Advancing Rare Disease Treatments in the COVID-19 Era and Beyond

Challenges and Opportunities

Advancing Rare Disease Treatments


Andrew Bevan, MRes, MRSB, CBiol
Executive Director, Peri- and Post-Approval Interventional Studies
Evidera, a PPD business

Ariel Berger, MPH
Executive Director, Integrated Solutions
Real-World Evidence
Evidera, a PPD business

Delphine Saragoussi, MD, MScPH
Executive Director
Real-World Evidence
Evidera, a PPD business

Mariah Baltezegar, MBA
Vice President and Head, Consumer Health & PPA Study Innovation
Real-World Evidence
Evidera, a PPD business

Linda Ross, MPH
Executive Director
Project Management
Real-World Evidence
Evidera, a PPD business

Timothy Miller, MD
Vice President and Therapeutic Area Head
Rare Disease and
Global Product Development
Evidera, a PPD business

Debra Schaumberg, ScD, OD, MPH
Vice President, Epidemiology and Head of Strategic & Scientific Affairs
Real-World Evidence
Evidera, a PPD business

Rare Disease and Orphan Drug Development Overview

Although there is no singular definition of rare diseases, the term is typically applied to diseases or subsets of common diseases affecting fewer than 200,000 people in the United States (US)1 ; fewer than five per 10,000 in the European Union (EU)2 ; and fewer than 50,000 patients in Japan.3 There are approximately 7,000 rare diseases, which collectively affect approximately 400 million people globally,4 many of whom suffer from high disease burden and limited treatment options. When we also consider caregiver burden, the impact of rare diseases impacts even more people. These rare pathologies, many of which are serious or life-threatening, include genetic defects, autoimmune disorders, infectious diseases, neurological disorders, and certain types of cancers,5 among others.

Since the enactment of the US Orphan Drug Act in 1983,6 the EU Regulation 141/2000 on orphan medicines in 2000,7 and, most recently, the US 21st Century Cures Act in 2016,8 many advancements have been made in the development of treatments for rare diseases. The drive for international collaboration on rare diseases has been further supported by laws such as the US Rare Diseases Act in 2002,9 which established the Rare Diseases Clinical Research Network (RDCRN) as an initiative of the Office of Rare Diseases Research (ORDR) at the National Institutes of Health’s (NIH’s) National Center for Advancing Translational Sciences (NCATS). The RDCRN comprises 23 research consortia and pioneered the creation of a collaborative and coordinated network of investigators and patient groups to support research into rare diseases.10 More recently, other rare disease organizations have encouraged the involvement of patients in the design of clinical studies in rare diseases. For example, the goal of the European Organisation for Rare Diseases (EURORDIS), which has published a charter for the collaboration between study sponsors and patient organizations, is to improve the quality of clinical research in rare diseases11 by statutes such as Directive 2011/24/ EU.12 This has resulted in the establishment of European reference networks between healthcare providers and centers of expertise in the EU member states, increasing access to cross-border healthcare and opportunities for patients to participate in research by facilitating crossborder enrollment.

Since the introduction of the Orphan Drug Act, more than 5,700 drugs and biologics have been given orphan drug designation by the US Food and Drug Administration (FDA). However, data from the FDA Orphan Drug Database (see Figure 1), demonstrate that the conversion rate to approval is low, with only 935 candidates (16.3% of all initially granted orphan drug designation) ultimately approved for use and almost the same number withdrawn (803 [14.0%]). Furthermore, this trend has not improved over the last decade. Similarly, an analysis of data from the EU demonstrated that 27.8% of all orphan drug designations granted in the period from 2000 to 2012 failed.13 Consequently, only 5% of rare diseases currently have an approved medicinal therapy.4 However, it is noteworthy that the number of US orphan drug designations granted over the last two years remains high, indicating continued strong interest in pursuing treatments for rare diseases, even in the midst of the COVID-19 pandemic.

