Unleashing AI’s potential in clinical trial recruitment
Multiple innovations using AI are transforming the trial recruitment process
The onerous cost of clinical trials in time, money and effort is well documented. The average clinical trial process lasts between 7.5 and 12 years, studies estimate, with costs ranging from $161M — $2.6B per drug. Just 14% of clinical trials are successful and only one in ten drugs entering Phase 1 ends up being approved by the FDA.
Recruitment is a significant source of the problem. According to Christina Busmalis, director of global life sciences at IBM Watson Health,
80% of clinical trials do not finish on time and the reason for this, in 86% of cases, is that they do not meet target recruitment on time. “It’s a massive problem across the board,” she explains, “which leads to delays and trial costs that can run into the multi-millions.”
The growing complexity and volume of data involved in trials is a chief reason for this, says Michelle Longmire, CEO and co-founder of California startup, Medable. The clinical trial world has gradually reached a point over the last decade where the number of patients and the amount of data has increased to such an extent that it has become impossible to deal with it manually.
“When you look at clinical trial protocol, the inclusion/exclusion criteria and the variety of parameters one needs to meet to be eligible, it is a significant challenge for both patients and companies,” says Longmire. “Whereas a clinical trial in the past would have had 20 variables, it will now have an average of 150.”
For Robert Jan-Sips, chief technical officer at myTomorrows, a Netherlands-based treatment delivery company specialising in expanded
access treatments and compassionate use programmes, another of the main hurdles in successful clinical trial recruitment is the ‘information gap’, which often results in a lose-lose situation where patients cannot find clinical trials and pharma companies are unable to find patients.
“Information is scattered across 17 different registries,” he says. “Patients have difficulty finding a concise overview of trials that are recruiting, many are out-dated and most are only available in the English language. Furthermore, it is very difficult for patients to understand the medical language in order to enroll.”
A new approach is needed and Artificial intelligence has the potential to disrupt and transform the clinical trials landscape, ultimately speeding drug discovery and cutting costs. One of its most promising applications is in streamlining the recruitment process for doctors and pharma by extracting information from patient records and matching it to ongoing trials.
“It’s extremely powerful. It is improving the process of identifying patients and the screening capability. We are the only enterprise cloud purpose built for life sciences with dedicated AI and machine learning,” she says. Medable’s technology is currently being used by over 750,000 patients across five continents, she says.
Matchmaking patients with trials
“This gave two perspectives. One gave Highlands a detailed view of which patient would fit which trial and the other gave Novartis an insight into how many applicable patients would be relevant to their trials.” The results of the study, which were published at ASCO in 2017, showed that screening time was reduced by 78% from 1hr 50mins to 24mins.
Democratising information access
Patient and physician decide together
This offers an unbiased overview of all treatment options for the patients while facilitating a shared decision-making process between the patient and the physician, says Dennis Akkaya, head of corporate development at the company.
“I think patient empowerment will change things a lot and at some stage in the future patients will be empowered to find trials that best suit them and to work directly in a different way with organisations through a trial perspective.” She believes the landscape will shift towards siteless trials as more and more data from wearable devices is harvested and analysed, and that adaptive trial design will enable a more robust and flexible process.
“From an IBM point of view, AI stands for augmented intelligence, not artificial intelligence. All of our solutions are about empowering someone to do things better and faster. An AI system will guide through a process more efficiently and augment the capabilities of humans. You have machine learning and humans — you bring those together and that’s quite a partnership.”
Visit our website at www.myTomorrows.com
Originally published at https://social.eyeforpharma.com.