse effects of drug-induced harm and toxicity. In this study, MALDI MSI was utilized to examine the spatial distribution of drug and renal metabolites within kidney tissues subsequent to a single oral dosage of the anticancer compound rotenone. The integration of ion mobility spectrometry with MALDI MSI enhanced the data acquisition and analysis, resulting to improved mass resolution. Subsequently, the MS/MS fragment ions of rotenone reference drug were detected and characterized using MALDI HDMS/MS imaging. Notably, drug accumulation was observed in the cortical region of the representative kidney tissue sections treated with rotenone. The histological examination of treated kidney tissues did not reveal any observable changes. Differential ion intensity of renal endogenous metabolites was observed between untreated and rotenone-treated tissues. In the context of treated kidney tissues, the ion intensity level of sphingomyelin (D18:1/16:0), a sphingolipid indicator of glomerular cell injury and renal damage, was found to be elevated significantly compared to untreated kidney tissues. Conversely, the ion intensities of choline, glycero-3-phosphocholine (GPC), inosine, and a lysophosphatidylcholine LysoPC(18:0) exhibited a significant decrease. The results of this study demonstrate the potential of MALDI MSI as a novel technique for investigating the in situ spatial distribution of drugs and renal endogenous molecules while preserving the anatomical integrity of the kidney tissue. This technique can be used to study drug-induced metabolism and toxicity in a dynamic manner.

Human metabolism of four synthetic benzimidazole opioids: isotonitazene, metonitazene, etodesnitazene, and metodesnitazene

Following isotonitazene scheduling in 2019, the availability of alternative 2-benzylbenzimidazole opioids (nitazenes) on the global drug market increased, resulting in many fatalities worldwide. Nitazenes are potent µ-opioid receptor agonists with strong narcotic/analgesic effects, and their concentrations in biological matrices are low, making the detection of metabolite biomarkers of consumption crucial to document use in clinical and forensic settings. However, there is little to no data on the metabolism of the most recently available nitazenes, especially desnitro-analogues. The aim of the research was to assess isotonitazene, metonitazene, etodesnitazene, and metodesnitazene human metabolism and identify specific metabolite biomarkers of consumption. The four analogues were incubated with 10-donor-pooled human hepatocytes, and the incubates were analyzed by liquid chromatography-high-resolution tandem mass spectrometry and data mining with Compound Discoverer (Thermo Scientific); the analysis was supported by in silico metabolite predictions with GLORYx open-access software. Metabolites were identified in postmortem blood and/or urine samples from two metonitazene-positive and three etodesnitazene-positive cases following the same workflow, with and without glucuronide hydrolysis in urine, to confirm in vitro results. Twelve, nine, twenty-two, and ten metabolites were identified for isotonitazene, metonitazene, etodesnitazene, and metodesnitazene, respectively. The main transformations were N-deethylation at the N,N-diethylethanamine side chain, O-dealkylation, and further O-glucuronidation. In vitro and autopsy results were consistent, demonstrating the efficacy of the 10-donor-pooled human hepatocyte model to predict human metabolism. We suggest the parent and the corresponding O-dealkyl- and N-deethyl-O-dealkyl metabolites as biomarkers of exposure in urine after glucuronide hydrolysis, and the corresponding N-deethyl metabolite as additional biomarker in blood.

Next-generation risk assessment read-across case study: application of a 10-step framework to derive a safe concentration of daidzein in a body lotion

Introduction: We performed an exposure-based Next Generation Risk Assessment case read-across study using New Approach Methodologies (NAMs) to determine the highest safe concentration of daidzein in a body lotion, based on its similarities with its structural analogue, genistein. Two assumptions were: (1) daidzein is a new chemical and its dietary intake omitted; (2) only in vitro data were used for daidzein, while in vitro and legacy in vivo data for genistein were considered. Methods: The 10-step tiered approach evaluating systemic toxicity included toxicokinetics NAMs: PBPK models and in vitro biokinetics measurements in cells used for toxicogenomics and toxicodynamic NAMs: pharmacology profiling (i.e., interaction with molecular targets), toxicogenomics and EATS assays (endocrine disruption endpoints). Whole body rat and human PBPK models were used to convert external doses of genistein to plasma concentrations and in vitro Points of Departure (PoD) to external doses. The PBPK human dermal module was refined using in vitro human skin metabolism and penetration data. Results: The most relevant endpoint for daidzein was from the ERα assay (Lowest Observed Effective Concentration was 100 ± 0.0 nM), which was converted to an in vitro PoD of 33 nM. After application of a safety factor of 3.3 for intra-individual variability, the safe concentration of daidzein was estimated to be 10 nM. This was extrapolated to an external dose of 0.5 μg/cm2 for a body lotion and face cream, equating to a concentration of 0.1%. Discussion: When in vitro PoD of 33 nM for daidzein was converted to an external oral dose in rats, the value correlated with the in vivo NOAEL. This increased confidence that the rat oral PBPK model provided accurate estimates of internal and external exposure and that the in vitro PoD was relevant in the safety assessment of both chemicals. When plasma concentrations estimated from applications of 0.1% and 0.02% daidzein were used to calculate bioactivity exposure ratios, values were >1, indicating a good margin between exposure and concentrations causing adverse effects. In conclusion, this case study highlights the use of NAMs in a 10-step tiered workflow to conclude that the highest safe concentration of daidzein in a body lotion is 0.1%.

Insights into the metabolism of CH-PIATA-A novel synthetic cannabinoid featuring an acetamide linker

The recent change from the popular carboxamide to an acetamide (ATA) linker scaffold in synthetic cannabinoid receptor agonists (SCRAs) can be interpreted as an attempt to circumvent legal regulations, setting new analytical challenges. Metabolites of N-cyclohexyl-2-(1-pentyl-1 H-indol-3-yl)acetamide: CH-PIATA, the second ATA type SCRA detected in the EU, were investigated in urine and serum samples by LC-HRMS-MS and LC-MS-MS. Two different in vitro models, a pHLM assay and HepG2-cells, as well as an in silico prediction by GLORYx freeware assisted in metabolite formation/identification. CH-PIATA was extensively metabolized, leading to metabolites formed primarily by mono- and dihydroxylation. For urine and serum specimens, monohydroxylation at the indole core or the methylene spacer of the acetamide linker (M1.8), carboxylic acid formation at the N-pentyl side chain (M3.1) and degradation of the latter leading to a tentatively identified N-propionic acid metabolite (M5.1) are suggested as reliable markers for substance intake. The N-propionic acid metabolite could not be confirmed in the in vitro assays as it includes multiple consecutive metabolic reactions. Furthermore, CH-PIATA could be detected as parent substance in blood samples, but not in urine. Both in vitro assays and the in silico tool proved suitable for predicting metabolites of CH-PIATA. Considering effort and costs, pHLM incubations seem to be more effective for metabolite prediction in forensic toxicology than HepG2 cells. The highlighted Phase I metabolites serve as reliable urinary targets for confirming CH-PIATA use. The in silico approach is advantageous when reference material is unavailable.

