Meloxicam methyl group determines enzyme specificity for thiazole bioactivation compared to sudoxicam

Barnette, Dustyn A.; Schleiff, Mary A.; Datta, Arghya; Flynn, Noah R.; Swamidass, S. Joshua; Miller, Grover P.

doi:10.1016/j.toxlet.2020.11.015

Cited by 20 publications

(7 citation statements)

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“…Previous studies have shown that meloxicam detoxification pathways are mediated in part by CYPs 2C9 and 3A4 [ 72 , 73 ]. Therefore, we expected that inhibitors of CYPs 2C9 or 3A4, when co-prescribed with meloxicam, may result in increased incidence of DILI.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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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).

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Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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“…The validity of predictions was improved by using the structure inference model to filter out highly predicted sites that do not produce an inferred structure. For the experimental studies, analysis of the reactive metabolites relied on set reaction conditions and no steady-state kinetics that could be used to extrapolate the potential in vivo relevance of bioactivation pathways [16,18]. Moreover, there are no reported studies investigating and confirming that the bioactivation of isoxazole-containing molecules contributes to in vitro or in vivo toxicity.…”

Section: Advances In Isoxazole Bioactivation Studies Revealed Current Limitations Of Those Effortsmentioning

confidence: 99%

“…These computational analyses are more accessible than experiments and provide an opportunity to readily explore potential relationships between molecular structure and bioactivation that lead to testable hypotheses. In practice, we couple high throughput computational studies with experimental efforts to validate predicted relationships and reveal model shortcomings for further refinement into practical tools, as shown through our work on terbinafine [16], thiazoles [17,18], and diphenylamine nonsteroidal anti-inflammatory drugs [19]. In this study, we applied our novel computational and experimental strategy to reveal the potential bioactivation liabilities of bromodomain and extra-terminal (BET) inhibitors.…”

Section: Introductionmentioning

confidence: 99%

Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors

et al. 2021

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The 3,5-dimethylisoxazole motif has become a useful and popular acetyl-lysine mimic employed in isoxazole-containing bromodomain and extra-terminal (BET) inhibitors but may introduce the potential for bioactivations into toxic reactive metabolites. As a test, we coupled deep neural models for quinone formation, metabolite structures, and biomolecule reactivity to predict bioactivation pathways for 32 BET inhibitors and validate the bioactivation of select inhibitors experimentally. Based on model predictions, inhibitors were more likely to undergo bioactivation than reported non-bioactivated molecules containing isoxazoles. The model outputs varied with substituents indicating the ability to scale their impact on bioactivation. We selected OXFBD02, OXFBD04, and I-BET151 for more in-depth analysis. OXFBD’s bioactivations were evenly split between traditional quinones and novel extended quinone-methides involving the isoxazole yet strongly favored the latter quinones. Subsequent experimental studies confirmed the formation of both types of quinones for OXFBD molecules, yet traditional quinones were the dominant reactive metabolites. Modeled I-BET151 bioactivations led to extended quinone-methides, which were not verified experimentally. The differences in observed and predicted bioactivations reflected the need to improve overall bioactivation scaling. Nevertheless, our coupled modeling approach predicted BET inhibitor bioactivations including novel extended quinone methides, and we experimentally verified those pathways highlighting potential concerns for toxicity in the development of these new drug leads.

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“…The evolution of drug-resistant strains, viz ., methicillin-resistant Staphylococcus aureus (MRSA) and Staphylococcus epidermidis (MRSE), has unfortunately augmented both nosocomial and inherent mortality rates, which poses an imperative threat to human health . Nowadays, the development of new antibacterial compounds to combat human bacterial infections has become a big challenge, and that may be related to the explicit and repeated use of antibiotics. , Recently, only four new classes of antibiotics have been approved by FDA over the past 17 years, while most current drugs have the same well-understood target. , The thiazole moiety is found in several substances, including thiamine (vitamin B1), thiamine pyrophosphate (TPP), bacitracin, amoxicillin, cefotaxime, and sulfathiazole. − Additionally, thiazole derivatives have emerged as a new class of potent antimicrobial agents, which are reported to inhibit bacteria by many different mechanisms such as blocking the biosynthesis of certain bacterial lipids and inhibiting DNA gyrase B , and dihydrofolate reductase …”

Section: Introductionmentioning

confidence: 99%

In Vitro Antimicrobial Evaluation, Single-Point Resistance Study, and Radiosterilization of Novel Pyrazole Incorporating Thiazol-4-one/Thiophene Derivatives as Dual DNA Gyrase and DHFR Inhibitors against MDR Pathogens

et al. 2022

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A series of thiazol-4-one/thiophene-bearing pyrazole derivatives as pharmacologically attractive cores were initially synthesized using a hybridization approach. All structures were confirmed using spectra analysis techniques (IR, 1 H NMR, and 13 C NMR). In vitro antimicrobial activities, including the minimum inhibitory concentration (MIC), minimum bactericidal/fungicidal concentration (MBC/MFC), and time-kill assay, were evaluated for the most active derivatives 4a, 5a, 7b, 10, and 13. These derivatives were significantly active against the tested pathogens, with compound 7b as the most active derivative (MIC values range from 0.22 to 0.25 μg/mL). In the MBC and MFC, the active target pyrazole derivatives showed -cidal activities toward the pathogenic isolates. Further, the inhibition of biofilm formation of Staphylococcus aureus and Staphylococcus epidermidis was also carried out. Additionally, these derivatives displayed significant antibiofilm potential with a superior % reduction in the biofilm formation compared with Ciprofloxacin. The target derivatives behaved synergistically with Ciprofloxacin and Ketoconazole, reducing their MICs. Hemolytic results revealed that these derivatives were nontoxic with a significantly low hemolytic activity (%lysis range from 3.23 to 15.22%) compared with Triton X-100 and showed noncytotoxicity activity with IC 50 values > 60 μM. In addition, these derivatives proved to be active DNA gyrase and DHFR inhibitors with IC 50 ranging between 12.27−31.64 and 0.52−2.67 μM, respectively. Furthermore, compound 7b showed bactericidal activity at different concentrations in the time-kill assay. Moreover, a gamma radiation dose of 10.0 kGy was efficient for sterilizing compound 7b and enhancing its antimicrobial activity. Finally, molecular docking simulation of the most promising derivatives exhibited good binding energy with different interactions.

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Meloxicam methyl group determines enzyme specificity for thiazole bioactivation compared to sudoxicam

Cited by 20 publications

References 32 publications

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

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

Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors

In Vitro Antimicrobial Evaluation, Single-Point Resistance Study, and Radiosterilization of Novel Pyrazole Incorporating Thiazol-4-one/Thiophene Derivatives as Dual DNA Gyrase and DHFR Inhibitors against MDR Pathogens

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