Deleterious effects of reactive metabolites

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted In Vivo and In Vitro Data

Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. Over the last decade, the existing suite of in vitro proxy-DILI assays has generally improved at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing the in silico prediction of DILI because it allows for evaluating large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predict nine proxy-DILI labels and then use them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILI data set (composed of DILIst and DILIrank) and tested them on a held-out external test set of 223 compounds from the DILI data set. The best model, DILIPredictor, attained an AUC-PR of 0.79. This model enabled the detection of the top 25 toxic compounds (2.68 LR+, positive likelihood ratio) compared to models using only structural features (1.65 LR+ score). Using feature interpretation from DILIPredictor, we identified the chemical substructures causing DILI and differentiated cases of DILI caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as nontoxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity and the potential for mechanism evaluation. DILIPredictor required only chemical structures as input for prediction and is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download.

Dulaglutide rescues the elevated testicular dysfunction in a mouse model of high-fat diet-induced obesity

Obesity is a well-known risk factor for testicular function; however, dulaglutide's effect on the testis in obesity has received little attention. Currently, clinicians prescribe the antidiabetic drug dulaglutide only off-label for weight management in non-diabetics. Investigating the impact of this novel compound on obesity is critical for determining whether it has any disruptive effects on testicular cells. We used a well-known animal model of high-fat diet-induced obesity in this investigation, and testicular dysfunction was determined by sperm DNA damage, spermatocyte chromosomal abnormalities, and spermiogram analysis. Following a 12-week high-fat diet challenge, mice were randomly assigned to dulaglutide (0.6 mg/kg/day) or saline treatments for five weeks. Testes and sperm cells were collected 24 h after the last dulaglutide injection. Untreated obese mice had a lower testes/body weight ratio, more sperm DNA damage, diakinesis-metaphase I chromosomal abnormalities, a lower sperm count/motility, more cell morphological defects, and an altered testicular redox balance. In obese mice, dulaglutide injection efficiently restored all disturbed parameters to their control levels. Dulaglutide injection into healthy mice exhibited no significant harmful effects at the applied regimen. As a result, we infer that dulaglutide therapy might bring obese men additional benefits by recovering testicular dysfunction induced by obesity.

Semaglutide ameliorated autism-like behaviors and DNA repair efficiency in male BTBR mice by recovering DNA repair gene expression

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is marked by impaired social interactions, and increased repetitive behaviors. There is evidence of genetic changes in ASD, and several of these altered genes are linked to the process of DNA repair. Therefore, individuals with ASD must have improved DNA repair efficiency to mitigate risks associated with ASD. Despite numerous milestones in ASD research, the disease remains incurable, with a high occurrence rate and substantial financial burdens. This motivates scientists to search for new drugs to manage the disease. Disruption of glucagon-like peptide-1 (GLP-1) signaling, a regulator in neuronal development and maintains homeostasis, has been associated with the pathogenesis and progression of several neurological disorders, such as ASD. Our study aimed to assess the impact of semaglutide, a new GLP-1 analog antidiabetic medication, on behavioral phenotypes and DNA repair efficiency in the BTBR autistic mouse model. Furthermore, we elucidated the underlying mechanism(s) responsible for the ameliorative effects of semaglutide against behavioral problems and DNA repair deficiency in BTBR mice. The current results demonstrate that repeated treatment with semaglutide efficiently decreased autism-like behaviors in BTBR mice without affecting motor performance. Semaglutide also mitigated spontaneous DNA damage and enhanced DNA repair efficiency in the BTBR mice as determined by comet assay. Moreover, administering semaglutide recovered oxidant-antioxidant balance in BTBR mice. Semaglutide restored the disrupted DNA damage/repair pathways in the BTBR mice by reducing Gadd45a expression and increasing Ogg1 and Xrcc1 expression at both the mRNA and protein levels. This suggests that semaglutide holds great potential as a novel therapeutic candidate for treating ASD traits.

Cyprocide selectively kills nematodes via cytochrome P450 bioactivation

Left unchecked, plant-parasitic nematodes have the potential to devastate crops globally. Highly effective but non-selective nematicides are justifiably being phased-out, leaving farmers with limited options for managing nematode infestation. Here, we report our discovery of a 1,3,4-oxadiazole thioether scaffold called Cyprocide that selectively kills nematodes including diverse species of plant-parasitic nematodes. Cyprocide is bioactivated into a lethal reactive electrophilic metabolite by specific nematode cytochrome P450 enzymes. Cyprocide fails to kill organisms beyond nematodes, suggesting that the targeted lethality of this pro-nematicide derives from P450 substrate selectivity. Our findings demonstrate that Cyprocide is a selective nematicidal scaffold with broad-spectrum activity that holds the potential to help safeguard our global food supply.

Dulaglutide reduces oxidative DNA damage and hypermethylation in the somatic cells of mice fed a high-energy diet by restoring redox balance, inflammatory responses, and DNA repair gene expressions

Obesity is an established risk factor for numerous malignancies, although it remains uncertain whether the disease itself or weight-loss drugs are responsible for a greater predisposition to cancer. The objective of the current study was to determine the impact of dulaglutide on genetic and epigenetic DNA damage caused by obesity, which is a crucial factor in the development of cancer. Mice were administered a low-fat or high-fat diet for 12 weeks, followed by a 5-week treatment with dulaglutide. Following that, modifications of the DNA bases were examined using the comet assay. To clarify the underlying molecular mechanisms, oxidized and methylated DNA bases, changes in the redox status, levels of inflammatory cytokines, and the expression levels of some DNA repair genes were evaluated. Animals fed a high-fat diet exhibited increased body weights, elevated DNA damage, oxidation of DNA bases, and DNA hypermethylation. In addition, obese mice showed altered inflammatory responses, redox imbalances, and repair gene expressions. The findings demonstrated that dulaglutide does not exhibit genotoxicity in the investigated conditions. Following dulaglutide administration, animals fed a high-fat diet demonstrated low DNA damage, less oxidation and methylation of DNA bases, restored redox balance, and improved inflammatory responses. In addition, dulaglutide treatment restored the upregulated DNMT1, Ogg1, and p53 gene expression. Overall, dulaglutide effectively maintains DNA integrity in obese animals. It reduces oxidative DNA damage and hypermethylation by restoring redox balance, modulating inflammatory responses, and recovering altered gene expressions. These findings demonstrate dulaglutide's expediency in treating obesity and its associated complications.

Anti-TNF Thioester Glucocorticoid Antibody-Drug Conjugate Fully Inhibits Inflammation with Minimal Effect on Systemic Corticosterone Levels in a Mouse Arthritis Model

We describe the discovery of a thioester-containing glucocorticoid receptor modulator (GRM) payload and the corresponding antibody-drug conjugate (ADC). Payload 6 was designed for rapid hepatic inactivation to minimize systemic exposure of nonconjugated GRM. Mouse PK indicated that 6 is cleared 10-fold more rapidly than a first-generation GRM payload, resulting in 10-fold lower exposure and 3-fold decrease in Cmax. The anti-mTNF conjugate ADC5 fully inhibited inflammation in mouse contact hypersensitivity with minimal effects on corticosterone, a biomarker for systemic GRM effects, at doses up to and including 100 mg/kg. Concomitant inhibition of P1NP suggests potential delivery to cells involved in the remodeling of bone, which may be a consequence of TNF-targeting or bystander payload effects. Furthermore, ADC5 fully suppressed inflammation in collagen-induced arthritis mouse model after one 10 mg/kg dose for 21 days. The properties of the anti-hTNF conjugate were suitable for liquid formulation and may enable subcutaneous dosing.

