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A disease progression model of longitudinal lung function decline in idiopathic pulmonary fibrosis patients

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Abstract

Pirfenidone and nintedanib are the first two FDA-approved therapies for treatment of idiopathic pulmonary fibrosis (IPF). The clinical programs for pirfenidone and nintedanib included 1132 patients in the placebo arms and 1691 patients in the treatment arms across 6 trials. We developed a disease progression model to characterize the observed variability in lung function decline, measured as percent predicted forced vital capacity (%p-FVC), and its decrease in decline after treatment. The non-linear longitudinal change in %p-FVC was best described by a Weibull function. The median decreased decline in %p-FVC after treatment was estimated to be 1.50% (95% CI [1.12, 1.79]) and 1.96% (95% CI [1.47, 2.36]) at week 26 and week 52, respectively. Smoking status, weight, %p-FVC, %p-DLco and oxygen use at baseline were identified as significant covariates affecting decline in %p-FVC. The decreased decline in %p-FVC were observed among all subgroups of interest, of which the effects were larger at 1 year compared to 6 months. Based on the disease progression model smoking status and oxygen use at baseline may affect the treatment effect size. At week 52, the decreased decline in %p-FVC for current smokers and patients with oxygen use at baseline were 1.56 (90% CI [1.02, 1.99]) and 2.32 (90% CI [1.74, 2.86]), respectively. These prognostic factors may be used to enrich studies with patients who are more likely to respond to treatment, by demonstrating a lesser decline in lung function, and therefore provide the potential to allow for IPF studies with smaller study populations or shorter durations.

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References

  1. Raghu G, Collard HR, Egan JJ et al (2011) An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med 183:788–824

    Article  Google Scholar 

  2. US Food and Drug Administration (FDA) Drug label for pirfenidone. https://www.accessdata.fda.gov/drugsatfda_docs/label/2017/208780s000lbl.pdf. Accessed 11 Jan 2017

  3. US Food and Drug Administration (FDA) Drug label for nintedanib. https://www.accessdata.fda.gov/drugsatfda_docs/label/2018/205832s010lbl.pdf. Accessed 9 Nov 2018

  4. Karimi-Shah BA, Chowdhury BA (2015) Forced vital capacity in idiopathic pulmonary fibrosis—FDA review of pirfenidone and nintedanib. N Engl J Med 372:1189–1191

    Article  Google Scholar 

  5. Kurashima K, Takayanagi N, Tsuchiya N et al (2010) The effect of emphysema on lung function and survival in patients with idiopathic pulmonary fibrosis. Respirology 15:843–848

    Article  Google Scholar 

  6. Peljto AL, Zhang Y, Fingerlin TE et al (2013) Association between the MUC5B promoter polymorphism and survival in patients with idiopathic pulmonary fibrosis. JAMA 309:2232–2239

    Article  CAS  Google Scholar 

  7. Moua T, Martinez ACZ, Baqir M, Vassallo R, Limper AH, Ryu JH (2014) Predictors of diagnosis and survival in idiopathic pulmonary fibrosis and connective tissue disease-related usual interstitial pneumonia. Respir Res 15:154

    Article  Google Scholar 

  8. Durheim MT, Collard HR, Roberts RS et al (2015) Association of hospital admission and forced vital capacity endpoints with survival in patients with idiopathic pulmonary fibrosis: analysis of a pooled cohort from three clinical trials. Lancet Respir Med 3:388–396

    Article  Google Scholar 

  9. Paterniti MO, Bi Y, Rekic D, Wang Y, Karimi-Shah BA, Chowdhury BA (2017) Acute exacerbation and decline in forced vital capacity are associated with increased mortality in idiopathic pulmonary fibrosis. Ann Am Thorac Soc 14:1395–1402

    Article  Google Scholar 

  10. Gobburu JV, Lesko LJ (2009) Quantitative Disease, Drug, and Trial Models*. Annu Rev Pharmacol Toxicol 49:291–301

    Article  CAS  Google Scholar 

  11. Mould D, Denman N, Duffull S (2007) Using disease progression models as a tool to detect drug effect. Clin Pharmacol Ther 82:81–86

    Article  CAS  Google Scholar 

  12. Chan P, Holford N (2001) Drug treatment effects on disease progression. Annu Rev Pharmacol Toxicol 41:625–659

    Article  CAS  Google Scholar 

  13. Holford N (2015) Clinical pharmacology= disease progression+ drug action. Br J Clin Pharmacol 79:18–27

    Article  Google Scholar 

  14. Romero K, Ito K, Rogers J et al (2015) The future is now: model-based clinical trial design for Alzheimer’s disease. Clin Pharmacol Ther 97:210–214

    Article  CAS  Google Scholar 

  15. King TE Jr, Bradford WZ, Castro-Bernardini S et al (2014) A phase 3 trial of pirfenidone in patients with idiopathic pulmonary fibrosis. N Engl J Med 370:2083–2092

    Article  Google Scholar 

  16. Noble PW, Albera C, Bradford WZ et al (2011) Pirfenidone in patients with idiopathic pulmonary fibrosis (CAPACITY): two randomised trials. Lancet 377:1760–1769

    Article  CAS  Google Scholar 

  17. Richeldi L, Costabel U, Selman M et al (2011) Efficacy of a tyrosine kinase inhibitor in idiopathic pulmonary fibrosis. N Engl J Med 365:1079–1087

