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Tremmel R, Hofmann U, Haag M, Schaeffeler E, Schwab M. Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes. Annu Rev Pharmacol Toxicol 2024; 64:65-87. [PMID: 37585662 DOI: 10.1146/annurev-pharmtox-032023-121106] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.
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Affiliation(s)
- Roman Tremmel
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Mathias Haag
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies," University of Tuebingen, Tuebingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies," University of Tuebingen, Tuebingen, Germany
- Departments of Clinical Pharmacology, and Pharmacy and Biochemistry, University of Tuebingen, Tuebingen, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center Heidelberg (DKFZ), Partner Site, Tübingen, Germany
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Hecker M, Frahm N, Zettl UK. Update and Application of a Deep Learning Model for the Prediction of Interactions between Drugs Used by Patients with Multiple Sclerosis. Pharmaceutics 2023; 16:3. [PMID: 38276481 PMCID: PMC10819178 DOI: 10.3390/pharmaceutics16010003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 01/27/2024] Open
Abstract
Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton's tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow's milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs.
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Affiliation(s)
- Michael Hecker
- Division of Neuroimmunology, Department of Neurology, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany; (N.F.); (U.K.Z.)
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Feick D, Rüdesheim S, Marok FZ, Selzer D, Loer HLH, Teutonico D, Frechen S, van der Lee M, Moes DJAR, Swen JJ, Schwab M, Lehr T. Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network. CPT Pharmacometrics Syst Pharmacol 2023; 12:1143-1156. [PMID: 37165978 PMCID: PMC10431052 DOI: 10.1002/psp4.12981] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/31/2023] [Accepted: 04/13/2023] [Indexed: 05/12/2023] Open
Abstract
The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P-glycoprotein (P-gp) and is therefore recommended for use in clinical drug-drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P-gp, it is susceptible to DDIs involving these proteins. Physiologically-based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug-drug(-gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P-gp perpetrators as well as CYP2D6 and P-gp victims. The quinidine parent-metabolite model including 3-hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1-600 mg). The model covers efflux transport via P-gp and metabolic transformation to either 3-hydroxyquinidine or unspecified metabolites via CYP3A4. The 3-hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two-fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two-fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.
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Affiliation(s)
- Denise Feick
- Clinical PharmacySaarland UniversitySaarbrückenGermany
| | - Simeon Rüdesheim
- Clinical PharmacySaarland UniversitySaarbrückenGermany
- Dr. Margarete Fischer‐Bosch‐Institute of Clinical PharmacologyStuttgartGermany
| | | | | | | | - Donato Teutonico
- Translational Medicine & Early DevelopmentSanofi‐Aventis R&DChilly‐MazarinFrance
| | - Sebastian Frechen
- Bayer AG, Pharmaceuticals, Research & DevelopmentSystems Pharmacology & MedicineLeverkusenGermany
| | - Maaike van der Lee
- Department of Clinical Pharmacy & ToxicologyLeiden University Medical CenterLeidenThe Netherlands
| | - Dirk Jan A. R. Moes
- Department of Clinical Pharmacy & ToxicologyLeiden University Medical CenterLeidenThe Netherlands
| | - Jesse J. Swen
- Department of Clinical Pharmacy & ToxicologyLeiden University Medical CenterLeidenThe Netherlands
| | - Matthias Schwab
- Dr. Margarete Fischer‐Bosch‐Institute of Clinical PharmacologyStuttgartGermany
- Departments of Clinical Pharmacology, Pharmacy and BiochemistryUniversity of TübingenTübingenGermany
- Cluster of Excellence iFIT (EXC2180) “Image‐guided and Functionally Instructed Tumor Therapies”University of TübingenTübingenGermany
| | - Thorsten Lehr
- Clinical PharmacySaarland UniversitySaarbrückenGermany
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Alsultan A, Alalwan AA, Alshehri B, Jeraisy MA, Alghamdi J, Alqahtani S, Albassam AA. Interethnic differences in drug response: projected impact of genetic variations in the Saudi population. Pharmacogenomics 2023; 24:685-696. [PMID: 37610881 DOI: 10.2217/pgs-2023-0105] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023] Open
Abstract
Ethnicity is known to have an impact on drug responses. This is particularly important for drugs that have a narrow therapeutic window, nonlinearity in pharmacokinetics and are metabolized by enzymes that demonstrate genetic polymorphisms. However, most clinical trials are conducted among Caucasians, which might limit the usefulness of the findings of such studies for other ethnicities. The representation of participants from Saudi Arabia in global clinical trials is low. Therefore, there is a paucity of evidence to assess the impact of ethnic variability in the Saudi population on drug response. In this article, the authors assess the projected impact of genetic polymorphisms in drug-metabolizing enzymes and drug targets on drug response in the Saudi population.
