Rethinking Trial Access in Lower-Income Regions Through Patient-Centric, AI-Driven Models

In a recent video interview Applied Clinical Trials, Gen Li, PhD, MBA, president, Phesi, highlighted results from the company’s new analysis on global inequity in breast cancer development. He discussed the geographic concentration of breast cancer trial leaders in the US and China, with a positive outlook on global diversity. Operational barriers in lower-income countries were attributed to traditional business models, with opportunities to implement a more patient-centric approach by leveraging artificial intelligence (AI) and machine learning. Li addressed the lack of local KOLs in underserved areas by emphasizing the importance of identifying local leaders. He also stressed the need to redistribute trial workloads and improve recruitment by using AI to identify and activate new sites.

The interview transcript was lightly edited for clarity.

ACT: Lower-income countries face rising breast cancer mortality, yet have little to no clinical trial leadership. What operational barriers prevent trials from being conducted in these regions?

Li: I think that’s a fascinating question. I think it’s very much to do with the traditional business model that the pharmaceutical industry has deployed, which means that if we go [into] planning a particular clinical trial, we’re looking at [whether] you have investigators [who] have done clinical trials in the past, [are] experienced, [and] deliver good results for us, and so on and so forth.

That has to change. If we keep using that kind of recycling of [the same] model, we are only going to [create] a downward spiral—getting worse. The situation will shrink because people [will] retire and people die. So therefore, you’re getting a smaller and smaller [pool of investigators] available for the industry to use.

Where I think it’s encouraging and promising is [in] technology advancement. So artificial intelligence and machine learning and all of those other things—if we combine those with a slightly transformed kind of mindset—it’s [about] not just looking at the clinical trials and the development-related activities, but [about] getting ourselves to be more patient-centric.

Remember this: there are people [who still] get breast cancer. They suffer from the disease; they’re still being treated. They still [have] those activities going on in those less-participating countries. If we start looking at those issues from the patient perspective, and we start looking at the characteristics and distribution of [patients] in those countries—then comparing those data with [data] from other countries—we [will] know better.

That will give us a very good view of those physicians working with them. Then those [physicians] must have a lot of commonality with [the investigators] we are familiar with—[those] we label as investigators in more advanced countries.

That is the path we are going [down] at Phesi: leveraging artificial intelligence [and] taking a patient-centric approach to allow us [to] go into some of those unknown territories. It’s not only for that, but it is also a very good technique we are using for rare diseases—or rarely studied diseases—even in advanced countries. We’re using the same technique: patient-centric, artificial intelligence.