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CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs.

Biochemical pharmacology

Schleiff MA, Crosby S, Blue M, Schleiff BM, Boysen G, Miller GP.
PMID: 34748821
Biochem Pharmacol. 2021 Dec;194:114824. doi: 10.1016/j.bcp.2021.114824. Epub 2021 Nov 05.

Diphenylamine NSAIDs are taken frequently for chronic pain conditions, yet their use may potentiate hepatotoxicity risks through poorly characterized metabolic mechanisms. Our previous work revealed that seven marketed or withdrawn diphenylamine NSAIDs undergo bioactivation into quinone-species metabolites, whose reaction...

Cite
Schleiff MA, Crosby S, Blue M, et al. CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochem Pharmacol. 2021;194:114824doi: 10.1016/j.bcp.2021.114824.
Schleiff, M. A., Crosby, S., Blue, M., Schleiff, B. M., Boysen, G., & Miller, G. P. (2021). CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochemical pharmacology, 194114824. https://doi.org/10.1016/j.bcp.2021.114824
Schleiff, Mary Alexandra, et al. "CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs." Biochemical pharmacology vol. 194 (2021): 114824. doi: https://doi.org/10.1016/j.bcp.2021.114824
Schleiff MA, Crosby S, Blue M, Schleiff BM, Boysen G, Miller GP. CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochem Pharmacol. 2021 Dec;194:114824. doi: 10.1016/j.bcp.2021.114824. Epub 2021 Nov 05. PMID: 34748821.
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Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites.

Journal of chemical information and modeling

Hughes TB, Dang NL, Kumar A, Flynn NR, Swamidass SJ.
PMID: 32881497
J Chem Inf Model. 2020 Oct 26;60(10):4702-4716. doi: 10.1021/acs.jcim.0c00360. Epub 2020 Sep 16.

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...

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Hughes TB, Dang NL, Kumar A, et al. Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites. J Chem Inf Model. 2020;60(10):4702-4716doi: 10.1021/acs.jcim.0c00360.
Hughes, T. B., Dang, N. L., Kumar, A., Flynn, N. R., & Swamidass, S. J. (2020). Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites. Journal of chemical information and modeling, 60(10), 4702-4716. https://doi.org/10.1021/acs.jcim.0c00360
Hughes, Tyler B, et al. "Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites." Journal of chemical information and modeling vol. 60,10 (2020): 4702-4716. doi: https://doi.org/10.1021/acs.jcim.0c00360
Hughes TB, Dang NL, Kumar A, Flynn NR, Swamidass SJ. Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites. J Chem Inf Model. 2020 Oct 26;60(10):4702-4716. doi: 10.1021/acs.jcim.0c00360. Epub 2020 Sep 16. PMID: 32881497; PMCID: PMC8716321.
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XenoNet: Inference and Likelihood of Intermediate Metabolite Formation.

Journal of chemical information and modeling

Flynn NR, Dang NL, Ward MD, Swamidass SJ.
PMID: 32525671
J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29.

Drug metabolism is a common cause of adverse drug reactions. Drug molecules can be metabolized into reactive metabolites, which can conjugate to biomolecules, like protein and DNA, in a process termed bioactivation. To mitigate adverse reactions caused by bioactivation,...

Cite
Flynn NR, Dang NL, Ward MD, et al. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020;60(7):3431-3449doi: 10.1021/acs.jcim.0c00361.
Flynn, N. R., Dang, N. L., Ward, M. D., & Swamidass, S. J. (2020). XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. Journal of chemical information and modeling, 60(7), 3431-3449. https://doi.org/10.1021/acs.jcim.0c00361
Flynn, Noah R, et al. "XenoNet: Inference and Likelihood of Intermediate Metabolite Formation." Journal of chemical information and modeling vol. 60,7 (2020): 3431-3449. doi: https://doi.org/10.1021/acs.jcim.0c00361
Flynn NR, Dang NL, Ward MD, Swamidass SJ. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29. PMID: 32525671.
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Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network.