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Affiliation(s)
- Abdullah Alsultan
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Abdullah A Alalwan
- Department of Clinical Pharmacy, College of Pharmacy, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Bashayer Alshehri
- Pharmaceutical Care Department, King Abdulaziz Medical City, Riyadh, Saudi Arabia
| | - Majed Al Jeraisy
- Pharmaceutical Care Department, King Abdulaziz Medical City, Riyadh, Saudi Arabia
- College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Jahad Alghamdi
- Saudi Food and Drug Authority, Drug Sector, Riyadh, Saudi Arabia
| | - Saeed Alqahtani
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Ahmed A Albassam
- Department of Clinical Pharmacy, College of Pharmacy, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
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A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug-Drug Interaction Perpetrators. Pharmaceutics 2023; 15:pharmaceutics15020679. [PMID: 36840001 PMCID: PMC9965990 DOI: 10.3390/pharmaceutics15020679] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/06/2023] [Accepted: 02/13/2023] [Indexed: 02/19/2023] Open
Abstract
The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing's syndrome, is prone to drug-food interactions (DFIs) and is well known for its strong drug-drug interaction (DDI) potential. Some of ketoconazole's potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole's metabolites on its DDI potential. The parent-metabolites model was developed with PK-Sim® and MoBi® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUClast and DFI Cmax ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUClast and 21/21 DDI Cmax ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUClast and 18/21 DDI Cmax ratios were within the success limits.
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Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone. Pharmaceutics 2022; 14:pharmaceutics14081734. [PMID: 36015360 PMCID: PMC9414337 DOI: 10.3390/pharmaceutics14081734] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/12/2022] [Accepted: 08/17/2022] [Indexed: 11/16/2022] Open
Abstract
The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUClast ratios and 9/12, 5/5 and 7/7 of DGI Cmax ratios were within the prediction success limits. Finally, the models were used to predict their compound’s exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUCss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.
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Yang S, Zeng L, Jin X, Lin H, Song J. Feature Genes in Neuroblastoma Distinguishing High-Risk and Non-high-Risk Neuroblastoma Patients: Development and Validation Combining Random Forest With Artificial Neural Network. Front Med (Lausanne) 2022; 9:882348. [PMID: 35911385 PMCID: PMC9336509 DOI: 10.3389/fmed.2022.882348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
There is a significant difference in prognosis among different risk groups. Therefore, it is of great significance to correctly identify the risk grouping of children. Using the genomic data of neuroblastoma samples in public databases, we used GSE49710 as the training set data to calculate the feature genes of the high-risk group and non-high-risk group samples based on the random forest (RF) algorithm and artificial neural network (ANN) algorithm. The screening results of RF showed that EPS8L1, PLCD4, CHD5, NTRK1, and SLC22A4 were the feature differentially expressed genes (DEGs) of high-risk neuroblastoma. The prediction model based on gene expression data in this study showed high overall accuracy and precision in both the training set and the test set (AUC = 0.998 in GSE49710 and AUC = 0.858 in GSE73517). Kaplan–Meier plotter showed that the overall survival and progression-free survival of patients in the low-risk subgroup were significantly better than those in the high-risk subgroup [HR: 3.86 (95% CI: 2.44–6.10) and HR: 3.03 (95% CI: 2.03–4.52), respectively]. Our ANN-based model has better classification performance than the SVM-based model and XGboost-based model. Nevertheless, more convincing data sets and machine learning algorithms will be needed to build diagnostic models for individual organization types in the future.
