A methodological framework for drug development in rare diseases

Orphanet Journal of Rare Diseases

Catherine Cornu¹  Behrouz Kassai¹  Nathalie Eymard⁴  Vitaly Volpert⁴  Sylvie Chabaud³  Charlotte Castellan²  Salma Malik¹  Agathe Bajard³  Polina Kurbatova⁴  Patrice Nony⁵ 

[1] Hôpital Louis Pradel, Centre d¿Investigation Clinique, INSERM CIC1407/UMR5558, Bron, France;University of Lyon 1, UMR 5558, CNRS, Lyon, France;Unité de Biostatistique et d¿Evaluation des Thérapeutiques, Centre Léon Bérard, Lyon, France;Institut Camille Jordan UMR 5208 Université Claude Bernard, Lyon 1, France;Service de Pharmacologie Clinique et Essais Thérapeutiques-HCL, Groupement Hospitalier Est, Hôpital Cardiovasculaire et Pneumologique Louis Pradel, 28, Avenue du Doyen Lépine, Bron Cedex, 69677, France

关键词: Clinical trial simulation;    Integrative modeling;    Drug development;    Rare diseases;   

Others  :  1149380
DOI  :  10.1186/s13023-014-0164-y

 received in 2014-06-02, accepted in 2014-10-14,  发布年份 2014

【 摘 要 】

Introduction

Developing orphan drugs is challenging because of their severity and the requisite for effective drugs. The small number of patients does not allow conducting adequately powered randomized controlled trials (RCTs). There is a need to develop high quality, ethically investigated, and appropriately authorized medicines, without subjecting patients to unnecessary trials.

Aims and Objectives

The main aim is to develop generalizable framework for choosing the best-performing drug/endpoint/design combinations in orphan drug development using an in silico modeling and trial simulation approach. The two main objectives were (i) to provide a global strategy for each disease to identify the most relevant drugs to be evaluated in specific patients during phase III RCTs, (ii) and select the best design for each drug to be used in future RCTs.

Methodological approach

In silico phase III RCT simulation will be used to find the optimal trial design and was carried out in two steps: (i) statistical analysis of available clinical databases and (ii) integrative modeling that combines mathematical models for diseases with pharmacokinetic-pharmacodynamics models for the selected drug candidates.

Conclusion

There is a need to speed up the process of orphan drug development, develop new methods for translational research and personalized medicine, and contribute to European Medicines Agency guidelines. The approach presented here offers many perspectives in clinical trial conception.

