University of Arizona researchers accelerate rare pediatric disease drug repurposing with AWS

For families of children with rare neurodevelopmental disorders, genome sequencing has made finding answers far more likely than it was a decade ago. Sequencing can identify a genetic cause in some children who might otherwise have spent years searching for a diagnosis. Still, about half of cases go unsolved after sequencing, and a diagnosis is only the first step toward treatment. Because these disorders are rare, pharmaceutical companies often have limited incentive to develop new drugs specifically for them, leaving much of the search for potential treatments to academic research labs.

At the University of Arizona (U of A) College of Medicine – Phoenix, a translational neuroscience lab of eight researchers is pursuing a mission to find and repurpose existing drugs as potential treatments for rare pediatric disorders. Working with Amazon Web Services (AWS), the team connected four AWS services into an integrated research pipeline, using generative artificial intelligence (AI) and specialized foundation models to turn raw sequencing data into insights that help identify promising drug candidates. In this post, learn how the lab is using generative AI to expand its research capacity with its current members to pursue scientific breakthroughs for a patient population that is often overlooked.

Finding treatments for children with rare disorders

The Schrauwen lab studies rare pediatric neurodevelopmental disorders, with a focus on uncovering their genetic causes and translating those discoveries into potential treatments. The team’s work spans identifying disease-associated genes, understanding how those genes contribute to disease, and searching for therapies that could improve patient outcomes.

“A lot of these diseases are rare, so the populations are smaller and clinical trials are more difficult,” said Isabelle Schrauwen, associate professor of translational neurosciences and principal investigator at the U of A College of Medicine – Phoenix. “It can be hard to put funding toward these rarer disorders.”

So instead of waiting on new medications, the team looks for existing ones that can be “repurposed” to treat these diseases. The team identifies potential drugs for repurposing by matching ribonucleic acid (RNA) signatures, the patterns of gene activity in a patient’s cells, against National Institutes of Health (NIH) datasets that link approved compounds to the same signatures. If a drug already on the market for one condition acts on the same pathway as a neurological disorder, it could become a candidate for further study.

“You’re trying to accelerate the time to drug discovery,” said Meghan Buder, principal tech business development manager for research computing at AWS. “You’re talking about the sickest kids in the world.”

Drug repurposing pipelines produce long lists of potential candidates. Matching one gene’s RNA signature against NIH data can turn up tens or hundreds of candidate compounds, each of which must be checked against what is already known about the drug and the gene — which can range from a single candidate gene to the entire human genome.

The lab also wanted to use deoxyribonucleic acid (DNA) and RNA foundation models to help predict individualized responses, but these models require graphics processing unit (GPU) computation, and the lab’s on-site computing cluster does not have it.

Connecting four AWS services into one research pipeline

Rather than replace the cluster, the lab decided to add cloud capacity around it, paying only for what it used. The pipeline work began in May 2025 with hands-on workshops and support from AWS specialists, starting with AWS HealthOmics. Because the lab was already using AWS, the team could get started quickly and build on the existing cloud environment.

“It can be pretty daunting to set up something in AWS, so if there is ever something our lab needs, we work with the AWS team,” said Daniel Mendoza, a biostatistician in the lab. “Since we don’t have a GPU server, AWS allowed us to start exploring AI analysis that we haven’t done before quickly.”

Sequencing data goes into AWS HealthOmics first, where the lab runs its own bioinformatics pipeline alongside open-source workflows to produce RNA signatures and an initial list of candidate compounds. From there, the team turns to Amazon SageMaker AI to run DNA and RNA foundation models from Hugging Face. These models are trained on the language of genetic sequences, and the lab uses them to predict how a particular patient’s cells might respond to a given drug. Amazon Bedrock rounds out the pipeline with access to large language models, including Claude by Anthropic in Amazon Bedrock, for AI-assisted coding and for checking a hypothesis against more than one model.

SageMaker AI is the latest addition. When the lab wanted to try running foundation models, it immediately reached out to its AWS account team, and the service was up within a day. “The team brings us questions, and we get to find out, yes, we’ve done it, or here’s how we could,” said Caleb Ferganchick, senior enterprise account executive at AWS who works with U of A.

The pipeline produces a list of candidate compounds. However, someone still has to decide which drugs are worth pursuing, which includes reading through the literature on each drug, which could take hours to days per compound. With Amazon Quick Research, a researcher can run a deep literature search across the whole list. The tool reviews the studies, pulls out relevant findings, and returns a summary, which helps when useful evidence is buried in research on other conditions. For example, Mendoza noted that “a lot of literature out there is cancer-based” and may not have evidence for neuro-focused drug candidates. Amazon Quick Research can close that gap.

Researchers also use Quick to get a fast overview of a single gene’s function and associated disorders by uploading papers alongside the lab’s unpublished data and getting an organized overview in minutes. Quick is a HIPAA eligible service, and the lab set it up with permissions that control who can access which data, so answers come back grounded in the sources the lab provides.

“Quick really helps us synthesize and narrow down the information and summarize the research findings we have from our datasets,” said Jash Trivedi, scientific analyst at the lab. And because the tool works in plain language, it has also opened the lab’s data to researchers who don’t code.

From one pilot user to the whole lab

The lab started with a single proof-of-concept user on AWS promotional credits. After everyone in the lab had tried it, the lab purchased seven Quick licenses, one for each member. Since then:

Literature synthesis that took hours of manual reading now takes 10 to 15 minutes.
Several research papers that used Quick in the research process are in development.
The team has run analyses it had never attempted before, some within a month of first trying the tool.

Quick also helps researchers error-check originally written documents, manuscripts and presentations more efficiently. Researchers run nearly finished drafts through it to catch typos and mismatched figure panels or numbers.

“When you’ve looked at a draft so many times, you need a different pair of eyes,” said Anushree Acharya, scientist III at the lab.

Because checking a candidate drug list against the literature now takes minutes instead of hours, the team evaluates more candidates per gene. Each of those candidates could be a therapy for a child who may have few other options.

Kanishka Manna, a postdoctoral researcher at the lab, sees the effect of Quick across the whole workflow: “It helps in every aspect of research, from the literature to organizing data to exploring AI models. It helps everything move along quicker,” he said.

Taking on more genes and reaching more families

Next, the team plans to explore more foundation models through SageMaker AI. Schrauwen’s advice to other labs in a similar position is to keep exploring.

“Set something up with AWS. Don’t forget it’s there, and try novel things in the environment,” she said.

In this lab, trying new things has meant scaling computational efforts to interrogate more genes and drug candidates, each one another chance at a treatment for a child who has been waiting, and for the community that forms around every newly identified disorder. Those communities often start with a Facebook group where parents follow new research, and some families go on to form foundations that fund researchers, like those in this lab.

“These parents are superheroes,” Schrauwen said. “It’s good to see how much more is available to these families now.”

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Read related stories on the AWS Public Sector Blog:

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University of Arizona researchers accelerate rare pediatric disease drug repurposing with AWS

Finding treatments for children with rare disorders

Connecting four AWS services into one research pipeline

From one pilot user to the whole lab

Taking on more genes and reaching more families

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