By 2024, the sale of orphan drugs is predicted to rise to US$217 billion, with a corresponding increase in rare disease Clinical trials. Substantial evidence of clinical safety and efficacy is required to obtain orphan drug approval. Clinical trials require an appropriately powered sample size, control group, validated biomarkers, and clinically meaningful outcome measures. Strategies to overcome the methodological challenges of clinical research in rare diseases and cellular and gene therapies and to subsequently achieve regulatory approvals are presented below.

Cellular and Gene Therapies (CGT)

Alfano et al. (2022) estimated that 31 gene therapy and 21 cell therapy launches will occur in 2024 alone. By 2030, cellular therapy is expected to become the third-largest segment across all modalities in oncology, with a compound annual growth rate (CAGR) of 35% in sales from 2021–30.

Rare Diseases Clinical Research Challenges

Clinical research in rare diseases is challenging for multiple reasons. Selection of meaningful and relevant endpoints is difficult as rare diseases are heterogeneous, understanding of genotype-phenotype relationships is often incomplete, and knowledge of natural history is often lacking, compounded by variable expression within diseases and subtypes. Limited potential participants and phenotypic heterogeneity in rare diseases may render the double-blind placebo-controlled randomized trial gold standard unlikely.

Rare disease clinical trials tend to be multicentre and multinational for good patient recruitment, which can affect protocol harmonization, ethical review, indemnity, organization of clinical services, standards of care, and cultural diversity. Running multicentre, multinational rare disease trials increases administrative burden and may discourage researchers. Additionally, sensitive, non-invasive biomarkers and clinical outcome measures are limited for rare diseases. CGT challenges include the complexity and heterogeneity of the solution space and the difficulty of appropriately matching therapies to suitable patient endotypes.

Comparatively fewer disease reference centers result in a significant time and cost burden for participants, while the burden of trial requirements, e.g., additional medication, completing diaries, recording symptoms or side effects, may be a further disincentive for participants.

Strategies for Achieving Approvals in Rare Disease Therapies

Rare disease trial design and delivery have specific considerations and potential pitfalls for researchers, patients, pharma, and regulators. Anticipating these early can increase the probability of success and regulatory approval.

1. Reach out to experts early on

Early involvement of a biostatistician and clinical trial design experts experienced in tackling challenges related to rare diseases clinical research is vital. As the CBER provides scientific and regulatory advice to researchers, discussing trials in advance with them will help inform study design to better align to requirements for approval. Working with regulatory agencies may be necessary to develop acceptable alternative trial parameters in ultrarare conditions. Partnerships with academia facilitate trial design through appropriate patient stratification and improved participation and outcome measurements.

2. Utilize artificial intelligence and advanced analytics

Bhandari et al. (2022) believe that embedding digital and analytics in CGT research is crucial for its success, capturing patient value, and boosting the return on R&D spending. The authors assert that artificial intelligence (AI) and advanced analytics accelerate the process, decrease clinical failures, decrease cost across the R&D value chain, and enable sustainable tech platforms.

3. Trial design and power

Innovative strategies exist to overcome the methodological challenges inherent in conducting rare disease clinical studies. According to Pizzamiglio et al. (2022), the size estimate of a likely effect in orphan drug trials should be at a reasonable power of 80% and a 5% confidence interval. Trial design strategies to address the challenges of trial power include:

• Parallel-group randomization: participants are randomly allocated to one of two or more treatment groups
  
• Cross-over: participants act as their own control by receiving a random sequence of different treatments, each followed by a wash-out period
  
• Delayed start: initial randomized placebo-controlled phase, followed by a phase during which all participants receive active treatment
  
• Randomized withdrawal: to identify responders, all participants receive open-label therapy during the first phase. In the second phase, only responders are randomized to treatment or placebo
  
• Group sequential: the number of participants is not preset. Clinical trial data is monitored through pre-determined interim analysis, which may, in turn, potentially terminate the trial early
  
• Adaptive: based on results obtained, the probability of randomization to a group shifts towards more promising treatments.

Strategies to reduce sample size and optimize recruitment include extending the study duration to increase events per patient; reducing disease heterogeneity by selecting participants who are likely to respond to treatment; and maximizing access using broad networks, which facilitate the running of multicentre multinational trials and accelerate patient recruitment.