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data

Drug-induced liver injury (DILI) has been significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. The existing suite of in vitro proxy-DILI assays is generally effective at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing in silico prediction of DILI because it allows for the evaluation of large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predicts nine proxy-DILI labels and then uses them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILIst dataset and tested on a held-out external test set of 223 compounds from DILIst dataset. The best model, DILIPredictor, attained an AUC-ROC of 0.79. This model enabled the detection of top 25 toxic compounds compared to models using only structural features (2.68 LR+ score). Using feature interpretation from DILIPredictor, we were able to identify the chemical substructures causing DILI as well as differentiate cases DILI is caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as non-toxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity as well as the potential for mechanism evaluation. DILIPredictor is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download and local implementation via https://pypi.org/project/dilipred/.

Ion Trap LC/MS reveals the generation of reactive intermediates in acalabrutinib metabolism: phase I metabolic profiling and bioactivation pathways elucidation

Acalabrutinib (CALQUENCE; ACB) is a Bruton tyrosine kinase inhibitor (BTKI) used to treat mantle cell lymphoma, small lymphocytic lymphoma (SLL), and chronic lymphocytic leukemia (CLL). On 21 November 2019, ACB was approved by the U.S. FDA for the use as a single therapy for the treatment of CLL/SLL. In silico studies were first done to propose vulnerable sites of metabolism and reactivity pathways by StarDrop software and Xenosite online software; respectively. ACB metabolites and stable adducts were characterized in vitro from rat liver microsomes (RLMs) using Ion Trap LC/MS. Generation of reactive intermediates (RIs) in the in vitro metabolism of ACB was investigated using glutathione, potassium cyanide, and methoxylamine as trapping nucleophiles for the RIs including iminopyridinone, iminium, and aldehyde, respectively, to form stable adducts that can be identified and characterized by Ion Trap LC/MS. Five phase I metabolites, seven 6-iminopyridin-3(6H)-one and five aldehyde RIs of ACB were identified. Based on literature reviews, the generation of RIs of ACB, and the subsequent drug-induced organ toxicity (DIOT) reactions may provide an explanation of ACB ADRs. Additional drug discovery investigations can be performed to facilitate the creation of novel medications with improved safety characteristics.

In vivo and in vitro metabolite profiling of nirmatrelvir using LC-Q-ToF-MS/MS along with the in silico approaches for prediction of metabolites and their toxicity

Nirmatrelvir (NRV), a 3C-like protease or Mpro inhibitor of SARS-CoV-2, is used for the treatment of COVID-19 in adult and paediatric patients. The present study was accomplished to investigate the comprehensive metabolic fate of NRV using in vitro and in vivo models. The in vitro models used for the study were microsomes (human liver microsomes, rat liver microsomes, mouse liver microsomes) and S9 fractions (human liver S9 fractions and rat liver S9 fractions) with the appropriate cofactors, whereas Sprague-Dawley rats were used as the in vivo models. Nirmatrelvir was administered orally to Sprague-Dawley rats, which was followed by the collection of urine, faeces and blood at pre-determined time intervals. Protein precipitation was used as the sample preparation method for all the samples. The samples were then analysed by liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-Q-ToF-MS/MS) using an Acquity BEH C18 column with 0.1% formic acid and acetonitrile as the mobile phase. Four metabolites were found to be novel, which were formed via amide hydrolysis, oxidation and hydroxylation. Furthermore, an in silico analysis was performed using Meteor Nexus software to predict the probable metabolic changes of NRV. The toxicity and mutagenicity of NRV and its metabolites were also determined using DEREK Nexus and SARAH Nexus.

Development and Application of a Multidimensional Database for the Detection of Quaternary Ammonium Compounds and Their Phase I Hepatic Metabolites in Humans

The COVID-19 pandemic has led to significantly increased human exposure to the widely used disinfectants quaternary ammonium compounds (QACs). Xenobiotic metabolism serves a critical role in the clearance of environmental molecules, yet limited data are available on the routes of QAC metabolism or metabolite levels in humans. To address this gap and to advance QAC biomonitoring capabilities, we analyzed 19 commonly used QACs and their phase I metabolites by liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS). In vitro generation of QAC metabolites by human liver microsomes produced a series of oxidized metabolites, with metabolism generally occurring on the alkyl chain group, as supported by MS/MS fragmentation. Discernible trends were observed in the gas-phase IM behavior of QAC metabolites, which, despite their increased mass, displayed smaller collision cross-section (CCS) values than those of their respective parent compounds. We then constructed a multidimensional reference SQLite database consisting of m/z, CCS, retention time (rt), and MS/MS spectra for 19 parent QACs and 81 QAC metabolites. Using this database, we confidently identified 13 parent QACs and 35 metabolites in de-identified human fecal samples. This is the first study to integrate in vitro metabolite biosynthesis with LC-IM-MS/MS for the simultaneous monitoring of parent QACs and their metabolites in humans.

In vitro and in vivo metabolic activation and hepatotoxicity of chlorzoxazone mediated by CYP3A

Chlorzoxazone (CZX), a benzoxazolone derivative, has been approved for the treatment of musculoskeletal disorders to relieve localized muscle spasm. However, its idiosyncratic toxicity reported in patients brought attention, particularly for hepatotoxicity. The present study for the first time aimed at the relationship between CZX-induced hepatotoxicity and identification of oxirane intermediate resulting from metabolic activation of CZX. Two N-acetylcysteine (NAC) conjugates (namely M1 and M2) and two glutathione (GSH) conjugates (namely M3 and M4) were detected in rat & human microsomal incubations with CZX (200 μM) fortified with NAC or GSH, respectively. The formation of M1-M4 was NADPH-dependent and these metabolites were also observed in urine or bile of SD rats given CZX intragastrically at 10 mg/kg or 25 mg/kg. NAC was found to attach at C-6' of the benzo group of M1 by sufficient NMR data. CYPs3A4 and 3A5 dominated the metabolic activation of CZX. The two GSH conjugates were also observed in cultured rat primary hepatocytes after exposure to CZX. Inhibition of CYP3A attenuated the susceptibility of hepatocytes to the cytotoxicity of CZX (10-400 μM). The in vitro and in vivo studies provided solid evidence for the formation of oxirane intermediate of CZX. This would facilitate the understanding of the underlying mechanisms of toxic action of CZX.

Metabolic bioactivation of antidepressants: advance and underlying hepatotoxicity

Many drugs that serve as first-line medications for the treatment of depression are associated with severe side effects, including liver injury. Of the 34 antidepressants discussed in this review, four have been withdrawn from the market due to severe hepatotoxicity, and others carry boxed warnings for idiosyncratic liver toxicity. The clinical and economic implications of antidepressant-induced liver injury are substantial, but the underlying mechanisms remain elusive. Drug-induced liver injury may involve the host immune system, the parent drug, or its metabolites, and reactive drug metabolites are one of the most commonly referenced risk factors. Although the precise mechanism by which toxicity is induced may be difficult to determine, identifying reactive metabolites that cause toxicity can offer valuable insights for decreasing the bioactivation potential of candidates during the drug discovery process. A comprehensive understanding of drug metabolic pathways can mitigate adverse drug-drug interactions that may be caused by elevated formation of reactive metabolites. This review provides a comprehensive overview of the current state of knowledge on antidepressant bioactivation, the metabolizing enzymes responsible for the formation of reactive metabolites, and their potential implication in hepatotoxicity. This information can be a valuable resource for medicinal chemists, toxicologists, and clinicians engaged in the fields of antidepressant development, toxicity, and depression treatment.