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    Impact of COVID-19 on Rare Disease Patients

    Patients with rare diseases have been widely impacted by the COVID-19 pandemic as evidenced by EURORDIS and the US National Organization of Rare Diseases (NORD) surveys, in which 90% and 74% respectively15,16 of these patients experienced interruptions in their continuity of care. The ability to access clinical research has also been impacted by the pandemic.17 Data obtained by the authors from (see Figure 2) show a greater than 500% increase in the number of studies suspended in 2020 (vs. 2019). Furthermore, a recent report by McKinsey,18 which surveyed 20 European and US cell and gene therapy companies, revealed that the COVID-19 pandemic has had a major impact on clinical development, with 65% of respondents reporting average delays to programs between 3-12 months, and 55% reporting paused site activations and enrollment and missed follow-up visits. Preliminary findings from the RDCRN’s COVID-19 impact survey indicate that approximately 40% of patients were unable to continue specialized treatments for their disease during the pandemic.19

    Figure 1. Distribution of FDA Orphan Drug Designations by Year14

    This paper examines the traditional challenges of drug development in rare diseases coupled with the once in a lifetime challenge of the COVID-19 pandemic, which has forced changes in nearly all spheres of life, including how we communicate, how we work, how healthcare is delivered, and how we conduct clinical research. Although COVID-19 has compounded many of the challenges of conducting research in rare diseases, it has paradoxically also led to advancements that have enabled research to continue and evolve. As we now begin to emerge from the pandemic, our challenge is to ensure we build back better and for the benefit of rare disease patients and their families.

    Figure 2. A Comparison of Suspended Clinical Studies 2019 to 2020


      Challenges of Conducting Rare Diseases Research

      Rare disease research and orphan drug development is challenging in the best of times, and the financial rewards for companies that develop treatments for rare disease populations can be low considering the R&D investment required versus the size of the affected population. Some of the main challenges to confront when conducting rare disease research are summarized in Figure 3 and discussed below.

      Enrollment of patients with rare diseases in clinical studies is challenging, and a recent analysis of studies terminated early found lack of patient accrual to be the main reason for study failure.20 Populations with rare diseases are, by their nature, small and typically geographically dispersed21 and are, therefore, challenging to include in sufficient numbers required for clinical studies. Further complicating this enrollment challenge is the lack of solid epidemiology on many rare diseases, including: (1) limited awareness of relevant signs and symptoms by healthcare professionals; and (2) heterogeneity of clinical presentations that can make a diagnosis via the appearance of clinical signs and symptoms alone difficult, necessitating genetic testing and genetic counseling. These complications often result in underdiagnosis or misdiagnosis, for example Fabry disease, where it is thought that only approximately 20% of cases in the US have been diagnosed.22 Heterogeneity of clinical presentations can also make it difficult to identify suitable cohorts of sufficient size for inclusion into clinical studies (including matching controls if using external control arms).

      Figure 3. Challenges of Conducting Clinical Research in Rare Disease

      Approximately 50% of patients with rare diseases are children, which presents additional challenges in terms of:

      • Dose selection – appropriate pediatric dose selection is required to maximize the likelihood that the studied dose will have a beneficial efficacy and safety profile in children
      • Endpoints and outcomes selection – endpoints and outcome measures of interest in children may differ from those typically used for adults
      • Blood sampling and tissue collection – pharmacokinetic/pharmacodynamic (PK/PD) investigations can be difficult in neonates and infants due to limited blood volume; invasive procedures such as tissue biopsies and cerebrospinal fluid (CSF) sampling may not be justifiable in younger age groups
      • Adverse event reporting – eliciting adverse event information can be challenging in children in whom vocabulary is limited and/or non-verbal communication with caregivers more common
      • Informed consent – the complex nature of assent; the impact of cultural variables and individual life experiences leading to reluctance on the part of parents and caregivers to expose dependents to experimental treatments; and gaps in local regulations all present challenges
      • Logistics and visit scheduling – participation may be hindered by mobility difficulties, school and family schedules, the need to travel long distances, and potential loss of income for parents due to the need to take time off work

