Journal of Pharmacokinetics and Pharmacodynamics

, Volume 41, Issue 6, pp 553–569 | Cite as

Population model of longitudinal FEV1 data in asthmatics: meta-analysis and predictability of placebo response

  • Eleonora Marostica
  • Alberto Russu
  • Shuying Yang
  • Giuseppe De Nicolao
  • Stefano Zamuner
  • Misba Beerahee
Original Paper


Asthma is an obstructive lung disease where the mechanism of disease progression is not fully understood hence motivating the use of empirical models to describe the evolution of the patient’s health state. With reference to placebo response, measured in terms of FEV1 (Forced Expiratory Volume in 1 s), a range of empirical models taken from the literature were compared at a single trial level. In particular, eleven GSK trials lasting 12 weeks in mild-to-moderate asthma were used for the modelling of longitudinal placebo responses. Then, the chosen exponential model was used to carry out an individual participant data meta-analysis on eleven trials. A covariate analysis was also performed to find relevant covariates in asthma to be accounted for in the meta-analysis model. Age, gender, and height were found statistically significant (e.g. the taller the patients the higher the FEV1, the older the patients the lower the FEV1, and females have lower FEV1). By truncating each trial at week 4, the predictive properties of the meta-analysis model were also investigated, showing its ability to predict long-term FEV1 response from truncated trials. Summarizing, the study suggests that: (i) the exponential model effectively describes the placebo response; (ii) the meta-analysis approach may prove helpful to simulate new trials as well as to reduce trial duration in view of its predictive properties; (iii) the inclusion of available covariates within the meta-analysis model provides a reduction of the inter-individual variability.


Asthma Population approach Meta-analysis Predictability analysis Covariate analysis 



The authors thank colleagues in the Respiratory Therapeutic Area in particular Shuyen Ho and Anna Ellsworth who supported preparation of the database for this work.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Eleonora Marostica
    • 1
  • Alberto Russu
    • 1
    • 2
  • Shuying Yang
    • 3
  • Giuseppe De Nicolao
    • 1
  • Stefano Zamuner
    • 3
  • Misba Beerahee
    • 3
  1. 1.Department of Industrial and Information EngineeringUniversity of PaviaPaviaItaly
  2. 2.Janssen Research and DevelopmentJanssen Pharmaceutical Companies of Johnson&JohnsonBeerseBelgium
  3. 3.Clinical Pharmacology Modelling and Simulation, GlaxoSmithKlineStockley ParkUK

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