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Jang HY, Song J, Kim JH, Lee H, Kim IW, Moon B, Oh JM. Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information. NPJ Digit Med 2022; 5:88. [PMID: 35817846 PMCID: PMC9273620 DOI: 10.1038/s41746-022-00639-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 06/16/2022] [Indexed: 11/27/2022] Open
Abstract
Many machine learning techniques provide a simple prediction for drug-drug interactions (DDIs). However, a systematically constructed database with pharmacokinetic (PK) DDI information does not exist, nor is there a machine learning model that numerically predicts PK fold change (FC) with it. Therefore, we propose a PK DDI prediction (PK-DDIP) model for quantitative DDI prediction with high accuracy, while constructing a highly reliable PK-DDI database. Reliable information of 3,627 PK DDIs was constructed from 3,587 drugs using 38,711 Food and Drug Administration (FDA) drug labels. This PK-DDIP model predicted the FC of the area under the time-concentration curve (AUC) within ± 0.5959. The prediction proportions within 0.8–1.25-fold, 0.67–1.5-fold, and 0.5–2-fold of the AUC were 75.77, 86.68, and 94.76%, respectively. Two external validations confirmed good prediction performance for newly updated FDA labels and FC from patients’. This model enables potential DDI evaluation before clinical trials, which will save time and cost.
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Affiliation(s)
- Ha Young Jang
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Jihyeon Song
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jae Hyun Kim
- School of Pharmacy, Jeonbuk National University, Jeonju, Republic of Korea
| | - Howard Lee
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - In-Wha Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Bongki Moon
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
| | - Jung Mi Oh
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea.
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Hanapi NA, Chear NJY, Azizi J, Yusof SR. Kratom Alkaloids: Interactions With Enzymes, Receptors, and Cellular Barriers. Front Pharmacol 2021; 12:751656. [PMID: 34867362 PMCID: PMC8637859 DOI: 10.3389/fphar.2021.751656] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 10/26/2021] [Indexed: 01/11/2023] Open
Abstract
Parallel to the growing use of kratom, there is a wealth of evidence from self-report, preclinical, and early clinical studies on therapeutic benefits of its alkaloids in particular for treating pain, managing substance use disorder, and coping with emotional or mental health conditions. On the other hand, there are also reports on potential health risks concerning kratom use. These two aspects are often discussed in reviews on kratom. Here, we aim to highlight specific areas that are of importance to give insights into the mechanistic of kratom alkaloids pharmacological actions. This includes their interactions with drug-metabolizing enzymes and predictions of clinical drug-drug interactions, receptor-binding properties, interactions with cellular barriers in regards to barrier permeability, involvement of membrane transporters, and alteration of barrier function when exposed to the alkaloids.
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Affiliation(s)
- Nur Aziah Hanapi
- Centre for Drug Research, Universiti Sains Malaysia, Minden, Malaysia
| | | | - Juzaili Azizi
- Centre for Drug Research, Universiti Sains Malaysia, Minden, Malaysia
| | - Siti R Yusof
- Centre for Drug Research, Universiti Sains Malaysia, Minden, Malaysia
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Model-based comparative analysis of rifampicin and rifabutin drug-drug interaction profile. Antimicrob Agents Chemother 2021; 65:e0104321. [PMID: 34228545 DOI: 10.1128/aac.01043-21] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Rifamycins are widely used for treating mycobacterial and staphylococcal infections. Drug-drug interactions (DDI) caused by rifampicin (RIF) is a major issue. We used a model-based approach to predict the magnitude of DDI with RIF and rifabutin (RBT) for 217 cytochrome P450 (CYP) substrates. On average, DDI caused by low-dose RIF were twice more potent than those caused by RBT. Contrary to RIF, RBT appears unlikely to cause severe DDI, even with sensitive CYP substrates.
