Rare Diseases: Treatment and Controls
A rare disease is defined by the European Union as one that affects less than 5 in 10,000 of the general population and in the US, the definition is one that affects less than 200,000 persons1. There are between 6,000 and 8,000 known rare diseases and around five new rare diseases are described in medical literature each week. Approximately 80% of rare disease have a genetic component, 75% of rare diseases affect children and 30% of rare disease patients die before the age of five2.
A single rare disease may affect up to 30,000 people in the UK alone1, meaning research into these diseases is urgently needed. However, research is often hindered by several factors3: diagnosis is often difficult, resulting in lack of proper diagnosis or a delay in diagnosis; the population affected is sparse and spread over a wide geographical area; clinical research centres specialising in the rare disease are often limited in number and in almost all cases most of the patient care is provided locally.
These issues mean that it is difficult to conduct clinical trials to assess effective treatments for rare diseases. Rare disease trials are more likely to have smaller target sample sizes, more likely to be early-phase, more likely to recruit to a single arm, more likely to be non-randomised and more likely to be unblinded4. Many solutions have been suggested to limit bias in rare disease trials, such as selection bias models5-7, Bayesian methodology7-8, adaptive randomisation9, utilisation of registries to inform clinical trial design10-12, substitution of the control group for external historical controls and others13.
If dramatic beneficial effects (e.g. cure) are likely, then it can be unethical to randomise patients within a trial to an alternative treatment. Therefore, the use of external (historical) controls is necessary. Several trials that utilise historical controls have shown that this type of data has a place in the hierarchy of evidence-based investigation. However, using external historical controls in clinical trials involves careful analysis and skilful adjustment. Stringent methodological requirements are needed, including: rigorous patient selection criteria; record of refusals (inasmuch as the intent-to-treat principle is even more important); identification of external controls in the protocol before any analysis; formalisation of statistical considerations as in a conventional randomized trial; and proper selection of end points (response, duration of response, survival)14.
The U.S. Food and Drug Administration (FDA) states in its guidelines that the use of natural history data as a historical comparator for patients treated in a clinical trial is often of interest, but it recognises that there are challenges associated with the use of historical controls15. It recommends that historical controls can be used in clinical development programs for rare diseases, comparing patients on known covariates, or in studies where the observed effect is large in comparison to variability in disease course (e.g., a substantial improvement in outcome is observed with treatment in a disease that does not naturally remit). In general, the FDA states that, provided the study design permits a valid comparison, the use of historical controls may be used in limited or special circumstances.
Recent research into the treatment of rare disease has focussed on gene therapy or cell therapy. Gene therapy is particularly relevant to rare disease patients, as more than 80 percent of rare diseases have a known monogenic (single-gene) cause16. Traditionally, drugs in these areas often work by minimising the symptoms, or managing the condition, whereas gene therapy has the potential to correct the underlying genetic defects. As an example, Adenosine Deaminase Severe Combined Immunodeficiencies (ADA-SCID) was the first primary immunodeficiency to be genetically characterised and one of the earliest targets for gene therapy17. ADA-SCID is an extremely rare immunodeficiency with incidence rates estimated between 1: 200,000 and 1: 1,000,000 live births18. Without treatment, most children die in the first year of life from overwhelming infection. In May 2016, treatment of ADA-SCID using autologous CD34+ cells transduced to express ADA was the first ex vivo gene therapy approved for use by the European Medicines Agency (EMA)19. This gene treatment has the potential to be a one-off corrective treatment of the immunological manifestations for eligible patients. The European Society for Immunodeficiencies (ESID) and the European Society for Blood and Marrow Transplantation (EBMT) have since updated their joint guidelines to recommend gene therapy as first-line treatment for ADA-SCID patients with no matched sibling or family donor.
A final consideration in rare disease treatment is adverse drug events. Many adverse events related to treatment are rare, only occur in a small subset of the population or are not observed in small clinical trials. This is especially true for rare diseases, which have smaller target sample sizes. To address this issue, the FDA, World Health Organisation, and Health Canada have created large adverse event reporting systems (AERSs) that collect data from clinicians, patients and pharmaceutical companies. These resources present an opportunity to monitor drug safety in a large and diverse population of patients20.
- Rare Disease UK. http://www.raredisease.org.uk. Accessed 27/06/2018
- EURODIS Rare Diseases Europe. https://www.eurordis.org. Accessed 27/06/2018
- Hilgers RD, et al. Design and analysis of clinical trials for small rare disease populations. Journal of Rare Diseases Research & Treatment. 2016;1:53-60.
- Tamm M, Cramer E, Kennes LN, et al. Influence of selection bias on the test decision. A simulation study. Methods of Information in Medicine. 2012; 51(2): 138‐143.
- Tamm M, Hilgers RD. Chronological bias in randomized clinical trials arising from different types of unobserved time trends. Methods of Information in Medicine. 2014;53:501‐510.
- Kennes LN, Rosenberger WF, Hilgers RD. Inference for blocked randomization under a selection bias model. Biometrics. 2015;71:979‐984.
- Bell SA, Tudur Smith C. A comparison of interventional clinical trials in rare versus non‐rare diseases: an analysis of ClinicalTrials.gov. Orphanet Journal of Rare Diseases. 2014;9:170.
- Lilford RJ, Thornton JG, Braunholtz D. Clinical trials and rare diseases: a way out of a conundrum. BMJ. 1995;311:1621-1625
- Chow SC, Chang M. Adaptive design methods in clinical trials - a review. Orphanet Journal of Rare Diseases. 2008;3:11
- Tudur Smith C, Williamson PR, Beresford MW. Methodology of clinical trials for rare diseases. Best Practice & Research Clinical Rheumatology. 2014;28:247‐262
- Cole JA, Taylor JS, Hangartner TN. Reducing selection bias in case‐control studies from rare disease registries. Orphanet Journal of Rare Diseases. 2011;6:61.
- Mann CJ. Observational research methods. Research design II: cohort, cross sectional, and case‐control studies. Emergency medicine journal. 2003;20:54‐60.
- Abrahamyan L, et al. Alternative designs for clinical trials in rare diseases. American Journal of Medical Genetics. 2016;172:313-331
- Casali PG, Bruzzi P, Bogaerts J, et al. Rare Cancers Europe (RCE) methodological recommendations for clinical studies in rare cancers: a European consensus position paper. Annals of Oncology. 2015;20:300‐306.
- Rare Diseases: Common Issues in Drug Development - Guidance for Industry. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM458485.pdf. Accessed 27/06/18 .
- National Center for Advancing Translational Sciences. https://ncats.nih.gov/trnd/projects/gene-therapy. Accessed 27/06/18.
- Stirnadel-Farrant H, et al. Gene therapy in rare diseases: the benefits and challenges of developing a patient-centric registry for Strimvelis in ADA-SCID. Orphanet Journal of Rare Diseases. 2018;13:49
- Hershfield M. Adenosine Deaminase Deficiency. Gene Reviews. 1995
- Aiuti A, Roncarolo MG, Naldini L. Gene therapy for ADA-SCID, the first marketing approval of an ex vivo gene therapy in Europe: paving the road for the next generation of advanced therapy medicinal products. EMBO Molecular Medicine. 2017;9:237-240
- Tatonetti NP, Ye PP, Daneshjou R, Altman RB. Data-Driven Prediction of Drug Effects and Interactions. Science Translational Medicine. 2012;4:125-131.