3-CMC, 4-CMC, and 4-BMC Human Metabolic Profiling: New Major Pathways to Document Consumption of Methcathinone Analogues?

Synthetic cathinones represent one of the largest and most abused new psychoactive substance classes, and have been involved in numerous intoxications and fatalities worldwide. Methcathinone analogues like 3-methylmethcathinone (3-MMC), 3-chloromethcathinone (3-CMC), and 4-CMC currently constitute most of synthetic cathinone seizures in Europe. Documenting their consumption in clinical/forensic casework is therefore essential to tackle this trend. Targeting metabolite markers is a go-to to document consumption in analytical toxicology, and metabolite profiling is crucial to support investigations. We sought to identify 3-CMC, 4-CMC, and 4-bromomethcathinone (4-BMC) human metabolites. The substances were incubated with human hepatocytes; incubates were screened by liquid chromatography-high-resolution tandem mass spectrometry and data were mined with Compound Discoverer (Themo Scientific). 3-CMC-positive blood, urine, and oral fluid and 4-CMC-positive urine and saliva from clinical/forensic casework were analyzed. Analyses were supported by metabolite predictions with GLORYx freeware. Twelve, ten, and ten metabolites were identified for 3-CMC, 4-CMC, and 4-BMC, respectively, with similar transformations occurring for the three cathinones. Major reactions included ketoreduction and N-demethylation. Surprisingly, predominant metabolites were produced by combination of N-demethylation and ω-carboxylation (main metabolite in 3-CMC-positive urine), and combination of β-ketoreduction, oxidative deamination, and O-glucuronidation (main metabolite in 4-CMC-positive urine). These latter metabolites were detected in negative-ionization mode only and their non-conjugated form was not detected after glucuronide hydrolysis; this metabolic pathway was never reported for any methcathinone analogue susceptible to undergo the same transformations. These results support the need for comprehensive screening strategies in metabolite identification studies, to avoid overlooking significant metabolites and major markers of consumption.

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.

D-CyPre: a machine learning-based tool for accurate prediction of human CYP450 enzyme metabolic sites

The advancement of graph neural networks (GNNs) has made it possible to accurately predict metabolic sites. Despite the combination of GNNs with XGBOOST showing impressive performance, this technology has not yet been applied in the realm of metabolic site prediction. Previous metabolic site prediction tools focused on bonds and atoms, regardless of the overall molecular skeleton. This study introduces a novel tool, named D-CyPre, that amalgamates atom, bond, and molecular skeleton information via two directed message-passing neural networks (D-MPNN) to predict the metabolic sites of the nine cytochrome P450 enzymes using XGBOOST. In D-CyPre Precision Mode, the model produces fewer, but more accurate results (Jaccard score: 0.497, F1: 0.660, and precision: 0.737 in the test set). In D-CyPre Recall Mode, the model produces less accurate, but more comprehensive results (Jaccard score: 0.506, F1: 0.669, and recall: 0.720 in the test set). In the test set of 68 reactants, D-CyPre outperformed BioTransformer on all isoenzymes and CyProduct on most isoenzymes (5/9). For the subtypes where D-CyPre outperformed CyProducts, the Jaccard score and F1 scores increased by 24% and 16% in Precision Mode (4/9) and 19% and 12% in Recall Mode (5/9), respectively, relative to the second-best CyProduct. Overall, D-CyPre provides more accurate prediction results for human CYP450 enzyme metabolic sites.

Predicting Retention Time in Unified-Hydrophilic-Interaction/Anion-Exchange Liquid Chromatography High-Resolution Tandem Mass Spectrometry (Unified-HILIC/AEX/HRMS/MS) for Comprehensive Structural Annotation of Polar Metabolome

The accuracy of the structural annotation of unidentified peaks obtained in metabolomic analysis using liquid chromatography/tandem mass spectrometry (LC/MS/MS) can be enhanced using retention time (RT) information as well as precursor and product ions. Unified-hydrophilic-interaction/anion-exchange liquid chromatography high-resolution tandem mass spectrometry (unified-HILIC/AEX/HRMS/MS) has been recently developed as an innovative method ideal for nontargeted polar metabolomics. However, the RT prediction for unified-HILIC/AEX has not been developed because of the complex separation mechanism characterized by the continuous transition of the separation modes from HILIC to AEX. In this study, we propose an RT prediction model of unified-HILIC/AEX/HRMS/MS, which enables the comprehensive structural annotation of polar metabolites. With training data for 203 polar metabolites, we ranked the feature importance using a random forest among 12,420 molecular descriptors (MDs) and constructed an RT prediction model with 26 selected MDs. The accuracy of the RT model was evaluated using test data for 51 polar metabolites, and 86.3% of the ΔRTs (difference between measured and predicted RTs) were within ±1.50 min, with a mean absolute error of 0.80 min, indicating high RT prediction accuracy. Nontargeted metabolomic data from the NIST SRM 1950-Metabolites in frozen human plasma were analyzed using the developed RT model and in silico MS/MS prediction, resulting in a successful structural estimation of 216 polar metabolites, in addition to the 62 identified based on standards. The proposed model can help accelerate the structural annotation of unknown hydrophilic metabolites, which is a key issue in metabolomic research.

Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation

The ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. Pharmaceutical companies typically have measured metabolism data available for several hundred to several thousand compounds. However, even for metabolism experts, interpreting these data and assigning SoMs are challenging and time-consuming. Therefore, a significant proportion of the potential of the existing metabolism data, particularly in machine learning, remains dormant. Here, we report on the development and validation of an active learning approach that identifies the most informative atoms across molecular data sets for SoM annotation. The active learning approach, built on a highly efficient reimplementation of SoM predictor FAME 3, enables experts to prioritize their SoM experimental measurements and annotation efforts on the most rewarding atom environments. We show that this active learning approach yields competitive SoM predictors while requiring the annotation of only 20% of the atom positions required by FAME 3. The source code of the approach presented in this work is publicly available.

