PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe.
PAGE 15 (2006) Abstr 986 [www.page-meeting.org/?abstract=986]
Poster: Applications- CNS
Lehr, T.(1), A. Staab (2), C. Tillmann (2), D.Trommeshauser (2), A. Raschig(2), H.G. Schaefer (2), C. Kloft (1,3)
(1) Dept. Clinical Pharmacy, Institute of Pharmacy, Freie Universitaet Berlin, Berlin, Germany (2) Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d.R., Germany (3) Martin-Luther-Universitšt Halle-Wittenberg, Halle, Germany
Objectives & Background: NS2330 is a new central nervous system active drug under clinical development for Alzheimerís disease and Parkinsonís disease. The objective of this analysis was to develop a population pharmacokinetic model for NS2330 and its major metabolite M1 based on data from a 14 week proof of concept study in Alzheimerís disease patients including a screening for covariates that might influence the pharmacokinetic characteristics of the drug and/or its metabolite.
Methods: Plasma concentration-time profiles of 320 subjects (0.25, 0.5 or 1 mg NS2330 orally) consisting of 1969 NS2330 and 1714 metabolite concentrations were included in the population PK analysis. The modelling was performed using the FOCE INTERACTION estimation method implemented in NONMEM.
Results: Plasma concentration-time profiles of NS2330 and M1 were best described by one-compartment models with first-order elimination processes for both compounds. Absorption of NS2330 was best modelled by a first-order absorption process. Low relative clearances in combination with the large relative volumes of distribution resulted in long half-lives of 295 h (NS2330) and 371 h (M1). The covariate analysis identified weight, sex, CLCR, BMI and age as having an influence on PK parameters of NS2330 and/or M1, respectively. However, simulations performed revealed that only CLCR and sex had a significant influence on the steady-state plasma concentration-time profiles. The robustness and predictively of the population model developed was demonstrated as it successfully predicted the observation of an external dataset.
Conclusion: A descriptive, robust and predictive model for the promising new compound and its major metabolite could successfully be developed. Important covariates for the therapeutic use were identified which might guide the future drug development to provide safe and efficacious long-term treatment in the Alzheimerís disease population.