    Article  CAS  Google Scholar 

  18. Richeldi L, Cottin V, Flaherty KR et al (2014) Design of the INPULSIS trials: two phase 3 trials of nintedanib in patients with idiopathic pulmonary fibrosis. Respir Med 108:1023–1030

    Article  Google Scholar 

  19. Keizer RJ, Karlsson M, Hooker A (2013) Modeling and simulation workbench for NONMEM: tutorial on Pirana, PsN, and Xpose. CPT Pharmacomet Syst Pharmacol 2:1–9

    Article  Google Scholar 

  20. Keizer RJ, Van Benten M, Beijnen JH, Schellens JH, Huitema AD (2011) Pirana and PCluster: a modeling environment and cluster infrastructure for NONMEM. Comput Methods Programs Biomed 101:72–79

    Article  Google Scholar 

  21. Lindbom L, Pihlgren P, Jonsson N (2005) PsN-Toolkit—a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Programs Biomed 79:241–257

    Article  Google Scholar 

  22. Jonsson EN, Karlsson MO (1998) Xpose—an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput Methods Programs Biomed 58:51–64

    Article  Google Scholar 

  23. RC Team (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0

  24. Bergstrand M, Hooker AC, Wallin JE, Karlsson MO (2011) Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J 13:143–151

    Article  Google Scholar 

  25. Karlsson M, Savic R (2007) Diagnosing model diagnostics. Clin Pharmacol Ther 82:17–20

    Article  CAS  Google Scholar 

  26. Jonsson EN, Karlsson MO (1998) Automated covariate model building within NONMEM. Pharm Res 15:1463–1468

    Article  CAS  Google Scholar 

  27. Collard HR, King TE Jr, Bartelson BB, Vourlekis JS, Schwarz MI, Brown KK (2003) Changes in clinical and physiologic variables predict survival in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 168:538–542

    Article  Google Scholar 

  28. du Bois RM, Weycker D, Albera C et al (2011) Ascertainment of individual risk of mortality for patients with idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 184:459–466

    Article  Google Scholar 

  29. Hanson D, Winterbauer RH, Kirtland SH, Wu R (1995) Changes in pulmonary function test results after 1 year of therapy as predictors of survival in patients with idiopathic pulmonary fibrosis. Chest 108:305–310

    Article  CAS  Google Scholar 

  30. Ekstrom M, Gustafson T, Boman K et al (2014) Effects of smoking, gender and occupational exposure on the risk of severe pulmonary fibrosis: a population-based case–control study. BMJ Open 4:e004018

    Article  Google Scholar 

  31. Kishaba T, Nagano H, Nei Y, Yamashiro S (2016) Clinical characteristics of idiopathic pulmonary fibrosis patients according to their smoking status. J Thorac Dis 8:1112–1120

    Article  Google Scholar 

  32. King TE Jr, Tooze JA, Schwarz MI, Brown KR, Cherniack RM (2001) Predicting survival in idiopathic pulmonary fibrosis: scoring system and survival model. Am J Respir Crit Care Med 164:1171–1181

    Article  Google Scholar 

  33. Oh CK, Murray LA, Molfino NA (2012) Smoking and idiopathic pulmonary fibrosis. Pulm Med 2012:808260

    Article  Google Scholar 

  34. Ryerson CJ, Cottin V, Brown KK, Collard HR (2015) Acute exacerbation of idiopathic pulmonary fibrosis: shifting the paradigm. Eur Respir J 46:512–520

    Article  Google Scholar 

  35. Song JW, Hong SB, Lim CM, Koh Y, Kim DS (2011) Acute exacerbation of idiopathic pulmonary fibrosis: incidence, risk factors and outcome. Eur Respir J 37:356–363

    Article  CAS  Google Scholar 

  36. Bjornsson MA, Friberg LE, Simonsson US (2015) Performance of nonlinear mixed effects models in the presence of informative dropout. AAPS J 17:245–255

    Article  Google Scholar 

  37. FDA (2012) Enrichment strategies for clinical trials to support approval of human drugs and biological products FDA guidance for industry

  38. Investigators S, Yusuf S, Pitt B, Davis CE, Hood WB, Jr, Cohn JN (1992) Effect of enalapril on mortality and the development of heart failure in asymptomatic patients with reduced left ventricular ejection fractions. N Engl J Med 327(10):685–691

    Article  Google Scholar 

  39. Shepherd J, Cobbe SM, Ford I et al (1995) Prevention of coronary heart disease with pravastatin in men with hypercholesterolemia. West of Scotland Coronary Prevention Study Group. N Engl J Med 333:1301–1307

    Article  CAS  Google Scholar 

  40. Romero K, Conrado D, Burton J, et al (2019) Molecular neuroimaging of the dopamine transporter as a patient enrichment biomarker for clinical trials for early parkinson's disease. Clin Transl Sci 12:240–246

    Article  Google Scholar 

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The views expressed in this paper are those of the authors and do not necessarily represent those of the FDA.

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DR, YB, MOP, JC, AM, BAC, BAK-S and YW wrote the manuscript, DiR and YB analyzed the data.

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Correspondence to Yaning Wang.

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Anshu Marathe, Dinko Rekić and Badrul A. Chowdhury: denotes authors performed this work when in the US FDA.

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Bi, Y., Rekić, D., Paterniti, M.O. et al. A disease progression model of longitudinal lung function decline in idiopathic pulmonary fibrosis patients. J Pharmacokinet Pharmacodyn 48, 55–67 (2021). https://doi.org/10.1007/s10928-020-09718-9

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  • DOI: https://doi.org/10.1007/s10928-020-09718-9

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