      Randomized clinical trials (RCTs), although the gold standard for evidence generation, are often not possible in rare disease research due to lack of comparator treatment options and ethical concerns with the use of placebocontrols, which, even if possible, may be difficult to sustain for long-term comparisons. Thus, it is not uncommon for studies in rare diseases to be single-arm trials focused on enrollment of patients from a limited number of specialized centers in countries with relatively high prevalence of the disease. This traditional approach seems to be an intuitive solution shaped by practical necessities and limitations, such as small patient populations, limited comparator options, and high unmet medical need, which can make a two-arm study impractical and/or unethical. However, this traditional study approach creates a barrier to access of novel and potentially life changing therapies for patients who live outside the catchment of a specialist treatment center or who are living with a high disease burden and medical needs that make travel difficult. In addition, the traditional model limits the conclusions that can be drawn from the research. External control arms (ECAs) are one possible solution to facilitate interpretation of single-arm studies. However, the heterogeneity of clinical presentations that can make the diagnosis of rare diseases challenging also poses challenges for ensuring comparability of cohorts when designing an ECA.

      Heterogeneity of clinical presentations of rare diseases also poses challenges for endpoint selection. Different mutations producing gene variants that result in different clinical manifestations of a disease, such as those seen in Fabry disease, can affect multiple organ systems to varying degrees and researchers must therefore consider which is most important, which is most likely to respond to therapy, and how to measure response with accuracy and precision. In addition, the type of treatment can present challenges for identifying the most appropriate endpoint or outcome, for example, long term treatment outcomes in advanced therapy medicinal products (ATMPs) such as gene and cell therapies. This underscores the importance of natural history studies to better understand disease progression and burden of disease.

      Several key challenges of rare disease research have been compounded by the COVID-19 pandemic, including the ability or willingness of patients and their families to travel, and reduced access to specialist medical care and testing facilities, resulting in potential additional delays in diagnosis and treatment. Risk of COVID-19 infection and its potential sequelae added to existing and significant medical challenges is another barrier. FDA guidance on conducting studies in the COVID-19 era emphasizes patient safety and sets forth the expectation that sponsors, investigators, and institutional review boards (IRBs)/independent ethics committees (IECs), respectively, put robust measures in place to maintain the safety of study participants and data integrity. While challenging in general, it is particularly keenly felt in the setting of rare diseases, where stakes are high and suspension of a study or no access to study medication outside of a study can be devastating and even life threatening. The greater than five-fold increase in the number of trials suspended in 2020 (vs. 2019) (see Figure 2) demonstrates that these concerns are not hypothetical or academic, but rather have already conspired to delay the evaluation of potentially life-changing therapies.

        Addressing Barriers to Rare Disease Research

        Decentralized Study Approaches to Remove Geographical Barriers

        As previously mentioned, the reality of disparate geographic distributions of patients with rare diseases often results in patients needing to travel long distances to access specialist treatment and studies. These realities, which challenge the traditional site-based clinical study model, have only been compounded by the COVID-19 pandemic. Simultaneously, COVID-19 has accelerated a paradigm shift towards digital enablement and decentralization of studies, which have enabled the continuation of clinical development programs. Removing geographical barriers by bringing studies to patients is a compelling solution that increases patient access to clinical research and may, if supported by carefully considered decentralized solutions, improve completeness of the data collected. There is no one-size-fits-all solution to clinical studies and certainly not studies in patients with a rare disease. These solutions exist along a continuum, from traditional site-based models that are supported by digital health tools, to fully decentralized studies (DCTs) where a full range of remote enablement solutions are deployed. Figure 4 illustrates the menu of DCT solutions that can be employed to support the full spectrum of studies.

