2 challenges to overcome in clinical trials for rare diseases to maximize the development of new treatments

Published on 14 May 2024 Read 25 min

According to the European Medicines Agency (EMA), rare diseases are conditions affecting fewer than 5 people in 10,000. They represent more than 7,000 conditions and affect a total population of more than 250 million people worldwide.

The market for drugs for rare diseases continues to grow. Over the last 40 years, the number of drugs developed in this market has quadrupled, representing on average between 30% and 40% of the products developed by pharmaceutical companies each year. This growth is explained by the introduction of health policies favoring funding and access to drugs, as well as by the increase in the number of patients suffering from rare diseases. It is also explained by improved diagnostics and advances in personalized medicine, and the development of genomic technologies, advances which are particularly well suited to treat rare diseases.

Despite this growing interest, less than 6% of rare diseases currently have a treatment. Why is this? How can we do better? In this article, Alcimed looks back at two major challenges associated with the development of drugs for rare diseases and proposes potential solutions for improving clinical trials.

Challenge n°1: Develop knowledge about rare diseases

Research into rare diseases is hampered by a lack of knowledge of the biological mechanisms involved and a limited understanding of how these diseases develop. It is therefore difficult to identify suitable in vitro and/or animal models for preclinical studies. Furthermore, the design of clinical trials for rare diseases is particularly complex, as it is often difficult to define precise diagnostic criteria, treatment doses and evaluation criteria.

These complex challenges can, however, be partly overcome by exploring three levers of action.

Leverage real-life observational studies

The use of real-life data, such as that obtained from patient registries, maximizes knowledge of diseases by providing detailed phenotypic and genetic data.

This is particularly true of natural history studies, which can help to overcome the lack of knowledge about rare diseases. These are real-life studies designed to follow the evolution of diseases in patients receiving current standard care. They make it possible to identify patient sub-populations, discover biomarkers, help design clinical trials and decipher the factors influencing the disease.

Include patient organizations in clinical trials for rare diseases

Patient organizations play a central role in the rare disease ecosystem, acting as the interface between the clinical, scientific, governmental, pharmaceutical and patient communities. They contribute to the sharing of knowledge on rare diseases, for example by promoting conferences on key issues bringing together experts, patients, government officials, and industry representatives.

Patient organizations also work directly with pharmaceutical companies. They enable the voice of patients and their needs to be included, which helps to guide and optimize development projects over the long term. This collaboration transforms the role of patients, moving them from observers to active players in research. Patient organizations also share their knowledge of the rare disease ecosystem with pharmaceutical companies, in particular by identifying relevant medical specialists.

Finally, patient organizations play a vital role in raising funds for research and awarding grants, often soliciting support from industry and philanthropy. For example, the LAM Foundation, which supports patients with lymphangioleiomyomatosis, has mobilized funds for research and career grants for clinician-researchers, considerably boosting support for research into lymphangioleiomyomatosis.

Harness artificial intelligence

Technological advances in the fields of artificial intelligence (AI) and machine learning (ML) can make a significant contribution to a better understanding of rare diseases. By enabling advanced statistical approaches, they can play a crucial role in processing unstructured natural history and real-world data (RWD) and building algorithms. All of this can be used to model disease progression, identify relevant outcomes and thoroughly evaluate the results of studies. In this way, AI can help to improve understanding of rare diseases and guide the design of more effective clinical trials. In the case of amyotrophic lateral sclerosis, for example, an AI tool was used to analyze clinical data contained in patients’ electronic health records, making it possible to characterize the demographic and clinical profile of patients while determining the chronology of clinical events specific to the disease as it progressed.

Challenge n°2: Improve recruitment of rare disease patients

The second challenge associated with the development of drugs for rare diseases is the recruitment of patients into clinical trials.

The first obstacle to recruitment is the difficulty of identifying and including patients in clinical trials. There are several reasons for this. Populations affected by rare diseases are often geographically dispersed, poorly or under-diagnosed, and the symptoms of rare diseases can vary considerably from one patient to another. As a result, the actual diagnosis of a rare disease is frequently delayed. Even when patients are diagnosed, they do not necessarily meet the clinical trial’s inclusion criteria, which require homogeneous groups of patients to obtain interpretable results. As a result, it is important to think carefully about inclusion criteria when designing clinical trials and to begin recruitment immediately after inclusion criteria are set.

Furthermore, in standard randomized clinical trials, the initial difficulties associated with patient selection and randomization make recruitment even more complex. According to the Tufts Center for the Study of Drug Development (CSDD), on average, 81% of patients selected are not eligible for inclusion in trials, while 56% fail to be randomized for rare diseases, compared with 57% and 36% [1] respectively for non-rare diseases.

There are two main levers for action to improve clinical trial recruitment in rare diseases.

Developing alternative clinical trial models

Alternative trial designs aim to optimize recruitment and study duration. They include crossover studies, where patients alternate between active treatment and placebo with delayed starts where all participants end up receiving active treatment at different times. There are also adaptive trials, which adjust the study according to initial results to maximize treatment efficacy and speed up recruitment.

In addition, the use of new control groups can be essential for rare diseases. These groups can be made up of cohorts from published data or registries, as well as data on the natural history of the disease, serving as control groups when the use of a placebo is inappropriate, provided that the disease characteristics between the two groups are similar.

Online recruitment platforms such as social media, email and patient support groups can be effective in reaching a wide audience. By using targeted messages and social media management tools, researchers can generate many leads, i.e. potential candidates for clinical trials. Although few of these leads often translate into enrolled patients, these web-based methods are valuable for complementing traditional recruitment because they generate large numbers of possible patients.

Finally, AI technologies can integrate and analyze data from different sources such as patient registries, which can help identify potential participants for clinical trials and improve the recruitment process. Data warehouse” (CDW) systems collect and reuse medical information from electronic health records (EHR). For example, Dr. Warehouse, an open source CDW, uses automatic natural language processing to speed up the diagnosis of rare diseases and the recruitment of clinical research participants based on phenotypic similarities in EHRs.

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Integrating rare disease patient organizations

Patient organizations, through their role as patient advocates and supporters, can help to stimulate recruitment to clinical trials. For example, providing travel grants for patients to consult with experts in their field and to connect with other patients in localized sites can help to identify and bring together eligible patients for recruitment more effectively. In addition, patient organizations can use their ability to mobilize patients to inform the development process and ongoing clinical trials.

Clinical trials in rare diseases face two major challenges: limited understanding of the diseases which makes study design more complex and difficulty recruiting patients. Solutions exist to overcome these challenges, and include the design of new study models, the use of real-life data, and the involvement of patient associations.

However, other challenges persist and hamper access to new treatments for rare diseases, notably the need to improve early diagnosis and the organization of patient care. In addition, the manufacture of these treatments often requires complex technologies, particularly in gene and cell therapy, forcing pharmaceutical companies to rise to the challenge of increasing their skills and complying with increasingly stringent regulatory requirements.

In this context, close collaboration between the players in the rare disease ecosystem, whether researchers, clinicians, patient associations or pharmaceutical companies, is becoming crucial to maximizing the development of new treatments and improving patients’ quality of life.

Alcimed can help you find and put you in touch with partners of interest in the rare disease sector. Don’t hesitate to contact our team!

About the author,

Justine, Consultant in Alcimed’s Healthcare team in Paris

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