A Strategy to Refine the Phenotyping Approach and Its Implementation to Predict Drug Clearance: A Physiologically Based Pharmacokinetic Simulation Study

Adiwidjaja, Jeffry; Boddy, Alan V.; McLachlan, Andrew J.

doi:10.1002/psp4.12355

Cited by 5 publications

(6 citation statements)

References 50 publications

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“…Our adherence to the FDA list aimed to minimize the selection bias and the number of studies with nonvalidated substrates. For instance, the oral clearance of nifedipine (the substrate in a phenobarbital study) is only modestly correlated with that of midazolam 36 . The oral clearance correlation between midazolam and simvastatin (which was included in the FDA list of sensitive CYP3A substrates) was even poorer than that of nifedipine 36 .…”

Section: Discussionmentioning

confidence: 99%

“…Because a key determinant of a drug interaction magnitude is the fraction of the substrate eliminated by the induced pathways(s), the CYP3A substrates constituted a source of heterogeneity. Therefore, we conducted a sensitivity analysis with midazolam (fraction metabolized by CYP3A [ f m,CYP3A ] = .93) 28 and triazolam ( f m,CYP3A = .90; oral clearance highly correlated with that of midazolam) 36 as the only sensitive CYP3A substrates.…”

Section: Methodsmentioning

confidence: 99%

“…36 The oral clearance correlation between midazolam and simvastatin (which was included in the FDA list of sensitive CYP3A substrates) was even poorer than that of nifedipine. 36 Yet in induction studies, simvastatin AUCR correlated with that of midazolam and other sensitive CYP34 substrates. 28 Another example is the exclusion of studies with substrates other than midazolam or triazolam (including one carbamazepine-quetiapine study), which made the difference between 200 mg/day cenobamate and 600 mg/day carbamazepine significant.…”

Section: Between-substrate Variationmentioning

confidence: 99%

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The extent of cytochrome P450 3A induction by antiseizure medications: A systematic review and network meta‐analysis

Cohen,

Mahajna,

Ben‐Shushan

et al. 2023

Epilepsia

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ObjectiveAntiseizure medications (ASMs) are commonly categorized as enzyme‐inducers and non‐enzyme‐inducers based on their propensity to enhance the metabolism of concomitantly administered drugs. This systematic review and network meta‐analysis aimed to rank ASMs as cytochrome P450 3A (CYP3A)‐inducers based on a comparative assessment of ASM‐induced reduction in the concentrations of sensitive substrate drugs.MethodsThe protocol was registered with PROSPERO (International Prospective Register of Systematic Reviews; CRD42022335846), and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analysis) standards were followed. We searched MEDLINE, Embase, and Cochrane until March 14, 2023 without an initial date restriction. Data were additionally obtained via the US Food and Drug Administration database. Studies had to be prospective, with ASM monotherapy for ≥5 days. The primary parameter was the magnitude of change in the area under the concentration–time curve of CYP3A substrates following treatment with the ASM. The standardized mean difference (SMD) was used as the point estimate for the indirect comparisons between ASMs using the pairwise method. Bias risk was assessed using the PKclin tool.ResultsWe identified 14 open‐label, fixed‐sequence studies with 370 participants. The effect size of 600 mg/day carbamazepine did not differ from those of 300 mg/day phenytoin (SMD = −.06, 95% confidence interval [CI] = −.18 to .07) and 200 mg/day cenobamate (SMD = −.11, 95% CI = −.26 to .04). Carbamazepine at 600 mg/day was the strongest CYP3A‐inducer (P‐score = .88), followed by carbamazepine 400 mg/day (.83), phenytoin 300 mg/day (.79), and cenobamate 200 mg/day (.73). Eslicarbazepine (800 mg/day) ranked higher than cenobamate 100 mg/day and oxcarbazepine 900 mg/day (.60, .39, and .37, respectively).SignificanceDespite the limited number of studies, our network meta‐analysis emphasizes that the magnitude of ASM effects on CYP3A substrate metabolism is a dose‐dependent continuum. When possible, ASM classification as inducers should apply cutoff values tailored to the outcome. Prescribers should monitor plasma concentrations or clinical effects of CYP3A substrates and consider selecting concomitant medications accordingly.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Between-substrate Variationmentioning

confidence: 99%

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The extent of cytochrome P450 3A induction by antiseizure medications: A systematic review and network meta‐analysis

Cohen,

Mahajna,

Ben‐Shushan

et al. 2023

Epilepsia

View full text Add to dashboard Cite

show abstract

“…During the last 20 years various modeling approaches and software solutions were utilized to investigate various aspects of DXM pharmacokinetics, e.g., using GastroPlus ( Bolger et al, 2019 ), P-Pharm ( Moghadamnia et al, 2003 ), SAS ( Ito et al, 2010 ; Chiba et al, 2012 ), SimCYP ( Dickinson et al, 2007 ; Ke et al, 2013 ; Sager et al, 2014 ; Chen et al, 2016 ; Rougée et al, 2016 ; Adiwidjaja et al, 2018 ; Storelli et al, 2019b ; Machavaram et al, 2019 ), MATLAB ( Kim et al, 2017 ), or PK-Sim ( Rüdesheim et al, 2022 ). However, most of the work is difficult/impossible to validate or to build up on due to a lack of accessibility of models and software, and platform-dependency of the models.…”

Section: Discussionmentioning

confidence: 99%

Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan

Grzegorzewski

Brandhorst²,

König³

2022

Front. Pharmacol.