In silico and in vitro human metabolism of IOX2, a performance-enhancing doping agent

IOX2 is a potent inhibitor of prolyl hydroxylase 2, a key enzyme in the regulation of hypoxia-inducible factor (HIF) and oxygen homeostasis. As such, it can be used to enhance athletic performance and is currently banned by the World Anti-Doping Agency (WADA). Detection of metabolites is critical to demonstrate drug use in doping. However, there is currently little data on IOX2 human metabolism. Our aim was to identify relevant biomarkers of IOX2 use in humans. For this purpose, IOX2 was incubated with 10-donor-pooled human hepatocytes for 3 h, incubates were analyzed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS), and LC-HRMS/MS data were screened with Compound Discoverer (Thermo Scientific) for a comprehensive identification of IOX2 metabolites. Additionally, IOX2 human metabolites were predicted with GLORYx open-access software (University of Hamburg, Germany) to assist in the LC-HRMS/MS analysis and data mining. Thirteen metabolites were identified, oxidation at the quinolinyl group, O-glucuronidation, and combinations being predominant biotransformations. The results were consistent with previous animal studies and a single case of oral microdose administration. We suggest hydroxyquinolinyl-IOX2 as major biomarker of IOX2 use in biological samples, glucuronide hydrolysis being critical to increase IOX2 and hydroxyquinolinyl-IOX2 detectability in urine.

Structural Elucidation of Agrochemical Metabolic Transformation Products Based on Infrared Ion Spectroscopy to Improve In Silico Toxicity Assessment

Toxicological assessments of newly developed agrochemical agents consider chemical modifications and their metabolic and biotransformation products. To carry out an in silico hazard assessment, understanding the type of chemical modification and its location on the original compound can greatly enhance the reliability of the evaluation. Here, we present and apply a method based on liquid chromatography-mass spectrometry (LC-MS) enhanced with infrared ion spectroscopy (IRIS) to better delineate the molecular structures of transformation products before in silico toxicology evaluation. IRIS facilitates the recording of IR spectra directly in the mass spectrometer for features selected by retention time and mass-to-charge ratio. By utilizing quantum-chemically predicted IR spectra for candidate molecular structures, one can either derive the actual structure or significantly reduce the number of (isomeric) candidate structures. This approach can assist in making informed decisions. We apply this method to a plant growth stimulant, digeraniol sinapoyl malate (DGSM), that is currently under development. Incubation of the compound in Caco-2 and HepaRG cell lines in multiwell plates and analysis by LC-MS reveals oxidation, glucuronidation, and sulfonation metabolic products, whose structures were elucidated by IRIS and used as input for an in silico toxicology assessment. The toxicity of isomeric metabolites predicted by in silico tools was also assessed, which revealed that assigning the right metabolite structure is an important step in the overall toxicity assessment of the agrochemical. We believe this identification approach can be advantageous when specific isomers are significantly more hazardous than others and can help better understand metabolic pathways.

Pharmacochemical Study of Multitarget Amino Acids' Hybrids: Design, Synthesis, In vitro, and In silico Studies

INTRODUCTION

Neuro-inflammation is a complex phenomenon resulting in several disorders. ALOX-5, COX-2, pro-inflammatory enzymes, and amino acid neurotransmitters are tightly correlated to neuro-inflammatory pathologies. Developing drugs that interfere with these targets will offer treatment for various diseases.

OBJECTIVE

Herein, we extend our previous research by synthesizing a series of multitarget hybrids of cinnamic acids with amino acids recognized as neurotransmitters.

METHODS

The synthesis was based on an In silico study of a library of cinnamic amide hybrids with glycine, γ- aminobutyric, and L - glutamic acids. Drug-likeness and ADMET properties were subjected to In silico analysis. Cinnamic acids were derived from the corresponding aldehydes by Knoevenagel condensation. The synthesis of the amides followed a two-step reaction with 1- hydroxybenzotriazole monohydrate and 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride in dry dichloromethane and the corresponding amino acid ester hydrochloride salt in the presence of N,N,-diisopropyl-Nethylamine.

RESULTS

The structure of the synthesized compounds was confirmed spectrophotometrically. The new compounds, such as lipoxygenase, cyclooxygenase-2, lipid peroxidation inhibitors, and antiinflammatories, were tested in vitro. The compounds exhibited LOX inhibition with IC50 values in the low μM region).

CONCLUSION

Compounds 18a, 23b, and 11c are strong lipid peroxidation inhibitors (99%, 78%, and 92%). Compound 28c inhibits SLOX-1 with IC50 =8.5 μM whereas 11a and 22a highly inhibit COX-2 (IC50 6 and 5 μM Hybrids 14c and 17c inhibit both enzymes. Compound 29c showed the highest anti-inflammatory activity (75%). The In silico ADMET properties of 14c and 11a support their drug-likeness.

In silico and in vitro metabolism studies of the new synthetic opiate AP-237 (bucinnazine) using bioinformatics tools

The recent emergence of new synthetic opioids (NSOs) compounds in the illicit market is increasingly related to fatal cases. Identification and medical care of NSO intoxication cases are challenging, particularly due to high frequency of new products and extensive metabolism. As the study of NSO metabolism is crucial for the identification of these drugs in cases of intoxication, we aimed to investigate the metabolism of the piperazine NSO AP-237 (= bucinnazine). Two complementary approaches (in silico and in vitro) were used to identify putative AP-237 metabolites which could be used as consumption markers. In silico metabolism studies were realized by combining four open access softwares (MetaTrans, SyGMa, Glory X, Biotransformer 3.0). In vitro experiments were performed by incubating AP-237 (20 µM) in differentiated HepaRG cells during 0 h, 8 h, 24 h or 48 h. Cell supernatant were extracted and analyzed by liquid chromatography coupled to high-resolution mass spectrometry and data were reprocessed using three strategies (MetGem, GNPS or Compound Discoverer®). A total of 28 phase I and six phase II metabolites was predicted in silico. Molecular networking identified seven putative phase I metabolites (m/z 203.154, m/z 247.180, m/z 271.180, two m/z 289.191 isomers, m/z 305.186, m/z 329.222), including four previously unknown metabolites. Overall, this cross-disciplinary approach with molecular networking on data acquired in vitro and in silico prediction enabled to propose relevant candidate as AP-237 consumption markers that could be added to mass spectrometry libraries to help diagnose intoxication.

