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BioPharma Industry Excited about the early use of AI in clinical trials

October 21, 2024 – Clinical Trials, Drug Discovery, Other, Pharmaceutical – AI and ML, AI in Biopharma, Indegene, Indegene Digital Summit, clinical trials

Experts discuss the long road for complete AI-led transformation at Indegene Digital Summit 2024

21 October 2024 — New Jersey, US, and London, UK — Clinical development experts from Novartis, GSK, Bayer and Johnson & Johnson called out the preliminary success indicators of AI adoption in designing and monitoring clinical trials during a panel discussion on AI in Biopharma R&D at the sixth annual Indegene Digital Summit.

Badhri Srivinasan, Head, Global Development Operations, Novartis, Christopher D. Corsico, Senior Vice President, Head of Development, GSK, Christoph Koenen, Head of Clinical Development and Operations, Bayer and Darren Weston, Senior Vice President, Head of Integrated Data Analytics & Reporting, Johnson & Johnson, unequivocally agreed on the transformative role of AI right along the value chain in clinical development albeit only with a solid foundation of data infrastructure, effort prioritization and rationalization within the R&D organizations, and approaching newer areas like Cart -T therapy, using AI/Gen AI with caution.

According to Darren Weston, Senior Vice President, Head of Integrated Data Analytics & Reporting, Johnson & Johnson, “The challenging part for a lot of R&D organizations is trying to prioritize what could we do, what should we do – it is almost like we are children in a candy store with so many opportunities with limited budgets for many of us, and we are trying to prioritize our efforts on the things that can have the biggest impact. An interesting use case – where the combination of AI and perhaps Gen AI come together as an example – is that many companies have been doing things like centralized data surveillance, whether that’s for informed monitoring, whether that’s for inspection, management etc. It is also perhaps useful to separate AI from GenAI in this case – because in the AI space, it relates to protocol optimization, feasibility, even document checking and all this kind of stuff.”

“I think AI models have been around for a while and are constantly being refined. These are things that companies can do now, and many companies are doing now in many areas, but I think they can be evolved or even enhanced by leveraging things like GenAI. Within J&J, we have deployed, for example, all the operational dashboards that our clinical trial teams used to monitor and manage the ongoing concept of the studies. We’ve created a system where you can ask a plain language question, like how many subjects were enrolled in Canada in the last week of my trial – and we can give you an answer almost immediately! I think the area where it’s a little more challenging is when we want it to create brand-new content that didn’t exist before – particularly in the R&D space. A lot of what we are doing is cutting-edge, it has not existed. If you asked ChatGPT 20 years ago to create your protocol for car-T, it would not know what to do because car-T didn’t exist.I think the combination between AI and Gen AI creates a ton of opportunity, but we need to rationalize the universe of opportunities and try to figure out what are the areas that have the highest degree of benefit.”

According to Christopher Corisco, Senior Vice President, Head of Development, GSK, “There are some specific ways that we have been thinking about the introduction of machine learning, large algorithms, reuse of data to be able to unlock some of the value that we believe, will have across the entire aspect of running our clinical studies. We have been looking at ways to use large language models to be able to assess how we translate documents, use GenAI to automate some of the documents based on content that already exists, and then go ahead and put that into a different format. We hope that at some point these algorithms and models will become smart enough that they will start to generate unique content. But we’re not there yet.”

“We’re also using mathematical modelling to be able to help us better predict where we want to put our sites, and where we may be able to identify patients. And this really builds off on real-world data to better understand where patients may reside, what site efficiency looks like and where those patients may be driven into joining clinical trials. And now taking it one step further, moving beyond the site to trying to use the technology to allow patients to remotely contribute their data in real time. At the top of everyone’s mind is: how do we demonstrate that whatever we generate can be replicated, is true and real as best as we can tell, based on the content? Can we go back to and show the regulators, where the source data came from and the quality of that data? Depending on where you are in that journey, the pilots may look different, but I think our big focus has been ‘do not disrupt’ the entire generation of what is a very important portfolio while you start to experiment and bring in new ways of working, ” added Christopher.

According to Christoph Koenen, Head of Clinical Development and Operations, Bayer, “Data access is extremely important. If the data we use to understand the patient population is recent and not from a clinical trial we did 5 years ago and the same indication, that makes all the difference in the world. For example, in cardiovascular disease, the standard of care is evolving and improving rather fast and prices are reducing, that understanding my patient population in a contemporary manner is extremely important to really find the appropriate power to define the design, the trial and in the end determine the size of the trial. Having the right access to the right data is extremely important to achieve this. The second thing is using AI in conducting the trial. This includes ensuring we acquire the right centres, we go even to the right countries to do a trial and use technology to acquire the data. But on the other hand, this also creates a dilemma for us because we suddenly collect so many more data points that we somehow need to hammer and monitor the data as well. This then brings me to the other important part of the conduct of a clinical trial which is to ensure the data quality in our clinical trials. To give you an example, depending on what trial we are talking about, 25 to 50% of the cost of the clinical trial right currently goes into ensuring the quality of the data. People literally travel around the world to look at source data and correct mistakes. I see an enormous potential in making it easier for clinical development function and at the same time produce even higher data quality, which will be much easier to be audited by regulators in the end. When we’re looking at large clinical trials, that is going to make a big difference.”

Panel moderator, Badhri Srinivasan, Head of Global Development Operations, Novartis, concluded the session by saying, “There are many aspects to think about and the maturity level where we are comfortable in bringing AI in. Are we fine to say let things be driven by AI? Is a broader digital landscape something that we can start to embrace much more comprehensively than before? Or even just point solutions? The way we have structured our current organization – is that fit for the AI world as we see it? How do we bring AI without disrupting the entire portfolio? Because we have to make sure we continue to deliver on the portfolio that is in front of us.”

“We don’t have a choice. We must bring AI in. It’s the right thing to do for patients. There is a regulatory aspect that we must explore a bit more. There is a change management aspect which we must explore in detail as well.”

*Indegene Digital Summit is an industry event exclusively devoted to tackling emerging challenges and tapping emerging opportunities to make the pharma and biopharma industry deliver future-ready outcomes.

To access this panel discussion, please visit here.