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Population pharmacokinetic modelling of NS2330 (tesofensine) and its major metabolite in patients with Alzheimer's disease

  1. Thorsten Lehr1,
  2. Alexander Staab2,
  3. Christiane Tillmann2,
  4. Dirk Trommeshauser2,
  5. Andreas Raschig3,
  6. Hans Guenter Schaefer2,
  7. Charlotte Kloft1,4,*

Article first published online: 23 FEB 2007

DOI: 10.1111/j.1365-2125.2007.02855.x

Issue

British Journal of Clinical Pharmacology

British Journal of Clinical Pharmacology

Volume 64, Issue 1, pages 36–48, July 2007

Additional Information

How to Cite

Lehr, T., Staab, A., Tillmann, C., Trommeshauser, D., Raschig, A., Schaefer, H. G. and Kloft, C. (2007), Population pharmacokinetic modelling of NS2330 (tesofensine) and its major metabolite in patients with Alzheimer's disease. British Journal of Clinical Pharmacology, 64: 36–48. doi: 10.1111/j.1365-2125.2007.02855.x

Author Information

  1. 1

    Freie Universitaet Berlin, Institute of Pharmacy, Department of Clinical Pharmacy, Kelchstrasse 31, 12169 Berlin,

  2. 2

    Boehringer Ingelheim Pharma GmbH & Co. KG, Department of Drug Metabolism and Pharmacokinetics, 88397 Biberach an der Riss,

  3. 3

    Boehringer Ingelheim Pharma GmbH & Co. KG, Department of Clinical Research, 88397 Biberach an der Riss and

  4. 4

    Martin-Luther-Universitaet Halle-Wittenberg, Faculty of Pharmacy, Department of Clinical Pharmacy, Wolfgang-Langenbeck-Strasse 4, 06120 Halle, Germany

*Professor Dr Charlotte Kloft, Department Clinical Pharmacy, Faculty of Pharmacy, Martin-Luther-Universitaet Halle-Wittenberg, Wolfgang-Langenbeck-Strasse 4, D-06120 Halle, Germany. Tel.: + 49 (0) 34 5552 5190 Fax: + 49 (0) 34 5552 7257 E-mail: charlotte.kloft@pharmazie.uni-halle.de

Publication History

  1. Issue published online: 23 FEB 2007
  2. Article first published online: 23 FEB 2007
  3. Received 6 March 2006 Accepted9 November 2006 Published OnlineEarly23 February 2007

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Keywords:

  • active metabolite;
  • Alzheimer's disease;
  • NONMEM;
  • NS2330;
  • population pharmacokinetic modelling;
  • sex

What is already known about this subject

• Several studies in predominantly healthy subjects have investigated the pharmacokinetics of NS2330 and its major metabolite M1.

• However, its pharmacokinetics have not been characterized in Alzheimer's disease patients, the target population for NS2330.

• In addition, no covariates have previously been found to influence the plasma concentration-time profiles of NS2330 and/or M1.

What this study adds

• A descriptive and predictive population pharmacokinetic model for NS2330 and its metabolite was successfully developed in a population of patients with Alzheimer's disease.

• A covariate analysis elucidated sex and creatinine clearance as having an influence on the plasma concentration-time profiles of NS2330 after long-term treatment.

Aims

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 patients with Alzheimer's disease, and to identify covariates that might influence the pharmacokinetic characteristics of the drug and/or its metabolite.

Methods

Plasma data from 320 subjects undergoing multiple oral dosing, and consisting of 1969 NS2330 and 1714 metabolite concentrations were fitted simultaneously using NONMEM.

Results

Plasma concentration-time profiles of NS2330 and M1 were best described by one-compartment models with first-order elimination for both compounds. Absorption of NS2330 was best modelled by a first-order process. Low apparent clearances together with large apparent volumes of distribution resulted in long half-lives of 234 h (NS2330) and 374 h (M1). The covariate analysis identified weight, sex, CLCR, BMI and age as influencing the pharmacokinetics of NS2330 and/or M1. However, simulations performed revealed that only CLCR and sex had a significant effect on the steady-state plasma concentration-time profiles. Females with a creatinine clearance of 35.6 ml min−1 showed a 62% increased exposure compared with males without renal impairment. The robustness and accuracy of the model were demonstrated by the successful predictivity of an external dataset.

Conclusions

A descriptive, robust and predictive model for NS2330 and its M1 metabolite was developed. Important covariates influencing pharmacokinetics were identified, which might guide the further development of NS2330 and optimize its long-term use in the treatment of Alzheimer's disease.

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