AI model identifies existing drugs that can be repurposed to treat rare diseases

There are more than 7,000 rare and undiagnosed diseases worldwide. Although each of these diseases affects only a small number of people, collectively they inflict a staggering human and economic toll, affecting approximately 300 million people worldwide.

However, since only 5 to 7% of these conditions have FDA-approved medications, most of them remain untreated or undertreated.

Developing new drugs is a daunting challenge, but a new tool based on artificial intelligence can accelerate the discovery of new therapies based on existing drugs and bring hope to patients with rare and neglected diseases and to the physicians who treat them.

The AI ​​model, called TxGNN, is the first specifically designed to identify drug candidates for rare diseases and conditions for which there is no treatment.

Drug candidates have been identified from existing drugs for more than 17,000 diseases, many of which have no treatment yet. This is the largest number of diseases that a single AI model can handle to date. The researchers point out that the model could be applied to even more diseases than the 17,000 it handled in the first experiments.

The work, described on 25 September in Natural medicinewas led by scientists at Harvard Medical School. The researchers have made the tool available free of charge and want to encourage clinicians and scientists to use it in the search for new therapies, especially for conditions for which there are no or limited treatment options.

“We hope to use this tool to identify new therapies across the entire spectrum of diseases. But when it comes to rare, ultra-rare and neglected diseases, we believe this model could help close or at least narrow a gap that leads to serious inequalities in health care,” said lead researcher Marinka Zitnik, assistant professor of biomedical informatics at the Blavatnik Institute at HMS.

“This is where we see the potential of AI in reducing the global burden of disease and finding new uses for existing drugs. In addition, developing therapies is faster and more cost-effective than developing new drugs from scratch,” added Zitnik, who is an associate professor at Harvard University's Kempner Institute for the Study of Natural and Artificial Intelligence.

The new tool has two key features: one identifies treatment candidates along with possible side effects, and another explains the reasons for the decision.

In total, the tool identified drug candidates for 17,080 diseases, including conditions for which there are no treatments, from nearly 8,000 drugs (both FDA-approved and experimental ones currently in clinical trials). It also predicted which drugs would have side effects and contraindications for certain conditions – something the current approach to drug discovery mostly determines through trial and error during early clinical trials, where the focus is on safety.

Compared to leading AI models for drug repurposing, the new tool was, on average, almost 50% better at identifying potential drugs and was also 35% more accurate at predicting which drugs would have contraindications.

Advantages of using already approved medicines

Repurposing existing drugs is a promising way to develop new treatments because it is based on drugs that have been studied, have well-established safety profiles, and have passed regulatory approval processes.

Most drugs have multiple effects beyond the specific targets for which they were originally developed and approved. However, many of these effects go undetected during initial testing, clinical trials, and reviews and are poorly studied, and only become apparent after years of use by millions of people. In fact, nearly 30% of FDA-approved drugs have received at least one additional treatment indication after initial approval, and many have received dozens of additional treatment indications over the years.

This approach to drug repurposing is haphazard at best. It relies on patient reports of unexpected beneficial side effects or on doctors' intuition about whether to use a drug for a condition for which it is not intended. This practice is called off-label use.

“We rely on luck and chance rather than strategy. This limits drug discovery to diseases for which there are already drugs,” says Zitnik.

The benefits of drug repurposing extend beyond incurable diseases, Zitnik noted.

“Even for more common diseases for which there are already approved treatments, new drugs could offer alternatives with fewer side effects or replace drugs that are ineffective in certain patients,” she said.

What makes the new AI tool better than existing models

Most current AI models used for drug discovery are trained on a single disease or a handful of conditions. Instead of focusing on specific diseases, the new tool has been trained to make new predictions using existing data. It does this by identifying common features across multiple diseases, such as shared genomic aberrations.

For example, the AI ​​model reveals common disease mechanisms based on shared genomic underpinnings. This allows it to draw conclusions about a well-understood disease with known treatment options to a poorly understood disease for which there are no treatment options.

This capability, the research team said, brings the AI ​​tool closer to the kind of reasoning a human clinician could use to develop new ideas if they had access to all the pre-existing knowledge and raw data that the AI ​​model has but that the human brain cannot possibly access or store.

The tool was trained on massive amounts of data, including DNA information, cell signals, gene activity levels, clinical notes, and more. The researchers tested and refined the model by having it perform various tasks. Finally, the tool's performance was validated using 1.2 million patient records and asked to identify drug candidates for various diseases.

The researchers also asked the tool to predict which patient characteristics would make the identified drug candidates contraindicated for certain patient groups.

Another task was to ask the tool to identify existing small molecules that could effectively block the activity of specific proteins involved in disease-causing pathways and processes.

In a test designed to measure the model's ability to reason like a human clinician, researchers asked the model to find drugs for three rare conditions that it had not been exposed to as part of its training: a neurodevelopmental disorder, a connective tissue disease and a rare genetic disorder that causes water imbalance.

The researchers then compared the model's drug therapy recommendations with current medical knowledge about how the suggested drugs work. In each example, the tool's recommendations were consistent with current medical knowledge.

In addition, the model not only identified medications for all three diseases, but also provided the reasons for its decision. This explanatory function ensures transparency and can increase doctors' trust.

The researchers point out that any therapies identified by the model would require additional research regarding dosing and timing of administration. However, they add that with this unprecedented capacity, the new AI model would accelerate drug repurposing in ways that were not previously possible. The team is already working with several rare disease foundations to identify potential treatments.