Is big pharma really on cusp of AI shake-out?
Artificial intelligence could herald a new era for drug development but caution remains about how revolutionary its impact will actually be
GlaxoSmithKline’s $43m deal with British AI firm Exscientia may signal a revolution for drug development that could radically speed up the drug discovery process, helping patients in urgent need of specialist treatments.
Other leading pharma giants – including Johnson & Johnson and Merck – are also looking at investments in the AI sphere on the expectation that AI platforms are poised to reshape the pharma industry.
In the same way that the financial industry has employed physicists and mathematicians to develop predictive software to fine-tune trading decisions, many experts predict that pharma industry will be particularly receptive to AI solutions because of its inherent grounding in science and innovation.
AI is poised to become “the primary drug discovery tool by 2027”, according to AstraZeneca’s Global Head of Enterprise Architecture.
But new artificial intelligence technologies remain ostensibly unproven in the field of pharma research and despite the hype and excitement it could yet take many years before patients reap the benefits.
The discovery challenge
The meticulous nature of pharma research and development means that drug discovery – taking a new pharmaceutical drug to market once a lead compound has been identified – can take more than a decade.
The development of a single drug can often cost more than $1bn because of inefficiencies in the sampling process and the vast number of screenings that are typically required – fewer than one in ten drugs that enter the discovery process actually end up being taken to market.
GSK hopes the Exscientia deal will allow it to use AI technology to isolate hard-to-find molecules in the drug discovery process to help speed up the development of treatments in a number of targeted areas. It could result in significantly shorter development times – and reduced costs – because screenings will be largely replaced by supercomputer simulations and predictive algorithms.
Exscientia’s deep learning systems look to balance the potency and selectiveness of a new drug with its pharmacokinetics – the speed at which the drug can pass through a human body. The Dundee-based company’s AI technologies have already attracted investment from a number of pharma groups including Germany’s Evotec (immuno-oncology), France’s Sanofi (metabolic diseases), and Japan’s Sumitomo Dainippon Pharma (central nervous system disorders).
Andrew Hopkins, CEO, Exscientia said the company’s AI-driven platform has the potential to accelerate the discovery of novel, high-quality drug candidates.
“Applying our approach to client discovery projects has already delivered candidate-quality molecules in roughly one-quarter of the time, and at one-quarter of the cost of traditional approaches,” he said.
Clinical stages could benefit from AI technology in regards to trial design, site selection and patient enrolment. Adverse events and diagnostic testing can be better forecasted with the help of artificial intelligence. Machine learning and analytics can speed up the examination of clinical trial data for example with marker identification. This software could also advance pharmacovigilance measures, further down the drug discovery process.