Skip to main content
SpringerLink
Log in

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

  • Original Paper
  • Published:
Journal of Pharmacokinetics and Pharmacodynamics Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. American Lung Association (2007) Trends in asthma morbidity and mortality. American Lung Association Epidemiology & Statistics Unit Research and Program Services

  2. Miller MR, Hankinson J, Brusasco V et al (2005) Standardisation of spirometry. Eur Respir J 26:319–338. doi:10.1183/09031936.05.00034805

    Article  CAS  PubMed  Google Scholar 

  3. Pierce R (2005) Spirometry: an essential clinical measurement. Aust Family Phys 34:535–539

    Google Scholar 

  4. American Thoracic Society (1991) Lung function testing: selection of reference values and interpretative strategies. Am Rev Respir Dis 144:1202–1218

  5. Wagner NL, Beckett WS, Steinberg R (2006) Using spirometry results in occupational medicine and research: common errors and good practice in statistical analysis and reporting. Indian J Occup Environ Med 10:5–10. doi:10.4103/0019-5278.22888

    Article  Google Scholar 

  6. David PJ, Pierce R (2008) Spirometry handbook, 3rd edn., Spirometry: the measurement and interpretation of ventilatory function in clinical practiceNational Asthma Campaign, Melbourne, pp 1–24

    Google Scholar 

  7. Gomeni R, Merlo-Pich E (2006) Bayesian modelling and ROC analysis to predict placebo responders using clinical score measured in the initial weeks of treatment in depression trials. Br J Clin Pharmacol 63:595–613. doi:10.1111/j.1365-2125.2006.02815.x

    Article  PubMed Central  Google Scholar 

  8. Nucci G, Gomeni R, Poggesi I (2009) Model-based approaches to increase efficiency of drug development in schizophrenia: a can’t miss opportunity. Expert Opin Drug Discov 4:837–856. doi:10.1517/17460440903036073

    Article  CAS  PubMed  Google Scholar 

  9. Holford N, Li J, Benincosa L, Birath M (2002) Population disease progress models for the time course of HAMD score in depressed patients receiving placebo in anti-depressant clinical trials. Population Approach Group in Europe (PAGE) 11th Meeting, Abstract 311. http://www.page-meeting.org/?abstract=311

  10. Reddy VP, Kozielska M, Johnson M, Vermeulen A, de Greef R, Liu J, Groothuis GMM, Danhof M, Proost JH (2011) Structural models describing placebo treatment effects in schizophrenia and other neuropsychiatric disorders. Clin Pharmacokinet 50:429–450. doi:10.2165/11590590-000000000-00000

    Article  Google Scholar 

  11. Russu A, Marostica E, De Nicolao G, Hooker AC, Poggesi I, Gomeni R, Zamuner S (2012) Joint modeling of efficacy, dropout, and tolerability in flexible-dose trials: a case study in depression. Clin Pharmacol Ther 91:863–871. doi:10.1038/clpt.2011.322

    Article  CAS  PubMed  Google Scholar 

  12. Atkinson AJ, Huang SM, Lertora JJL, Markey SP (2012) Principles of clinical pharmacology. Academic Press, London

    Google Scholar 

  13. Shang EY, Gibbs MA, Landen JW, Krams M, Russell T, Denman NG, Mould DR (2009) Evaluation of structural models to describe the effect of placebo upon the time course of major depressive disorder. J Pharmacokinet Pharmacodyn 36:63–80. doi:10.1007/s10928-009-9110-3

    Article  PubMed  Google Scholar 

  14. Matre D, Casey KL, Knardahl S (2006) Placebo-induced changes in spinal cord pain processing. J Neurosci 26:559–563. doi:10.1523/JNEUROSCI.4218-05.2006

    Article  CAS  PubMed  Google Scholar 

  15. Mayberg HS, Silva JA, Brannan SK, Tekell JL, Mahurin RK, McGinnis S, Jerabek PA (2002) The functional neuroanatomy of the placebo effect. Am J Psychiatry 159:728–737

    Article  PubMed  Google Scholar 

  16. de la Fuente-Fernandez R, Ruth TJ, Sossi V, Schulzer M, Calne DB, Stoessl AJ (2001) Expectation and dopamine release: mechanism of the placebo effect in Parkinson’s disease. Science 293:1164–1166. doi:10.1126/science.1060937

    Article  PubMed  Google Scholar 

  17. Delahoy P, Thompson S, Marschner IC (2010) Pregabalin versus gabapentin in partial epilepsy: a meta-analysis of dose–response relationships. BMC Neurol 10:104. doi:10.1186/1471-2377-10-104

