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With few options available for the treatment of rare diseases, the practice of drug repurposing has gained traction as an effective strategy. With genome sequencing revealing new rare diseases, it is now known that at least 5% of the world population lives with a rare disease. Drug development must pick up the pace to keep up with rare disease diagnoses. This is however incredibly difficult for a lot of reasons, but mainly the fact that rare diseases, by definition, have few patients for clinical trials. Beyond expedited regulations and incentives, drug development for rare disease patients needs creative solutions that accelerate the pipeline. Drug repurposing, also known as drug repositioning, is one such increasingly popular solution. The approach navigates existing literature and data on disease mechanics and the action of potential therapeutics to identify new uses for existing drugs. Compared to a full-blown drug development process, drug repurposing significantly reduces time and costs, making it a more economical option for rare disease drug development. This is because it is already known that a particular drug is safe for use and only its efficacy in treating a different condition is in question. Drug repurposing: old drugs, new benefits Drug repurposing has found wide success in the treatment of non-rare conditions. The most famous example is that of aspirin, which was first developed to treat pain and was later repurposed for heart diseases. More recently, researchers around the world repurposed multiple drugs for COVID-19. The rationale behind drug repurposing is that almost all drugs interact with multiple biological pathways and that similar drugs should act on similar targets. Unlike serendipitous discoveries of the past, biotech researchers and companies now actively seek new applications for existing drugs, including those that made it to clinical trials but were not commercialized. High-throughput screening assays allow companies to investigate the action of drugs on multiple pathways at once, enabling a systematic approach to drug repurposing. Scottish biotech NovaBiotics does exactly this with a focus on immunology assays. The company is repurposing cysteamine, a drug that has been used for the treatment of cystinosis, a rare metabolic condition, for decades. The company is reformulating this drug as a potential cure for the treatment of cystic fibrosis (CF), another rare metabolic condition. The repurposed drug, Lynovex, has received orphan drug designation in both the U.S. and Europe. Speaking of the company’s technology, CEO Deborah A. O’Neil said that it “uses innate immune effector molecules as templates for novel therapeutic approaches for an unmet disease where there is an inflammatory and/or infectious component.” With this, NovaBiotics is able to test and develop the same drug for multiple conditions. For example, Novabiotics “developed NM001 for CF in oral and inhaled (dry powder) form.” Early results show that the oral form helps with intermittent episodes in which CF symptoms worsen and the inhaled form maintains healthy lung function during treatment. Additionally, the company is repurposing cysteamine for the treatment of community-acquired pneumonia, a far more common condition. Repurposing the same drug for multiple conditions in this manner has the potential to further derisk the process for companies. Computational approaches reveal hidden mechanisms Conventional drug repurposing works well when investigating a drug that’s similar to a drug that already works for the same disease, or when investigating the action of a drug on a disease that’s similar to the one it already works for. Such data are often not available for rare diseases when it comes to looking deeper into the interactions between drugs and targets. For rare diseases, computational drug repurposing offers a quicker and more scalable method. Over the last two decades, biologists have produced tons of omics data for all kinds of conditions. There is a lot of other data available thanks to the digitalization of clinical trials and healthcare in general. Artificial intelligence (AI)-based approaches make these data accessible to drug designers looking to find new purposes for existing drugs. AI-based solutions tease out hidden interactions between drugs and phenotypes, allowing researchers to better understand disease mechanisms and identify drugs that target them. U.K.-based biotech Healx tackles this challenge with its graph-based approach to computational drug repurposing. The graph here refers to the complex network of interactions between drugs and target molecules. Scientists traverse these networks and find shared pathways between mechanisms of drugs or diseases. By integrating omics and phenotyping with this approach, Healx advances a hypothesis-free model of drug discovery. Instead of looking at one drug’s action on one disease at a time, it looks into multiple possibilities for enhancing existing drugs, repurposing them, or combining them for improved action at once. Daniel O’Donovan, principal machine learning engineer at Healx, said that their model analyzes “structured and unstructured data sources and uses a variety of algorithms to solve different problems in the drug repurposing pipeline.” Healx’s main focus is on Fragile X syndrome, a rare genetic disorder that causes intellectual disability in patients. It is repurposing sulindac, a drug originally developed decades ago as a treatment for inflammatory conditions. Donovan added that Healx’s AI platform analyzes interactions of multiple drugs with pathways involved in the Fragile X disease mechanism. This way, it identifies combination therapies as well as relative concentrations required to optimize synergistic activity between the drugs in combination therapy. The company is testing sulindac in combination with gaboxadol, a drug that failed in clinical trials for Angelman syndrome and as a sleeping pill at another pharma company. Bridging the gap Rare disease patients may also benefit from personalized repurposed drugs. This is true for ultra-rare diseases with only one or few patients globally as well as rare diseases for which existing therapies produce highly variable responses in different patients. In recent years, there has been a steady rise in N-of-1 studies for drug repurposing. As the name suggests, these are studies with a sample size of one individual for highly personalized drug repurposing. A successful case of N-of-1 drug repurposing for a rare disease is that of Dr. David Fagjenbaum. When diagnosed with the life-threatening Castleman disease, he found a cure by researching known drugs that prevent cytokine storms, a characteristic trait of this disease in which an overproduction of cytokines sends the immune system into overdrive. Like Dr. Fajgenbaum, much of rare disease drug development is led by patients or those close to them. However, they don’t always have the resources required to research or repurpose a drug. The industry needs new business models that allow rare disease patient groups to commercialize drug repurposing. For example, patient-led decentralized autonomous organizations empower patient groups to crowdfund research and have a financial stake in drug development. Most of the nearly 7,000 rare diseases known to date have no cure. Sustained innovations in drug repurposing are necessary to better serve rare disease patients. Bridging the gap between its promise and cures requires incentives for systematic drug repurposing — including those particularly targeted at rare diseases, new methods to repurpose drugs for individual patients, and better computational tools that gather as many insights as possible from a few patients. This story was made possible with support from the National Press Foundation. The Foundation did not influence the research or reporting of this article.

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