Unraveling biomarkers of exposure for tenuazonic acid through urinary metabolomics

Mycotoxins are secondary metabolites produced by fungi such as Aspergillus, Alternaria, and Penicillium, affecting nearly 80% of global food crops. Tenuazonic acid (TeA) is the major mycotoxin produced by Alternaria alternata, a prevalent pathogen affecting plants, fruits, and vegetables. TeA is notably prevalent in European diets, however, TeA biomarkers of exposure and metabolites remain unknown. This research aims to bridge this knowledge-gap by gaining insights about human TeA exposure and metabolization. Nine subjects were divided into two groups. The first group received a single bolus of TeA at the Threshold of Toxicological Concern (TTC) to investigate the presence of TeA urinary biomarkers, while the second group served as a control. Sixty-nine urinary samples were prepared and analyzed using UPLC-Xevo TQ-XS for TeA quantification and UPLC-Orbitrap Exploris for polar metabolome acquisition. TeA was rapidly excreted during the first 13 h and the fraction extracted was 0.39 ± 0.22. The polar metabolome compounds effectively discriminating the two groups were filtered using Orthogonal Partial Least Squares-Discriminant Analysis and subsequently annotated (n = 122) at confidence level 4. Finally, the urinary metabolome was compared to in silico predicted TeA metabolites. Nine metabolites, including oxidized, N-alkylated, desaturated, glucuronidated, and sulfonated forms of TeA were detected.

Using in silico tools to predict flame retardant metabolites for more informative exposomics-based approaches

Introduction: The positive identification of xenobiotics and their metabolites in human biosamples is an integral aspect of exposomics research, yet challenges in compound annotation and identification continue to limit the feasibility of comprehensive identification of total chemical exposure. Nonetheless, the adoption of in silico tools such as metabolite prediction software, QSAR-ready structural conversion workflows, and molecular standards databases can aid in identifying novel compounds in untargeted mass spectral investigations, permitting the assessment of a more expansive pool of compounds for human health hazard. This strategy is particularly applicable when it comes to flame retardant chemicals. The population is ubiquitously exposed to flame retardants, and evidence implicates some of these compounds as developmental neurotoxicants, endocrine disruptors, reproductive toxicants, immunotoxicants, and carcinogens. However, many flame retardants are poorly characterized, have not been linked to a definitive mode of toxic action, and are known to share metabolic breakdown products which may themselves harbor toxicity. As U.S. regulatory bodies begin to pursue a subclass- based risk assessment of organohalogen flame retardants, little consideration has been paid to the role of potentially toxic metabolites, or to expanding the identification of parent flame retardants and their metabolic breakdown products in human biosamples to better inform the human health hazards imposed by these compounds. Methods: The purpose of this study is to utilize publicly available in silico tools to 1) characterize the structural and metabolic fates of proposed flame retardant classes, 2) predict first pass metabolites, 3) ascertain whether metabolic products segregate among parent flame retardant classification patterns, and 4) assess the existing coverage in of these compounds in mass spectral database. Results: We found that flame retardant classes as currently defined by the National Academies of Science, Engineering and Medicine (NASEM) are structurally diverse, with highly variable predicted pharmacokinetic properties and metabolic fates among member compounds. The vast majority of flame retardants (96%) and their predicted metabolites (99%) are not present in spectral databases, posing a challenge for identifying these compounds in human biosamples. However, we also demonstrate the utility of publicly available in silico methods in generating a fit for purpose synthetic spectral library for flame retardants and their metabolites that have yet to be identified in human biosamples. Discussion: In conclusion, exposomics studies making use of fit-for-purpose synthetic spectral databases will better resolve internal exposure and windows of vulnerability associated with complex exposures to flame retardant chemicals and perturbed neurodevelopmental, reproductive, and other associated apical human health impacts.

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.

Metabolism Study of Anamorelin, a GHSR1a Receptor Agonist Potentially Misused in Sport, with Human Hepatocytes and LC-HRMS/MS

Anamorelin, developed for the treatment of cancer cachexia, is an orally active medication that improves appetite and food intake, thereby increasing body mass and physical functioning. It is classified as a growth hormone secretagogue and strictly monitored by the World Anti-Doping Agency (WADA), owing to its anabolic enhancing potential. Identifying anamorelin and/or metabolite biomarkers of consumption is critical in doping controls. However, there are currently no data available on anamorelin human metabolic fate. The aim of this study was to investigate and identify biomarkers characteristic of anamorelin intake using in silico metabolite predictions with GLORYx, in vitro incubation with 10-donor-pooled human hepatocytes, liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS) analysis, and data processing with Thermo Scientific's Compound Discoverer. In silico prediction resulted in N-acetylation at the methylalanyl group as the main transformation (score, 88%). Others including hydroxylation at the indole substructure, and oxidation and N-demethylation at the trimethylhydrazino group were predicted (score, ≤36%). Hepatocyte incubations resulted in 14 phase I metabolites formed through N-demethylation at the trimethylhydrazino group, N-dealkylation at the piperidine ring, and oxidation at the indole and methylalanyl groups; and two phase II glucuronide conjugates occurring at the indole. We propose four metabolites detected as specific biomarkers for toxicological screening.

Design, synthesis, and pharmacological evaluation of [1, 3] dioxolo-chromeno[2,3-b]pyridines as anti-seizure agents

An efficient one-pot three-component reaction for the synthesis of [1,3]dioxolo[4',5':6,7]chromeno[2,3-b]pyridines 4(a-i) has been developed. Synthesis was achieved by reacting sesamol (1), aromatic aldehydes 2(a-i), and 2-aminopropene-1,1,3-tricarbonitrile (3) in the presence of triethylamine at 100 °C under neat reaction condition. Simple operational procedure, broad substrate scope, column chromatography free separations, and high yield of products make it an efficient and largely acceptable synthetic strategy. Synthesized compounds 4(a-i) were further screened for preliminary anticonvulsant activity using MES and scPTZ tests. These analogs were also checked for neurotoxicity and hepatotoxicity. Selected active compounds have been then screened quantitatively to determine ED50 and TD50 values. Analog 4h was found effective in both preclinical seizure models with significant therapeutic/toxicity profile (4h: ED50 = 34.7 mg/kg, MES test; ED50 = 37.9 mg/kg, scPTZ test; TD50 = 308.7 mg/kg). Molecular dynamic simulation for 100 ns of compound 4h-complexed with GABAA receptor revealed good thermodynamic behavior and fairly stable interactions (4h, Docking score =  - 10.94). In conclusion, effective synthetic strategy, significant anticonvulsant activity with good toxicity profile and detailed molecular modeling studies led us to anticipate the emergence of these analogs as valid leads for the development of future effective neurotherapeutic agents.

