Signal detection in rare diseases

What are the rare disease definitions?

No single global definition of rare diseases: 

Europe: Defined as incidence < 1 in 2,000 people. 

United States: Defined as incidence < 1 in 1,250 people.

Prevalence and scope: Affects 6–10% of the global population. encompasses around 6,000–8,000 disorders. Each year, 250–280 additional diseases are classified as rare diseases.

Impact on patients: They primarily affect children and significantly reduce quality of life and life expectancy. Typically chronic and progressive.

Challenges in diagnosis:

• Rarity of cases makes diagnosis difficult and delayed.
• Require multiple, often invasive and repetitive tests.
• Diagnosis may take up to 5 years from first symptoms.
• Involves multiple visits to different specialists.

Treatment gaps:

• Majority of rare diseases have no approved treatments.
• Available treatments are often very costly.
• Access to treatment remains limited for most patients.

  Improvements:

·       However, all is not black but more grey in rare diseases now.

·       Better diagnostics, affordable genetic testing reduce the time and accessibility of tests.

·       Better treatments that are affordable and accessible

·       Early diagnosis improved the treatment outcomes

 

Traditional Signal detection Vs Signal detection in rare disease 

Traditional signal detection encompasses sources such as interventional studies, ICSR, Literature, real-world data, and regulatory databases.

But when we are applying the same sources, methods, and processes for rare diseases that we apply for other diseases, we face challenges. This is due to rare diseases having fewer patients, data availability is lower, so statistical methods will offer false negatives, and fewer clinical studies are conducted. The disease itself has its own limitations; genetic pathways are not identified, and the complete pathophysiology of the disease is not well recognised or well known. There is heterogeneity for disease and a lack of baseline data for standardised diagnostics, risks, and treatments.

Critical elements to consider for Signal detection in rare diseases

The potential safety concerns identified from the animal studies or pharmacological properties are monitored closely.

Focus on immunogenicity testing for therapeutic proteins, gene therapies, or cellular products. Additionally, expanding the safety database to include class drug effects, usage of patient records, market research surveys, and collaborative registries.

Utilization of technology, such as machine learning algorithms, for identifying patterns and determining the cause of adverse events. Other methods to utilize real-world evidence should be explored.

Involving multiple datasets like proteomics by leveraging biomarkers, genomics to see how genetic variations affect drug efficacy, utilization, and then AE profile, and transcriptomics to understand drug toxicity levels.

Regulatory Intelligence:

FDA: Potential safety concerns identified from animal studies or pharmacologic properties require proactive monitoring and mitigation strategies. This may include immunogenicity testing for therapeutic proteins gene therapies, or cellular products.

EMA : Database to retrieve actual patient medical records, collaborative registries to identify any drug event pair.

Initiative on rare and undiagnosed disease (IRUD) Japan: Comprehensive diagnostic system encompassing clinical information and other genetic analysis via data sharing. Promote pathology research that may lead to the development of new treatments and therapies. A database that allows a data sharing system.

Written by:

Dr.Shraddha Bhange.

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References :

Segura-Bedmar, I., Camino-Perdones, D. & Guerrero-Aspizua, S. Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts. BMC Bioinformatics 23, 263 (2022). https://doi.org/10.1186/s12859-022-04810-y

Schaefer, J., Lehne, M., Schepers, J. et al. The use of machine learning in rare diseases: a scoping review. Orphanet J Rare Dis 15, 145 (2020). https://doi.org/10.1186/s13023-020-01424-6

Banerjee J, Taroni JN, Allaway RJ, Prasad DV, Guinney J, Greene C. Machine learning in rare disease. Nat Methods. 2023 Jun;20(6):803-814. doi: 10.1038/s41592-023-01886-z. Epub 2023 May 29. PMID: 37248386.

Monaco L, Zanello G, Baynam G, Jonker AH, Julkowska D, Hartman AL, O'Connor D, Wang CM, Wong-Rieger D, Pearce DA. Research on rare diseases: ten years of progress and challenges at IRDiRC. Nat Rev Drug Discov. 2022 May;21(5):319-320. doi: 10.1038/d41573-022-00019-z. PMID: 35079160; PMCID: PMC7613920.

Austin CP, et al. Future of rare diseases research 2017-2027: an IRDiRC perspective. Clin Transl Sci. 2018;11:21–27. doi: 10.1111/cts.12500.

Lochmüller H, et al. The International Rare Diseases Research Consortium: policies and guidelines to maximize impact. Eur J Hum Genet. 2017;25:1293–1302. doi: 10.1038/s41431-017-0008-z