    Article  PubMed Central  PubMed  Google Scholar 

  18. Houk BE, Bello CL, Kang D, Amantea M (2009) A population pharmacokinetic meta-analysis of sunitinib malate (SU11248) and its primary metabolite (SU12662) in healthy volunteers and oncology patients. Clin Cancer Res 15:2497–2506. doi:10.1158/1078-0432.ccr-08-1893

    Article  CAS  PubMed  Google Scholar 

  19. Chan PL, Weatherley B, McFadyen L (2008) A population pharmacokinetic meta-analysis of maraviroc in healthy volunteers and asymptomatic HIV-infected subjects. Br J Clin Pharmacol 65:76–85. doi:10.1111/j.1365-2125.2008.03139.x

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Ito K, Ahadieh S, Corrigan B, French J, Fullerton T, Tensfeld T (2010) Disease progression meta-analysis model in Alzheimer’s disease. Alzheimers Dement 6:39–53. doi:10.1016/j.jalz.2009.05.665

    Article  CAS  PubMed  Google Scholar 

  21. Forey BA, Thornton AJ, Lee PN (2011) Systematic review with meta-analysis of the epidemiological evidence relating smoking to COPD, chronic bronchitis and emphysema. BMC Pulm Med 11:36. doi:10.1186/1471-2466-11-36

    Article  PubMed Central  PubMed  Google Scholar 

  22. Wang X, Shang D, Ribbing J, Ren Y, Deng C, Zhou T, Guo F, Lu W (2012) Placebo effect model in asthma clinical studies: longitudinal meta-analysis of forced expiratory volume in 1 second. Eur J Clin Pharmacol 68:1157–1166. doi:10.1007/s00228-012-1245-2

    Article  PubMed  Google Scholar 

  23. Rowland M, Sheiner LB, Steimer JL (1985) Variability in drug therapy: description, estimation and control. Raven Press, New York

    Google Scholar 

  24. Lunn DJ, Thomas A, Best N, Spiegelhalter DJ (2000) WinBUG: a Bayesian modeling framework: concepts, structure and extensibility. Stat Comput 10:325–337

    Article  Google Scholar 

  25. Development Core Team R (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  26. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002) Bayesian measures of model complexity and fit. J R Statist Soc B 64(Part 4):583–639. doi:10.1111/1467-9868.00353

    Article  Google Scholar 

  27. Karlsson M, Holford N (2008) A tutorial on Visual Predictive Checks. Population Approach Group in Europe (PAGE) 17th Meeting, Abstract 1434. http://www.page-meeting.org/default.asp?abstract=1434

  28. Sheiner LB, Beal SL (1981) Some suggestions for measuring predictive performance. J Pharmacokinet Biopharm 9:503–512

    Article  CAS  PubMed  Google Scholar 

  29. Fisher RA (1925) Statistical methods for research workers. Oliver & Boyd, Edinburgh

    Google Scholar 

  30. Ette EL, Williams PJ (2013) Pharmacometrics: the science of quantitative pharmacology. Wiley, Hoboken

    Google Scholar 

  31. Barraclough KA, Isbel NM, Kirkpatrick CM, Lee KJ, Taylor PJ, Johnson DW, Campbell SB, Leary DR, Staatz CE (2011) Evaluation of limited sampling methods for estimation of tacrolimus exposure in adult kidney transplant recipients. Br J Clin Pharmacol 71:207–223. doi:10.1111/j.1365-2125.2010.03815.x

    Article  PubMed Central  PubMed  Google Scholar 

  32. Sasakawa T, Masui K, Kazama T (2013) Iwasaki H. Comparison of Five Parameter Sets. American Society of Anesthesiologists, Performance of Compartmental Pharmacokinetic Model for Rocuronium

Download references

Acknowledgments

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.

Author information

Author notes

  1. Alberto Russu

    Present address: Janssen Research and Development, Janssen Pharmaceutical Companies of Johnson&Johnson, Beerse, Belgium

Authors and Affiliations

  1. Department of Industrial and Information Engineering, University of Pavia, Via Ferrata 1, 27100 , Pavia, Italy

    Eleonora Marostica, Alberto Russu & Giuseppe De Nicolao

  2. Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, Stockley Park, UK

    Shuying Yang, Stefano Zamuner & Misba Beerahee

Authors
  1. Eleonora Marostica
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alberto Russu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuying Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giuseppe De Nicolao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stefano Zamuner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Misba Beerahee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eleonora Marostica.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Marostica, E., Russu, A., Yang, S. et al. Population model of longitudinal FEV1 data in asthmatics: meta-analysis and predictability of placebo response. J Pharmacokinet Pharmacodyn 41, 553–569 (2014). https://doi.org/10.1007/s10928-014-9373-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10928-014-9373-1

Keywords

Search

Navigation