Roles of mixed nutrient intakes on metabolic syndrome among korean adults 19-80 years old: molecular mechanisms involved

Purpose

We aim to identify the association between nutrient intake mixtures (22 micro-macro nutrients) and metabolic syndrome (MetS) or its components, including molecular mechanisms involved, among 16,807 Korean adults aged 19-80.

Methods

The associations of mixed nutrient intakes on MetS or its components were identified using linear regression models, WQS regression, qgcomp, and BKMR regression. Genes, transcription factors, miRNA, biological processes, and pathways were assessed using GeneMania, CHEA3, MIENTURNET, and ToppFun functions.

Results

We found that the overall effect of mixed nutrient intakes was also related to MetS and its components. In silico analysis, we found that a mixture of nutrients interacted with the IL6 gene and was linked with MetS. Physical interactions were the key interactions (77%) involved in the mutual genes and MetS targeted by a mixture of nutrients. IL6 related pathways, "positive regulation of type B pancreatic cell apoptotic process", "regulation of glucagon secretion", "LDL pathway during atherogenesis", and "IL-10 anti-inflammatory signaling pathway" were identified as key molecular mechanisms that may be targeted by mixed nutrients implicated in MetS. The key miRNAs and transcription factors involved in the process of MetS targeted by a mixture of nutrients were also described. The cutoff levels for nutrient intake levels associated with MetS and its components were also described.

Conclusion

Our findings will pave the way for further research to evaluate the interactions between a mixture of nutrients and non-communicable diseases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-022-01158-1.

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

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

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).

Natural inhibitors for acetylcholinesterase and autophagy modulators as effective antagonists for tau and β-amyloid in Alzheimer's rat model

Background: Phytochemicals have amazing biological effects in relation to age-related illnesses and are increasingly being studied in clinical trials. The goal of this study was to examine the effectiveness of the aqueous extracts of Rosmarinus officinalis L. (Rosemary) and Crocus sativus L. (Saffron) and their combinations as tau and β-amyloid antagonists in an Alzheimer's rat model. Methods: AlCl₃ and D-galactose (150 & 300 mg/kg) were used to create the Alzheimer's neuroinflammation rat model. The animals were subsequently given the two extracts and their combinations (500 mg/kg) along 15 days. The cognitive impairment, oxidative stress, tau & amyloid neuroproteins, acetylcholine, acetylcholinesterase neurotransmitters, proinflammatory cytokines, LC3 as an autophagy marker, computational analysis, and morphological alterations were all assessed. Results: When compared to the conventional donepezil and normal groups, the treated groups showed a significant improvement in all calculated parameters. The cortex and hippocampus have a better morphological appearance. In silico analysis found that these extracts may have an affinity for and impede the activity of some proteins thought to be essential regulators of disease progression. Conclusion: Rosemary and Saffron extracts by the power of their constituents were able to alleviate the neurotoxicity of AlCl₃ & D-galactose and regulate the natural autophagy process.

β'-Phenylfentanyl Metabolism in Primary Human Hepatocyte Incubations: Identification of Potential Biomarkers of Exposure in Clinical and Forensic Toxicology

From 2014 onwards, illicit fentanyl and analogues have caused numerous intoxications and fatalities worldwide, impacting the demographics of opioid-related overdoses. The identification of cases involving fentanyl analogues is crucial in clinical and forensic settings to treat patients, elucidate intoxications, address drug use disorders and tackle drug trends. However, in analytical toxicology, the concentration of fentanyl analogues in biological matrices is low, making their detection challenging. Therefore, the identification of specific metabolite biomarkers is often required to document consumption. β'-Phenylfentanyl (N-phenyl-N-[1-(2-phenylethyl)-4-piperidinyl]-benzenepropanamide) is a fentanyl analogue that was first detected in Sweden in 2017 and has recently reemerged onto the American illicit drug market. There is little data available on β'-phenylfentanyl effects and toxicokinetics and its metabolism is yet to be studied. We aimed to investigate β'-phenylfentanyl human metabolism to identify potential biomarkers of use. To assist in β'-phenylfentanyl metabolite identification, a list of putative reactions was generated using in silico predictions with GLORYx freeware. β'-phenylfentanyl was incubated with cryopreserved 10-donor-pooled human hepatocytes, analyses were performed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS-MS) and data were processed using a partially automated targeted/untargeted approach with Compound Discoverer. We identified 26 metabolites produced by N-dealkylation, oxidation, hydroxylation, O-glucuronidation, O-methylation and combinations thereof. We suggest β'-phenylnorfentanyl (N-phenyl-N-4-piperidinyl-benzenepropanamide) and further metabolites 1-oxo-N-phenyl-N-4-piperidinyl-benzenepropanamide and 1-hydroxy-N-phenyl-N-4-piperidinyl-benzenepropanamide as major biomarkers of β'-phenylfentanyl use. In silico predictions were mostly wrong, and β'-phenylfentanyl metabolic fate substantially differed from that of a closely related analogue incubated in the same conditions, highlighting the value of the experimental assessment of new psychoactive substance human metabolism. In vivo data are necessary to confirm the present results. However, the present results may be necessary to help analytical toxicologists identify β'-phenylfentanyl-positive cases to provide authentic samples.

Drug Metabolism: A Half-Century Plus of Progress, Continued Needs, and New Opportunities

The systematic study of drug metabolism began in the 19th Century, but most of what we know now has been learned in the last 50 years. Drug metabolism continues to play a critical role in pharmaceutical development and clinical practice, as well as contributing to toxicology, chemical carcinogenesis, endocrinology, and drug abuse. The importance of the field will continue, but its nature will continue to develop with changes in analytical chemistry, structural biology, and artificial intelligence. Challenges and opportunities include toxicology, defining roles of genetic variations, and application to clinical issues. Although the focus of this Minireview is cytochrome P450, the same principles apply to other enzymes and transporters involved in drug metabolism. SIGNIFICANCE STATEMENT: Progress in the field of drug metabolism over the past 50 years has helped make the pharmaceutical enterprise what it is today. Drug metabolism will continue to be important. Challenges and opportunities for the future are discussed.

Biomarkers of 4-hydroxy-N,N-methylpropyltryptamine (4-OH-MPT) intake identified from human hepatocyte incubations

BACKGROUND

4-Hydroxy-N,N-methylpropyltryptamine (4-OH-MPT) is a psychedelic tryptamine whose use is regulated in several countries. Due to unspecific effects, consumption can be ascertained only through toxicological analyses. However, the trace amounts of tryptamines are usually challenging to detect in biological samples. 4-OH-MPT metabolism was characterized to identify optimal metabolite markers of intake in clinical/forensic toxicology.