Serious rare diseases with no alternative treatments: Pizzamiglio et al. (2022) suggest that Phases I–III be adapted to integrate traditional Phases II and III within a single study design to accelerate drug approval. A successful example of this is elamipretide in Barth syndrome.

4. Identifying Potential Trial Participants

Natural history data can be applied to identify and stratify appropriate patients to participate in a trial. Similarly, comprehensive patient registries with phenotypic and, where relevant, genotypic data can help identify potential trial participants.

5. Control Groups

Strategies for addressing challenges associated with control groups include using external controls for severe diseases with no alternative treatment or when a placebo is inappropriate, applying natural history data for an external control population, and using historical cohorts and registries with relevant endpoint data.

6. Biomarkers and Outcomes Measures

In clinical trials, biomarkers are surrogate endpoints that help predict the clinical benefit or harm of the intervention. As biomarker changes may precede clinical parameters, including them in trial design can accelerate regulatory approval. According to Mellerio (2022), regulatory bodies are starting to acknowledge the challenges associated with endpoint selection and agree that understanding the rare disease’s natural history is essential. Natural history data may be pivotal in developing and validating biomarkers and clinical outcome measures. It is important to note that biomarkers often reflect a single pathophysiological pathway and provide limited data on drug effects related to other aspects of efficacy.

Patient voice is critical in determining meaningful outcomes. Outcome measures should reflect relevance and significance for the patient, e.g., diminished pain. Patient groups like the US National Organization for Rare Disorders (NORD) are invaluable in identifying meaningful outcomes.

Strategies for Achieving Approvals for Cellular and Gene Therapies

1. Technology like AI can improve R&D

Bhandari et al. (2022) studied the unique opportunities of applying AI along the R&D value chain to accelerate the innovation of cellular and gene therapies. The authors explored three novel pharma modalities: mRNA-based therapeutics and vaccines, viral therapeutics that aim to edit the genome, and ex vivo therapeutics. They found that AI can facilitate the development of a novel therapy throughout the R&D value chain, including target identification, payload design optimization, and translational and clinical development.

2. Target identification

AI can play a role in target identification for viral therapeutics whereby algorithms predict CRISPR target sites and help identify genomic sites that permit increased efficiency of editing with minimal off-target activity. For cellular and gene therapies that aim to harness the immune system, AI and machine learning (ML) can be used to predict tumor epitopes that could be bound by a therapeutic molecule.

3. Payload design optimization

AI and ML models can rapidly screen many candidates and select payload designs that fulfill desired criteria. The models can be part of an AI-enabled closed-loop research system, with initial primary screening results fed into an ML pipeline. The pipeline learns how the assay responds to each payload and suggests the next batch of optimized payload candidates. Experimental data are automatically fed back to continue the learning and close the loop. The design of the delivery vehicle can similarly be part of an AI-enabled closed-loop research system.

4. Translational and clinical development

AI and ML can assist in speeding up regulatory approval for cellular and gene therapies by minimizing safety risks in clinical trials and increasing the overall probability of success. This includes finding translational biomarkers indicative of trial success; using AI to optimize trial design, especially with typically small patient population sizes, long treatment processes, and potential for severe adverse events; using AI and ML algorithms to identify the right patients, estimate optimal dosing, and predict severe adverse events; and by training models to screen patient records for comorbidities and to use genetic profiles to identify patient subgroups that will have the greatest treatment response.

Approvals Are Easier with a Trusted CRO Partner

Having a trusted Contract Research Organization (CRO) partner is paramount for biotech and pharma sponsors as it can significantly increase their chances of receiving a higher approval rate for new therapies, drugs, and medical devices. A reliable CRO brings extensive expertise, experience, and resources to the table, ensuring efficient and well-organized clinical trials. This helps streamline the development process, from planning and protocol design to data collection and analysis, adhering to regulatory requirements and best practices.

A trusted CRO also offers access to a broader patient population, enhancing recruitment and retention rates, while maintaining compliance with ethical considerations. The seamless collaboration between the sponsor and the CRO fosters a strong understanding of project goals, timely communication, and proactive problem-solving, all of which are crucial elements for obtaining regulatory approval and expediting the path to better healthcare innovations.

Vial is a next-generation, tech-first CRO delivering faster, more efficient trials at dramatically lower costs for biotech sponsors. Our mission is to empower scientists to discover groundbreaking scientific therapeutics that help people live happier, healthier lives.*

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