Phesi has launched ClinSite, a self-service, artificial intelligence (AI)-powered tool which enables biopharmaceutical companies to search for top-performing investigator sites for clinical trials in all therapeutic areas.
The Software as a Service (SaaS) solution ClinSite incorporates natural language processing and machine learning and was fully automated last year.
The platform incorporates data from 4.2 million physicians and nearly 600,000 investigator sites, identified from over 80,000 sources and 330,000 clinical trials in 240 countries.
Investigator sites which are selected through ClinSite in more than 250 clinical trials have enrolled participants up to 40% faster than other sites.
Phesi founder and president Gen Li said: “ClinSite isn’t just an investigator site search engine; it also updates, cleans, and structures millions of data points automatically and in real-time, for easy filtering and analysis by the users.
“Phesi’s algorithms combine data and model ’what if‘ scenarios, enabling clinical trial sponsors and designers to make well-informed and quick decisions – saving precious time and money in getting new therapies to patients.”
According to a recent survey of 761 clinical trials from Phesi’s database, it was analysed that 30% of Phase III trials experienced a delay of at least three months.
Even one such delay can have tremendous financial effects, considering recently published data estimating the median cost of a Phase III clinical trial at $19m and a per-patient cost of $44,117.
Phesi board member Steve Arlington said: “It is nearly impossible to gauge investigator site performance, one of the main reasons for poor site selection when using traditional trial planning methods.
“Using an AI-driven tool like ClinSite enables sponsors to automatically compare relevant trials and pinpoint global sites with the right expertise and proven performance for a given protocol design in any therapeutic area.”
Next year, the company also intends to launch a fully integrated module that will help trial sponsors optimise design of study protocols.