RESEARCH DESIGN AND METHODS

4-OH-MPT was incubated with 10-donor-pooled human hepatocytes to simulate in vivo conditions; samples were analyzed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS), and data were processed with Compound Discoverer from Thermo Scientific. LC-HRMS/MS and data mining were supported by in silico metabolite predictions (GLORYx).

RESULTS

Three phase I and four phase II metabolites were identified, including N-oxidation and N-demethylation at the alkylamine chain, and O-glucuronidation and sulfation at the hydroxylindole core.

CONCLUSIONS

4-OH-MPT metabolic fate was consistent with the human metabolism of tryptamine analogues: we suggest 4-OH-MPT-N-oxide and 4-hydroxy-N,N-propyltryptamine (4-OH-PT) as metabolite biomarkers of 4-OH-MPT consumption after glucuronide/sulfate hydrolysis in biological samples to improve detection of 4-OH-MPT and phase I metabolites; 4-OH-MPT-glucuronide is suggested as an additional biomarker when hydrolysis is not performed. Further research on the metabolism of structural analogues is necessary to evaluate the specificity of 4-OH-MPT metabolite biomarkers.

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.

Developing Role for Artificial Intelligence in Drug Discovery in Drug Design, Development, and Safety Assessment

Artificial intelligence (AI) is a rapidly growing discipline in the field of chemical toxicology. Herein, we provide a broad overview of research presented at the Fall 2022 American Chemical Society meeting, highlighting how AI is being applied across various facets of drug design, development, and safety assessment.

Human Hepatocyte 4-Acetoxy-N,N-Diisopropyltryptamine Metabolite Profiling by Reversed-Phase Liquid Chromatography Coupled with High-Resolution Tandem Mass Spectrometry

Tryptamine intoxications and fatalities are increasing, although these novel psychoactive substances (NPS) are not controlled in most countries. There are few data on the metabolic pathways and enzymes involved in tryptamine biotransformation. 4-acetoxy-N,N-diisopropyltryptamine (4-AcO-DiPT) is a synthetic tryptamine related to 4-hydroxy-N,N-diisopropyltryptamine (4-OH-DiPT), 4-acetyloxy-N,N-dipropyltryptamine (4-AcO-DPT), and 4-acetoxy-N,N-dimethyltryptamine (4-AcO-DMT). The aim of this study was to determine the best 4-AcO-DiPT metabolites to identify 4-AcO-DiPT consumption through human hepatocyte metabolism and high-resolution mass spectrometry. 4-AcO-DiPT metabolites were predicted in silico with GLORYx freeware to assist in metabolite identification. 4-AcO-DiPT was incubated with 10-donor-pooled human hepatocytes and sample analysis was performed with reversed-phase liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS/MS) in positive- and negative-ion modes. Software-assisted LC-HRMS/MS raw data mining was performed. A total of 47 phase I and II metabolites were predicted, and six metabolites were identified after 3 h incubation following ester hydrolysis, O-glucuronidation, O-sulfation, N-oxidation, and N-dealkylation. All second-generation metabolites were derived from the only first-generation metabolite detected after ester hydrolysis (4-OH-DiPT). The metabolite with the second-most-intense signal was 4-OH-iPT-sulfate followed by 4-OH-DiPT-glucuronide, indicating that glucuronidation and sulfation are common in this tryptamine’s metabolic pathway. 4-OH-DiPT, 4-OH-iPT, and 4-OH-DiPT-N-oxide are suggested as optimal biomarkers to identify 4-AcO-DiPT consumption.

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.

Discovery and Anticancer Activity of Novel 1,3,4-Thiadiazole- and Aziridine-Based Indolin-2-ones via In Silico Design Followed by Supramolecular Green Synthesis

Three crucial anticancer scaffolds, namely indolin-2-one, 1,3,4-thiadiazole, and aziridine, are explored to synthesize virtually screened target molecules based on the c-KIT kinase protein. The stem cell factor receptor c-KIT was selected as target because most U.S. FDA-approved receptor tyrosine kinase inhibitors bearing the indolin-2-one scaffold profoundly inhibit c-KIT. Molecular hybrids of indolin-2-one with 1,3,4-thiadiazole (IIIa-m) and aziridine (VIa and VIc) were afforded through a modified Schiff base green synthesis using β-cyclodextrin-SO₃H in water as a recyclable proton-donor catalyst. A computational study found that indolin-2,3-dione forms a supramolecular inclusion complex with β-cyclodextrin-SO₃H through noncovalent interactions. A molecular docking study of all the synthesized compounds was executed on the c-KIT kinase domain, and most compounds displayed binding affinities similar to that of Sunitinib. On the basis of the pharmacokinetic significance of the aryl thioether linkage in small molecules, 1,3,4-thiadiazole hybrids (IIIa-m) were extended to a new series of 3-((5-(phenylthio)-1,3,4-thiadiazol-2-yl)imino)indolin-2-ones (IVa-m) via thioetherification using bis(triphenylphosphine)palladium(II)dichloride as the catalyst for C-S bond formation. Target compounds were tested against NCI-60 human cancer cell lines for a single-dose concentration. Among all three series of indolin-2-ones, the majority of compounds demonstrated broad-spectrum activity toward various cancer cell lines. Compounds IVc and VIc were further evaluated for a five-dose anticancer study. Compound IVc showed a potent activity of IC₅₀ = 1.47 μM against a panel of breast cancer cell lines, whereas compound VIc exhibited the highest inhibition for a panel of colon cancer cell lines at IC₅₀ = 1.40 μM. In silico ADME property descriptors of all the target molecules are in an acceptable range. Machine learning algorithms were used to examine the metabolites and phase I and II regioselectivities of compounds IVc and VIc, and the results suggested that these two compounds could be potential leads for the treatment of cancer.