ACS central science

Hughes TB, Miller GP, Swamidass SJ.
PMID: 27162970
ACS Cent Sci. 2015 Jul 22;1(4):168-80. doi: 10.1021/acscentsci.5b00131. Epub 2015 Jun 09.

Drug toxicity is frequently caused by electrophilic reactive metabolites that covalently bind to proteins. Epoxides comprise a large class of three-membered cyclic ethers. These molecules are electrophilic and typically highly reactive due to ring tension and polarized carbon-oxygen bonds....

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Hughes TB, Miller GP, Swamidass SJ. Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS Cent Sci. 2015;1(4):168-80doi: 10.1021/acscentsci.5b00131.
Hughes, T. B., Miller, G. P., & Swamidass, S. J. (2015). Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS central science, 1(4), 168-80. https://doi.org/10.1021/acscentsci.5b00131
Hughes, Tyler B, et al. "Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network." ACS central science vol. 1,4 (2015): 168-80. doi: https://doi.org/10.1021/acscentsci.5b00131
Hughes TB, Miller GP, Swamidass SJ. Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS Cent Sci. 2015 Jul 22;1(4):168-80. doi: 10.1021/acscentsci.5b00131. Epub 2015 Jun 09. PMID: 27162970; PMCID: PMC4827534.
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Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity.

Scientific reports

Pan Y, Daito T, Sasaki Y, Chung YH, Xing X, Pondugula S, Swamidass SJ, Wang T, Kim AH, Yano H.
PMID: 27649847
Sci Rep. 2016 Sep 21;6:33766. doi: 10.1038/srep33766.

No abstract available.

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Pan Y, Daito T, Sasaki Y, et al. Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity. Sci Rep. 2016;6:33766doi: 10.1038/srep33766.
Pan, Y., Daito, T., Sasaki, Y., Chung, Y. H., Xing, X., Pondugula, S., Swamidass, S. J., Wang, T., Kim, A. H., & Yano, H. (2016). Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity. Scientific reports, 633766. https://doi.org/10.1038/srep33766
Pan, Yanchun, et al. "Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity." Scientific reports vol. 6 (2016): 33766. doi: https://doi.org/10.1038/srep33766
Pan Y, Daito T, Sasaki Y, Chung YH, Xing X, Pondugula S, Swamidass SJ, Wang T, Kim AH, Yano H. Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity. Sci Rep. 2016 Sep 21;6:33766. doi: 10.1038/srep33766. PMID: 27649847; PMCID: PMC5030520.
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XenoNet: Inference and Likelihood of Intermediate Metabolite Formation.

Journal of chemical information and modeling

Flynn NR, Dang NL, Ward MD, Swamidass SJ.
PMID: 32525671
J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29.

Drug metabolism is a common cause of adverse drug reactions. Drug molecules can be metabolized into reactive metabolites, which can conjugate to biomolecules, like protein and DNA, in a process termed bioactivation. To mitigate adverse reactions caused by bioactivation,...

Cite
Flynn NR, Dang NL, Ward MD, et al. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020;60(7):3431-3449doi: 10.1021/acs.jcim.0c00361.
Flynn, N. R., Dang, N. L., Ward, M. D., & Swamidass, S. J. (2020). XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. Journal of chemical information and modeling, 60(7), 3431-3449. https://doi.org/10.1021/acs.jcim.0c00361
Flynn, Noah R, et al. "XenoNet: Inference and Likelihood of Intermediate Metabolite Formation." Journal of chemical information and modeling vol. 60,7 (2020): 3431-3449. doi: https://doi.org/10.1021/acs.jcim.0c00361
Flynn NR, Dang NL, Ward MD, Swamidass SJ. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29. PMID: 32525671; PMCID: PMC8716322.
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Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network.

ACS central science

Hughes TB, Dang NL, Miller GP, Swamidass SJ.
PMID: 27610414
ACS Cent Sci. 2016 Aug 24;2(8):529-37. doi: 10.1021/acscentsci.6b00162. Epub 2016 Jul 29.

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict...