6-Shogaol prevents benzo (A) pyrene-exposed lung carcinogenesis via modulating PRDX1-associated oxidative stress, inflammation, and proliferation in mouse models

In this study, we have investigated the chemopreventive role of 6-shogaol (6-SGL) on benzopyrene (BaP) exposed lung carcinogenesis by modulating PRDX1-associated oxidative stress, inflammation, and proliferation in Swiss albino mouse models. Mice were exposed to BaP (50 mg/kg b.wt) orally twice a week for four consecutive weeks and maintained for 16 weeks, respectively. 6-SGL (30 mg/kg b.wt) were orally administered to mouse 1 h before BaP exposure for 16 weeks. After the experiment's termination, 6-SGL (30 mg/kg b.wt) prevented the loss in body weight, increased lung weight, and the total number of tumors in the mice. Moreover, we observed that 6-SGL treatment reverted the activity of BaP-induced lipid peroxidation and antioxidants in mice. Also, 6-SGL impeded the phosphorylation of MAPK family proteins such as Erk1, p38, and Jnk1 in BaP-exposed mice. PRDX1 is an essential antioxidant protein that scavenges toxic radicals and enhances several antioxidant proteins. Overexpression of PRDX1 substantially inhibits MAPKs, proliferation, and inflammation signaling axis. Hence, PRDX1 is thought to be a novel targeting protein for preventing BaP-induced lung cancer. In this study, we have obtained the 6-SGL treatment in a mouse model that reverted BaP-induced depletion of PRDX1 expression. Moreover, pretreatment of 6-SGL (30 mg/kg b.wt) significantly inhibited enhanced proinflammatory cytokines (TNF-α, IL-6, IL-β1, IL-10) and proliferative markers (Cyclin-D1, Cyclin-D2, and PCNA) in BaP-exposed mice. The histopathological studies also confirmed that 6-SGL effectively protected the cells with less damage. Thus, the study demonstrated that 6-SGL could be a potential phytochemical and act as a chemopreventive agent in BaP-induced lung cancer by enhancing PRDX1 expression.

Machine Learning Enables Accurate Prediction of Quinone Formation during Drug Metabolism

Metabolism helps in the elimination of drugs from the human body by making them more hydrophilic. Sometimes, drugs can be bioactivated to highly reactive metabolites or intermediates during metabolism. These reactive metabolites are often responsible for the toxicities associated with the drugs. Identification of reactive metabolites of drug candidates can be very helpful in the initial stages of drug discovery. Quinones are soft electrophiles that are generated as reactive intermediates during metabolism. Quinones make up more than 40% of the reactive metabolites. In this work, a reliable data set of 510 molecules was used to develop machine learning and deep learning-based predictive models to predict the formation of quinone-type metabolites. For representing molecules, two-dimensional (2D) descriptors, PubChem fingerprints, electro-topological state (E-state) fingerprints, and metabolic reactivity-based descriptors were used. Developed models were compared to the existing Xenosite web server using the untouched test set of 102 molecules. The best model achieved an accuracy of 86.27%, while the Xenosite server could achieve an accuracy of only 52.94% on the test set. Descriptor analysis revealed that the presence of greater numbers of polar moieties in a molecule can prevent the formation of quinone-type metabolites. In addition, the presence of a nitrogen atom in an aromatic ring and the presence of metabolophores V51, V52, and V53 (SMARTCyp descriptors) decrease the probability of quinone formation. Finally, a tool based on the best machine learning models was developed, which is accessible at http://14.139.57.41/quinonepred/.

The association between the incidence of postmenopausal breast cancer and occupational exposure to selected organic solvents, Montreal, Canada, 2008-2011

BACKGROUND

Breast cancer is the most diagnosed cancer among women and recognized risk factors explain 25%-47% of cases. Organic solvents are used widely in the workplace and exposure may increase the risk of developing breast cancer, yet there are insufficient data to confirm this hypothesis. We sought to determine whether past occupational exposures to selected organic solvents were associated with the incidence of invasive breast cancer in postmenopausal women in Montréal, Canada.

METHODS

From a population-based case-control study (2008-2011), using in-depth interviews we elicited information on risk factors and lifetime occupational histories. Industrial hygienists and chemists translated job descriptions into specific chemical and physical exposures. We assessed 11 individual solvents and four solvent groups. Unconditional logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for metrics of past exposures to selected solvents. Exposure metrics included any previous exposure, average frequency in hours per week, duration in years, and average cumulative concentration weighted by hours per workweek exposed.

RESULTS

We enrolled 695 cases and 608 controls. We found increased ORs for average cumulative concentration of exposure to mononuclear aromatic hydrocarbons (OR: 1.52, 95% CI: 1.04, 2.28), chlorinated alkanes (OR: 2.42, 95% CI: 1.23, 5.68), toluene (OR: 1.59, 95% CI: 1.02, 2.59), and a group of organic solvents with reactive metabolites (OR: 1.53, 95% CI: 1.08, 2.24). Positive associations were found across all exposure metrics and were higher among women with estrogen-positive/progesterone-negative tumors.

CONCLUSION

Our findings suggest occupational exposure to certain organic solvents may increase the risk of incident postmenopausal breast cancer.

Electrochemistry/mass spectrometry (EC/MS) for fast generation and identification of novel reactive metabolites of two unsymmetrical bisacridines with anticancer activity

The development of a new drug requires knowledge about its metabolic fate in a living organism, regarding the comprehensive assessment of both drug therapeutic activity and toxicity profiles. Electrochemistry (EC) coupled with mass spectrometry (MS) is an efficient tool for predicting the phase I metabolism of redox-sensitive drugs. In particular, EC/MS represents a clear advantage for the generation of reactive drug transformation products and their direct identification compared to biological matrices. In this work, we focused on the characterization of novel electrochemical products of two representative unsymmetrical bisacridines (C-2028 and C-2045) with demonstrated high anticancer activity. The electrochemical thin-layer flow-through cell μ-PrepCell 2.0 (Antec Scientific) was used here for the effective metabolite electrosynthesis. The electrochemical simulation of C-2028 reductive and C-2045 oxidative metabolism resulted in the generation of new products that were not observed before. The formation of nitroso [M-O+H]+ and azoxy [2M-3O+H]+ species from C-2028, as well as a series of hydroxylated and/or dehydrogenated products, including possible quinones [M-2H+H]+ and [M+O-2H+H]+ from C-2045, was demonstrated. For the latter, a glutathione S-conjugate (m/z 935.3130) was also obtained in measurements supplemented with the excess of reduced glutathione. For the identification of the products of interest, structural confirmation based on MS/MS fragmentation experiments was performed. Novel products of electrochemical conversions of unsymmetrical bisacridines were discussed in the context of their possible biological effect on the human organism.