        Figure 4. The Spectrum of Study Solutions From Traditional to Fully Decentralized

        eCOA = electronic clinical outcomes assessment; DTP/DFP = direct to patient/direct from patient; EMR = electronic medical records

        A recent study by the Tufts Center for the Study of Drug Development (Tufts CSDD), which interviewed staff from 25 US-based pharmaceutical, biotechnology, and non-profit research institutions, found that telemedicine and eConsent were the most frequently mentioned remote technologies adopted during COVID-19; and telemedicine was implemented by 19 of the interviewed organizations.23 Home health, direct to patient (DTP) supply, and decentralized labs were the most utilized strategies in studies of rare diseases. Despite the ability of these DCTs to reduce delays in clinical research, some have predicted that studies will return to the traditional approach postCOVID-19—especially for rare diseases and oncology, where many stakeholders believe that in-person care is critical. However, a recent survey by the rare disease patient network, Raremark, found that rare disease patients are open to a decentralized approach,24 suggesting that in-person care may not be such a key consideration for patients.

        The ability to collect digital biomarkers via wearables has the potential to expand the possibilities for conducting DCT studies in rare diseases. A wearable that enables continuous monitoring of physical activity has been piloted in patients with Gaucher disease,25 a rare lysosomal storage disorder. This disorder is categorized into three subtypes: Type 1 which is associated with pathology of the liver, spleen, and bone tissue, but does not affect the central nervous system (CNS); and Types 2 and 3, which do affect the CNS, resulting in neuromuscular impairment as well as nonneurological disease. The study, which monitored physical activity via a 3D accelerometer worn on the wrist paired to a mobile phone app that enabled patients to complete selfreported outcome measures, demonstrated the feasibility and utility of this technology to monitor physical activity as a surrogate of disease activity in a real-world setting. If further validated, this could expand the possibilities of performing DCTs in this patient population and potentially add to the list of more traditional surrogate endpoints already approved by the FDA for rare disease indications that lend themselves to remote collection (Table 1).

        Mining Electronic Medical Records (EMRs) to Accelerate Rare Disease Diagnosis and Narrow the Search for Rare Disease Populations

        The increasing availability of rich EMR data that are potentially linkable to medical claims data, and our ability to mine those data using natural language processing (NLP), artificial intelligence (AI), and other advanced analytic methods, have expanded the possibilities of identifying:

        1. Patients with rare diseases through computable clinical phenotypes (i.e., observable and searchable physical, morphologic, or biochemical characteristics), 26,27 and
        2. Those not yet diagnosed whose clinical profiles suggest confirmatory testing may be warranted (providing at least in theory the opportunity for improved case finding, with benefits to affected individuals that include and exceed enrollment in clinical studies).

        Table 1. Surrogate Endpoints Used as the Basis for FDA Approval in Rare Disease Indications That Can Be Collected Remotely


        Several resources exist that can aid in the development of probabilistic search algorithms that can be applied to EMR for the purposes of identifying rare disease populations. For example, the Orphanet Rare Disease Ontology (ORDO), jointly developed by Orphanet and the European Bioinformatics Institute (EMBL-EBI), is a structured vocabulary for rare diseases that provides a useful resource for computational analysis.28 The encyclopedia of Rare disease Annotation for Precision Medicine (eRAM) provides computational annotations (a process that attributes a biological function to genes) for approximately 16,000 rare diseases, producing more than 6,000 human disease related phenotype terms.29 Furthermore, the Human Phenotype Ontology (HPO), developed from medical literature and various rare disease resources, provides standardized vocabulary of phenotypic abnormalities encountered in human disease.30 These resources are being used to generate algorithms to extract phenotype-disease associations to support rare disease differential diagnosis.31 One such innovative tool is Dx29, which is being developed by Foundation 29, a non-profit organization focused on applying the latest AI technology to support rare disease diagnosis as well as building the rare disease knowledge base.32 Dx29 is part of a key technology pilot program supported by The Global Commission to End the Diagnostic Odyssey for Children with a Rare Disease33 that combines the HPO with natural language processing and next generation sequencing technology to extract rare disease phenotypes from free text medical records to narrow down possibilities for diagnosis.34