【 授权许可】

   
2014 Nony et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1][http://www.eurordis.org/content/what-rare-disease] webcite EURORDIS. What is a rare disease? () Accessed on: 31 October. 2011.
• [2][http://www.eurordis.org/IMG/pdf/princeps_document-EN.pdf] webcite EURORDIS:Rare diseases: understanding this public health priority european organisation for rare diseases () Accessed on: 17 February. 2013.
• [3]Friedman LM, Furberg CD, DeMets DL: Fundamentals of Clinical Trials. Mosby-Year Book, St Louis; 1996.
• [4]Chalmers I: Why transition from alternation to randomisation in clinical trials was made. BMJ 1999, 319:1372.
• [5]Urbach P: The value of randomization and control in clinical trials. Stat Med 1993, 12:1421-1431.
• [6]Feinstein AR: Current problems and future challenges in randomized clinical trials. Circulation 1984, 70:767-774.
• [7]Concato J, Shah N, Horwitz RI: Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med 2000, 342:1887-1892.
• [8]MacMahon S, Collins R: Reliable assessment of the effects of treatment on mortality and major morbidity, II: observational studies. Lancet 2001, 357:455-462.
• [9]McKee M, Britton A, Black N, McPherson K, Sanderson C, Bain C: Methods in health services research. Interpreting the evidence: choosing between randomised and non-randomised studies. BMJ 1999, 319:312-315.
• [10]Grimes DA, Schulz KF: Bias and causal associations in observational research. Lancet 2002, 359:248-252.
• [11]Grimes DA, Schulz KF: Descriptive studies: what they can and cannot do. Lancet 2002, 359:145-149.
• [12]Jepsen P, Johnsen SP, Gillman MW, Sorensen HT: Interpretation of observational studies. Heart 2004, 90:956-960.
• [13]Spilker B: Guide to clinical Trials. Raven Press, New York; 1991.
• [14]Cornu C, Kassai B, Fisch R, Chiron C, Alberti C, Guerrini R, Rosati A, Pons G, Tiddens H, Chabaud S, Caudri D, Ballot C, Kurbatova P, Castellan AC, Bajard A, Nony P: Experimental designs for small randomised clinical trials: an algorithm for choice. Orphanet J Rare Dis 2013, 8:48. BioMed Central Full Text
• [15]Gupta S, Faughnan ME, Tomlinson GA, Bayoumi AM: A framework for applying unfamiliar trial designs in studies of rare diseases. J Clin Epidemiol 2011, 64:1085-1094.
• [16]Senn S: Cross-over Trials in Clinical Research. Wiley, Chichester; 1993.
• [17]Nathan N, Taam RA, Epaud R, Delacourt C, Deschildre A, Reix P, Chiron R, De PU, Brouard J, Fayon M: A national internet-linked based database for pediatric interstitial lung diseases: the French network. Orphanet J Rare Dis 2012, 7:40. BioMed Central Full Text
• [18]Salvatore D, Buzzetti R, Baldo E, Forneris MP, Lucidi V, Manunza D, Marinelli I, Messore B, Neri AS, Raia V, Furnari ML, Mastella G: An overview of international literature from cystic fibrosis registries 2. Neonatal screening and nutrition/growth. J Cyst Fibros 2010, 9(2):75-83. doi:10.1016/j.jcf.2009.11.002. Epub 2009 Dec 2
• [19]Salvatore D, Buzzetti R, Baldo E, Forneris MP, Lucidi V, Manunza D, Marinelli I, Messore B, Neri AS, Raia V, Furnari ML, Mastella G: An overview of international literature from cystic fibrosis registries. Part 3. Disease incidence, genotype/phenotype correlation, microbiology, pregnancy, clinical complications, lung transplantation, and miscellanea. J Cyst Fibros 2011, 10(2):71-85. doi:10.1016/j.jcf.2010.12.005. Epub 2011 Jan 22
• [20]Salvatore D, Buzzetti R, Baldo E, Furnari ML, Lucidi V, Manunza D, Marinelli I, Messore B, Neri AS, Raia V, Mastella G: An overview of international literature from cystic fibrosis registries. Part 4: update 2011. J Cyst Fibros 2012, 11(6):480-493. doi:10.1016/j.jcf.2012.07.005. Epub 2012 Aug 9
• [21]Buzzetti R, Salvatore D, Baldo E, Forneris MP, Lucidi V, Manunza D, Marinelli I, Messore B, Neri AS, Raia V, Furnari ML, Mastella G: An overview of international literature from cystic fibrosis registries: 1. Mortality and survival studies in cystic fibrosis. J Cyst Fibros 2009, 8(4):229-237. doi:10.1016/j.jcf.2009.04.001. Epub 2009 May 6
• [22]Buyse M, Michiels S, Sargent DJ, Grothey A, de GA Matheson A: Integrating biomarkers in clinical trials. Expert Rev Mol Diagn 2011, 11:171-182.
• [23]Klungel OH, Martens EP, Psaty BM, Grobbee DE, Sullivan SD, Stricker BH, de BA Leufkens HG: Methods to assess intended effects of drug treatment in observational studies are reviewed. J Clin Epidemiol 2004, 57:1223-1231.
• [24]Rosenbaum PR: Observational Studies. Springer, New York; 2002.
