How Does AI Support Orphan Drug Development?

By Rasit Dinc

Orphan drugs, developed to treat rare diseases, represent a significant challenge for the pharmaceutical industry. These diseases, affecting a small percentage of the population, often lack effective treatments due to the high costs and complexities associated with drug development. The traditional drug development pipeline is a long and arduous journey, often taking over a decade and costing billions of dollars. For rare diseases, these challenges are amplified due to small patient populations, limited understanding of the disease pathology, and difficulties in conducting clinical trials. However, the advent of artificial intelligence (AI) is offering a beacon of hope, promising to revolutionize the landscape of orphan drug development and bring much-needed therapies to patients with rare diseases [1].

One of the most significant contributions of AI in orphan drug development is its ability to accelerate drug discovery and repurposing. AI algorithms can analyze vast and complex datasets, including genomic, proteomic, and clinical data, to identify novel drug targets and potential drug candidates with unprecedented speed and accuracy. For instance, machine learning models can be trained to predict the efficacy and toxicity of drug compounds, significantly reducing the time and cost of preclinical research [2]. Furthermore, AI is proving to be a powerful tool for drug repurposing, which involves identifying new uses for existing drugs. By mining extensive databases of scientific literature, clinical trial data, and electronic health records, AI can uncover hidden relationships between drugs and diseases, leading to the discovery of new therapeutic applications for already approved drugs. This approach is particularly valuable for rare diseases, as it can significantly shorten the development timeline and reduce costs [3].

Optimizing clinical trials is another critical area where AI is making a substantial impact. Clinical trials for rare diseases are notoriously difficult to conduct due to the challenges in recruiting a sufficient number of patients and designing trials that can demonstrate efficacy with a small sample size. AI can help overcome these hurdles by identifying eligible patients for clinical trials from large-scale electronic health records and other data sources. This not only accelerates patient recruitment but also ensures that the right patients are enrolled in the right trials [4]. Moreover, AI can contribute to the design of more efficient and effective clinical trials. For example, in-silico trials, which use computer simulations to model the effects of a drug on a virtual patient population, can help optimize trial design and reduce the need for large patient cohorts. AI can also be used to identify patient subgroups that are most likely to respond to a particular treatment, enabling the development of personalized therapies and increasing the chances of trial success [5].

The diagnostic odyssey is a common and frustrating experience for many patients with rare diseases, who often spend years searching for an accurate diagnosis. AI-powered diagnostic tools are emerging as a powerful solution to this problem. By analyzing medical images, clinical notes, and genomic data, AI algorithms can help clinicians identify rare diseases earlier and more accurately. This not only reduces the burden on patients and their families but also enables timely intervention and treatment [6]. Furthermore, AI can assist in patient stratification, which involves grouping patients based on their specific genetic, molecular, and clinical characteristics. This allows for a more precise understanding of the disease and the development of targeted therapies that are tailored to the individual needs of each patient. By enabling a more personalized approach to treatment, AI has the potential to significantly improve outcomes for patients with rare diseases [7].

In conclusion, artificial intelligence is poised to transform the field of orphan drug development, offering innovative solutions to some of the most significant challenges in this area. From accelerating drug discovery and repurposing to optimizing clinical trials and enhancing diagnosis, AI is paving the way for a new era of precision medicine for rare diseases. While there are still challenges to be addressed, such as data privacy and algorithmic bias, the potential of AI to bring hope and new treatments to millions of patients with rare diseases is undeniable. The continued development and responsible implementation of AI will be crucial in realizing this promise and ensuring that no patient is left behind.

References

[1] Irissarry, C., & Burger-Helmchen, T. (2024). Using artificial intelligence to advance the research and development of orphan drugs. Businesses, 4(3), 28. [2] Gangwal, A., & Lavecchia, A. (2024). AI-driven drug discovery for rare diseases. Journal of Chemical Information and Modeling, 64(13), 4183-4197. [3] Debnath, A., Mazumder, R., & Mazumder, A. (2025). Challenges and Progress of Orphan Drug Development for Rare Diseases. Current Pharmaceutical Biotechnology, 26(1), 1-15. [4] Germain, D. P. (2025). a literature review highlighting lessons from Fabry disease. Orphanet Journal of Rare Diseases, 20(1), 1-15. [5] Amorim, A. M. B., et al. (2025). Artificial intelligence in rare diseases: toward clinical impact. Trends in Pharmacological Sciences, 46(1), 1-15. [6] Karako, K., et al. (2025). Artificial intelligence applications in rare and intractable diseases. Journal of Human Genetics, 70(1), 1-9. [7] Huang, K., et al. (2024). A foundation model for clinician-centered drug repurposing. Nature Medicine, 30(7), 2086-2096.