Chemical metabolite synthesis and profiling: Mimicking in vivo biotransformation reactions

Biotransformation was previously viewed as merely the structural characterization of drug metabolites, and it was performed only when drug candidates entered clinical development. The synthesis of drug metabolites is crucial to the drug development process because it generates either pharmacologically active, inactive, or reactive molecules and hence their characterization and comprehensive pharmacological evaluation is necessary. The chemical metabolite synthesis is very challenging due to the complex structures of many drug molecules, presence of multiple stereocenters, poor reaction yields, and the formation of unwanted by-products. Drug metabolites and their chemical synthesis have immense significance in the drug discovery process. The chemical synthesis of metabolites facilitates on- or off-target pharmacological and toxicological evaluations at the easiest. In a broader view metabolite could be a target lead molecule for drug design, toxic reactive metabolites, pharmaceutical standards for bioanalytical methods, etc. Collectively these metabolite information dossiers will aid regulatory agencies such as the EMA and FDA in maintaining strict vigilance over drug manufacturers with regard to the safety of NCE's and their hidden metabolites. Herein, we are presenting a systematic compilation of chemical and biocatalytic strategies reported to date for pharmaceutical drug metabolite synthesis. This review report is very useful for the laboratory synthesis of new drug metabolites, and their preclinical biological evaluation could aid in the detection of early threats (alerts) in drug discovery, eliminate the toxicity profile, explore newer pharmacology, and delivering a promising and safe drug candidate to humankind.

The Effect of Carbamazepine on Performance, Carcass Value, Hematological and Biochemical Blood Parameters, and Detection of Carbamazepine and Its Metabolites in Tissues, Internal Organs, and Body Fluids in Growing Rabbits

Antiepileptic drugs (e.g., carbamazepine; CBZ) are widely prescribed for various conditions beyond epilepsy, including neurologic and psychiatric disorders. These medications can have both favorable and unfavorable impacts on mood, anxiety, depression, and psychosis. CBZ has been found at low concentrations (in the unit of nanograms per liter) in rivers, surface water, and even drinking water. As a result, when reclaimed wastewater is used for irrigation in agricultural ecosystems, CBZ can be reintroduced into the environment. That is why we tested different doses of CBZ in rabbits' feed as the meat is consumed in every community, has no religious barriers, and the potential risk of consuming meat which has been exposed to CBZ treatment is not known. Also, the evidence of the effect of CBZ on rabbits is missing. Mainly, the CBZ doses affected the count of leukocytes and other blood traits, meaning the higher the dose, the higher the reduction. Moreover, there were only low amounts of CBZ in rabbits' meat or tissues when they were exposed to the treatment.

Aneugenic and clastogenic alterations in the DBA/IJ mouse model of rheumatoid arthritis treated with rituximab, an anti-CD20 antibody

Rheumatoid arthritis (RA), an autoimmune disorder in which the immune system attacks healthy cells, is associated with elevated risk of lymphoma. Rituximab, a treatment for non-Hodgkin's lymphoma, has been approved as a treatment for RA. We studied the effects of rituximab on chromosomal stability in collagen-induced arthritis DBA/1J animal models. Micronucleus levels were increased in the mouse models, mainly due to chromosome loss, as detected by fluorescence in situ hybridization; rituximab-treated arthritic mice had significantly less micronucleus formation. Serum 8-hydroxydeoxyguanosine, a DNA oxidative stress marker, was increased in the mice models but reduced following rituximab administration.

Naringenin protects hepato-renal tissues against antituberculosis drugs induced toxic manifestations by modulating interleukin-6, insulin like growth factor-1, biochemical and ultra-structural integrity

BACKGROUND

The antituberculosis drugs (ATDs), isoniazid, rifampicin, pyrazinamide and ethambutol prompt extreme hepatic and renal damage during treatment of tuberculosis. The present study aimed to investigate protective potential of naringenin against ATDs induced hepato-renal injury.

METHODS

Rats were administered with ATDs (pyrazinamide; 210, ethambutol; 170, isoniazid; 85, rifampicin; 65 mg/kg b.wt) orally for 8 weeks (3 days/week) followed by naringenin at three different doses (10, 20 and 40 mg/kg b.wt) conjointly for 8 weeks (3 days/week alternately to ATDs administration) and silymarin (50 mg/kg b.wt) as positive control.

RESULTS

Exposure to ATDs caused significant increase in interleukin-6 (IL-6), triglycerides, cholesterol, bilirubin whereas depletion in insulin like growth factor-1 (IGF-1), albumin and glucose in serum. Endogenous antioxidant enzymes glutathione reductase (GR), glutathione peroxidase (GPx) and glucose-6-phosphate-dehydrogenase (G-6-PDH) were diminished in liver and kidney tissues with parallel increase in triglycerides, cholesterol, microsomal LPO and aniline hydroxylase (CYP2E1 enzyme). Ultra-structural observations of liver and kidney showed marked deviation in plasma membranes of various cellular and sub-cellular organelles after 8 weeks of exposure to ATDs.

CONCLUSIONS

Conjoint treatment of naringenin counteracted ATDs induced toxic manifestations by regulating IL-6, IGF-1, CYP2E1, biochemical and ultra-structural integrity in a dose dependent manner. Naringenin has excellent potential to protect ATDs induced hepato-renal injury by altering oxidative stress, modulation of antioxidant enzymes, serum cytokines and ultra-structural changes.

Crosstalk of TNF-α, IFN-γ, NF-kB, STAT1 and redox signaling in lipopolysaccharide/D-galactosamine/dimethylsulfoxide-induced fulminant hepatic failure in mice

Purpose

The clinical study of fulminant hepatic failure is challenging due to its high mortality and relative rarity, necessitating reliance on pre-clinical models to gain insight into its pathophysiology and develop potential therapies.

Methods and Results

In our study, the combination of the commonly used solvent dimethyl sulfoxide to the current-day model of lipopolysaccharide/d-galactosamine-caused fulminant hepatic failure was found to cause significantly greater hepatic damage, as indicated by alanine aminotransferase level. The effect was dose-dependent, with the maximum increase in alanine aminotransferase observed following 200 μl/kg dimethyl sulfoxide co-administration. Co-administration of 200 μl/kg dimethyl sulfoxide also remarkably increased histopathological changes induced by lipopolysaccharide/d-galactosamine. Importantly, alanine aminotransferase levels and survival rate in the 200 μl/kg dimethyl sulfoxide co-administration groups were both greater than those in the classical lipopolysaccharide/d-galactosamine model. We found that dimethyl sulfoxide co-administration aggravated lipopolysaccharide/d-galactosamine-caused liver damage by stimulating inflammatory signaling, as indicated by tumor necrosis factor alpha (TNF-α), interferon gamma (IFN-γ), inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2) levels. Further, nuclear factor kappa B (NF-kB) and transcription factor activator 1 (STAT1) were upregulated, as was neutrophil recruitment, indicated by myeloperoxidase activity. Hepatocyte apoptosis was also increased, and greater nitro-oxidative stress was noted, as determined based on nitric oxide, malondialdehyde, and glutathione levels.

Conclusion

Co-treatment with low doses of dimethyl sulfoxide enhanced the lipopolysaccharide/d-galactosamine-caused hepatic failure in animals, with higher toxicity and greater survival rates. The current findings also highlight the potential danger of using dimethyl sulfoxide as a solvent in experiments involving the hepatic immune system, suggesting that the new lipopolysaccharide/d-galactosamine/dimethyl sulfoxide model described herein could be used for pharmacological screening with the goal to better understand hepatic failure and evaluate treatment approaches.

The endoplasmic reticulum participated in drug metabolic toxicity

Covalent binding of reactive metabolites formed by drug metabolic activation with biological macromolecules is considered to be an important mechanism of drug metabolic toxicity. Recent studies indicate that the endoplasmic reticulum (ER) could play an important role in drug toxicity by participating in the metabolic activation of drugs and could be a primarily attacked target by reactive metabolites. In this article, we summarize the generation and mechanism of reactive metabolites in ER stress and their associated cell death and inflammatory cascade, as well as the systematic modulation of unfolded protein response (UPR)-mediated adaptive pathways.