• [25]Sacks H, Chalmers TC, Smith H Jr: Randomized versus historical controls for clinical trials. Am J Med 1982, 72:233-240.
• [26]Greenland S: Confounding and exposure trends in case-crossover and case-time-control designs. Epidemiology 1996, 7:231-239.
• [27]Maclure M: The case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol 1991, 133:144-153.
• [28]Suissa S: The case-time-control design. Epidemiology 1995, 6:248-253.
• [29]Suissa S: The case-time-control design: further assumptions and conditions. Epidemiology 1998, 9:441-445.
• [30]Cochran WG: The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 1968, 24:295-313.
• [31]Cook EF, Goldman L: Asymmetric stratification. An outline for an efficient method for controlling confounding in cohort studies. Am J Epidemiol 1988, 127:626-639.
• [32]Peduzzi P, Concato J, Feinstein AR, Holford TR: Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 1995, 48:1503-1510.
• [33]Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR: A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996, 49:1373-1379.
• [34]D'Agostino RB Jr: Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998, 17:2265-2281.
• [35]Rosenbaum PR, Rubin DB: The central role of the propensity score in observational studies for causal effects. Biometrika 1983, 70:41-55.
• [36]Miettinen OS: Stratification by a multivariate confounder score. Am J Epidemiol 1976, 104:609-620.
• [37]Newhouse JP, McClellan M: Econometrics in outcomes research: the use of instrumental variables. Annu Rev Public Health 1998, 19:17-34.
• [38]Angrist JD, Imbens GW: Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity. Journal of the American Statistical Association 1995, 90:431-442.
• [39]Boissel JP, Cucherat M, Nony P, Chabaud S, Gueyffier F, Wright JM, Lievre M, Leizorovicz A: New insights on the relation between untreated and treated outcomes for a given therapy effect model is not necessarily linear. J Clin Epidemiol 2008, 61:301-307.
• [40]Boissel JP, Ribba B, Grenier E, Chapuisat G, Dronne MA: Modelling methodology in physiopathology. Prog Biophys Mol Biol 2008, 97:28-39.
• [41]Chapuisat G, Dronne MA, Grenier E, Hommel M, Gilquin H, Boissel JP: A global phenomenological model of ischemic stroke with stress on spreading depressions. Prog Biophys Mol Biol 2008, 97:4-27.
• [42]Dronne MA, Grenier E, Dumont T, Hommel M, Boissel JP: Role of astrocytes in grey matter during stroke: a modelling approach. Brain Res 2007, 1138:231-242.
• [43]Ribba B, Saut O, Colin T, Bresch D, Grenier E, Boissel JP: A multiscale mathematical model of avascular tumor growth to investigate the therapeutic benefit of anti-invasive agents. J Theor Biol 2006, 243:532-541.
• [44]Gabreilsson J, Weiner D, Weiner D: Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications. Swedish Pharmaceutical Press, Sweden; 2007.
• [45]Blesius A, Chabaud S, Cucherat M, Mismetti P, Boissel JP, Nony P: Compliance-guided therapy: a new insight into the potential role of clinical pharmacologists. Clin Pharmacokinet 2006, 45:95-104.
• [46]Nony P, Cucherat M, Boissel JP: Revisiting the effect compartment through timing errors in drug administration. Trends Pharmacol Sci 1998, 19:49-54.
• [47]Smith DJ, Gaffney EA, Blake JR: Modelling mucociliary clearance. Respir Physiol Neurobiol 2008, 163:178-188.
• [48]Shak S, Capon DJ, Hellmiss R, Marsters SA, Baker CL: Recombinant human DNase I reduces the viscosity of cystic fibrosis sputum. Proc Natl Acad Sci U S A 1990, 87:9188-9192.
• [49]Yeh HC, Schum GM: Models of human lung airways and their application to inhaled particle deposition. Bull Math Biol 1980, 42:461-480.
• [50]Evans CH, IIdstad ST: Small Clinical Trials, Issues and Challenges; Committee on Strategies for Small-Number-Participant Clinical Research Trials, Board on Health Sciences Policy, Institute of Medicine. National Academy Press, Washington D.C; 2003.
• [51]Chabaud S, Girard P, Nony P, Boissel JP: Clinical trial simulation using therapeutic effect modeling: application to ivabradine efficacy in patients with angina pectoris. J Pharmacokinet Pharmacodyn 2002, 29:339-363.
• [52]Bajard A, Chabaud S, Perol D, Boissel JP, Nony P: Revisiting the level of evidence in randomized controlled clinical trials: A simulation approach. Contemp Clin Trials 2009, 30:400-410.
• [53]Nony P, Boissel JP: Use of sensitivity functions to characterise and compare the forgiveness of drugs. Clin Pharmacokinet 2002, 41:371-380.