DISMANTLING BARRIERS IN PATIENT-LED RESEARCH: UTILIZING AN AI-ENHANCED ACCESS-CENTERED RESEARCH MODEL TO EVALUATE THE INTEGRATION OF PATIENT-REPORTED OUTCOMES IN FDA ORPHAN DRUG APPROVALS
Author(s)
Erin Crutcher, MSG1, Brielan Smiechowski, MSc2, Mukul Mohanty, BS1, Ayush Ketan Shah, BTech3;
1University of Southern California, Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, USA, 2Reliant AI, Montreal, QC, Canada, 3University of Southern California, Viterbi School of Engineering, Los Angeles, CA, USA
1University of Southern California, Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, USA, 2Reliant AI, Montreal, QC, Canada, 3University of Southern California, Viterbi School of Engineering, Los Angeles, CA, USA
OBJECTIVES: Despite federal initiatives in the United States (US) intended to elevate patient voices in rare disease development, it is unclear if these expedited or specialized pathways prioritizing patient-centered evidence has increased patient involvement. This study evaluates the prevalence and characteristics of patient-reported outcomes (PROs) in the development of US Food and Drug Administration (FDA) approved therapies with orphan drug designation (ODD). Additionally, this study demonstrates an adaptive research model designed to dismantle access barriers for patient-investigators.
METHODS: A dataset of orphan-designated therapies (ODTs) receiving FDA approval from 2018-2024 was compiled from FDA data sources, clinicaltrials.gov and PubMed. Data was analyzed utilizing an innovative access-centered research (ACR) model. This inclusive approach utilized assistive technologies and life-science-specific AI tools (Tabular by Reliant AI) to extract variables such as PRO inclusion, named instruments, indication, approval year and different FDA regulatory categories (Breakthrough Therapy designation (BTD) and Regenerative Medicine Advanced Therapy (RMAT) designation). Descriptive statistics were used to summarize frequencies.
RESULTS: Of the 150 ODT approvals, 17% included PROs in their clinical development. Of the 96 ODTs with BTD status only 14.6% reported the PROs, compared with 20.4% of the 54 non-BTD ODTs. No ODTs received RMAT designation during this period. The AI-enhanced ACR model reduced manual barriers, significantly sped up timelines, and enabled equitable scientific leadership by diverse contributors.
CONCLUSIONS: While PRO visibility in orphan drug labels has nearly doubled since earlier research reported in 2019 (8.3% to 17%), integration remains inconsistent and limited in expedited designations. Some improvements in PRO instrument naming were observed but overall patient involvement is still inconsistently documented, leading to incomplete visibility into how patient perspectives are being considered. Furthermore, the success of the ACR model demonstrates that inclusive research infrastructures, supported by AI, can empower patient-researchers to lead high-level scientific analysis.
METHODS: A dataset of orphan-designated therapies (ODTs) receiving FDA approval from 2018-2024 was compiled from FDA data sources, clinicaltrials.gov and PubMed. Data was analyzed utilizing an innovative access-centered research (ACR) model. This inclusive approach utilized assistive technologies and life-science-specific AI tools (Tabular by Reliant AI) to extract variables such as PRO inclusion, named instruments, indication, approval year and different FDA regulatory categories (Breakthrough Therapy designation (BTD) and Regenerative Medicine Advanced Therapy (RMAT) designation). Descriptive statistics were used to summarize frequencies.
RESULTS: Of the 150 ODT approvals, 17% included PROs in their clinical development. Of the 96 ODTs with BTD status only 14.6% reported the PROs, compared with 20.4% of the 54 non-BTD ODTs. No ODTs received RMAT designation during this period. The AI-enhanced ACR model reduced manual barriers, significantly sped up timelines, and enabled equitable scientific leadership by diverse contributors.
CONCLUSIONS: While PRO visibility in orphan drug labels has nearly doubled since earlier research reported in 2019 (8.3% to 17%), integration remains inconsistent and limited in expedited designations. Some improvements in PRO instrument naming were observed but overall patient involvement is still inconsistently documented, leading to incomplete visibility into how patient perspectives are being considered. Furthermore, the success of the ACR model demonstrates that inclusive research infrastructures, supported by AI, can empower patient-researchers to lead high-level scientific analysis.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
PCR143
Topic
Patient-Centered Research
Topic Subcategory
Patient-reported Outcomes & Quality of Life Outcomes
Disease
No Additional Disease & Conditions/Specialized Treatment Areas, SDC: Rare & Orphan Diseases