The Combination of Electrochemistry and Microfluidic Technology in Drug Metabolism Studies

Drugs are metabolized within the liver (pH 7.4) by phase I and phase II metabolism. During the process, reactive metabolites can be formed that react covalently with biomolecules and induce toxicity. Identifying and detecting reactive metabolites is an important part of drug development. Preclinical and clinical investigations are conducted to assess the toxicity and safety of a new drug candidate. Electrochemistry coupled to mass spectrometry is an ideal complementary technique to the current preclinical studies, a pure instrumental approach without any purification steps and tedious protocols. The combination of microfluidics with electrochemistry towards the mimicry of drug metabolism offers portability, low volume of reagents and faster reaction times. This review explores the development of microfluidic electrochemical cells for mimicking drug metabolism.

Glutathione-Mediated Conjugation of Anticancer Drugs: An Overview of Reaction Mechanisms and Biological Significance for Drug Detoxification and Bioactivation

The effectiveness of many anticancer drugs depends on the creation of specific metabolites that may alter their therapeutic or toxic properties. One significant route of biotransformation is a conjugation of electrophilic compounds with reduced glutathione, which can be non-enzymatic and/or catalyzed by glutathione-dependent enzymes. Glutathione usually combines with anticancer drugs and/or their metabolites to form more polar and water-soluble glutathione S-conjugates, readily excreted outside the body. In this regard, glutathione plays a role in detoxification, decreasing the likelihood that a xenobiotic will react with cellular targets. However, some drugs once transformed into thioethers are more active or toxic than the parent compound. Thus, glutathione conjugation may also lead to pharmacological or toxicological effects through bioactivation reactions. My purpose here is to provide a broad overview of the mechanisms of glutathione-mediated conjugation of anticancer drugs. Additionally, I discuss the biological importance of glutathione conjugation to anticancer drug detoxification and bioactivation pathways. I also consider the potential role of glutathione in the metabolism of unsymmetrical bisacridines, a novel prosperous class of anticancer compounds developed in our laboratory. The knowledge on glutathione-mediated conjugation of anticancer drugs presented in this review may be noteworthy for improving cancer therapy and preventing drug resistance in cancers.

Structural Modification in Anesthetic Drug Development for Prodrugs and Soft Drugs

Among the advancements in drug structural modifications, the increased focus on drug metabolic and pharmacokinetic properties in the anesthetic drug design process has led to significant developments. Drug metabolism also plays a key role in optimizing the pharmacokinetics, pharmacodynamics, and safety of drug molecules. Thus, in the field of anesthesiology, the applications of pharmacokinetic strategies are discussed in the context of sedatives, analgesics, and muscle relaxants. In this review, we summarize two approaches for structural optimization to develop anesthetic drugs, by designing prodrugs and soft drugs. Drugs that both failed and succeeded during the developmental stage are highlighted to illustrate how drug metabolism and pharmacokinetic optimization strategies may help improve their physical and chemical properties.

Integration of a microfluidic multicellular coculture array with machine learning analysis to predict adverse cutaneous drug reactions

Adverse cutaneous reactions are potentially life-threatening skin side effects caused by drugs administered into the human body. The availability of a human-specific in vitro platform that can prospectively screen drugs and predict this risk is therefore of great importance to drug safety. However, since adverse cutaneous drug reactions are mediated by at least 2 distinct mechanisms, both involving systemic interactions between liver, immune and dermal tissues, existing in vitro skin models have not been able to comprehensively recapitulate these complex, multi-cellular interactions to predict the skin-sensitization potential of drugs. Here, we report a novel in vitro drug screening platform, which comprises a microfluidic multicellular coculture array (MCA) to model different mechanisms-of-action using a collection of simplistic cellular assays. The resultant readouts are then integrated with a machine-learning algorithm to predict the skin sensitizing potential of systemic drugs. The MCA consists of 4 cell culture compartments connected by diffusion microchannels to enable crosstalk between hepatocytes that generate drug metabolites, antigen-presenting cells (APCs) that detect the immunogenicity of the drug metabolites, and keratinocytes and dermal fibroblasts, which collectively determine drug metabolite-induced FasL-mediated apoptosis. A single drug screen using the MCA can simultaneously generate 5 readouts, which are integrated using support vector machine (SVM) and principal component analysis (PCA) to classify and visualize the drugs as skin sensitizers or non-skin sensitizers. The predictive performance of the MCA and SVM classification algorithm is then validated through a pilot screen of 11 drugs labelled by the US Food and Drug Administration (FDA), including 7 skin-sensitizing and 4 non-skin sensitizing drugs, using stratified 4-fold cross-validation (CV) on SVM. The predictive performance of our in vitro model achieves an average of 87.5% accuracy (correct prediction rate), 75% specificity (prediction rate of true negative drugs), and 100% sensitivity (prediction rate of true positive drugs). We then employ the MCA and the SVM training algorithm to prospectively identify the skin-sensitizing likelihood and mechanism-of-action for obeticholic acid (OCA), a farnesoid X receptor (FXR) agonist which has undergone clinical trials for non-alcoholic steatohepatitis (NASH) with well-documented cutaneous side effects.

Formation of Redox-Active Duroquinone from Vaping of Vitamin E Acetate Contributes to Oxidative Lung Injury

In late 2019, the outbreak of e-cigarette or vaping-associated lung injuries (EVALIs) in the United States demonstrated to the public the potential health risks of vaping. While studies since the outbreak have identified vitamin E acetate (VEA), a diluent of tetrahydrocannabinol (THC) in vape cartridges, as a potential contributor to lung injuries, the molecular mechanisms through which VEA may cause damage are still unclear. Recent studies have found that the thermal degradation of e-liquids during vaping can result in the formation of products that are more toxic than the parent compounds. In this study, we assessed the role of duroquinone (DQ) in VEA vaping emissions that may act as a mechanism through which VEA vaping causes lung damage. VEA vaping emissions were collected and analyzed for their potential to generate reactive oxygen species (ROS) and induce oxidative stress-associated gene expression in human bronchial epithelial cells (BEAS-2B). Significant ROS generation by VEA vaping emissions was observed in both acellular and cellular systems. Furthermore, exposure to vaping emissions resulted in significant upregulation of NQO1 and HMOX-1 genes in BEAS-2B cells, indicating a strong potential for vaped VEA to cause oxidative damage and acute lung injury; the effects are more profound than exposure to equivalent concentrations of DQ alone. Our findings suggest that there may be synergistic interactions between thermal decomposition products of VEA, highlighting the multifaceted nature of vaping toxicity.