Cite
Hughes TB, Dang NL, Miller GP, et al. Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. ACS Cent Sci. 2016;2(8):529-37doi: 10.1021/acscentsci.6b00162.
Hughes, T. B., Dang, N. L., Miller, G. P., & Swamidass, S. J. (2016). Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. ACS central science, 2(8), 529-37. https://doi.org/10.1021/acscentsci.6b00162
Hughes, Tyler B, et al. "Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network." ACS central science vol. 2,8 (2016): 529-37. doi: https://doi.org/10.1021/acscentsci.6b00162
Hughes TB, Dang NL, Miller GP, Swamidass SJ. Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. ACS Cent Sci. 2016 Aug 24;2(8):529-37. doi: 10.1021/acscentsci.6b00162. Epub 2016 Jul 29. PMID: 27610414; PMCID: PMC4999971.
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CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs.

Biochemical pharmacology

Schleiff MA, Crosby S, Blue M, Schleiff BM, Boysen G, Miller GP.
PMID: 34748821
Biochem Pharmacol. 2021 Dec;194:114824. doi: 10.1016/j.bcp.2021.114824. Epub 2021 Nov 05.

Diphenylamine NSAIDs are taken frequently for chronic pain conditions, yet their use may potentiate hepatotoxicity risks through poorly characterized metabolic mechanisms. Our previous work revealed that seven marketed or withdrawn diphenylamine NSAIDs undergo bioactivation into quinone-species metabolites, whose reaction...

Cite
Schleiff MA, Crosby S, Blue M, et al. CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochem Pharmacol. 2021;194:114824doi: 10.1016/j.bcp.2021.114824.
Schleiff, M. A., Crosby, S., Blue, M., Schleiff, B. M., Boysen, G., & Miller, G. P. (2021). CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochemical pharmacology, 194114824. https://doi.org/10.1016/j.bcp.2021.114824
Schleiff, Mary Alexandra, et al. "CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs." Biochemical pharmacology vol. 194 (2021): 114824. doi: https://doi.org/10.1016/j.bcp.2021.114824
Schleiff MA, Crosby S, Blue M, Schleiff BM, Boysen G, Miller GP. CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochem Pharmacol. 2021 Dec;194:114824. doi: 10.1016/j.bcp.2021.114824. Epub 2021 Nov 05. PMID: 34748821; PMCID: PMC8710044.
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Learning a Local-Variable Model of Aromatic and Conjugated Systems.

ACS central science

Matlock MK, Dang NL, Swamidass SJ.
PMID: 29392176
ACS Cent Sci. 2018 Jan 24;4(1):52-62. doi: 10.1021/acscentsci.7b00405. Epub 2018 Jan 03.

A collection of new approaches to building and training neural networks, collectively referred to as deep learning, are attracting attention in theoretical chemistry. Several groups aim to replace computationally expensive

Cite
Matlock MK, Dang NL, Swamidass SJ. Learning a Local-Variable Model of Aromatic and Conjugated Systems. ACS Cent Sci. 2018;4(1):52-62doi: 10.1021/acscentsci.7b00405.
Matlock, M. K., Dang, N. L., & Swamidass, S. J. (2018). Learning a Local-Variable Model of Aromatic and Conjugated Systems. ACS central science, 4(1), 52-62. https://doi.org/10.1021/acscentsci.7b00405
Matlock, Matthew K, et al. "Learning a Local-Variable Model of Aromatic and Conjugated Systems." ACS central science vol. 4,1 (2018): 52-62. doi: https://doi.org/10.1021/acscentsci.7b00405
Matlock MK, Dang NL, Swamidass SJ. Learning a Local-Variable Model of Aromatic and Conjugated Systems. ACS Cent Sci. 2018 Jan 24;4(1):52-62. doi: 10.1021/acscentsci.7b00405. Epub 2018 Jan 03. PMID: 29392176; PMCID: PMC5785769.
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CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs.

Biochemical pharmacology

Schleiff MA, Crosby S, Blue M, Schleiff BM, Boysen G, Miller GP.
PMID: 34748821
Biochem Pharmacol. 2021 Dec;194:114824. doi: 10.1016/j.bcp.2021.114824. Epub 2021 Nov 05.

