AI boom set to revolutionise healthcare in Europe – Pharmaceutical Technology

Among the areas in which he believes AI will contribute are research and development, clinical trials, diagnostics, imaging and patient care. Indeed, the companies in which van Lerberghe is invested show a similar variety in their use of the technology. 

France-based Wandercraft is an example he says he offers readily given the visible benefits it offers. The company specialises in lower-body exoskeletons that help paralysed people to walk again, with AI used to aid balance. 

“Their vision was, ‘We need to allow people who are in wheelchairs to walk again’,” he says. “Something you have to know: there’s a 30% to 50% life expectancy decrease when you’re in a wheelchair. AI is being used to solve one of the main challenges or obligations of exoskeletons – they cannot fall.” 

Van Lerberghe also points to Elixir Health, which is using AI to raise the reproductive chances of fertility patients by examining their medical data; LynxCare, an AI-powered clinical data platform aimed at unlocking structured and unstructured hospital data to improve patient outcomes; and Butterfly Therapeutics, whose Bliss DTx virtual reality headset is used as a digital therapy to treat acute pain induced by care.

AI in use

While many health tech companies using AI are still finding their feet, others are already making major contributions to the industry. 

France-based Owkin was founded in 2016 and uses AI to identify new drug candidates, de-risk and accelerate clinical trials and build diagnostic solutions that improve patient outcomes. The platform has been funded to the tune of around $304m and is partnered with the likes of Sanofi, BMS and MSD, as well as over 60 academic institutions globally. 

“We also are our own clinical development company,” Andrew Pierce, the company’s SVP for drug discovery and development, tells Pharmaceutical Technology. “We have our own drugs. We’re running our own clinical studies … So, we’re using the AI to inform our own decisions in clinical development, in addition to being able to partner with other pharma companies to help them make better AI-informed and enabled decisions in their studies.” 

Pierce describes Owkin as “a joint venture between data science and clinical medicine” and explains that it employs a federated learning approach that has allowed it to build up “the world’s largest data set for spatially resolved biomarkers in tissues that are of relevance to cancer”. 

By partnering with medical centres around the world, the company is able to examine tissue without it or the data involved leaving the premises. 

“The AI framework comes to the hospital and does what it needs to do to learn enough to help with clinical development,” he explains. “But the data, the actual foundational data, doesn’t leave the hospital. Just the conclusions from the data leave the hospital.  

“That really was the key to unlocking the willingness of world-leading medical centres to agree to sign up to this. I think at last count, there’s something like 60 different medical centres all over the world that have contributed the underpinning data that has led to the AI machine that can draw these conclusions.” 

Among its areas of focus, Owkin is seeking to improve outcomes in oncology. The company is using AI to identify the best clinical settings for drugs and identify the best patient groups for trials, with a view to raising efficacy and reducing drug development timelines in the process.

“I’m super excited by the technology,” says Pierce. “I think it is going to be transformative. And I think what you’re going to see is more drugs, better drugs, and faster.” 

Impact of AI

This is a sentiment shared by Dr Steven Bishop, chief data officer at Qureight, a UK-based company also aiming to accelerate clinical trials.

Like Owkin, Qureight (pronounced ‘curate’ after its elevator pitch of ‘Data curation to accelerate drug development’) is partnered with several major players, including biopharma firms AstraZeneca, Roche and MSD, contract research organisations PPD, Clario and Criterium and a number of NHS trusts. 

“Key to building robust, accurate and precise AI models is actually establishing the right data partnerships with hospitals, healthcare groups, academic institutions and commercial entities and being able to establish a really diverse data set across a range of diseases, across a range of geographies, across a range of patient demographics,” Bishop tells Pharmaceutical Technology. 

A round of Series A funding secured earlier this year is being channelled towards accelerating the company’s AI product portfolio, with a particular focus on lung cancer. 

“We’re an AI biotech – or tech-bio – company on a mission to advance what we can do in respiratory and cardiac disease,” says Bishop. “The company is particularly looking at ways to predict disease progression and utilise that to speed up clinical trials and get some of the benefits out of those trials through that drug development pipeline to patients sooner.”

Of the role AI plays for the company, he explains that much of it is to do with the automation of highly skilled work at pace and also interrogating data in ways that humans cannot. 

“In general, across healthcare data, there’s a lot of hidden signals which aren’t visible to the human eye,” he says. “The likes of you and I, even a trained radiologist, can’t look at a scan and necessarily discern it …  

“All of our current focus is really looking at, in our disease space, how do we build models that start to leverage that additional information that you can’t see by eye? And how does that improve the effectiveness of the things we are measuring – clinical trials and the indicators we’re coming up with. And how far can we push that barrier to really improve the effectiveness of trials, reduce size of trials and ultimately reduce costs of the trials and get those drugs to market faster.”

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