MetNC: Predicting Metabolites in vivo for Natural Compounds

Natural compounds (NCs) undergo complicated biotransformation in vivo to produce diverse forms of metabolites dynamically, many of which are of high medicinal value. Predicting the profiles of chemical products may help to narrow down possible candidates, yet current computational methods for predicting biotransformation largely focus on synthetic compounds. Here, we proposed a method of MetNC, a tailor-made method for NC biotransformation prediction, after exploring the overall patterns of NC in vivo metabolism. Based on 850 pairs of the biotransformation dataset validated by comprehensive in vivo experiments with sourcing compounds from medicinal plants, MetNC was designed to produce a list of potential metabolites through simulating in vivo biotransformation and then prioritize true metabolites into the top list according to the functional groups in compound structures and steric hindrance around the reaction sites. Among the well-known peers of GLORYx and BioTransformer, MetNC gave the highest performance in both the metabolite coverage and the ability to short-list true products. More importantly, MetNC seemed to display an extra advantage in recommending the microbiota-transformed metabolites, suggesting its potential usefulness in the overall metabolism estimation. In summary, complemented to those techniques focusing on synthetic compounds, MetNC may help to fill the gap of natural compound metabolism and narrow down those products likely to be identified in vivo.

Considerations for Improving Metabolism Predictions for In Vitro to In Vivo Extrapolation

High-throughput (HT) in vitro to in vivo extrapolation (IVIVE) is an integral component in new approach method (NAM)-based risk assessment paradigms, for rapidly translating in vitro toxicity assay results into the context of in vivo exposure. When coupled with rapid exposure predictions, HT-IVIVE supports the use of HT in vitro assays for risk-based chemical prioritization. However, the reliability of prioritization based on HT bioactivity data and HT-IVIVE can be limited as the domain of applicability of current HT-IVIVE is generally restricted to intrinsic clearance measured primarily in pharmaceutical compounds. Further, current approaches only consider parent chemical toxicity. These limitations occur because current state-of-the-art HT prediction tools for clearance and metabolite kinetics do not provide reliable data to support HT-IVIVE. This paper discusses current challenges in implementation of IVIVE for prioritization and risk assessment and recommends a path forward for addressing the most pressing needs and expanding the utility of IVIVE.

In silico, in vitro, and in vivo human metabolism of acetazolamide, a carbonic anhydrase inhibitor and common "diuretic and masking agent" in doping

Acetazolamide (ACZ) is a carbonic anhydrase inhibitor prescribed for the treatment of various pathologies. It is also used in doping and is prohibited in and out of sportive competitions. ACZ was reported not to undergo metabolization. However, the detection of ACZ metabolites may be critical for documenting ACZ use. We aimed to further investigate ACZ metabolic fate in humans. ACZ putative metabolites were generated in silico to assist in metabolite identification. ACZ was incubated with primary human hepatocytes to identify in vitro metabolites (10 µmol/l ACZ and 10⁶ cells/ml), and urine and plasma samples from patients receiving a single 5.0 mg/kg BW PO ACZ dose were analyzed to confirm the results in vivo. Analyses were performed with reversed-phase liquid chromatography and hydrophilic interaction chromatography coupled with high-resolution tandem mass spectrometry (RPLC-HRMS/MS and HILIC-HRMS/MS, respectively). Data were screened with a software-assisted targeted/untargeted workflow. ACZ was quantified in urine samples with creatinine normalization. We identified two metabolites in hepatocyte incubations and three additional metabolites in urine and plasma. Major transformations included cysteine conjugation, glucuronidation, and N-acetylation. All metabolites were detected in plasma, 1.5 h after intake. Major metabolites were detected in urine from 0.25 to 24 h (last collection) after intake. As opposed to the literature, ACZ does undergo metabolization in humans. We propose ACZ, ACZ-Cys, and N-acetyl-ACZ in urine, and ACZ and N-acetyl-ACZ in plasma as specific biomarkers of ACZ intake in doping.

3F-α-pyrrolydinovalerophenone (3F-α-PVP) in vitro human metabolism: Multiple in silico predictions to assist in LC-HRMS/MS analysis and targeted/untargeted data mining

Synthetic cathinones (SCs) constitute a heterogenous class of new psychoactive substances (NPS), structurally related to cathinone. SCs represent the widest NPS class, second to synthetic cannabinoids, accounting for approximately 160 different analogues with substitution at the phenyl group, the amine group, or the alkyl chain. In 2020, α-pyrrolidonophenone analogues were the most trafficked SCs, and were involved in many fatalities and intoxication cases. In particular, 3F-α-pyrrolidinovalerophenone (3F-α-PVP) was the cause of the highest number of SC-related fatal intoxications in Sweden in 2018. Minor structural modifications are used to avoid legal controls and analytical detection, but may also induce different toxicological profile. Therefore, the identification of specific markers of consumption is essential to discriminate SCs in clinical and forensic toxicology. In this study, we assessed 3F-α-PVP metabolic profile. 3F-α-PVP was incubated with 10-donor-pooled human hepatocytes, LC-HRMS/MS analysis, and software-assisted data mining. This well-established workflow was completed by in silico metabolite predictions using three different freeware. Ten metabolites were identified after 3 h incubation, including hydrogenated, hydroxylated, oxidated, and N-dealkylated metabolites. A total of 51 phase I and II metabolites were predicted, among which 7 were detected in the incubations. We suggest 3F-α-PVP N-butanoic acid, 3F-α-PVP pentanol, and 3F-α-PVP 2-ketopyrrolidinyl-pentanol as specific biomarkers of 3F-α-PVP consumption. This is the first time that an N-ethanoic acid is detected in the metabolic pathway of a pyrrolidine SC, demonstrating the importance of a dual targeted/untargeted data mining strategy.

Recent advances and limitations in the application of kahalalides for the control of cancer

Since the discovery of the kahalalide family of marine depsipeptides in 1993, considerable work has been done to develop these compounds as new and biologically distinct anti-cancer agents. Clinical trials and laboratory research have yielded a wealth of data that indicates tolerance of kahalalides in healthy cells and selective activity against diseased cells. Currently, two molecules have attracted the greates level of attention, kahalalide F (KF) and isokahalalide F (isoKF, Irvalec, PM 02734, elisidepsin). Both compounds were originally isolated from the sarcoglossan mollusk Elysia rufescens but due to distinct structural characteristics it has been hypothesized and recently shown that the ultimate origin of the molecules is microbial. The search for their true source has been a subject of considerable research in the anticipation of finding new analogs and a culturable expression system that can produce sufficient material through fermentation to be industrially relevant.

