Novel models for the prediction of drug–gene interactions

Türk, Denise; Fuhr, Laura Maria; Marok, Fatima Zahra; Rüdesheim, Simeon; Kühn, Anna Luisa; Selzer, Dominik; Schwab, Matthias; Lehr, Thorsten

doi:10.1080/17425255.2021.1998455

Cited by 12 publications

(13 citation statements)

References 100 publications

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“…For this, physiologically based pharmacokinetic (PBPK) modeling allows the study of a drug's PK in different genotypes and interactions with food components or coadministered drugs while covering various (patho-)physiological characteristics. 20,21 By incorporating factors contributing to a drug's interindividual variability, PBPK modeling can be applied to support model-informed precision dosing. 22 Moreover, PBPK modeling is widely acknowledged for the use in model-informed drug discovery and development (MID3), demonstrated by a growing number of PBPK applications submitted to regulatory agencies to address specific research questions, mainly related to DDIs.…”

Section: How Might This Change Drug Discovery Development And/or Ther...mentioning

confidence: 99%

Physiologically based pharmacokinetic modeling of tacrolimus for food–drug and CYP3A drug–drug–gene interaction predictions

Loer

Feick

Rüdesheim

et al. 2023

CPT Pharmacom & Syst Pharma

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The immunosuppressant and narrow therapeutic index drug tacrolimus is metabolized mainly via cytochrome P450 (CYP) 3A4 and CYP3A5. For its pharmacokinetics (PK), high inter‐ and intra‐individual variability can be observed. Underlying causes include the effect of food intake on tacrolimus absorption as well as genetic polymorphism in the CYP3A5 gene. Furthermore, tacrolimus is highly susceptible to drug–drug interactions, acting as a victim drug when coadministered with CYP3A perpetrators. This work describes the development of a whole‐body physiologically based pharmacokinetic model for tacrolimus as well as its application for investigation and prediction of (i) the impact of food intake on tacrolimus PK (food–drug interactions [FDIs]) and (ii) drug–drug(−gene) interactions (DD[G]Is) involving the CYP3A perpetrator drugs voriconazole, itraconazole, and rifampicin. The model was built in PK‐Sim® Version 10 using a total of 37 whole blood concentration–time profiles of tacrolimus (training and test) compiled from 911 healthy individuals covering the administration of tacrolimus as intravenous infusions as well as immediate‐release and extended‐release capsules. Metabolism was incorporated via CYP3A4 and CYP3A5, with varying activities implemented for different CYP3A5 genotypes and study populations. The good predictive model performance is demonstrated for the examined food effect studies with 6/6 predicted FDI area under the curve determined between first and last concentration measurements (AUClast) and 6/6 predicted FDI maximum whole blood concentration (Cmax) ratios within twofold of the respective observed ratios. In addition, 7/7 predicted DD(G)I AUClast and 6/7 predicted DD(G)I Cmax ratios were within twofold of their observed values. Potential applications of the final model include model‐informed drug discovery and development or the support of model‐informed precision dosing.

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Section: How Might This Change Drug Discovery Development And/or Ther...mentioning

confidence: 99%

Physiologically based pharmacokinetic modeling of tacrolimus for food–drug and CYP3A drug–drug–gene interaction predictions

Loer

Feick

Rüdesheim

et al. 2023

CPT Pharmacom & Syst Pharma

Self Cite

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“…The usefulness of parent–metabolite PBPK modeling for the investigation of drug interactions by imidazole derivatives was previously demonstrated in the application of an itraconazole–metabolites PBPK model within an extensive CYP3A4–P-gp–DDI network by Hanke et al [ 22 ]. In general, the application of PBPK modeling is recommended by both the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA) for the different stages of the drug development pipeline [ 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators

et al. 2023

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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 (PBPK) modeling has been a capable tool for such approaches for many years, as it allows the study of complex metabolic processes and pharmacokinetic aspects. Where relevant, different genotypes as well as interactions involving multiple drugs can be considered [ 37 , 38 ]. Additionally, it is well recognized for model-informed drug development and discovery (MID3).…”

Section: Introductionmentioning

confidence: 99%

Physiologically Based Pharmacokinetic (PBPK) Modeling of Clopidogrel and Its Four Relevant Metabolites for CYP2B6, CYP2C8, CYP2C19, and CYP3A4 Drug–Drug–Gene Interaction Predictions

et al. 2022

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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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Novel models for the prediction of drug–gene interactions

Cited by 12 publications

References 100 publications

Physiologically based pharmacokinetic modeling of tacrolimus for food–drug and CYP3A drug–drug–gene interaction predictions

Physiologically based pharmacokinetic modeling of tacrolimus for food–drug and CYP3A drug–drug–gene interaction predictions

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators

Physiologically Based Pharmacokinetic (PBPK) Modeling of Clopidogrel and Its Four Relevant Metabolites for CYP2B6, CYP2C8, CYP2C19, and CYP3A4 Drug–Drug–Gene Interaction Predictions

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