Novel (Q)SAR models for prediction of reversible and time-dependent inhibition of cytochrome P450 enzymes
The 2020 FDA drug-drug interaction (DDI) guidance includes a consideration for metabolites with structural alerts for potential mechanism-based inhibition (MBI) and describes how this information may be used to determine whether in vitro studies need to be conducted to evaluate the inhibitory potent...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1451164/full |
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| author | Sadegh Faramarzi Arianna Bassan Kevin P. Cross Xinning Yang Glenn J. Myatt Donna A. Volpe Lidiya Stavitskaya |
| author_facet | Sadegh Faramarzi Arianna Bassan Kevin P. Cross Xinning Yang Glenn J. Myatt Donna A. Volpe Lidiya Stavitskaya |
| author_sort | Sadegh Faramarzi |
| collection | DOAJ |
| description | The 2020 FDA drug-drug interaction (DDI) guidance includes a consideration for metabolites with structural alerts for potential mechanism-based inhibition (MBI) and describes how this information may be used to determine whether in vitro studies need to be conducted to evaluate the inhibitory potential of a metabolite on CYP enzymes. To facilitate identification of structural alerts, an extensive literature search was performed and alerts for mechanism-based inhibition of cytochrome P450 enzymes (CYP) were collected. Furthermore, five quantitative structure-activity relationship (QSAR) models were developed to predict not only time-dependent inhibition of CYP3A4, an enzyme that metabolizes approximately 50% of all marketed drugs, but also reversible inhibition of 3A4, 2C9, 2C19 and 2D6. The non-proprietary training database for the QSAR models contains data for 10,129 chemicals harvested from FDA drug approval packages and published literature. The cross-validation performance statistics for the new CYP QSAR models range from 78% to 84% sensitivity and 79%–84% normalized negative predictivity. Additionally, the performance of the newly developed QSAR models was assessed using external validation sets. Overall performance statistics showed up to 75% in sensitivity and up to 80% in normalized negative predictivity. The newly developed models will provide a faster and more effective evaluation of potential drug-drug interaction caused by metabolites. |
| format | Article |
| id | doaj-art-c2e6fa59b9f9491eb4fb6cceb855d21a |
| institution | Kabale University |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-c2e6fa59b9f9491eb4fb6cceb855d21a2025-02-12T05:14:57ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-02-011510.3389/fphar.2024.14511641451164Novel (Q)SAR models for prediction of reversible and time-dependent inhibition of cytochrome P450 enzymesSadegh Faramarzi0Arianna Bassan1Kevin P. Cross2Xinning Yang3Glenn J. Myatt4Donna A. Volpe5Lidiya Stavitskaya6Office of Clinical Pharmacology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United StatesInstem Inc., Conshohocken, PA, United StatesInstem Inc., Conshohocken, PA, United StatesOffice of Clinical Pharmacology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United StatesInstem Inc., Conshohocken, PA, United StatesOffice of Clinical Pharmacology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United StatesOffice of Clinical Pharmacology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United StatesThe 2020 FDA drug-drug interaction (DDI) guidance includes a consideration for metabolites with structural alerts for potential mechanism-based inhibition (MBI) and describes how this information may be used to determine whether in vitro studies need to be conducted to evaluate the inhibitory potential of a metabolite on CYP enzymes. To facilitate identification of structural alerts, an extensive literature search was performed and alerts for mechanism-based inhibition of cytochrome P450 enzymes (CYP) were collected. Furthermore, five quantitative structure-activity relationship (QSAR) models were developed to predict not only time-dependent inhibition of CYP3A4, an enzyme that metabolizes approximately 50% of all marketed drugs, but also reversible inhibition of 3A4, 2C9, 2C19 and 2D6. The non-proprietary training database for the QSAR models contains data for 10,129 chemicals harvested from FDA drug approval packages and published literature. The cross-validation performance statistics for the new CYP QSAR models range from 78% to 84% sensitivity and 79%–84% normalized negative predictivity. Additionally, the performance of the newly developed QSAR models was assessed using external validation sets. Overall performance statistics showed up to 75% in sensitivity and up to 80% in normalized negative predictivity. The newly developed models will provide a faster and more effective evaluation of potential drug-drug interaction caused by metabolites.https://www.frontiersin.org/articles/10.3389/fphar.2024.1451164/fullCYP = cytochrome P450reversible inhibitiontime dependent inhibitionQSARSARcomputational model |
| spellingShingle | Sadegh Faramarzi Arianna Bassan Kevin P. Cross Xinning Yang Glenn J. Myatt Donna A. Volpe Lidiya Stavitskaya Novel (Q)SAR models for prediction of reversible and time-dependent inhibition of cytochrome P450 enzymes Frontiers in Pharmacology CYP = cytochrome P450 reversible inhibition time dependent inhibition QSAR SAR computational model |
| title | Novel (Q)SAR models for prediction of reversible and time-dependent inhibition of cytochrome P450 enzymes |
| title_full | Novel (Q)SAR models for prediction of reversible and time-dependent inhibition of cytochrome P450 enzymes |
| title_fullStr | Novel (Q)SAR models for prediction of reversible and time-dependent inhibition of cytochrome P450 enzymes |
| title_full_unstemmed | Novel (Q)SAR models for prediction of reversible and time-dependent inhibition of cytochrome P450 enzymes |
| title_short | Novel (Q)SAR models for prediction of reversible and time-dependent inhibition of cytochrome P450 enzymes |
| title_sort | novel q sar models for prediction of reversible and time dependent inhibition of cytochrome p450 enzymes |
| topic | CYP = cytochrome P450 reversible inhibition time dependent inhibition QSAR SAR computational model |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1451164/full |
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