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We represent a community with a shared interest in data analysis using the population approach.

   Paris, France

Efficiency of Using Population Pharmacokinetics to Demonstrate Bioequivalence with Sparse Sampling in Cancer Patients- A Trial Simulation with Etoposide

Eric Masson, 1, Eliane Fuseau, 2, Valérie Cosson, 2

(1):ANAPHARM, Canada (2):EMF-Consulting, France

Purpose Etoposide (VP16) is an antineoplastic agent used in various malignancies. Like many anticancer drugs, toxicities preclude testing VP-16 in healthy subjects. Current FDA guidances on bioequivalence (BE) are based on two stage approach with calculation of pharmacokinetic (PK) parameters by non-compartmental analysis (NCA), followed by standard statistical analysis using average BE. Several factors preclude applying these guidances in cancer patients: anemia limiting extensive sampling, sampling time which vary, and presence co- factors affecting VP16 PK. One alternative is to use population PK which allows estimation of BE parameters (AUC, Cmax) with confidence intervals. The objective of the simulation is to evaluate the effect of two sampling schemes (sparse versus extensive), and designs (complete vs incomplete) to assess BE of VP16 using NONMEM.

Methods Monte-Carlo simulations and population PK analyses were performed using NONMEM. Using priors from the literature on VP16, plasma concentrations of VP16 were simulated in subjects receiving two formulations of VP-16 in a randomised crossover fashion. Six different scenarios were tested using 2 way crossover studies in 50 subjects. Each scenarios were simulated 100 times each without blocking, period or sequence effects: 3 scenarios with full PK profiles (12 points), and 3 scenarios with reduced PK profiles (7 points selected on D- optimality) with none, 25% and 50% of drop-out after one cycle of drop-out. BE was evaluated for Cmax and F based on the average BE criteria. Success rate defined as percentage of studies for which 90% CI for Cmax and AUC ratio are within 80-125%.

Results Full and sparse sampling with no dropout yielded 100% success compared to 98% success rate for sparse sampling and 50% dropout. Conclusion Population PK analysis allows accurate assessment of BE despite reduced sampling, and partial study completion. Thus, this method offers a significant advantage over average BE in cancer patients.