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Le Corvaisier C, Capelle A, France M, Bourguignon L, Tod M, Goutelle S. Drug interactions between emergency contraceptive drugs and cytochrome inducers: literature review and quantitative prediction. Fundam Clin Pharmacol 2020; 35:208-216. [DOI: 10.1111/fcp.12601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 08/04/2020] [Accepted: 08/17/2020] [Indexed: 01/10/2023]
Affiliation(s)
- Claire Le Corvaisier
- Groupement Hospitalier Nord Hôpital Pierre Garraud Service de Pharmacie Hospices Civils de Lyon 136 rue du Commandant Charcot 69005 Lyon France
| | - Aude Capelle
- Centre Hospitalier Universitaire de Saint‐EtiennePharmacie DMS 25 Boulevard Pasteur Saint‐Étienne 42100 France
| | - Mathilde France
- Groupement Hospitalier Nord Hôpital Pierre Garraud Service de Pharmacie Hospices Civils de Lyon 136 rue du Commandant Charcot 69005 Lyon France
| | - Laurent Bourguignon
- Groupement Hospitalier Nord Hôpital Pierre Garraud Service de Pharmacie Hospices Civils de Lyon 136 rue du Commandant Charcot 69005 Lyon France
- Univ Lyon, Université Lyon 1, ISPB Faculté de Pharmacie de Lyon 8 avenue Rockefeller Lyon 69373 France
- Univ Lyon Université Lyon 1 UMR CNRS 5558 Laboratoire de Biométrie et Biologie Evolutive Bât. Grégor Mendel, 43 bd du 11 novembre 1918 Villeurbanne 69622 France
| | - Michel Tod
- Groupement Hospitalier Nord Hôpital Pierre Garraud Service de Pharmacie Hospices Civils de Lyon 136 rue du Commandant Charcot 69005 Lyon France
- Univ Lyon, Université Lyon 1, ISPB Faculté de Pharmacie de Lyon 8 avenue Rockefeller Lyon 69373 France
| | - Sylvain Goutelle
- Groupement Hospitalier Nord Hôpital Pierre Garraud Service de Pharmacie Hospices Civils de Lyon 136 rue du Commandant Charcot 69005 Lyon France
- Univ Lyon, Université Lyon 1, ISPB Faculté de Pharmacie de Lyon 8 avenue Rockefeller Lyon 69373 France
- Univ Lyon Université Lyon 1 UMR CNRS 5558 Laboratoire de Biométrie et Biologie Evolutive Bât. Grégor Mendel, 43 bd du 11 novembre 1918 Villeurbanne 69622 France
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Tod M, Bourguignon L, Bleyzac N, Goutelle S. Quantitative Prediction of Interactions Mediated by Transporters and Cytochromes: Application to Organic Anion Transporting Polypeptides, Breast Cancer Resistance Protein and Cytochrome 2C8. Clin Pharmacokinet 2019; 59:757-770. [DOI: 10.1007/s40262-019-00853-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Tod M, Goutelle S, Bleyzac N, Bourguignon L. A Generic Model for Quantitative Prediction of Interactions Mediated by Efflux Transporters and Cytochromes: Application to P-Glycoprotein and Cytochrome 3A4. Clin Pharmacokinet 2018; 58:503-523. [DOI: 10.1007/s40262-018-0711-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Tod M, Goutelle S, Bourguignon L, Bleyzac N. Quantitative Prediction of Drug-Drug Interactions Involving Inhibitory Metabolites by Physiologically Based Pharmacokinetic Models: Is it Worth It? CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2016; 6:226. [PMID: 27984676 PMCID: PMC5397559 DOI: 10.1002/psp4.12164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 12/06/2016] [Indexed: 01/26/2023]
Affiliation(s)
- M Tod
- Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France.,EMR3738, Université Claude Bernard Lyon 1, Lyon, France
| | - S Goutelle
- Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France.,UMR5758, Université Claude Bernard Lyon 1, Lyon, France
| | - L Bourguignon
- Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France.,UMR5758, Université Claude Bernard Lyon 1, Lyon, France
| | - N Bleyzac
- EMR3738, Université Claude Bernard Lyon 1, Lyon, France.,Institut d'Hémato-Oncologie Pédiatrique, Hospices Civils de Lyon, Lyon, France
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Tod M, Bourguignon L, Bleyzac N, Goutelle S. A Model for Predicting the Interindividual Variability of Drug-Drug Interactions. AAPS JOURNAL 2016; 19:497-509. [PMID: 27924615 DOI: 10.1208/s12248-016-0021-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 11/28/2016] [Indexed: 11/30/2022]
Abstract
Pharmacokinetic drug-drug interactions are frequently characterized and quantified by an AUC ratio (Rauc). The typical value of the AUC ratio in case of cytochrome-mediated interactions may be predicted by several approaches, based on in vitro or in vivo data. Prediction of the interindividual variability of Rauc would help to anticipate more completely the consequences of a drug-drug interaction. We propose and evaluate a simple approach for predicting the standard deviation (sd) of Ln(Rauc), a metric close to the interindividual coefficient of variation of Rauc. First, a model was derived to link sd(Ln Rauc) with the substrate fraction metabolized by each cytochrome and the potency of the interactors, in case of induction or inhibition. Second, the parameters involved in these equations were estimated by a Bayesian hierarchical model, using the data from 56 interaction studies retrieved from the literature. Third, the model was evaluated by several metrics based on the fold prediction error (PE) of sd(Ln Rauc). The median PE was 0.998 (the ideal value is 1) and the interquartile range was 0.96-1.03. The PE was in the acceptable interval (0.5 to 2) in 52 cases out of 56. Fourth, a surface plot of sd(Ln Rauc) as a function of the characteristics of the substrate and the interactor has been built. The minimal value of sd(Ln Rauc) was about 0.08 (obtained for Rauc = 1) while the maximal value, 0.7, was obtained for interactions involving highly metabolized substrates with strong interactors.
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Affiliation(s)
- M Tod
- Pharmacie, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France. .,EMR3738, Faculté de médecine Lyon-sud, Université Lyon 1, Lyon, France. .,Faculté de pharmacie, Université Lyon 1, Lyon, France.
| | - L Bourguignon
- Pharmacie, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France.,Faculté de pharmacie, Université Lyon 1, Lyon, France.,UMR CNRS 5558, Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, Lyon, France
| | - N Bleyzac
- EMR3738, Faculté de médecine Lyon-sud, Université Lyon 1, Lyon, France.,Pharmacie, Institut d'Hématologie et d'Oncologie Pédiatrique, Hospices Civils de Lyon, Lyon, France
| | - S Goutelle
- Pharmacie, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France.,Faculté de pharmacie, Université Lyon 1, Lyon, France.,UMR CNRS 5558, Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, Lyon, France
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