It is estimated that rare diseases affect more than 400 million people worldwide, having a negative impact on their quality of life or even putting them in life-threatening conditions. Even though the treatment options available to rare disease patients have increased significantly in the past few years, it is still disappointing that only 5% of rare diseases have an approved treatment.
The term “rare diseases” describes a set of conditions affecting fewer than 200,000 people in the US or 1 in 2,000 people in the EU.
As mentioned in this Discussion Guide (Utilizing Innovative Statistical Methods and Trial Designs in Rare Disease Settings) issued by Margolis Center for Health Policy – Duke University, rare diseases represent a largely heterogeneous set of disorders with an estimate of 7,000 distinct diseases, more than 80% of which are genetic, clinically progressive, and impact the quality of or even threaten the patient’s life.
Limitations on data collection and analysis – The overarching barrier to rare disease drug development.
The major challenge in rare disease drug development is the lack of adequate knowledge about each entity and the inability to collect enough data to provide a meaningful result. Obstacles in collecting data include:
- the limited number of patients
- the limited number of researchers and the lack of collaboration among different teams
- delays in patient recruitment due to the small number of participating sites
- difficulty in determining the trial design (including trial duration, included population diversity, endpoints, and outcome measures)
- the limited funding (due to the small interest pharmaceutical companies have in investing in treatments for small populations)
The above data collection and analysis challenges lead to regulatory and reimbursement obstacles, further impeding the development of rare disease treatments.
Working toward data collection for rare diseases drug development: successful examples.
Below we discuss a technology solution (Atom5 by Aparito), an initiative (DevelopAKUre consortium), and the role of natural history studies, real-world and historical data, and registries in overcoming the data collection and analysis bottleneck in rare disease drug development.
- The Atom5 app Aparito employs wearables to collect data from Gaucher Disease patients.
Aparito, a health technology company, launched Atom5 to support rare disease patients and shares its success story regarding the case of Gaucher Disease (GD). The challenge impeding the development of a disease-modifying treatment for neurological GD (nGD) was the lack of meaningful clinical outcome measures.
As Apparito explains, 21 patients, 5 with Type 1 GD and 16 nGD, participated in a study for up to 12 months. Pairing Atom 5TM with a 3D accelerometer wearable device enabled assessing the 21 patients with “a neurological examination, the mSST (modified Severity Scoring Tool that indicates disease severity in nGD), 6-Minute Walk Test (6MWT), and a GAITIRite or Zeno Walkway gait analysis”. The study resulted in measuring data successfully and constructing PROs and QoL scales.
- The DevelopAKUre consortium: Academia, industry, and patient organizations fighting against alkaptonuria (AKU)
Academia, industry, and patient organizations collaborated to develop a treatment for alkaptonuria (AKU), with the industry facilitating international, multicenter, complex clinical studies, manufacturing the asset, negotiating with the regulatory bodies, and ensuring patient access, and academia providing scientific knowledge and facilitating research.
The patient advocacy groups contributed to increasing the patient pool and shedding light on the patient perspective. Their initial involvement boosted recruitment in the SONIA 2 clinical trial through cross-country networking with other patient groups. And the real-time patient feedback improved retention and contributed to the definition of meaningful clinical outcome measures— which are helpful in discussions with regulatory authorities. Finally, bringing together expertise from all parties led to securing considerable funding.
- Real-world data, historical data, and registries are employed to overcome data collection and analysis barriers.
Below, we present examples in which the employment of natural history studies real-world and historical data and registries helped overcome the data collection and analysis barriers in rare diseases drug development.
- The Neuromuscular Research Group Duchenne Natural History Study for the treatment of Duchenne Muscular Dystrophy
- The RESTORE registry study for treating Spinal Muscular Atrophy
- The ENROLL-HD study for Huntigton’s Disease treatment
- Myozyme–enzyme replacement therapy study for Pompe (here, historical data were used as control when evaluating the long-term efficacy of the asset)
- Fabrazyme–enzyme replacement therapy study for Fabry disease (here, a clinical outcome trial was conducted under a post-marketing commitment with data analysis demonstrating the efficacy of the treatment.)