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The cytochrome P450 2D6 (CYP2D6) is a key xenobiotic-metabolizing enzyme involved in the clearance of many drugs. Genetic polymorphisms in CYP2D6 contribute to the large inter-individual variability in drug metabolism and could affect metabolic phenotyping of CYP2D6 probe substances such as dextromethorphan (DXM). To study this question, we (i) established an extensive pharmacokinetics dataset for DXM; and (ii) developed and validated a physiologically based pharmacokinetic (PBPK) model of DXM and its metabolites dextrorphan (DXO) and dextrorphan O-glucuronide (DXO-Glu) based on the data. Drug-gene interactions (DGI) were introduced by accounting for changes in CYP2D6 enzyme kinetics depending on activity score (AS), which in combination with AS for individual polymorphisms allowed us to model CYP2D6 gene variants. Variability in CYP3A4 and CYP2D6 activity was modeled based on in vitro data from human liver microsomes. Model predictions are in very good agreement with pharmacokinetics data for CYP2D6 polymorphisms, CYP2D6 activity as described by the AS system, and CYP2D6 metabolic phenotypes (UM, EM, IM, PM). The model was applied to investigate the genotype-phenotype association and the role of CYP2D6 polymorphisms for metabolic phenotyping using the urinary cumulative metabolic ratio (UCMR), DXM/(DXO + DXO-Glu). The effect of parameters on UCMR was studied via sensitivity analysis. Model predictions indicate very good robustness against the intervention protocol (i.e. application form, dosing amount, dissolution rate, and sampling time) and good robustness against physiological variation. The model is capable of estimating the UCMR dispersion within and across populations depending on activity scores. Moreover, the distribution of UCMR and the risk of genotype-phenotype mismatch could be estimated for populations with known CYP2D6 genotype frequencies. The model can be applied for individual prediction of UCMR and metabolic phenotype based on CYP2D6 genotype. Both, model and database are freely available for reuse.

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“…During the last 20 years various modeling approaches and software solutions were utilized to investigate various aspects of DXM pharmacokinetics, e.g. using GastroPlus (Bolger et al, 2019), P-Pharm (Moghadamnia et al, 2003), SAS (Ito et al, 2010; Chiba et al, 2012), SimCYP (Dickinson et al, 2007; Ke et al, 2013; Sager et al, 2014; Chen et al, 2016; Rougée et al, 2016; Adiwidjaja et al, 2018; Machavaram et al, 2019; Storelli et al, 2019b), MATLAB (Kim et al, 2017), or PK-Sim (Rüdesheim et al, 2022). However, most of the work is difficult/impossible to validate or to build up on due to a lack of accessibility of models and software, and platform-independence of the models.…”

Section: Discussionmentioning

confidence: 99%

Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan

Grzegorzewski

Brandhorst

König

2022

Preprint

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The cytochrome P450 2D6 (CYP2D6) is a key xenobiotic-metabolizing enzyme involved in the clearance of many drugs. Genetic polymorphisms in CYP2D6 contribute to the large inter-individual variability in drug metabolism and could affect metabolic phenotyping of CYP2D6 probe substances such as dextromethorphan (DXM). To study this question, we (i) established an extensive pharmacokinetics dataset for DXM; and (ii) developed and validated a physiologically based pharmacokinetic (PBPK) model of dextromethorphan (DXM) and its metabolites dextrorphan (DXO) and dextrorphan O-glucuronide (DXO-Glu) based on the data. Drug-gene interactions (DGI) were introduced by accounting for changes in CYP2D6 enzyme kinetics depending on activity score (AS), which in combination with AS for individual polymorphisms allowed us to model of CYP2D6 gene variants. Variability in CYP3A4 and CYP2D6 activity was modeled based on in vitro data from human liver microsomes. Model predictions are in very good agreement with pharmacokinetics data for CYP2D6 polymorphisms, CYP2D6 activity as measured by AS, and CYP2D6 metabolic phenotypes (UM, EM, IM, PM). The model was applied to investigate the genotype-phenotype association and the role of CYP2D6 polymorphisms for metabolic phenotyping with dextromethorphan using the urinary cumulative metabolic ratio DXM/(DXO+DXO-Glu) (UCMR). The effect of parameters on UCMR was studied via sensitivity analysis. Model predictions indicate very good robustness against the intervention protocol (i.e. application form, dosing amount, dissolution rate, and sampling time) and good robustness against physiological variation. The model is capable of estimating the UCMR dispersion within and across populations depending on activity scores. Moreover, the distribution of UCMR and the risk of genotype-phenotype mismatch could be estimated for populations with known CYP2D6 genotype frequencies. The model can be applied as a tool for individual prediction of UCMR and metabolic phenotype based on CYP2D6 genotype. Both, model and database are freely available for reuse.

show abstract

A Strategy to Refine the Phenotyping Approach and Its Implementation to Predict Drug Clearance: A Physiologically Based Pharmacokinetic Simulation Study

Cited by 5 publications

References 50 publications

The extent of cytochrome P450 3A induction by antiseizure medications: A systematic review and network meta‐analysis

The extent of cytochrome P450 3A induction by antiseizure medications: A systematic review and network meta‐analysis

Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan

Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan

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