How AI Can Improve Patient Identification and Recruitment for Clinical Trials

Dr. Shaalan Beg and Dr. Arturo Loaiza-Bonilla discuss the potential of artificial intelligence to assist with patient recruitment and clinical trial matching using real-world data and next-generation sequencing results.

TRANSCRIPT

Dr. Shaalan Beg: Hello, and welcome to the ASCO Daily News Podcast. I'm Dr. Shaalan Beg, your guest host for the podcast today. I'm an adjunct associate professor at UT Southwestern's Simmons Comprehensive Cancer Center in Dallas and senior advisor for clinical research at the National Cancer Institute. On today's episode, we will be discussing the promise of artificial intelligence to improve patient recruitment in clinical trials and advanced clinical research. Joining me for this discussion is Dr. Arturo Loaiza-Bonilla, the medical director of oncology research at Capital Health in Philadelphia. He's also the co-founder and chief medical officer at Massive Bio, an AI-driven platform that matches patients with clinical trials and novel therapies. 

Our full disclosures are available in the transcript of this episode.  

Arturo, it's great to have you on the podcast today. 

Dr. Arturo Loaiza-Bonilla: Thanks so much, Shaalan. It's great to be here and talking to you today. 

Dr. Shaalan Beg: So we're all familiar with the limitations and inefficiencies in patient recruitment for clinical trials, but there are exciting new technologies that are addressing these challenges. Your group developed a first-in-class, AI-enabled matching system that's designed to automate and expedite processes using real-world data and integrating next-generation sequencing results into the algorithm. You presented work at the ASCO Annual Meeting this year where you showed the benefits of AI and NGS in clinical trial matching and you reported about a twofold increase in potential patient eligibility for trials. Can you tell us more about this study? 

Dr. Arturo Loaiza-Bonilla: Absolutely. And this is just part of the work that we have seen over the last several years, trying to overcome challenges that are coming because of all these, as you mentioned, inefficiencies and limitations, particularly in the manual patient trial matching. This is very time consuming, as all of us know; many of those in the audience as well experience it on a daily basis, and it’s resource intensive. It takes specialized folks who are able to understand the nuances in oncology, and it takes, on average, even for the most experienced research coordinator or principal investigator oncologist, 25 minutes per trial. Not only on top of that, but in compound there's a lack of comprehensive genomic testing, NGS, and that complicates the process in terms of inability to know what patients are eligible for, and it can delay also the process even further. 

So, to address those issues, we at Massive Bio are working with other institutions, and we're part of this … called the Precision Cancer Consortium, which is a combination of 7 of the top 20 top pharma companies in oncology, and we got them together. And let's say, okay, the only way to show something that is going to work at scale is people have to remove their silos and barriers and work as a collaborative approach. If we're going to be able to get folks tested more often and in more patients, assess for clinical trials, at least as an option, we need to understand further the data. And after a bunch of efforts that happened, and you're also seeing those efforts in CancerX and other things that we're working on together, but what we realize here is using an AI-enabled matching system to basically automate and expedite the process using what we call real-world data, which is basically data from patients that are actuall