- GALA maralixibat study for Alagille syndrome (ALGS) in kids. The pediatric patient registry was used to evaluate the effect of maralixibat use on transplant-free survival
As mentioned in the “Natural History and Real‐World Data in Rare Diseases: Applications, Limitations, and Future Perspectives article, in cases 1-3, the researchers gained insights into the etiology, pathophysiology, and the outcomes achieved with standard‐of‐care treatments. In cases 4 and 5, the use of real-world and historical data allowed researchers to obtain results that would lead to drug approval. In case 6, the study data was used as a comparator to assess event-free survival time before treatment subjects experience their first clinical event, and the results provided evidence that regulators could potentially review and create demand for further research.
Addressing rare disease drug development challenges beyond data collection. Navigating data analysis and regulatory bottlenecks.
Challenges in rare disease drug development go beyond data collection and hamper all stages of the drug development process, including data analysis and review for regulatory approval.
Below we present the four primary study design categories employed by researchers in RW data statistical analysis and discuss the critical role of early engagement with regulatory agencies.
- RW data statistical analysis: Researchers’ tool for informed decisions
Statistical analysis of RW data, especially when used prospectively, allows researchers to make an informed decision on study design and overcome the barrier of limited sample size. There are four primary study design categories:
- Use of external control
- Evidence synthesis
- Pragmatic trials
- Use of RWE to fulfill a post-marketing requirement for safety/effectiveness
- Early discussion with authorities: the answer to regulatory challenges for rare disease drug development.
Discussions with the authorities at the stage of trial design enable understanding of requirements and minimize the chances of application and approval rejection.
Considering that in many cases (e.g., France), the regulators view trials for rare diseases as “proof-of-concept” and RW data are required to inform HTA’s decisions, drug providers could make use of the accelerated approval process, available in several countries, to start collecting real-world evidence early.
Finally, pharmaceutical companies should shape their pricing and evidence-generation strategy according to each country’s policy to secure reimbursement and patient access.
Shaping a new landscape in rare disease drug development. The key takeaways.
The factors contributing to an “unfriendly” environment for developing novel, effective therapies for patients with rare diseases are multiple; however, most obstacles share a common denominator: the limitation in data availability, an inevitable barrier given the nature of these diseases (“rare”).
In light of the above limitation, the involved parties should focus on making the most out of the already available and new tools. All stakeholders should collaborate and strengthen their efforts in utilizing their expertise.
Implementing new technologies will produce clinically significant and real-world data to add to the already existing information, i.e., historical data and employment of statistical designs that will be fit for use in smaller population samples aiding the effort to move forward.
As we are going through the AI era, it might make sense to explore whether such a breakthrough tool could further support efforts in shaping a better landscape for rare diseases.While gathering as much data as possible is the initial step towards early and precise identification of the disease and potential treatments, analyzing large datasets would also be a challenge. AI algorithms in Machine Learning have allowed the analysis of large amounts of data. At the same time, Deep Learning models could assist in forecasting the response to drugs based on certain biomarkers, thus leading to better treatment planning using targeted therapies.
However, AI implementation requires exhaustive testing in every TA and careful consideration of associated challenges.
The landscape is and will most likely continue to remain challenging; however, the already achieved small steps forward and the successful examples noted give hope for the future.
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#rarediseases #drugdevelopment #clinicaltrials #clinicaltrialsdesign #aparito #AIinhealthcare #Duchenne #MuscularDystrophy #SpinalMuscularAtrophy #huntington #Maralixibat#AlagilleSyndrome #FabryDisease #fabrazyme
Sources:
A Rare Public Health Challenge – NIH Director’s Blog
The building blocks to make rare disease treatments more common | Research and Innovation
Exploring Novel Statistical Methods for Rare Disease and Small Population Clinical Trials
Digital Tools for Outcome Measures in Gaucher Disease | Aparito
Rare Disease Clinical Trials: Strategies Learned from Duchenne Muscular Dystrophy
strategies-for-rare-diseases—-international-landscape-report–dec-2021-en.pdf (takeda.com)
Research advances in treatment methods and drug development for rare diseases
The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.