Diphenylamine NSAIDs are taken frequently for chronic pain conditions, yet their use may potentiate hepatotoxicity risks through poorly characterized metabolic mechanisms. Our previous work revealed that seven marketed or withdrawn diphenylamine NSAIDs undergo bioactivation into quinone-species metabolites, whose reaction...

Cite
Schleiff MA, Crosby S, Blue M, et al. CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochem Pharmacol. 2021;194:114824doi: 10.1016/j.bcp.2021.114824.
Schleiff, M. A., Crosby, S., Blue, M., Schleiff, B. M., Boysen, G., & Miller, G. P. (2021). CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochemical pharmacology, 194114824. https://doi.org/10.1016/j.bcp.2021.114824
Schleiff, Mary Alexandra, et al. "CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs." Biochemical pharmacology vol. 194 (2021): 114824. doi: https://doi.org/10.1016/j.bcp.2021.114824
Schleiff MA, Crosby S, Blue M, Schleiff BM, Boysen G, Miller GP. CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochem Pharmacol. 2021 Dec;194:114824. doi: 10.1016/j.bcp.2021.114824. Epub 2021 Nov 05. PMID: 34748821.
Copy Download .nbib Format: NLM
XenoNet: Inference and Likelihood of Intermediate Metabolite Formation.

Journal of chemical information and modeling

Flynn NR, Dang NL, Ward MD, Swamidass SJ.
PMID: 32525671
J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29.

Drug metabolism is a common cause of adverse drug reactions. Drug molecules can be metabolized into reactive metabolites, which can conjugate to biomolecules, like protein and DNA, in a process termed bioactivation. To mitigate adverse reactions caused by bioactivation,...

Cite
Flynn NR, Dang NL, Ward MD, et al. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020;60(7):3431-3449doi: 10.1021/acs.jcim.0c00361.
Flynn, N. R., Dang, N. L., Ward, M. D., & Swamidass, S. J. (2020). XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. Journal of chemical information and modeling, 60(7), 3431-3449. https://doi.org/10.1021/acs.jcim.0c00361
Flynn, Noah R, et al. "XenoNet: Inference and Likelihood of Intermediate Metabolite Formation." Journal of chemical information and modeling vol. 60,7 (2020): 3431-3449. doi: https://doi.org/10.1021/acs.jcim.0c00361
Flynn NR, Dang NL, Ward MD, Swamidass SJ. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29. PMID: 32525671.
Copy Download .nbib Format: NLM
CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs.

Biochemical pharmacology

Schleiff MA, Crosby S, Blue M, Schleiff BM, Boysen G, Miller GP.
PMID: 34748821
Biochem Pharmacol. 2021 Dec;194:114824. doi: 10.1016/j.bcp.2021.114824. Epub 2021 Nov 05.

Diphenylamine NSAIDs are taken frequently for chronic pain conditions, yet their use may potentiate hepatotoxicity risks through poorly characterized metabolic mechanisms. Our previous work revealed that seven marketed or withdrawn diphenylamine NSAIDs undergo bioactivation into quinone-species metabolites, whose reaction...

Cite
Schleiff MA, Crosby S, Blue M, et al. CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochem Pharmacol. 2021;194:114824doi: 10.1016/j.bcp.2021.114824.
Schleiff, M. A., Crosby, S., Blue, M., Schleiff, B. M., Boysen, G., & Miller, G. P. (2021). CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochemical pharmacology, 194114824. https://doi.org/10.1016/j.bcp.2021.114824
Schleiff, Mary Alexandra, et al. "CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs." Biochemical pharmacology vol. 194 (2021): 114824. doi: https://doi.org/10.1016/j.bcp.2021.114824
Schleiff MA, Crosby S, Blue M, Schleiff BM, Boysen G, Miller GP. CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs. Biochem Pharmacol. 2021 Dec;194:114824. doi: 10.1016/j.bcp.2021.114824. Epub 2021 Nov 05. PMID: 34748821.
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Showing 1 to 12 of 15 entries