        While promising, challenges exist in implementing data mining approaches to rare diseases research in multicenter/multi-country studies. For example, applying coding algorithms across diverse EMRs likely require the enablement of different software systems and associated data formats to interact with others (i.e., interoperability). Common data models (CDMs), such as the one that is part of the FDA’s Sentinel System, have been used to standardize EMR data across multiple sources for research purposes. However, CDMs have traditionally relied on the extraction and mapping of structured data such as ICD-9- CM diagnosis and procedure codes. These alone may not translate to a broad clinical research setting or be sufficient to identify potential participants in rare diseases studies, because clues to the diagnosis may be buried in the unstructured portions of patient records, such as physician notes or discharge summaries (which often contain misspellings, abbreviations, and local colloquialisms that require substantial cleaning and review before they can be fully analyzed/incorporated into case-finding algorithms). The mapping of data can also result in loss of detail and precision, further limiting the usefulness of this approach in rare disease research. However, recently HL7’s Fast Healthcare Interoperability Resources (FHIR) has emerged as a new data standard and Application Programming Interface (API) that is able to integrate both structured and unstructured EMR data. FHIR has recently been used with semi-structured discharge summaries to identify patients with obesity and its multiple associated comorbidities and could be similarly used to narrow the search for patients with (at least some) rare diseases. However, heterogeneity of clinical presentation in patients with a rare disease can make identification through these automated means alone difficult (in the aforementioned Gaucher disease example alone, there are three different clinical manifestations that each have different hallmark symptomatology). Therefore, confirmation of diagnosis via genetic testing is often needed, although use of the EMR and advanced analytics such as machine learning can likely maximize efficiencies in identification of the pool of patients requiring this confirmatory step.

        The use of EMR extraction to obtain endpoint data can also streamline data collection and reduce site burden. However, this can be challenging in patients with rare diseases for whom specialist examinations and test results may be buried in the unstructured data portions of ERMs or locked in paper records; information from visits with specialists outside the patient’s typical network of providers may not be entered completely into the EMR, further limiting their usefulness. The use of specialized EMR software (e.g., ophthalmology-specific systems such as Softalmo [Corilus]), that contains specific fields for capturing specialist information and examination results, has been demonstrated to improve the reliability of EMR data in patients with rare eye disorders, and may facilitate the collection of endpoint data via EMR extraction.35 This technology may also facilitate the design and conduct of pragmatic clinical studies, as it would allow for a more “hands off” approach without a corresponding threat to the capture of necessary endpoint data.

        Genetic Screening to Identify Patients with Mutations of Interest

        Approximately 80% of rare diseases have a genetic component, and advances in technology, such as next generation sequencing (NGS), have led to the identification of new molecular biomarkers. These are integral to the development of novel treatments and have the potential to identify subgroups of patients suffering from, or at risk of, more common diseases (e.g., genetic mutations linked to obesity). This has led to progress in rare disease drug development that previously had been difficult to treat (e.g., PARP inhibitor olaparib approved for the treatment of a rare and lethal form of breast cancer36). Many patients with rare diseases may already be participants in clinical studies; consequently, genetic screening to identify newly diagnosed, treatment-naive cases may be warranted. Genetic testing of potential patients for rare disease mutations identified through case-selection algorithms developed through EMR mining and machine learning is one approach. Another important strategy, although not new to rare disease research, is cascade testing of family members of patients diagnosed with a rare disease. Figure 5 shows a schematic for a virtual genetic screening program that the authors have successfully deployed in rare disease clinical programs. This strategy is now being supported by companies like PreventionGenetics, an accredited clinical DNA testing laboratory (Marshfield, Wisconsin, USA), which has partnered with several biopharmaceutical companies developing treatments for rare disorders to offer no-cost sponsored testing programs.37

        Figure 5. Identifying Patients for Rare Disease Studies Through Genetic Screening

