As the pharmaceutical industry transitions to a patient-centric model, real-world data is increasingly used to inform drug development and regulatory decisions. Including the perspectives of patients, caregivers, and advocates in drug development improves the design of clinical trials, selection of endpoints, and regulatory review processes. This strategy aims to concentrate research on treatments that address the functional or symptomatic needs of patients, reduce patient burden, and enhance outcomes.

To support this, the FDA has issued four methodological guidance documents on Patient-Focused Drug Development (PFDD). These guidances outline how stakeholders can systematically collect, submit, and apply patient experience data throughout the drug development process.

Guidance 1 provides an overview of methods and approaches for collecting patient experience data. It directs stakeholders on how to ensure that patient input is comprehensive, covering the full range of relevant experiences, and representative, reflecting the diversity of the patient population. It also establishes the relationship between research questions and the selection of appropriate sampling methods and target populations.¹

Guidance 2 offers a framework for gathering information about what is important to patients, including symptoms, disease impacts, and treatment outcomes. This is done through both qualitative, quantitative and mixed research methods. The guidance highlights the need to avoid priming or influencing patients to ensure that results accurately reflect patients’ true priorities. Special considerations are suggested for populations such as children, cognitively impaired individuals, and those with rare diseases, including the involvement of caregivers or remote assessments when appropriate.²

Guidance 3 concentrates on the selection, development, or modification of clinical outcome assessments (COAs) that are considered “fit-for-purpose,” indicating their scientific robustness and suitability for measuring outcomes deemed significant to patients within the context of clinical trials and drug development. The guidance advocates for the use of electronic and digital health technologies for COA data collection, indicating the FDA’s receptiveness to innovation and contemporary data capture methodologies. It addresses considerations for employing COAs in special populations, such as children or individuals unable to self-report, emphasizing the significance of observer or clinician input in these scenarios.³

Guidance 4 constitutes the concluding document in the FDA’s Patient-Focused Drug Development (PFDD) series. It primarily addresses the integration of Clinical Outcome Assessments (COAs) into clinical trial endpoints utilized for regulatory decision making. The guidance offers recommendations on the development, analysis, interpretation, and submission of COA-based endpoints to substantiate treatment benefits within drug development programs. Furthermore, it discusses the challenges associated with implementing COAs and endpoints in special populations, including individuals with rare disorders, and presents potential solutions for conducting robust assessments in these contexts.⁴

The FDA’s PFDD guidelines specifically recognize the unique challenges of rare diseases, such as small patient populations and limited clinical trial sites.

Guidance 1 acknowledges that collecting comprehensive and representative input may require different sampling strategies for rare diseases, while Guidance 4 addresses the complexities of evaluating treatment benefits and selecting clinical endpoints in these contexts.

Rare diseases present additional data challenges due to their low prevalence, which limits data collection, analysis, and sharing. There are many challenges associated.

• Small patient populations: Rare diseases have very limited numbers of patients, making it difficult to recruit enough participants for clinical trials and to design statistically powered studies.
• Poorly understood natural history: Many rare diseases have incomplete or poorly characterized disease progression, complicating trial design, endpoint selection, and assessment of treatment effects.
• Heterogeneous disease presentation: Variability in symptoms and disease severity within the patient population challenges the identification of sensitive and meaningful clinical endpoints.
• Lack of validated outcome measures: There is often no consensus on clinical outcome measures or fit-for-purpose endpoints, requiring development and validation of new measures, including patient-reported outcomes that reflect patient and caregiver perspectives.⁵
• Difficulty in patient identification and recruitment: Patients are geographically dispersed and often see multiple specialists before diagnosis, making it challenging to find and enroll eligible patients for trials.
• Regulatory complexities: Navigating regulatory requirements is complicated by novel therapies, pediatric populations (since ~70% of rare diseases start in childhood), and the need for adaptive or accelerated approval pathways.⁶
• Limited clinical trial infrastructure and resources: There is often a lack of local infrastructure, funding, and trained researchers focused on rare diseases, which hinder trial conduct and collaboration.⁷
• Need for innovative trial designs: Traditional randomized controlled trials are often not feasible; alternative designs such as adaptive, crossover, or N-of-1 trials are necessary to generate meaningful data from small patient populations.
• Challenges in post-marketing data collection: Long-term data collection and registry development are essential but require clear accountability and coordination among stakeholders.

Advanced AI and the integration of comprehensive patient data are crucial for overcoming these barriers and advancing precision medicine in rare disease research.

Clinakos’ Integrated Patient Data™ platform is designed to address these rare disease challenges by aggregating high-quality, longitudinal patient data from multiple sources-including historical and real-time records, unstructured EMR notes, patient surveys, interviews, and PROs-into comprehensive profiles. Their AI/ML engine, optimized for healthcare, uses natural language processing to extract insights from unstructured documents and identify patterns that might otherwise be missing. This approach provides dynamic, real-time insights rather than static, retrospective analyses.⁸

By eliminating data silos, enabling earlier and more accurate diagnoses, supporting patient management, and aiding research and drug development with robust data, Clinakos’ platform helps improve care and quality of life for rare disease patients. This aligns with the FDA’s PFDD initiative, ensuring that the patient voice is central to drug development and regulatory decision-making, especially in the context of rare and complex diseases.

References:

1.   https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-focused-drug-development-collecting-comprehensive-and-representative-input
2.     https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-focused-drug-development-methods-identify-what-important-patients
3.     https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-focused-drug-development-selecting-developing-or-modifying-fit-purpose-clinical-outcome
4.     https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-focused-drug-development-incorporating-clinical-outcome-assessments-endpoints-regulatory
5.     FDA Patient-Focused Drug Development Guidances: Considerations for Trial Readiness in Rare Developmental and Epileptic Encephalopathies – PubMed
6.     Challenges of developing and conducting clinical trials in rare disorders – PubMed
7.     Rare Disease Day: Challenges in Rare Disease Drug Development