Clinical trials platform based on machine learning launched by Israel’s BGU

The yet-to-be-named programme reportedly “leverages machine learning to optimise a clinical trial’s chances of success” through the analysis of factors such as patient population recruitment and dropout rate.
By Rachel McArthur
11:20 am
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Pharma, machine learning, medical devices

Credit: BGU

A new platform aimed at streamlining clinical trials in order to lower costs – and increase the efficiency and success rate of a drug or the development of a medical device – has been unveiled by Ben-Gurion University of the Negev (BGU).

The yet-to-be-named programme reportedly “leverages machine learning to optimise a clinical trial’s chances of success” through the analysis of factors including patient population recruitment and dropout rate, and the identification of monitored markers. In turn, the platform provides pre-trial recommendations, in-trial interim analysis, and post-trial insights for “next trial preparation, as well as potential salvage options in case of failure,” the Israeli public research university said in a statement.

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Panacea, a new company founded by BGN Technologies – the technology transfer company of the university – is licensing the technology for development and commercialisation.

It is said to have been already used in the clinical studies of various neurodegenerative disorders, including amyotrophic lateral sclerosis (ALS), Parkinson’s disease, and Alzheimer’s disease.

“Clinical trials haven’t fundamentally changed in the past two decades. They are extremely costly, and the probability of success for new drugs is in the single digits,” said Boaz Lerner, scientific founder of Panacea, and member of the BGU Department of Industrial Engineering and Management. “Therefore, our platform is highly beneficial for pharma and biotech companies, enabling them to increase efficiency and the chances of success by streamlining the trial and selecting the optimal participants and markers. 

“Conversely, we can also help in understanding when to terminate a trial and what lessons can be derived from a failed trial.”

ON THE RECORD

“In the age of artificial intelligence and machine learning, it seems only natural that drug development should benefit from these sophisticated tools that can take into account large amounts of data, and integrate and analyse numerous parameters in order to optimise clinical trials and increase their probability of success,” said Josh Peleg, CEO of BGN Technologies. “We are happy to see that the technology has already received interest from several biopharma companies who have begun collaborating with Panacea on improving their ongoing clinical studies.”

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