Trial design
were looking to conduct, he adds, as this could help you decide which to move forward with. Sun and his team found that the factors most predictive of success are the condition and disease area, followed by the drug molecule itself – including its mechanism of action and its chemical features. Factors such as the eligibility criteria and trial design play a smaller role.
A third AI model by Sun’s lab, SPOT, takes this approach a step further by adding weight to trials conducted more recently. The thinking is that because scientific knowledge is evolving and expanding, newer research would be more valuable and thus lead to a more accurate prediction.
Digital tools and AI are streamlining trial design, improving efficiency, and predicting success to bring new medicines to market faster.
have found very compelling, because they’d rather know now before they’ve made up their mind and get too wedded to a particular study design.” Generative AI can even suggest aspects of trial design, including eligibility criteria, based on what’s been done before. Sun’s lab has developed one such model called AutoTrial, which is trained on the eligibility criteria of thousands of past trials. Much like ChatGPT, you’d input the condition you’re looking at and AutoTrial would create draft criteria – saving the time needed to go through the literature manually.
“People are all of a sudden waking up and going, ‘Maybe we should pay attention to this digital protocol thing’.”
Todd Georgieff, digital clinical trials consultant Predicting success
Then there are systems offering perhaps the most sought-out metric of all: a trial’s odds of success. HINT, another AI model by Sun’s lab, predicts the outcome of a trial before it starts. As with AutoTrial, HINT is trained on historical trial data, from sites like clinical
trial.gov. Yet because the data on trial outcomes on those websites can often be vague – they will tell you if a trial is terminated or completed but not why, for instance – HINT fills in the gaps using publicly available information, such as from news articles. HINT collects information about the drug molecule (biochemical information about many drugs can be found online) and target disease, plus past trial protocols and outcomes. Based on what has worked before, the model can predict how your trial will fare. “You can predict the trial outcome before it starts. Then maybe you can optimise the trial in terms of adjusting the design,” says Sun. This would be particularly useful if you had a portfolio of trials you
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Models like this might also be used to gauge the probability of regulatory success. If you had the relevant database, you could plug in your endpoints and see how similar clinical research turned out, says Georgieff. “I could connect to a database that says, ‘Well, there have been 10 other clinical trials aimed at [those endpoints] before but only three of them have become successful label claims.”
Looking ahead
Georgieff and Sun agree we won’t have to wait too long for these digital solutions to hit the market. Tools and models that could support a digital protocol are currently in the works. For instance, the Digital Data Flow initiative by non-profit TransCelerate BioPharma and the Clinical Data Interchange Standards Consortium (CDISC) is developing a universal data standard that can be used across the industry. This will allow for the easy exchange of trial information between different systems and organisations. And since the International Council of Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) has released its new M11 guideline, which contains a standard protocol template that supports digital data exchange, more people are realising how impactful digitalising trials could be. “People are all of a sudden waking up and going, ‘Maybe we should pay attention to this digital protocol thing’,” says Georgieff. He sees digital protocol systems on the market within five years. With predictive AI models, it could be even sooner. “I think the backbone of the capability is already there,” says Sun. “You can ask ChatGPT or any cloud type of model, and it can already give you a very informative answer.” And ideally, one day we will be able to merge both technologies, says Georgieff. That is, having a digital protocol that can integrate predictive AI models. “[What] if I had that kind of database that could predict not only regulatory success but also clinical success as well?” he asks, “That’s the ultimate prize… The more I know, the better off I am.” ●
Clinical Trials Insight /
www.worldpharmaceuticals.net
elenabsl/
Shutterstock.com
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