We always tell our clients to start small with AI. It’s important to start with clear, specific use cases. For some aspects of the business, it may take some time to carefully consider what these specific use cases are and what opportunities AI can create. Clinical trials present no such difficulties. The opportunities to improve efficiencies are numerous and clear, and AI has begun to make an impact. The industry is aware of the inefficiencies that abound in clinical trial design and execution and is eager to solve them.
To delve into what AI can do for clinical trials, I spoke with my colleague, Venkat Sethuraman, who is the global clinical lead for ZS and sits at the forefront of AI adoption in the clinical trials space.
A: The greatest potential I see for AI is in clinical development. You can use AI to design trials in an intelligent way and this is where you start to see a potential cure for clinical trial inefficiencies. AI can scan clinical trial data in literature in real time and give you a landscape view of how your trials are performing and evolving, and it can alert you to challenges as the landscape shifts. You can predict the potential impact of design decisions before making them. For example, how should you sequence indications within clinical programs to maximize trial success? Also, having such rich landscape data enables organizations to better advance clinical development plans that have been archaic and mostly static in nature. When there’s new external information and topline results, the view can be refreshed, and design recommendations can be made by AI, which can then be further validated by a researcher.
A: There are a lot of opportunities for AI in clinical trials, which are typically run and analyzed in old-school ways. For example, companies have access to vast historical clinical trial data. To gain insights, you still manually pull data from multiple clinical trial studies and translate the data so that it meets a single data standard. You may have some fields that say “gender” and others that say “sex,” and you need to resolve these variations when you pull the data together. We are working with clients to automate this process with AI, and their efficiency rate has improved tenfold. Once you bring data into one common data model such as CDISC standards, you can gain rich insights. Companies need to trust AI to improve productivity, but it’s hard for them to believe that they don’t need humans to do this work.
Another area where AI can drive impact is in authoring clinical trial protocols or extracting value from a written protocol. Most companies have standard templates and processes when it comes to authoring protocols. However, “human variation” leads to review delays, amendment issues and increased time to train investigative sites. Algorithms can learn protocol writing from existing protocols. You can develop a process to structure 75% of a protocol with AI and thereby save at least four to six weeks from authoring and review. Most pharma companies have not leveraged AI in this space, and that’s why it’s due for disruption.
All of this requires adaptation to new technology and a new way of thinking. Clinical trials have nowhere to go but up, so why not embrace this latest technology? Cars can drive themselves and many people have started to trust them. Pharma companies need to jump in and start experimenting with AI rather than questioning it and over-thinking how they may use it. That’s the advice I give most of my clients.
A: There are three that come immediately to mind. Diverse data, problem design and solution, and an adaptive mindset.
As is the case with most AI, it all begins with data. Most companies have historical clinical trial data, but today there is a wealth of newer sources, both structured and unstructured data, including real-world data (RWD), that is emerging as a valuable.
We have to leverage this data to say, “I know we can complete this trial on time with this global footprint and these investigators." In addition, we should be able to say "this particular trial will excite this investigator, and we know that there are patients in this ZIP code who need this particular treatment and, therefore, we can connect these two together." So the critical element here is being more specific and understanding what the trial requirements are rather than working on a one-size-fits-all approach.
And, finally, develop an adaptive mindset. Typically, companies plan for a certain patient population and then launch the trial. As the enrollment data emerge, we have to adapt models as well as our approach. Clearly having real time data and making frequent adjustments requires an agile mindset that’s lacking today.