Customer: Fred Hutchinson Cancer Center
Dr. Soheil Meshinchi, a pioneer in pediatric oncology, is dedicated to advancing therapies for high-risk pediatric AML leukemia. As a result of decades of collecting patient samples and modern sequence analysis of those samples, the Meshinchi lab at Fred Hutchinson Cancer Center has identified several protein targets of interest that are differentially expressed in the cancerous cells of pediatric AML patients.
A very promising line of therapy is to identify existing drugs that may be effective against those protein targets, and repurpose those drugs to treat pediatric AML patients. The best opportunity to find such drugs is from the thousands of ongoing medical trials aimed at other more common forms of cancer.
The key initial step in the research process is to analyze ongoing trials to identify the protein targets of those trials and the drugs used, and explore related publications to collect information about those drugs. This is a highly demanding task requiring the research team to meticulously read hundreds of clinical trial documents, and then extract and verify information from them.
The Meshinchi lab at Fred Hutchinson Cancer Center collaborated with the Thunk.AI platform to fully automate this crucial initial stage of the research pipeline, placing the clinical trial analysis in the hands of AI agents.
In the span of just 6 months and with no additional staff, the Meshinchi lab automated its clinical trials analysis workflow using AI agents implemented on the Thunk.AI platform.
“Thunk.AI transformed a painstaking time-consuming task into an efficient and simple workflow.”
Now that the workflow has been automated on Thunk.AI, work that might have taken months to perform is completed in a matter of hours, verified in a matter of days, and the research team is freed up to work immediately on the downstream stages of research.
There were various challenges with the “human-powered” approach to this research process.
Analyzing each clinical trial required highly skilled professionals to dedicate substantial time and effort. On average, it took about 20 minutes per trial to thoroughly read and understand the documents, extract relevant entities like gene targets, interventions, study details, and sponsors, and verify all information. The process was labor-intensive and repetitive, with tasks such as performing web searches for sponsors and checking if interventions were FDA-regulated drugs. For antibody drug conjugates (ADCs), additional effort was required to identify the payload linked to the antibody by researching relevant online publications. These detailed steps made scaling the process difficult, especially as trial volumes continued to grow.
With hundreds of ADC and CAR-T-related clinical trials and thousands more expected, the volume of work rapidly outpaced the capacity of the existing team. Manual workflows, while thorough, could not keep up with the increasing demands. This bottleneck risked delays in extracting critical insights needed for downstream decision-making, impacting the ability to stay ahead in research and innovation.
Despite the high workloads, there was no room for error in clinical trial analysis. Precision was critical for ensuring the integrity of research findings and compliance with regulatory standards. Exhaustive data collection and validation were essential, yet the risk of human fatigue and errors increased with the growing workload. Ensuring high accuracy and consistency under these challenging circumstances was a constant struggle for the team.
After attempting to execute this workflow solely through manual effort, it became clear that a smarter, faster solution was essential to maintain the pace of innovation.
For example, the instructions for the last workflow step (“Research drug and manufacturer”) were specified using straightforward English instructions along with an example of the desired result format:
That’s it! Notice how the instructions are exactly how they would be specified to a human teammate who would be expected to exercise judgment and subjectivity in making decisions. That is sufficient for the AI agent to do its work.
Every action taken by the AI or the human within the thunk was logged for complete transparency and auditability. This traceability allowed users to see detailed records of the AI's activities, such as which web searches were performed and the sources of the extracted answers. This ensured a high level of accountability and trust in the workflow.
In the span of just a month in production, the Meshinchi lab has analyzed more than 1000 clinical trials using AI agent automation – an undertaking that would otherwise have taken many months of focused effort. AI automation has provided tangible and as well as intangible benefits for the AML research process at the Meshinchi lab:
“What would have been an error prone, manual process, became rapid, reliable, and reproducible.”
Try out a sample thunk that replicates how our customer, Fred Hutch, automated their work. Walk through this sample with our step-by-step article.
