Background & Objectives:
Efficacy benchmarking is an important decision making component during clinical drug development and comparative effectiveness in many countries would ultimately determine ranking and pricing among alternative treatments. In chronic obstructive pulmonary disease (COPD), the forced expiratory volume in 1 second (FEV1) is used to assess lung function , and serves as biomarker for dose selection . A model based meta-analysis of literature data resulting in a model for FEV1 can facilitate this process.
Materials & Methods:
Randomized COPD maintenance trials on long-acting direct bronchodilators (BD) and anti-inflammatory (AI) treatments published until July 2013 were identified from literature, and suitable summary level FEV1 data, treatment information and covariates were extracted.
The literature data was analysed using NONMEM 7.2 by expanding and revising a previously developed database and FEV1 model . Of particular interest were the inclusion of new compounds, dose-response relationships, drug-drug interactions, and a priori available covariates to ensure predictive performance of the model.
In total, 142 studies were included in the database, comprising 106,422 subjects who received 19 compounds (11 BD, 8 AI) in 105 treatment combinations across 419 study arms. 1982 FEV1 observations were available for analysis. Each observation represents the mean FEV1 for a treatment arm at a specific time point.
The final model included baseline, disease progression, placebo effect, and efficacy estimates for all 19 compounds. Dose-response was identifiable for 10 compounds. Time course for on/offset of drug effects were included where information was available. Drug-drug interactions for direct bronchodilators were accounted for, as well as the effect of concomitant open-label background COPD treatment, which was important for handling drug-drug interactions and non-steady state in washout of background medication. Significant covariates included on baseline were mean age at study start and study inclusion criteria regarding disease severity and exacerbation history. The individual baseline predicted for each arm was found to be correlated with disease progression and treatment effects. Random effects included inter-study variability on all structural components and inter-arm variability on baseline.
The updated literature model well described the data and also consolidated unexpected findings of poor efficacies seen in literature (e.g. [4-6]).
The model can serve as a tool for efficacy benchmarking across different compounds and provides FEV1 predictions for the development of another model linking FEV1 with exacerbation rate in COPD.
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