AI drug studies coming
Experts say artificial intelligence (AI) should affect drug development.
Drug companies are grappling with a dilemma - the era of blockbuster drugs is coming to an end, but adding new drugs to their portfolios is slow and expensive.
It takes on average 10 to 15 years and $1.5 billion to $2 billion to get a new drug to market; approximately half of this time and investment is devoted to clinical trials.
Although AI has not yet had a significant impact on clinical trials, AI-based models are helping trial design, AI-based techniques are being used for patient recruitment, and AI-based monitoring systems aim to boost study adherence and decrease dropout rates.
“AI is not a magic bullet and is very much a work in progress, yet it holds much promise for the future of healthcare and drug development,” says lead author and computer scientist Stefan Harrer, a researcher at IBM Research-Australia.
IBM’s research found a list of ways that AI can potentially boost the success rate of clinical trials, including efficiently measuring biomarkers that reflect the effectiveness of the drug being tested
Thinking machines can also be used to identify and characterise patient subpopulations best suited for specific drugs.
Less than a third of all phase II compounds advance to phase III, and one in three phase III trials fail - not because the drug is ineffective or dangerous, but because the trial lacks enough patients or the right kinds of patients.
Start-ups, large corporations, regulatory bodies, and governments are all exploring and driving the use of AI for improving clinical trial design, Harrer says.
“What we see at this point are predominantly early-stage, proof-of-concept, and feasibility pilot studies demonstrating the high potential of numerous AI techniques for improving the performance of clinical trials,” he said.
The review also evaluated the potential implications for pharmaceutical companies.
It found computer vision algorithms could potentially pinpoint relevant patient populations through a range of inputs from handwritten forms to digital medical imagery.
They suggested applying AI analysis to failed clinical trial data, to uncover insights for future trial design.
Additionally, the researchers found AI capabilities such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) could be used for correlating large and diverse data sets such as electronic health records, medical literature, and trial databases to help pharma improve trial design, patient-trial matching, and recruiting, as well as for monitoring patients during trials.