Metabolism of a Selective Serotonin and Norepinephrine Reuptake Inhibitor Duloxetine in Liver Microsomes and Mice

Duloxetine (DLX) is a dual serotonin and norepinephrine reuptake inhibitor, widely used for the treatment of major depressive disorder. Although DLX has shown good efficacy and safety, serious adverse effects (e.g., liver injury) have been reported. The mechanisms associated with DLX-induced toxicity remain elusive. Drug metabolism plays critical roles in drug safety and efficacy. However, the metabolic profile of DLX in mice is not available, although mice serve as commonly used animal models for mechanistic studies of drug-induced adverse effects. Our study revealed 39 DLX metabolites in human/mouse liver microsomes and mice. Of note, 13 metabolites are novel, including five N-acetyl cysteine adducts and one reduced glutathione (GSH) adduct associated with DLX. Additionally, the species differences of certain metabolites were observed between human and mouse liver microsomes. CYP1A2 and CYP2D6 are primary enzymes responsible for the formation of DLX metabolites in liver microsomes, including DLX-GSH adducts. In summary, a total of 39 DLX metabolites were identified, and species differences were noticed in vitro. The roles of CYP450s in DLX metabolite formation were also verified using human recombinant cytochrome P450 (P450) enzymes and corresponding chemical inhibitors. Further studies are warranted to address the exact role of DLX metabolism in its adverse effects in vitro (e.g., human primary hepatocytes) and in vivo (e.g., Cyp1a2-null mice). SIGNIFICANCE STATEMENT: This current study systematically investigated Duloxetine (DLX) metabolism and bioactivation in liver microsomes and mice. This study provided a global view of DLX metabolism and bioactivation in liver microsomes and mice, which are very valuable to further elucidate the mechanistic study of DLX-related adverse effects and drug-drug interaction from metabolic aspects.

Evaluation of toxicological aspects of three new benzoxazole compounds with sunscreen photophysical properties using in silico and in vitro methods

Sunscreening chemicals protect against damage caused by sunlight most absorbing UVA or UVB radiations. In this sense, 2-(2'-hydroxyphenyl)benzoxazole derivatives with amino substituents in the 4' and 5' positions have an outstandingly high Sun Protection Factor and adequate photostability, but their toxicity is not yet known. This study aimed to evaluate the toxicity of three synthetic 2-(2'-hydroxyphenyl)benzoxazole derivatives for their possible application as sunscreens. In silico tools were used in order to assess potential risks regarding mutagenic, carcinogenic, and skin sensitizing potential. Bioassays were performed in L929 cells to assess cytotoxicity in MTT assay and genotoxic activities in the Comet assay and micronucleus test. Also, the Salmonella/microsome assay was performed to evaluate gene mutations. The in silico predictions indicate a low risk of mutagenicity and carcinogenicity of the compounds while the skin sensitizing potential was low or inconclusive. The 2-(4'-amino-2'-hydroxyphenyl)benzoxazol compound was the most cytotoxic and genotoxic among the compounds evaluated in L929 cells, but none induced mutations in the Salmonella/microsome assay. The amino substituted at the 4' position of the phenyl ring appears to have greater toxicological risks than substituents at the 5' position of 2-(phenyl)benzoxazole. The findings warrant further studies of these compounds in cosmetic formulations.

Frequency of CYP1A1*2A polymorphisms and deletion of the GSMT1 gene in a Peruvian mestizo population

The polymorphic variants of CYP1A1 and the deletion of GSTM1 are present in the Peruvian mestizo population. Wild type and mutated genotypes (WT/*2A and *2A/ *2A) were identified, whose allele frequencies are 0.31 (T allele) and 0.69 (C allele), respectively; 53% with wild type GSTM1 (+) and 47% with null GSTM1. The frequency in Iquiteño emigrants was 0.72 CYP1A1*2A and 25% GSTM1 (-); from Lima 0.67 CYP1A1*2A and 33% of GSTM1 (-). The Hardy-Weinberg equilibrium test for the studied population showed that both frequencies are out of balance, p > .05.

The presence of the risk allele of the CYP1A1*2A polymorphism and the deletion in the GSTM1 gene are high, which could be indicative of a phase I and II metabolic imbalance in this group of Peruvian populations, with potential risks of activating agents procarcinogens thus affecting the incidence of tumor pathologies with an environmental component.

Sulfite as the substrate of C-sulfonate metabolism of α, β-unsaturated carbonyl containing andrographolide: analysis of sulfite in rats' intestinal tract and the reaction kinetics of andrographolide with sulfite

One-sixth of the currently known natural products contain α, β-unsaturated carbonyl groups. Our previous studies reported a rare C-sulfonate metabolic pathway. Sulfonate groups were linked to the β-carbon of α, β-unsaturated carbonyl-based natural compounds through this pathway. However, the mechanism of this type of metabolism is still not fully understood, especially whether it is formed through enzyme-mediated biotransformation or direct sulfite addition. In this work, the enzyme-mediated and non-enzymatic pathways were studied. First, the sulfite content in rat intestine was determined by LC-MS/MS. The results showed that the amount of sulfite in rat intestinal contents was from 41.5 to 383 μg·g^-1, whereas the amount of sulfite in rat feed was lower than the lower limit of quantitation (20 μg·g^-1). Second, the reaction kinetics of sulfite-andrographolide reactions in phosphate buffer solutions (pH 6-8) was studied. The half-lives of andrographolide ranged from minutes to hours. This was suggested that the C-sulfonate reaction of andrographolide was very fast. Third, the C-sulfonate metabolites of andrographolide were both detected when andrographolide and L-cysteine-S-conjugate andrographolide were incubated with the rat small intestine contents or sulfite, indicating that the sulfite amount in rat intestine contents was high enough to react with andrographolide, which assisted a significant portion of andrographolide metabolism. Finally, the comparison of andrographolide metabolite profiles among liver homogenate (with NADPH), liver S9 (with NADPH), small intestine contents homogenate (with no NADPH), and sulfite solution incubations showed that the C-sulfonate metabolites were predominantly generated in the intestinal tract by non-enzymatic pathway. In summary, sulfite can serve as a substrate for C-sulfonate metabolism, and these results identified non-enzymatically nucleophilic addition as the potential mechanism for C-sulfonate metabolism of compounds containing α, β-unsaturated carbonyl moiety.

Development and evaluation of tofacitinib transdermal system for the treatment of rheumatoid arthritis in rats

CONTEXT

Tofacitinib tablet is approved for the treatment of rheumatoid arthritis (RA). However, tofacitinib (Tfc) faces extensive first-pass metabolism following oral administration.

AIM

To develop transdermal systems of Tfc and evaluate their efficacies against RA using Freund's Complete Adjuvant immunized arthritis rat model.

METHODS

These systems were prepared by solvent casting method and evaluated for texture, needle strength, skin penetrability, in vitro drug release, skin permeation, stability, and in vivo anti-arthritic activity.

RESULTS AND DISCUSSION

Transdermal patch (TS) showed smooth texture, good mechanical strength, slow-release, and slow permeation through the skin. Microneedle array (MNS) showed good needle strength, with required skin penetrability. MNS and TS showed 95% and 24% drug release, and 82% and 12% drug permeation, respectively in 4 h. The developed systems were found to be stable for 90 days at very stressful conditions, that is, 40 ± 2 °C and 75 ± 5% RH. MNS and TS both reduced arthritic scores (at p < 0.01 and p < 0.001 level, respectively) and the level of inflammatory cytokines (at p < 0.05 and p < 0.01 level, respectively) significantly as compared to that of the drug solution (DS). MNS and TS were found to be effective in restoring histological alterations (annum, synovial hyperplasia, synovial constriction, and cartilage and articular erosions) toward normal.

CONCLUSION

TS and MNS were found to be stable and effective for the treatment of arthritis and hence considered a good alternative for the treatment of RA with better clinical pertinence.

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort

Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations' data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).

