Dandelion Health CEO Elliott Green said that the company hopes that the data harnessed in the software will be able to be used as control arms in future clinical trials. Image credit: Shutterstock / PopTika.

An AI database that utilises real-world data (RWD) could help sponsors decide where to next investigate glucagon-like peptide-1 receptor agonists (GLP1-RA).

US-based Dandelion Health launched its AI-powered RWD library for GLP1-RAs on 14 May. The database will be available to sponsors and clinicians, among others in the medical community, to use patients’ medical records available about GLP1-RA’s impact on metabolic disorders and beyond.

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CEO Elliott Green told Medical Device Network that the RWD collected can be used to help sponsors understand how else their drug is working without the need for a trial.

“We can evaluate points like, is this candidate more effective in men or women? Are there certain age ranges where this works best? That kind of level of investigation is normally not done unless you’re running a clinical trial,” Green explained.

“We would love to bring this information to the forefront so that sponsors can actually look at it in advance of maybe running a clinical trial, or use the information to supplement a clinical trial.”

GLP1-RAs are being investigated in indications beyond obesity and type 2 diabetes, including neurodegenerative, cardiometabolic and central nervous system.

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The data for the software is pulled and processed using AI from a patient’s electronic medical records (EMR) in a deidentified manner. The software can process both structured and unstructured data, including ECGs and radiography scans.

This tech will not only help sponsors in planning what other indications to investigate with their drugs but could also be potentially used as a synthetic arm or an RWD control arm in a trial. Green added that this is something which the company is currently discussing with sponsors and the US Food and Drug Administration (FDA).

As a result, in a therapeutic setting, a clinician would be able to look at a patient’s gender, ethnicity, comorbidities and more to determine whether GLP1-RA would be the most effective for their needs, allowing them to become precision medicines.

As well as this, if a patient wanted to see benefits from the drug in other areas such as cardiometabolic benefits, the data would also be able to determine which candidate would be most effective there.

Ignite Data is working on a similar product in Europe, however, it is clinical trial specific, transferring data inputted into a patient’s electronic health record to electronic data capture.