Design and Data Analysis in Drug Interaction Studies

Nix, David E.; Gallicano, Keith

doi:10.1007/978-1-59259-025-4_13

Cited by 2 publications

(1 citation statement)

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“…The recommended strategy for analyzing comparative data from drug interaction studies is to adapt the confidence interval approach used in large bioequivalence studies [31], where the aim is to show that an interaction is not clinically meaningful by the similarity of exposure (AUC (0,∞) ) and C max . Confidence limits around mean ratios for within‐subject comparisons in crossover studies are constructed from the residual mean‐square error (MSE) term in anova , which is converted to a coefficient of variation, CV W .…”

Section: Methodsmentioning

confidence: 99%

Desipramine, substrate for CYP2D6 activity: population pharmacokinetic model and design elements of drug–drug interaction trials

Gueorguieva¹,

Jackson²,

Wrighton³

et al. 2010

Brit J Clinical Pharma

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WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Desipramine is extensively metabolized by CYP2D6 to its main metabolite 2-OH-desipramine.• Alternative metabolic pathways are 10-hydroxylation and further demethylation to didesipramine, but the concentration of these metabolites in plasma upon therapeutic oral doses are very low both in extensive and poor metabolizers of substrates of CYP2D6. • Desipramine has been used widely as a probe drug for CYP2D6 activity. WHAT THIS STUDY ADDS• The population pharmacokinetics of desipramine in healthy subjects were characterized.• The model allows performing simulations of trials which assess CYP2D6 interaction potential of novel compounds.• Study design elements, such as required sample size based on desipramine within and between-subject variance and optimal sampling schedules for the three subgroups, intermediate, extensive and ultrarapid metabolizers of CYP2D6 are suggested. AIMSTo develop a population pharmacokinetic model to describe the pharmacokinetics of desipramine in healthy subjects, after oral administration of a 50 mg dose. Additional objectives were to develop a semi-mechanistic population pharmacokinetic model for desipramine, which allowed simulation of CYP2D6-mediated inhibition, when using desipramine as a probe substrate, and to evaluate certain study design elements, such as duration of desipramine pharmacokinetic sampling, required sample size and optimal pharmacokinetic sampling schedule for intermediate, extensive and ultrarapid metabolizers of CYP2D6 substrates. RESULTSThe mean population estimates of the first order absorption rate constant (ka), apparent clearance (CL/F) and apparent volume of distribution at steady state (Vss/F) were 0.15 h -1, 111 l h -1 and 2950 l, respectively. Further, using the proposed semi-mechanistic hepatic intrinsic clearance model with Bayesian inference, mean population desipramine hepatic intrinsic clearance was estimated to be 262 l h -1 with between-subject variability of 84%. D-optimal PK sampling times for intermediate metabolizers were calculated to be approximately 0.25, 24, 75 and 200 h. Similar sampling times were found for ultrarapid and extensive metabolizers except that the second D-optimal sample was earlier at 14 and 19 h, respectively, compared with 24 h for intermediate metabolizers. This difference in sampling times between the three genotypes can be attributed to the different intrinsic clearances and elimination rates. CONCLUSIONSA two compartment population pharmacokinetic model best described desipramine disposition. The semi-mechanistic population model developed is suitable to describe the pharmacokinetic behaviour of desipramine for the dose routinely used in drug-drug interaction (DDI) studies. Based on this meta-analysis of seven trials, a sample size of 21 subjects in cross-over design is appropriate for assessing CYP2D6 interaction with novel compounds.

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Section: Methodsmentioning

confidence: 99%