KPD modelling of trough FEV1 in chronic obstructive pulmonary disease (COPD).
F. Musuamba(1), D. Teutonico(1), H.J. Maas(2), 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;
Objectives: FEV1 (forced expiratory volume in one second) is the most frequently used endpoint in clinical trials in COPD, with change from baseline (ΔFEV1) corrected for placebo being the primary measure of efficacy. However, this approach ignores the time course of disease progression, which partly contributes to failure in identifying treatment effects in longitudinal studies in COPD. Recently, a KPD modelling approach was proposed to characterise the time course of treatment effect which overcomes some of the limitations associated with lack of pharmacokinetic data [1].
The aims of this investigation were: (1) to develop and validate a KPD model to describe the time-course of FEV1 in COPD including relevant patient demographics- and disease-related covariates; (2) to explore the influence of these covariates on the outcome of clinical trials.
Methods: A KPD model was developed using data from 6 Phase III studies, in which pacebo and two different active drugs (salmeterol and tiotrpium) were available. Patient demographics and disease related factors were tested as covariates on model parameters. Model diagnostics and performance included bootstrapping, visual predictive check and NPDE. Subsequently, simulations were performed to evaluate treatment effect across different scenarios. Scenarios were based on a typical placebo-controlled parallel group design with 100 and 150 patients per treatment arm. The influence of relevant covariates on the treatment effect size (ΔFEV1) was explored by varying drug dose levels and inclusion and exclusion criteria (i.e., reversibility to salbutamol/albuterol, disease severity, gender, body height and previous use of inhaled corticosteroids (PICS)). NONMEM v.7.1.2 and R were used in an integrated manner for data handling and subsequent statistical analysis. Statistical significance of the treatment effect was assessed using mixed effect for repeated measurement modelling.
Results: The use of a KPD model permits the characterisation of the time course of FEV1 in the absence of PK data. Severity, gender, PICS and body height were found to affect baseline FEV1, whilst reversibility and severity are covariates on drug-related parameters (Emax). Our results show that covariates do not only alter the treatment effect size at completion of treatment, but can also influence the onset of response.
Conclusions: Demographic and disease-related factors can affect the decline of FEV1 during the course of treatment in COPD patientsl. Model-based simulations should be used prior to the design of a clinical protocol to assess the implications of patient stratification and other relevant confounders on treatment outcome in COPD trials.
References:
[1] Jacqmin P, et al (2000). Modelling response time profiles in the absence of drug concentrations: definition and performance evaluation of the K-PD model. J Pharmacokinet Pharmacodyn.