In Vitro Metabolism of Donepezil in Liver Microsomes Using Non-Targeted Metabolomics

Donepezil is a reversible acetylcholinesterase inhibitor that is currently the most commonly prescribed drug for the treatment of Alzheimer’s disease. In general, donepezil is known as a safe and well-tolerated drug, and it was not associated with liver abnormalities in several clinical trials. However, rare cases of drug-related liver toxicity have been reported since it has become commercially available. Few studies have investigated the metabolic profile of donepezil, and the mechanism of liver damage caused by donepezil has not been elucidated. In this study, the in vitro metabolism of donepezil was investigated using liquid chromatography–tandem mass spectrometry based on a non-targeted metabolomics approach. To identify metabolites, the data were subjected to multivariate data analysis and molecular networking. A total of 21 donepezil metabolites (17 in human liver microsomes, 21 in mice liver microsomes, and 17 in rat liver microsomes) were detected including 14 newly identified metabolites. One potential reactive metabolite was identified in rat liver microsomal incubation samples. Metabolites were formed through four major metabolic pathways: (1) O-demethylation, (2) hydroxylation, (3) N-oxidation, and (4) N-debenzylation. This study indicates that a non-targeted metabolomics approach combined with molecular networking is a reliable tool to identify and detect unknown drug metabolites.

Advancements in practical and scientific bioanalytical approaches to metabolism studies in drug development

Advancement in metabolism profiling approaches and bioanalytical techniques has been revolutionized over the last two decades. Different in vitro and in vivo approaches along with advanced bioanalytical techniques are enabling the accurate qualitative and quantitative analysis of metabolites. This review summarizes various modern in vitro and in vivo approaches for executing metabolism studies with special emphasis on the recent advancement in the field. Advanced bioanalytical techniques, which can be employed in metabolism studies, have been discussed suggesting their particular application based on specific study objectives. This article can efficiently guide the researchers to scientifically plan metabolism studies and their bioanalysis during drug development programs taking advantage of a detailed understanding of instances of failure in the past.

Galeon: A Biologically Active Molecule with In Silico Metabolite Prediction, In Vitro Metabolic Profiling in Rat Liver Microsomes, and In Silico Binding Mechanisms with CYP450 Isoforms

Galeon, a natural cyclic-diarylheptanoid (CDH), which was first isolated from Myrica gale L., is known to have potent cytotoxicity against A549 cell lines, anti-tubercular activity against Mycobacterium tuberculosis H37Rv, chemo-preventive potential, and moderate topoisomerase inhibitory activity. Here, in silico metabolism and toxicity prediction of galeon by CYP450, in vitro metabolic profiling study in rat liver microsomes (RLMs), and molecular interactions of galeon-CYP450 isoforms were performed. An in silico metabolic prediction study showed demethyl and mono-hydroxy galeon were the metabolites with the highest predictability. Among the predicted metabolites, mono-hydroxy galeon was found to have plausible toxicities such as skin sensitization, thyroid toxicity, chromosome damage, and carcinogenicity. An in vitro metabolism study of galeon, incubated in RLMs, revealed eighteen Phase-I metabolites, nine methoxylamine, and three glutathione conjugates. Identification of possible metabolites and confirmation of their structures were carried out using ion-trap tandem mass spectrometry. In silico docking analysis of galeon demonstrated significant interactions with active site residues of almost all CYP450 isoforms.

The MAP kinase inhibitor PD98059 reduces chromosomal instability in the autoimmune encephalomyelitis SJL/J-mouse model of multiple sclerosis

Multiple sclerosis (MS), a disease in which the immune system attacks nerve cells, has been associated with both genetic and environmental risk factors. We observed increased micronucleus (MN) formation in SJL/J mouse experimental autoimmune encephalomyelitis (EAE), an animal model of MS. Most of these MN were due to chromosomal loss. Increased activation of MAP kinases, which leads to disruption of the mitotic spindle and improper segregation of chromosomes, is associated with MS. MAP kinase inhibitors, such as PD98059, may therefore be beneficial for MS. In the EAE model, PD98059 treatment reduced adverse effects, including MN formation, lipid peroxidation, and GSH oxidation. Interventions that mitigate chromosomal instability may have therapeutic value in MS.

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined system-termed the metabolic forest-for generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.

Drug Withdrawal Due to Safety: A Review of the Data Supporting Withdrawal Decision

INTRODUCTION

Several drugs were withdrawn from the market due to safety.

OBJECTIVE

The aim of this study was to describe data supporting drug withdrawal from the market due to safety reasons in countries belonging to the World Health Organization.

METHODS

We analyzed drugs withdrawn from the market between 1990 and 2010. All medicine agencies of the countries belonging to the Program for International Drug Monitoring of the World Health Organization were contacted. To complete data, Medline, reference books and available drug databases were also searched. Information sources on which authorities based their withdrawal were categorized and the average time between the first date of exposure and withdrawal was calculated and stratified.

RESULTS

A total of 133 drugs that met the inclusion/exclusion criteria were withdrawn from the market due to safety reasons in the period reviewed (1990 - 2010). Hepatotoxicity (n=36, 27.1%), cardiac disorders (n=25, 18.8%), hypersensitivity (n=17, 12.8%) and nephrotoxicity (n=14, 9.8%) were the major reasons responsible for 69.2% of all drugs withdrawn. In most cases, Information Sources for drug withdrawal were spontaneous reports and/or case reports (n=86, 64.7%), followed by clinical trials (n=24, 18.0%). The average time between the introduction of a drug and its withdrawal due to safety reasons was 20.3 years (SD±13.8).

CONCLUSION

According to available and published evidence, there is no gold standard to identify risks associated with drug exposure. These findings strengthen the role of different information sources within the drug safety review process.

Site-Level Bioactivity of Small-Molecules from Deep-Learned Representations of Quantum Chemistry

Atom- or bond-level chemical properties of interest in medicinal chemistry, such as drug metabolism and electrophilic reactivity, are important to understand and predict across arbitrary new molecules. Deep learning can be used to map molecular structures to their chemical properties, but the data sets for these tasks are relatively small, which can limit accuracy and generalizability. To overcome this limitation, it would be preferable to model these properties on the basis of the underlying quantum chemical characteristics of small molecules. However, it is difficult to learn higher level chemical properties from lower level quantum calculations. To overcome this challenge, we pretrained deep learning models to compute quantum chemical properties and then reused the intermediate representations constructed by the pretrained network. Transfer learning, in this way, substantially outperformed models based on chemical graphs alone or quantum chemical properties alone. This result was robust, observable in five prediction tasks: identifying sites of epoxidation by metabolic enzymes and identifying sites of covalent reactivity with cyanide, glutathione, DNA and protein. We see that this approach may substantially improve the accuracy of deep learning models for specific chemical structures, such as aromatic systems.

A Comprehensive Review on Importance and Quantitation of Atypical Antipsychotic Drugs and their Active Metabolites in Commercial Dosage Forms

Background:

Schizophrenia is a severe mental illness that affects more than twenty-one million people throughout the world. Schizophrenia also causes early death. Schizophrenia and other related psychotic ailments are controlled by the prescription of antipsychotic drugs, which act by blocking certain chemical receptors in the brain and thus relieves the symptoms of psychotic disorder. These drugs are present in the different dosage forms in the market and provided in a certain amount as per the need of the patients.

Objective:

Since such medications treat mental disorders, it is very important to have a perfect and accurate dose so that the risk factor is not affected by a higher or lower dose, which is not sufficient for the treatment. For accurate assay of these kinds of drugs, different analytical methods were developed ranging from older spectrophotometric techniques to latest hyphenated methods.