Fisetin as a Senotherapeutic Agent: Biopharmaceutical Properties and Crosstalk between Cell Senescence and Neuroprotection

Like other organs, brain functions diminish with age. Furthermore, for a variety of neurological disorders-including Alzheimer's disease-age is one of the higher-risk factors. Since in many Western countries the average age is increasing, determining approaches for decreasing the effects of aging on brain function is taking on a new urgency. Neuroinflammation and oxidative stress are two convoluted key factors in brain aging and chronic neurodegenerative diseases. The diverseness of factors, causing an age-related decrease in brain functions, requires identifying small molecules that have multiple biological activities that can affect all these factors. One great source of these small molecules is related to polyphenolic flavonoids. Recently, 3,3',4',7-tetrahydroxyflavone (fisetin) has been reported as a potent senotherapeutic capable of extending lifespan by reducing peroxidation levels and enhancing antioxidant cell responses. The neuroprotective effects of fisetin have been shown in several in vitro and in vivo models of neurological disorders due to its actions on multiple pathways associated with different neurological disorders. The present work aims to collect the most recent achievements related to the antioxidant and neuroprotective effects of fisetin. Moreover, in silico pharmacokinetics, pharmacodynamics, and toxicity of fisetin are also comprehensively described along with emerging novel drug delivery strategies for the amelioration of this flavonol bioavailability and chemical stability.

Roles of cytochrome P450 enzymes in pharmacology and toxicology: Past, present, and future

The development of the cytochrome P450 (P450) field has been remarkable in the areas of pharmacology and toxicology, particularly in drug development. Today it is possible to use the knowledge base and relatively straightforward assays to make intelligent predictions about drug disposition prior to human dosing. Much is known about the structures, regulation, chemistry of catalysis, and the substrate and inhibitor specificity of human P450s. Many aspects of drug-drug interactions and side effects can be understood in terms of P450s. This knowledge has also been useful in pharmacy practice, as well as in the pharmaceutical industry and medical practice. However, there are still basic and practical questions to address regarding P450s and their roles in pharmacology and toxicology. Another aspect is the discovery of drugs that inhibit P450 to treat diseases.

In silico prediction, LC-HRMS/MS analysis, and targeted/untargeted data-mining workflow for the profiling of phenylfentanyl in vitro metabolites

Illicit fentanyl and analogues have been involved in many fatalities and cases of intoxication across the United States over the last decade, and are becoming a health concern in Europe. New potent analogues emerge onto the drug market every year to circumvent analytical detection and legislation, and little pharmacological/toxicological data are available when the substances first appear. However, pharmacokinetic data are crucial to determine specific biomarkers of consumption in clinical and forensic settings, considering the low active doses and the rapid metabolism of fentanyl analogues. Phenylfentanyl is a novel analogue that was first detected in seized material in 2017, and little is currently known about this substance and its metabolism. We studied phenylfentanyl metabolic fate using in silico predictions with GLORYx freeware, human hepatocyte incubations, and liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS). We applied a specific targeted/untargeted workflow using data-mining software to allow the rapid and partially automated screening of LC-HRMS/MS raw data. Approximately 90,000 substances were initially individuated after 3-h incubation with hepatocytes, and 115 substances were automatically selected for a manual check by the operators. Finally, 13 metabolites, mostly produced by N-dealkylation, amide hydrolysis, oxidation, and combinations thereof, were identified. We suggest phenylnorfentanyl as the main biological marker of phenylfentanyl use, and we proposed the inclusion of its fragmentation pattern in mzCloud and HighResNPS online libraries. Other major metabolites include N-Phenyl-1-(2-phenylethyl)-4-piperidinamine (4-ANPP), 1-(2-phenylethyl)-4-piperidinol, and other non-specific metabolites. Phase II transformations were infrequent, and the hydrolysis of the biological samples is not required to increase the detection capability of non-conjugated metabolites. The overall workflow is easily adaptable for the metabolite profiling of other novel psychoactive substances.

Recent Advances in the Prediction of Pharmacokinetics Properties in Drug Design Studies: A Review

This review presents the main aspects related to pharmacokinetic properties, which are essential for the efficacy and safety of drugs. This topic is very important because the analysis of pharmacokinetic aspects in the initial design stages of drug candidates can increase the chances of success for the entire process. In this scenario, experimental and in silico techniques have been widely used. Due to the difficulties encountered with the use of some experimental tests to determine pharmacokinetic properties, several in silico tools have been developed and have shown promising results. Therefore, in this review, we address the main free tools/servers that have been used in this area, as well as some cases of application. Finally, we present some studies that employ a multidisciplinary approach with synergy between in silico, in vitro, and in vivo techniques to assess ADME properties of bioactive substances, achieving successful results in drug discovery and design.

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

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

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop

We report the major conclusions of the online open-access workshop "Computational Applications in Secondary Metabolite Discovery (CAiSMD)" that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads. During 3 days, the participants of this online workshop received an overview of modern computer-based approaches for exploring NP discovery in the "omics" age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. This was followed by oral presentations with much interaction between the speakers and the audience. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) and present posters in the form of flash presentations (5 min) upon submission of an abstract. The final program available on the workshop website ( https://caismd.indiayouth.info/ ) comprised of 4 keynote lectures (KLs), 12 oral presentations (OPs), 2 round table discussions (RTDs), and 5 hands-on sessions (HSs). This meeting report also references internet resources for computational biology in the area of secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community. The workshop concluded with an online survey form to be completed by speakers and participants for the goal of improving any subsequent editions.

Novel, selective acrylamide linked quinazolines for the treatment of double mutant EGFR-L858R/T790M Non-Small-Cell lung cancer (NSCLC)

T790M mutation is the most common mechanism of acquired resistance to first-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). To overcome this resistance, 4-anilinoquinazoline-based irreversible inhibitors afatinib, dacomitinib has been developed. However, the clinical application of these irreversible inhibitors is limited due to its narrow selectivity against L858R/T790M mutant EGFR. In an attempt to develop potent and selective EGFR T790M inhibitors, we have designed and synthesized two series of novel acrylamide linked quinazolines. Among them, compounds 2i (IC₅₀ 0.171 µM) and 11h (IC₅₀ 0.159 µM) were identified as potent compounds, which displayed selective and potent anti-proliferative activity on gefitinib-resistant cell line NCI-H1975 as compared to the gefitinib and WZ4002 in cellular assay. Furthermore, a molecular dynamic simulation of 11h was carried out to assess the stability to form a complex with the L858R/T790M EGFR Kinase domain, which demonstrated that complex was stable for the 100 ns and form strong crucial covalent binding contacts with the thiol group of Cys797 residue. Finally, satisfactory in silico pharmacokinetics properties of 2i, 11h and 11i compounds were predicted. The synthesized compounds were also evaluated for in vitro cytotoxic activity/hepatotoxicity against HepG2 cell line through MTT assay. The results revealed that compounds exhibited lower cytotoxicity to HepG2 cells.