Leading pharmaceutical companies are harnessing artificial intelligence (AI) to find patients for clinical trials quickly, or to reduce the number of people needed to test medicines, accelerating drug development while potentially saving millions of dollars.
Human studies are the most expensive and time-consuming part of drug development as it can take years to recruit patients and trial new medicines, the process sometimes costing more than $1bn from the drug’s discovery to the finishing line.
Companies have been experimenting with AI for a number of years in the hope that machines can discover the next blockbuster drug. Although a few compounds picked by AI are now in development, those bets will take years to play out.
Interviews by Reuters with more than a dozen pharmaceutical company executives, regulators, public health experts and AI firms show, however, that the technology is playing a growing role in human drug trials.
Companies like Amgen, Bayer and Novartis are training AI to scan billions of public health records, prescription data, medical insurance claims and their internal data to find trial patients – in some cases halving the time it takes to sign them up.
The US Food and Drug Administration (FDA) said it had received about 300 applications that incorporate AI or machine-learning in drug development from 2016 to 2022. More than 90% of those were in the past two years, and most were for the use of AI at some point in the clinical development stage.
Before AI, Amgen would spend months sending surveys to doctors – from Johannesburg to Texas – to ask whether a clinic or hospital had patients with relevant clinical and demographic characteristics to participate in a trial.
Existing relationships with facilities or doctors would often sway the decision on which trial sites are selected.
However, Deloitte estimates about 80% of studies miss their recruitment targets because clinics and hospitals over-estimate the number of available patients; there are high dropout rates, or patients do not adhere to trial protocols.
Amgen’s AI tool, Atomic, scans troves of internal and public data to identify and rank clinics and doctors based on past performance in recruiting patients for trials.
Enrolling patients for a mid-stage trial could take up to 18 months, depending on the disease, but Atomic can cut that in half in the best-case scenario, Amgen said.
Amgen has used Atomic in a handful of trials testing drugs for conditions, including cardiovascular disease and cancer, and aims to use it for most studies by 2024.
The company said by 2030, it anticipates that AI would have helped it shave two years off the decade or more it typically takes to develop a drug.
The AI tool that Novartis uses has also made enrolling patients in trials faster, cheaper and more efficient, said Badhri Srinivasan, its head of global development operations. But he said AI in this context is only as good as the data it gets.
In general, less than 25% of health data is publicly available for research, according to Sameer Pujari, an AI expert at the WHO.
External control arms
German drugmaker Bayer said it used AI to slash, by several thousand, the number of participants needed for a late-stage trial for asundexian, an experimental drug aimed at reducing the long-term risk of strokes in adults.
It used AI to link the mid-stage trial results to real-world data from millions of patients in Finland and the US to predict the long-term risks in a population similar to the trial.
Armed with the data, Bayer started the late-stage trial with fewer participants. Without AI, Bayer said it would have spent millions more, and taken up to nine months longer to recruit volunteers.
Now the company wants to take it a step further.
For a study to test asundexian in children with the same condition, Bayer plans to use real-world patient data to generate a so-called external control arm, potentially eliminating the need for patients taking a placebo.
That is because the condition is so rare in the age group that it would be difficult to recruit patients, and could raise concerns about whether it was ethical to give trial participants a placebo when there are no proven treatments available.
Instead, Bayer aims to mine anonymised real-world data of children with similar vulnerabilities, hoping that will be enough to help discern how effective the drug is.
Finding real-world patients by mining electronic patient data can be done manually, but using AI speeds up the process dramatically.
While unusual, external control arms have been used in the past instead of traditional randomised control arms where half the participants take a placebo – mainly for rare diseases where there are few patients or no existing treatments.
Amgen’s drug Blincyto, designed to treat a rare form of leukaemia, received US approval after adopting this approach, though the company had to conduct a follow-up study to confirm the drug’s benefit once it was on sale.
Blythe Adamson, senior principal scientist at Roche subsidiary Flatiron Health, said AI enabled scientists to examine real-world patient data quickly, and at scale.
She said it could take months to trawl through data from 5 000 patients using traditional methods: “Now we can learn those same things for millions of patients in days.”
Over-estimation risk
Drugmakers typically seek prior approval from regulators to test a drug using an external control arm.
Bayer said it was in discussions with regulators – like the FDA – about now relying on AI to create an external arm for its paediatric trial, but did not offer additional detail.
The European Medicines Agency (EMA) said it had not received any applications from companies seeking to use AI in this way.
Some scientists, including the FDA’s oncology chief, are worried drug companies will try to use AI to come up with external arms for a broader range of diseases.
“When you’re comparing one arm without randomisation to another arm, you are assuming you have the same populations in both. That doesn’t account for the unknown,” said Richard Pazdur, director of the FDA’s Oncology Centre of Excellence.
Patients in trials tend to feel better than people in the real world because they believe they are getting an effective treatment and also get more medical attention, which could, in turn over-estimate the success of a drug.
This risk is one of the reasons regulators tend to insist on randomised trials, as all patients believe they are getting the drug, even though half are on a placebo.
Regulators say that although AI has the potential to augment the clinical trial process, evidentiary standards for a drug’s safety and effectiveness will not change.
“The main risks are that we want to make sure we don’t get the wrong answer to the question of whether a drug works,” said John Concato, associate director for real-world evidence analytics in the Office of Medical Policy in the FDA’s Centre for Drug Evaluation and Research.
Reuters article – Insight: Big Pharma bets on AI to speed up clinical trials (Open access)
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