        Use of Natural History Studies and Registries to Characterize Rare Disease Patient Populations and Serve as ECAs

        The FDA’s updated draft guidance on rare diseases38 includes a recommendation to conduct natural history (NH) studies to better characterize patient populations and delineate target populations. Further FDA draft guidance in 201939 underscores the importance of performing NH studies to expand on the paucity of data for many rare diseases; this expanded knowledge base can then be used to support and guide the design of clinical studies. NH studies can inform clinical product development by:

        • Providing better insight into disease characteristics, patient populations, and identification of disease subtypes
        • Identifying patients for clinical studies
        • Serving as an historical external comparator in case of single-arm studies, thereby addressing some limitations inherent in single-arm studies, ethical concerns with use of placebo or sham comparators, and reducing the number of patients that need to be enrolled40
        • Identifying the most sensitive and relevant endpoints or the optimal duration of follow-up
        • Providing the ability to characterize disease burden and levels of unmet need associated with current standard of care, thereby demonstrating the need for new therapies in the indication and potentially providing input values for economic models typically required for newly approved products

        Global rare disease patient registries created through collaboration between multinational rare disease organizations such as NORD, EURORDIS, and the Canadian Organization for Rare Disorders (CORD) are powerful webbased data repositories containing treatment-related health information and biological sample data. Furthermore, their corresponding biobanks, with a Global Unique Identifier (GUID), enable the tracking of patient information. These registries are excellent potential sources of data that could serve as historical ECAs in the case of single-arm studies and help identify appropriate endpoints, thereby reducing the number of patients that need to be enrolled and yielding potentially more comprehensive results. However, certain data elements are recommended for rare disease registries in order to inform clinical study design, including patient characteristics, demographic characteristics, specific diagnosis (e.g., for genotype/phenotype classification or other factors that may affect outcomes), comorbidities, treatments, mortality, life impact, and pathophysiological manifestations.41 This level of detail is more likely to be captured in disease and treatment registries, but not captured in public health registries, which are focused on helping to inform epidemiological research, healthcare service planning, and disease surveillance.

        If natural history studies or registry data are intended to serve as ECAs, it is recommended to seek preliminary regulatory agency agreement for the use of such designs ahead of submitting final protocols. ECAs have been successfully employed in several indications, and examples of FDA approvals based on clinical studies that incorporated ECAs include:42

        • Blincyto (blinatumomab) for the treatment of relapsed/ refractory Philadelphia chromosome-negative acute lymphoblastic leukemia: historical controls were used to demonstrate effectiveness (vs. standard of care), based on weighted analysis of patient-level data from medical chart review
        • Bavencio (avelumab) for the treatment of metastatic Merkel cell carcinoma and urothelial carcinoma: historical controls identified via EMRs and a German patient registry were used as a benchmark to characterize the natural history of the disease


          The COVID-19 pandemic has widely impacted clinical research opportunities for patients with rare disease indications, many of whom fall into highly vulnerable categories with high disease burden and limited treatment options. This impact has been compounded by disparate geographic distributions of rare disease populations, often requiring patients to travel long distances to access specialized treatment, further exposing the limitations of traditional, site-based, clinical study models to recruit and retain enough patients to generate statistically meaningful results. Natural history studies and rare disease registries can serve as sources of external comparators, reducing the number of patients needing to be enrolled and yielding potentially more meaningful results. Leveraging technology can facilitate the research required to bring these lifechanging medications to market, including but not limited to, identification of rare disease populations via computable clinical phenotypes using AI algorithms to mine EMRs, remote genetic screening, DCT solutions to bring clinical research into patients’ homes, and the use of wearable technologies and EMRs to collect necessary endpoints. The use and acceptance of these technologies has largely evolved during the COVID-19 pandemic out of necessity. However, as we emerge from the pandemic, it is incumbent on us all to remember the lessons we have learned and continue to innovate and lead in the design and conduct of rare disease studies. These patients, and the caregivers who support them, deserve nothing but our best.


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