Results:

The current review highlights the role of different analytical techniques that were employed in the determination and identification of antipsychotic drugs and their metabolites. Techniques such as spectrophotometry, fluorimetry, liquid chromatography, liquid chromatography-mass spectrometry, gas chromatography, and gas chromatography-mass spectrometry employed in the method development of such antipsychotic drugs were reported in the review. Different metabolites, identified using the hyphenated techniques, were also mentioned in the review. The synthesis pathways of few of the metabolites were mentioned.

Conclusion:

The review summarizes the analyses of different antipsychotic drugs and their metabolites. A brief introduction of illnesses and their symptoms and possible medications were highlighted. Synthesis pathways of the associated metabolites were also mentioned.

Toxicological testing of syringaresinol and enterolignans

Lignans are secondary plant constituents with dibenzylbutane skeletons found in cereals, oilseeds, and nuts. Two members of this class, syringaresinol (Syr) and secoisolariciresinol (Seco), occur at relatively high levels in cereals and processed food products as well as in coniferous trees. In vitro studies have shown that Seco and its metabolites enterodiol (END) and enterolactone (ENL), which are formed by intestinal microbes, exhibit strong antioxidant activity because of their phenolic character. The biological activity and discussion of dietary supplementation with these substances led to questions about the potential adverse health effects of these compounds, which are explored here. Syr and the metabolites END and ENL were investigated by combining structural information generated in silico with practical testing in vitro. An in silico structure-activity analysis was performed using ToxTree and NexusPrediction to suggest plausible mechanisms of toxicity and estimate toxicological endpoints of these compounds. Structural alerts were generated based on the presence of phenolic units with coordinating substituents that could potentially form quinoid structures, promote reactive oxygen species (ROS) formation, bind to cellular structures, or damage chromosomes. To assess the in silico results, the cytotoxicity and genotoxic potential of the studied compounds were tested in vitro using the resazurin reduction and comet assays, respectively. Incubating HepG2 and HT29 cells for 1 h or 24 h with 0–100 μM Syr, END, or ENL induced no cytotoxic effects. Additionally, even the highest tested concentrations of END and ENL showed no modulation of background and total DNA damage. The initial in silico screen thus generated structural alerts linked to toxicological endpoints, but experimental assessments of the studied compounds revealed no detectable toxicity, demonstrating the need for individual mechanistic experimental verification of in silico predictions. This approach makes it possible to connect known biological activity, such as reported antioxidative effects, to underlying mechanisms such as proton abstraction or donation. This in turn can yield insights – for example, that a compound's tendency to act as a pro- or anti-oxidant (and hence to exert adverse or beneficial health effects) may depend on its concentration and the cellular state.

•

Potential of toxicologic mechanisms: cellular stress and chromosomal damage were identified in silico for syringaresinol, enterdiol and enterlactone.

•

However, in confirmatory in vitro assays (cytotoxicity, DNA damage and DNA strand breaks) in HepG2 and HT29 cells no such toxicities were induced by physiological and higher concentrations of syringaresinol and enterolignans.

•

This study serves as a cautionary tale of using in silico prediction of toxicity mechanisms. Experimental verification of in silico predictions is needed as these methodologies are still under development.

Two novel fish paralogs provide insights into the Rid family of imine deaminases active in pre-empting enamine/imine metabolic damage

Reactive Intermediate Deaminase (Rid) protein superfamily includes eight families among which the RidA is conserved in all domains of life. RidA proteins accelerate the deamination of the reactive 2-aminoacrylate (2AA), an enamine produced by some pyridoxal phosphate (PLP)-dependent enzymes. 2AA accumulation inhibits target enzymes with a detrimental impact on fitness. As a consequence of whole genome duplication, teleost fish have two ridA paralogs, while other extant vertebrates contain a single-copy gene. We investigated the biochemical properties of the products of two paralogs, identified in Salmo salar. _SsRidA-1 and _SsRidA-2 complemented the growth defect of a Salmonella enterica ridA mutant, an in vivo model of 2AA stress. In vitro, both proteins hydrolyzed 2-imino acids (IA) to keto-acids and ammonia. _SsRidA-1 was active on IA derived from nonpolar amino acids and poorly active or inactive on IA derived from other amino acids tested. In contrast, _SsRidA-2 had a generally low catalytic efficiency, but showed a relatively higher activity with IA derived from L-Glu and aromatic amino acids. The crystal structures of _SsRidA-1 and _SsRidA-2 provided hints of the remarkably different conformational stability and substrate specificity. Overall, _SsRidA-1 is similar to the mammalian orthologs whereas _SsRidA-2 displays unique properties likely generated by functional specialization of a duplicated ancestral gene.

Quantitative structure activity relationship and molecular simulations for the exploration of natural potent VEGFR-2 inhibitors: an in silico anti-angiogenic study

VEGFR-2 has recently become an eye-catching molecular target for the novel therapeutic designs against cancer for its well known role in persuading angiogenesis in tumor cells. The current study set sights on the exploration of novel potent natural compound targeting VEGFR-2 via computational ligand-based modeling and database screening followed by binding pattern analysis, reactivity site prediction and MD simulation studies. The known 53 VEGFR-2 inhibitors (with IC50 ranging from 0.7 nM to 9700 nM) were headed for development of Ligand based pharmacophore model using 3 D QSAR pharmacophore generation module of DS Client. Training set inhibitors (23 compounds) were exploited to create pharmacophore model based on their chemical features. The model was validated through 30 test set inhibitors and exploited further for screening of 62,082 natural compounds from InterBioscreen natural compound database. Screened compounds further went through Drug-Likeliness study, ADMET prediction, Binding pattern analysis, In silico prediction of reactivity sites, Biological activity spectra prediction, pan assay interference compound identification and MD simulation analysis. Out of 5 screened compounds, Compound A and Compound B exhibited highest binding energy judged against the standard drug "Sorafenib". On further conducting reactivity site prediction, BAS prediction, and pan assay interference compound identification, Compound B exhibited better result which was carried forward for MD simulation study for 50 ns. MD simulation results suggested that Compound B exhibited more stable binding to the active site of VEGFR-2 without causing any conformational changes in protein-ligand complex. Thereby, the investigation proposes Compound B to hold potent antiangiogenic potential targeting VEGFR-2. [Formula: see text] Communicated by Ramaswamy H. Sarma.

The Discovery of 7-Methyl-2-[(7-methyl[1,2,4]triazolo[1,5-a]pyridin-6-yl)amino]-9-(tetrahydro-2H-pyran-4-yl)-7,9-dihydro-8H-purin-8-one (AZD7648), a Potent and Selective DNA-Dependent Protein Kinase (DNA-PK) Inhibitor

DNA-PK is a key component within the DNA damage response, as it is responsible for recognizing and repairing double-strand DNA breaks (DSBs) via non-homologous end joining. Historically it has been challenging to identify inhibitors of the DNA-PK catalytic subunit (DNA-PKcs) with good selectivity versus the structurally related PI3 (lipid) and PI3K-related protein kinases. We screened our corporate collection for DNA-PKcs inhibitors with good PI3 kinase selectivity, identifying compound 1. Optimization focused on further improving selectivity while improving physical and pharmacokinetic properties, notably co-optimization of permeability and metabolic stability, to identify compound 16 (AZD7648). Compound 16 had no significant off-target activity in the protein kinome and only weak activity versus PI3Kα/γ lipid kinases. Monotherapy activity in murine xenograft models was observed, and regressions were observed when combined with inducers of DSBs (doxorubicin or irradiation) or PARP inhibition (olaparib). These data support progression into clinical studies (NCT03907969).