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Affiliation(s)
- Sha Yang
- Department of Surgery, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
- Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Lingfeng Zeng
- Department of Nephrology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xin Jin
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
- Children’s Hospital of Chongqing Medical University, Chongqing, China
- Department of Cardiacthoracic, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Huapeng Lin
- Department of Intensive Care Unit, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianning Song
- Department of General Surgery, Guiqian International General Hospital, Guiyang, China
- *Correspondence: Jianning Song, ,
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Türk D, Müller F, Fromm MF, Selzer D, Dallmann R, Lehr T. Renal Transporter-Mediated Drug-Biomarker Interactions of the Endogenous Substrates Creatinine and N 1 -Methylnicotinamide: A PBPK Modeling Approach. Clin Pharmacol Ther 2022; 112:687-698. [PMID: 35527512 DOI: 10.1002/cpt.2636] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 04/28/2022] [Indexed: 01/06/2023]
Abstract
Endogenous biomarkers for transporter-mediated drug-drug interaction (DDI) predictions represent a promising approach to facilitate and improve conventional DDI investigations in clinical studies. This approach requires high sensitivity and specificity of biomarkers for the targets of interest (e.g., transport proteins), as well as rigorous characterization of their kinetics, which can be accomplished utilizing physiologically-based pharmacokinetic (PBPK) modeling. Therefore, the objective of this study was to develop PBPK models of the endogenous organic cation transporter (OCT)2 and multidrug and toxin extrusion protein (MATE)1 substrates creatinine and N1 -methylnicotinamide (NMN). Additionally, this study aimed to predict kinetic changes of the biomarkers during administration of the OCT2 and MATE1 perpetrator drugs trimethoprim, pyrimethamine, and cimetidine. Whole-body PBPK models of creatinine and NMN were developed utilizing studies investigating creatinine or NMN exogenous administration and endogenous synthesis. The newly developed models accurately describe and predict observed plasma concentration-time profiles and urinary excretion of both biomarkers. Subsequently, models were coupled to the previously built and evaluated perpetrator models of trimethoprim, pyrimethamine, and cimetidine for interaction predictions. Increased creatinine plasma concentrations and decreased urinary excretion during the drug-biomarker interactions with trimethoprim, pyrimethamine, and cimetidine were well-described. An additional inhibition of NMN synthesis by trimethoprim and pyrimethamine was hypothesized, improving NMN plasma and urine interaction predictions. To summarize, whole-body PBPK models of creatinine and NMN were built and evaluated to better assess creatinine and NMN kinetics while uncovering knowledge gaps for future research. The models can support investigations of renal transporter-mediated DDIs during drug development.
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Affiliation(s)
- Denise Türk
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
| | - Fabian Müller
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Martin F Fromm
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dominik Selzer
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
| | - Robert Dallmann
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
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Physiologically Based Pharmacokinetic (PBPK) Modeling of Clopidogrel and Its Four Relevant Metabolites for CYP2B6, CYP2C8, CYP2C19, and CYP3A4 Drug–Drug–Gene Interaction Predictions. Pharmaceutics 2022; 14:pharmaceutics14050915. [PMID: 35631502 PMCID: PMC9145019 DOI: 10.3390/pharmaceutics14050915] [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: 02/24/2022] [Revised: 04/19/2022] [Accepted: 04/20/2022] [Indexed: 11/23/2022] Open
Abstract
The antiplatelet agent clopidogrel is listed by the FDA as a strong clinical index inhibitor of cytochrome P450 (CYP) 2C8 and weak clinical inhibitor of CYP2B6. Moreover, clopidogrel is a substrate of—among others—CYP2C19 and CYP3A4. This work presents the development of a whole-body physiologically based pharmacokinetic (PBPK) model of clopidogrel including the relevant metabolites, clopidogrel carboxylic acid, clopidogrel acyl glucuronide, 2-oxo-clopidogrel, and the active thiol metabolite, with subsequent application for drug–gene interaction (DGI) and drug–drug interaction (DDI) predictions. Model building was performed in PK-Sim® using 66 plasma concentration-time profiles of clopidogrel and its metabolites. The comprehensive parent-metabolite model covers biotransformation via carboxylesterase (CES) 1, CES2, CYP2C19, CYP3A4, and uridine 5′-diphospho-glucuronosyltransferase 2B7. Moreover, CYP2C19 was incorporated for normal, intermediate, and poor metabolizer phenotypes. Good predictive performance of the model was demonstrated for the DGI involving CYP2C19, with 17/19 predicted DGI AUClast and 19/19 predicted DGI Cmax ratios within 2-fold of their observed values. Furthermore, DDIs involving bupropion, omeprazole, montelukast, pioglitazone, repaglinide, and rifampicin showed 13/13 predicted DDI AUClast and 13/13 predicted DDI Cmax ratios within 2-fold of their observed ratios. After publication, the model will be made publicly accessible in the Open Systems Pharmacology repository.
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