Taking a leaf out of Amul’s textbook to help rare-disease patients
Just as Amul and the other cooperatives it inspired made India milk-secure and secured the livelihoods of millions of farmers, so too could a patient data collective have an outsized impact on drug discovery for most rare diseases, contributing to the health of many patients who currently have few treatment options
Patient data, encompassing medical records and patient perspectives, are vital for researchers to develop novel treatments, particularly for rare diseases. These data enable identification of diagnostic biomarkers, prediction of disease progression, and design of improved clinical trials. Clinical trials are necessary to demonstrate a treatment's safety and efficacy before regulatory approval.
However, for rare diseases, insufficient eligible participants and lack of control groups pose challenges. Patient registries and natural-history studies offer real-world data that can function as external controls, modeling disease progression in the absence of treatments. Patient data also assists in identifying appropriate participants and determining meaningful clinical endpoints for evaluating complex treatments.
By enhancing trial design, reducing uncertainty, and expediting the approval process, comprehensive patient datasets can expedite the development of safe and effective treatments for rare disease patients. For instance, patient information amassed over years aided in understanding spinal muscular atrophy's progression, identifying optimal improvement measurement methods, designing effective clinical trials, proving treatments' efficacy, and demonstrating early treatment benefits.
Recent AI advancements can further aid these efforts, assuming adequate patient data is available. A suggested approach involves forming a 'Patient Data Collective' (PDC), inspired by India's dairy cooperative Amul. This PDC would serve as a cooperative that holds patients' data, returning most, if not all, revenue to them. It would access data from patient advocacy groups, Ayushman Bharat Digital Health Mission, hospital records, Centres of Excellence for Rare Diseases, clinicians, and other repositories.
The PDC would also incorporate AI-based data analytics with appropriate safeguards, ensuring data collection, storage, analysis, and protection after patient consent. Global patient advocacy groups have already aided rare disease research and regulatory navigation, a movement now present in India. These active disease-specific groups could play a crucial role in establishing and operating the PDC.
The Indian Council of Medical Research has already established a rare disease registry based on data uploaded by a few experts from 19 specialized hospitals, collecting data on around 4,000 patients with specific diseases over the past five years. While this resource is valuable, it is still minor compared to India's population.
To overcome this, the PDC's data collection should be flexible, inclusive, accurate, and compliant with high ethical and legal standards, necessitating a patient-centric initiative with non-governmental organization support. Although setting up the proposed portal and backend database to accept data from all stakeholders while adhering to accuracy, ethics, safety, and legality standards will be challenging, it is feasible, as demonstrated by the Citizen Health Platform in the U.S. Once the portal is established, patient data must be easily accessible to the PDC through digital health records maintained by both public and private health providers.
Leveraging India's Ayushman Bharat Digital Health Mission, which mandates a secure digital health locker, can facilitate the aggregation of scattered medical histories, genetic reports, and clinical notes. Incorporating generative AI to synthesize medical records' large volumes of information can inform medical and patient decisions, with an AI model trained in local languages to analyze medical information and suggest possible diagnoses and treatment options, encouraging participation from diverse patient groups.
Government or philanthropic organizations' support is essential to develop such a platform with AI capabilities, complete with clear governance and oversight. The PDC should actively encourage natural-history registries led by patient advocacy groups to generate longitudinal real-world evidence datasets, funded to initiate natural history studies for targeted diseases, with data channeled to the PDC.
By aggregating granular, every-day patient-reported metrics across India's genetically diverse endogamous populations, the PDC could attract drug developers from abroad seeking unique data.
Written by urgent.news from The Hindu Health's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
