The clinical trial is a foundational pillar of the pharmaceutical drug discovery process. Essentially, clinical trials are research studies which seek to determine if a medical treatment or device is safe and effective for humans. While the pharmaceutical drug industry has experienced some fluctuations it remains a profitable market. Global prescription drug expenditures are estimated to reach nearly $1.5 trillion by 2021 according to Quintiles IMS Holding.
However, major challenges facing researchers structuring clinical trials are high costs and have low rates of success. According to the U.S. Food and Drug Administration, roughly 1 in 10 of drugs tested in human subjects receive FDA approval and millions of dollars are invested in the research process.
In this report, we set out to determine the answers to the following important questions:
- What types of AI applications are emerging to improve clinical trial efficiency and optimization?
- How is the healthcare market implementing these AI applications?
The majority of AI use-cases and emerging technologies for clinical trials appear to center around three primary applications:
- Patient Recruitment: Companies are developing software platforms to better match patients to clinical trials based on specified criteria.
- Clinical Trial Design: Companies are developing machine learning algorithms to help researchers manage clinical trial workflows.
- Clinical Trial Optimization: Companies are developing machine learning models to predict which patients are at risk of dropping out of clinical trials to prevent threats to trial validity.
Founded in 2010 with headquarters in London, England and offices in New York, NY and Carmel, Indiana, Antidote claims that it uses machine learning to connect patients to medical research studies through its clinical trial matching platform.
Using algorithms trained on high volumes of patient eligibility criteria for clinical trials, thousands of open trials are searched by the Antidote Match search engine based on user responses to a questionnaire.
Upon accessing Antidote’s website, patients searching for clinical trials are prompted to provide responses to a series of questions about their health such as “for what condition are you looking to find a clinical trial?” and about their location to be matched to opportunities nearby.
In the 2 minute video below, CEO and Founder Pablo Graiver and Antidote staff discuss the mission of the company and its potential value to the drug discovery process.
According to a case study, a clinical trial relating to Alzheimer’s disease required 10,000 participants in 2 months. Antidote claims that through its platform and partnerships, it “delivered 8,000 of the required 10,000 referrals in less than 2 months [and its referrals] were seven times more likely to complete the required follow-up than referrals from other sources.”
Through Antidote’s Connect Network, the company has expanded its platform access to health organizations by allowing the Match search engine to be embedded into websites. As a result, the company claims that its platform reaches over millions of patients through more than 180 online communities.
Companies partnering with Antidote include the National Kidney Foundation, Lupus Research Alliance, and Healthline.
Staff members with machine learning experience include Tom Reeve, a London-based Data Engineer trained at the University of Essex. To date, Antidote has reportedly raised $28.9 million in funding and is backed by the Merck Global Health Innovation Fund.