Time is of the essence

Analysing the worsening mental health landscape across Europe

The exponentially advancing capabilities of AI may be the only match for the rapidly evolving chronic and infectious disease landscape. Will big pharma and governments embrace the shift from lab to computer suite, or will a lack of funding, skilled workforce, data and regulatory frameworks prove too much?

Earlier this month, the world marked the seventh annual One Health Day – a campaign brought about to raise awareness of the shared health threats at the human-animal-environment interface.

A big part of this – and something that was discussed at COP27 – is how the erosion of barriers between different species’ habitats is increasing the prevalence of infectious zoonotic diseases by ultimately allowing viruses to jump from animal hosts into the human population.

It’s a discussion that has heated up since the outbreak of COVID-19, and while much of it has been rightly centred on prevention strategies, we also need to be talking about the flipside of the coin – improving drug development to combat the inevitable uptick in virus transmission that’s on its way.

Clearly, the human workforce involved in the process of drug development and clinical trials has an upper capacity limit to deal with growing demands. At the moment, it takes approximately 12-14 years for a new drug to reach market, and costs an average $3 billion.

This is a time frame and a bill that we increasingly cannot afford, particularly now with pharmaceutical companies facing rising energy costs, along with the rest of the world.

In good company

Fortunately, new technologies are emerging that are already dramatically reducing both the time to market and overheads of the drug development process.

New artificial intelligence (AI) and machine learning (ML) tools have proven particularly game-changing at the drug discovery stage, which is accountable for around a third of overall cost and time, with potentially thousands of molecules required to develop just a single pre-clinical lead candidate.

Applying advanced computational techniques to vast data sets can streamline drug R&D by building holistic and reproducible disease models and developing more specific scalable therapies.

With healthcare companies acquiring phenomenal quantities of new molecules every day, AI could play an enormous role in unlocking their potential faster and more effectively, and ultimately developing new and better medicines and drug combinations. Such technology has already enabled several drug discoveries so far.  Nuritas proved this to be true when, on 2020, it announced the first anti-inflammatory to be identified with the help of AI.

The technology is also a potential game changer in the repurposing of drugs, which saves costs and shortens the time to market by 30-60%. This is how Britain’s BenevolentAI helped Eli Lilly identify baricitinib, a rheumatoid arthritis drug, as a COVID-19 treatment.

Indeed, partnerships between big pharma and medtech start-ups have spiked in recent years, suggesting that many companies are seeing the enormous potential at the intersection of medicine and AI.

AI and new statistical analyses also make it possible to use real-life data captured from patients along their care pathway to supplement or even inform live clinical trials. Such evidence on the potential benefits or risks associated with medical products allows for innovation in clinical trial design – with fewer and/or more specific recruits, faster and cheaper trials, and predictive information that may increase the chances of approval.

There’s even the potential to create a population of virtual patients on which to simulate the effects of a medical product, intervention or device. These ‘in silico’ studies would use real-life data brought together from multiple sources, including patient monitoring devices, clinic check-ups, registers and records.

Fork in the road

Of course, there are several roadblocks that could stand in the way of the uptake of such technology. Availability of data sets is a big one, particularly in light of the recent Medibank data leak in Australia, which could see patients and health companies alike become more antsy about the sharing of medical data in future.

The upfront investment required to onboard the right technologies is another cause for industry-wide hesitation, with profits currently being squeezed across the board.
Even during COVID-19, when asked as part of Ayming’s 2022 International Innovation Barometer (IIB) whether they believed they had sufficient funding to navigate the crisis, only 58% of pharmaceutical sector respondents said yes. Additionally, a whopping 32.7% reported having no defined budget for R&D.

There’s also a broader shortage of workers with the necessary technological skill sets, and a lack of consistent, comprehensive regulatory guidelines to allay companies’ understandable concerns about entering the new space.

But the long-term benefits of investing the necessary time and resources in innovation and R&D to overcome these challenges are exponential for all involved. Organisations that recognize this, and invest in the right digital tools and upskilling, will have the opportunity to realise the full potential of AI and data-driven approaches to medicine – from drug analysis and trials, to diagnostic capabilities.
Jurisdictions that develop the necessary regulatory regimes, and provide subsidies, grants and R&D tax credits to businesses, will also benefit, making faster scientific breakthroughs and pushing their way to the forefront of global health sciences innovation.

The UK is one such jurisdiction already jostling to the head of the pack, with the Medicines and Healthcare products Regulatory Agency recently establishing an innovative licensing and access pathway to expedite access to essential new drugs in response to the outbreak of COVID-19. It is granted to medicines that address the needs of patients with life-threatening and unmet medical needs, with a view to getting the drug to the market as quickly as possible.

It’s clear that, in the current ‘post-pandemic’ paradigm, there is a massive opportunity for the sector to reposition the economic landscape for R&D.

Stakeholders across the entire global health sciences market must join forces to support and embrace this momentum while it’s high, or we face a future where the evolution of chronic and infectious diseases outpaces the evolution of our technological capabilities, which could see the entire industry reach a point of overwhelm. The clock is ticking.

Naomi Ikeda is R&D Tax Incentives Senior Manager at Ayming. Go to ayming.co.uk