The role of data revolution in rare disease research

Science-led innovations have transformed medical research in several critical disease areas over the last few years and rare diseases is no exception. While each disease is individually rare, collectively, they affect over 300 million people worldwide, with India alone accounting for approximately 70-90 million cases. 

Despite this significant burden, there are still substantial gaps in their identification, largely due to a lack of comprehensive research data. Early diagnosis and treatment options remain constrained largely due to lack of data processing and analysis tools that can help in extracting meaningful insights from the large amounts of collected data.

Today, the arrival of big data, artificial intelligence (AI), and shared databases, offers unparallelled potential for advancing the study and treatment of rare diseases. The combination research with technology is revolutionizing how investigations into rare diseases are conducted. 

By analysing large sets of genomic data, identifying subtle patterns, and connecting previously unrelated data points, scientists have successfully discovered new genetic variants that were once beyond the reach of traditional means, facilitating wider treatment options for patients.

Role of genomic databases in research
The completion of the Human Genome Project laid the foundation for more research in the field of genomics, resulting in the creation of specialized databases that aggregate both genotypic and phenotypic data. These databases have become invaluable resources for clinicians, researchers, and pharmaceutical companies striving to develop innovative treatments for genetic conditions.

Global Rare Disease Registries: Organizations such as the National Organization for Rare Disorders (NORD) and EURORDIS-Rare Disease Europe maintain extensive databases that compile critical patient information, genetic data, and clinical responses . These registries play a crucial role in supporting research and guiding the development of targeted therapies.

Genotypic and Phenotypic Databases: Resources such as the Online Mendelian Inheritance in Man (OMIM) catalogue human genes and their related genetic disorders, specifically focusing on rare inherited disorders . ClinVar, another significant database, offers a detailed information on genomic variation and their clinical relevance, helping researchers understand vital genotype-phenotype correlations.

Public Genomic Databases: While many of the publicly available databases have focussed on Caucasian populations, large-scale initiatives such as the GenomeAsia 100K project, which aims to map genetic diversity in South Asian populations, and MedGenome's SAS ATLAS database, which aggregates South Asian genomic data for rare diseases, are broadening the scope of research. 

These databases have been instrumental in recent discoveries, including the identification of new genetic variants linked to neurodegenerative diseases among younger population.

Cross-Border Data Sharing: Global collaborations have resulted in the development of integrated data platforms that transcend geographical boundaries in rare disease research. The International Rare Diseases Research Consortium (IRDiRC) exemplifies how collaborative data sharing can accelerate scientific advancements and therapeutic innovation.

AI and ML in rare disease research
Machine learning (ML) and AI have revolutionized the way scientists approach the diagnosis and treatment of rare diseases. These technologies enable the automation of analytical tools, enhancing both efficiency and accuracy. 

Powerful platforms like MedGenome’s VarMiner powered by machine learning pathogenicity score calculator - VaRTK and deep variant annotation tool - VariMAT have streamlined genomic analysis, making it possible for faster and accurate identification of disease-causing mutations.

Disease Identification: AI-driven algorithms can analyze massive genomic datasets to detect subtle genetic markers and subtypes of a disease, leading to significant improvements in early diagnosis.

Drug Discovery and Repurposing: Machine learning algorithms predict drug-target interactions, enabling the repurposing of existing medication for orphan diseases, accelerating drug development and reducing associated costs and timelines.

Clinical Trial Optimization: AI automates patient recruitment and trial design, ensuring more efficient trials and enabling the precise identification of suitable candidates.

Future of data-driven research and drug discovery
With the progress in research methodologies, data-driven approaches have become vital in drug discovery and precision medicine. Here are some of the new technologies that are shaping the future of rare disease research:

• Single-cell sequencing has deepened genetic understanding of diseases by enabling the study of individual cells, unearthing genetic variations previously undetectable.
• Blockchain ensures secure data sharing and enhances transparency, fostering better collaboration among institutions.
• Cloud computing facilitates large-scale genomic sequencing and observation of vast numbers of specimens with great efficiency.
• Digital twins—virtual representations of patients—simulate disease progression and responses to treatments, allowing for more personalised therapeutic interventions.

To fully leverage these technologies and build comprehensive databases, focus on strong investments in data infrastructure, standardization of data collection protocols, and public-private partnerships will be needed from all the stakeholders. Additionally, researchers must receive access to continued training for the new trends and advancements in data science to effectively use new and evolved technologies.

As technology continues to evolve, new tools will play even more significant roles in diagnostics, treatment, and overall disease management. Continued collaboration, ethical data sharing practices, investment in building the required skillset and ensuring accessibility to the available resources across emerging populations will be the key for accelerating breakthroughs that will benefit millions of people whose lives are affected by rare diseases worldwide.

-- Dr. Ravi Gupta, VP, Bioinformatics, MedGenome.