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

Abstract

Aims: To develop a population pharmacokinetic model to describe the pharmacokinetics of desipramine in healthy subjects, after oral administration of a 50mg 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.

Results: The mean population estimates of the first order absorption rate constant (k(a) ), apparent clearance (CL/F) and apparent volume of distribution at steady state (V(ss) /F) were 0.15h(-1) , 111 lh(-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 lh(-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 200h. Similar sampling times were found for ultrarapid and extensive metabolizers except that the second d-optimal sample was earlier at 14 and 19h, respectively, compared with 24h for intermediate metabolizers. This difference in sampling times between the three genotypes can be attributed to the different intrinsic clearances and elimination rates.

Conclusions: A 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.

Figure 3

Observed desipramine concentration data overlaid onto (A) 95% prediction intervals from population desipramine model with parameters as given in Table 2 and (B) 95% credible intervals from semi-mechanistic hepatic intrinsic clearance of desipramine with parameters as listed in Table 4, column 3 posterior estimated with informative priors. 5th percentile (); 95th percentile (); median (); data (); LLQ (); LLQ:800;900 ()

Figure 1

Desipramine concentration–time data from all studies

Figure 2

Goodness-of-fit plots of desipramine two-compartment first order absorption pharmacokinetic model. Upper panels: Measured concentrations vs. predictions for the population (left) and for the individuals (right). Middle panels: weighted residuals vs. observed data (left) weighted residuals vs. time (right) Lower panels: weighted residuals vs. population predictions (left) and individual predicted concentrations vs. individual weighted residuals (right)

Figure 4

Mean desipramine concentration–time profiles in extensive, intermediate and ultrarapid metabolizers of CYP2D6. EMs (—); IMs (); UMs (); d-optimal design EMs (); d-optimal design IMs (); d-optimal design UMs ()

Figure 5

Desipramine concentration–time profiles for study 300 (A) desipramine only arm (period 1) and (B) desipramine plus potential inhibitor arm (period 2)