2010 - Berlin - Germany

PAGE 2010: Methodology- Design
Flora Musuamba-Tshinanu

Evaluation of disease covariates in chronic obstructive pulmonary disease (COPD).

F. Musuamba (1), D. Teutonico (1), H.J. Maas (2), C. van Kesteren (1), A. Facius (3), S. Yang (2), M. Danhof (1), O. Della Pasqua (1,2)

(1)Division of Pharmacology, Leiden/Amsterdam Center for Drug Research, Leiden, The Netherlands; (2) Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, United Kingdom; (3). Dept. of Pharmacometrics, Nycomed GmbH, Konstanz, Germany

Objectives: Prospective longitudinal studies in COPD often fail to clearly show improvement of the disease condition in patients under active vs. placebo arm. In addition to a potential lack of efficacy, this finding could be partly explained by the sensitivity of FEV1 (forced expiratory volume in one second) to patient demographics and disease-related inclusion criteria [1].  Thus far,   the relevance of these factors on treatment effect size has not been assessed.

The aim of this exercise was to explore the influence of different patient demographics and disease-related factors on the outcome of clinical trials.  A decline of FEV1of at least 50mL was considered to be clinically relevant for the purposes of this evaluation.

Methods: A KPD model was used to simulate the time course of treatment effect. Relevant continuous and categorical covariates were randomly sampled from a unique multivariate normal distribution constructed from the frequencies, distribution and correlations of these covariates in patients enrolled in three Phase III clinical studies [2]. Comparisons between simulated correlations and real patients were performed to assess the reliability of the estimated correlations.   Subsequently, scenarios based on a typical placebo-controlled parallel group design were simulated with 100, 150 and 200 patients per treatment arm. The influence of relevant covariates on the treatment effect size (ΔFEV1) was explored by varying inclusion and exclusion criteria (i.e., reversibility to salbutamol/albuterol, disease severity, gender, smoking status, age and previous use of inhaled corticosteroids (PICS)). Each scenario consisted of at least 500 simulations. NONMEM v.6.2 and R were used in an integrated manner for data handling and subsequent statistical analysis. Statistical significance was assessed by hypothesis testing using analysis of covariance (ANCOVA). 

Results: The proposed methodology enables generation of accurate summary statistics for covariates in the target population. In addition, demographic and disease-related factors were found to influence outcome not only in terms of magnitude of ΔFEV1 at completion of treatment, but also in terms of the onset of response. Whilst disease severity, reversibility, height, gender and age of patients seem correlated to each other, no significant association was observed between smoking status and PICS.

Conclusions: : Demographic and disease-related factors can affect the decline of FEV1 during the course of treatment in a clinical setting. Simulation scenarios can be used to quantify the implications of patient stratification and other relevant confounders on treatment outcome in COPD trials.

[1] Tashkin DP et al (2005) A 4-year trial of tiotropium in chronic obstructive pulmonary disease. the New  England Journal of Medicine. 359, 1543-54
[2] Tannenbaum SJ et al (2006). Simulation of correlated continuous and categorical variables using a single multivariate distribution.  Journal of Pharmacokinetics and Pharmacodynamics. 33, 773-94

Reference: PAGE 19 (2010) Abstr 1864 [www.page-meeting.org/?abstract=1864]
Poster: Methodology- Design