03/03/2022 | Spotlight
One of the more intriguing of the countless statistical reports that cross my desk every week was a recent one that calculated the rising number of times the words Artificial Intelligence had been used within the filings of companies in the pharmaceutical industry. It reported a rise of 24 per cent between the first and second quarters of last year – and an overall 105 per cent increase since 2016, the year information service providers, Global Data, began tracking these things.
I realise that, without close examination of any of these references, it’s difficult to provide proper context, but it did give a strong indication in numbers of the significant increase in activity, along with a global insight and what researchers called an “approximate indication” of the main players involved in the subject.
Asia scored highest overall, which is why of the 50 biggest employers Lupin Ltd of India made the most references, with 141 – or, to put it another way, 0.9 per cent of all sentences – followed by F. Hoffmann-La Roche, along with Grifols SA, Astellas Pharma Inc and Johnson & Johnson. The issue came to the fore again shortly after Prof. Dame Sarah Roberts – a co-creator of the Oxford-AstraZeneca COVID-19 vaccine issued what was widely seen as a rallying cry for increase investment and support against a potentially more deadly and economically devasting pandemic that has to come.
Her speech at the 44th Richard Dimbleby Lecture struck a chord with many who have championed for far greater investment within healthcare as well as pandemic protection and preparedness, citing huge strides made since the original COVID-19 outbreak in 2020.
She echoed the likes of Philip’s healthcare division, who believe a decade of normal healthcare progress was made in just three months early in the pandemic.
But she also warned that the future outbreak could be more contagious and lethal than Covid and that the pace of research that drove the earlier delivery of vaccines and other anti-virus measures must be maintained. In her words: “We cannot allow a situation where we have gone through all we have gone through, and then find that the enormous economic losses we have sustained mean that there is still no funding for pandemic preparedness.
“The advances we have made, and the knowledge we have gained, must not be lost.” She was using the lecture to highlight the launch of a UK project to create a 100-day vaccine strategy against future pandemics; one hunting £3.5 billion of investment to enable pre-prepared vaccines and to grow manufacturing capacity. It aims to develop 100 prototype vaccines for the 25 viral families known to infect humans, so that any new virus with pandemic potential could be met with a bespoke vaccine within 100 days.
Her comments came as the global death toll passed 5.25 million and case numbers reach 266 million, and were seized upon by the wider community as a rallying cry that “governments and the investor community needed to hear”.
That struck a chord with Paul Sheedy, co-founder of the not-for-profit World Nano Foundation, who said: “We have made so much progress, not only in fighting this pandemic, but others potentially lurking in the shadows, and we cannot afford to now sit back an allow pandemic protection and preparedness or overall healthcare to shrink back to pre-Covid levels of research and investment.
“Over 220 pathogens have emerged in the past 100 years with the potential to impact global healthcare, so we need universal vaccines and therapeutic solutions to stop these viruses finding hosts in the first place.”
And Paul Stannard, Chairman of the Luxembourg-based Vector Innovation Fund (VIF), which specialises in identifying and attracting healthcare technology investment was quick to point out the “transformational innovations using nanomedicines as well as computational AI drug delivery”, adding that he was “staggered by the speed of innovation coming through our investment pipeline”.
Four days later, Nano Magazine sent me an in-depth report highlighting how the need for vaccines and antiviral drugs has renewed focus on what it called the limits to what medicine can currently treat and the speed with which we discover and develop new drugs for any disease.
“This focus has further fuelled AI-centric efforts to improve the drug development process. Joining over 100 other companies focused on an AI-driven approach, Google’s parent Alphabet has launched a company called Isomorphic Labs to utilise deep learning in drug discovery,” authors pointed out. “As in many other industries, using AI to solve challenges in drug discovery has captured the imagination of companies, investors, and the public.”
Some of the leading drugmakers, from Pfizer, Johnson & Johnson, Merck, AbbVie in the US, Roche and Novartis in Switzerland and the likes of Sanofi in France and the UK’s AstraZeneca and GSK – are currently investing in AI, often as a result of specialist partnerships.
AI systems are also helping to improve basic patient care in the likes of rural China and Africa, where there are shortages of healthcare professionals. Using data and an advanced technology, machines can help to put patients in touch with the most relevant physicians or help doctors make diagnoses and determine treatment remotely.
“There’s more and more data available, and the power of AI to recognise patterns helps us understand things that we otherwise couldn’t have done,” said Ameet Nathwani, MD and Chief Medical Officer at Sanofi.
“We’re only beginning to learn how to apply it usefully to many parts of life. In health, where there’s so much information – genetic information, proteomic information, or the study of proteins, clinical data, social data – it’s allowing us to see patterns and gain insights into outcomes for patients that we couldn’t have dreamt about. AI will fundamentally change how we look at disease and health. The medical future in 10 years’ time will be totally different because of AI.”
The factory of the future, which Sanofi is currently implementing, will include connected and intelligent equipment, with sensors capable of taking thousands of measurements throughout the production process and generating billions of data points used to monitor, analyse and control the manufacturing process. State-of-the-art analytical techniques will predict and prevent variations and ensure the quality of biological medicines.
Arda Ural, of Ernst & Young Health Sciences and Wellness Practice recently highlighted the significance of emerging technologies at a time when financial confidence is increasing, when he wrote how “the rapid development and mass deployment of COVID-19 vaccines, including the pioneering mRNA vaccines, highlighted to stakeholders what the industry is capable of achieving. At the same time, new technological advances are opening up the possibility of the life sciences industry making other breakthroughs that will transform the health experiences of patients, while potentially saving millions of lives.”
One promising AI-pharma partnership is that involving Paris-based Sanofi and Exscientia, an Oxford-headquartered specialist in using AI to modernise drug discovery. Together, they plan to develop up to 15 novel small molecule candidates across oncology and immunology, leveraging Exscientia’s end-to-end AI-driven platform and utilising actual patient samples. The companies have been working together since 2016 and in 219 Sanofi in-licensed Exscientia’s novel bispecific small molecule candidate capable of targeting two distinct targets in inflammation and immunology.
Frank Nestle, Sanofi’s Chief Scientific Officer, said they aim to “transform how we discover and develop new small molecule medicines for cancer and immune-mediated diseases”. He added: “Application of sophisticated AI and machine learning methods will not only shorten drug discovery timelines, but will also help to design higher quality and better-targeted medicines.”
Key to this will be Exscientia’s personalised medicine platform which enables a “patient-first” approach by integrating primary human tissue samples into early target and drug discovery research. Scientists can then integrate patient, disease, and clinically relevant data into decisions on potential new medicine candidates earlier in the drug creation process. Exscientia will also lead small molecule drug design and optimisation activites up to development candidate nomination, with Sanofi assuming responsibility for development, manufacturing and commercialisation.
Exscientia CEO Andrew Hopkins, said: “Our expanded collaboration with Sanofi will utilise the breadth of our platform to test AI-designed drug candidates against patient tissue models, potentially providing far better accuracy than conventional approaches such as mouse models. When you consider the change that this will represent – testing candidates against actual human tissue years before a clinical trial – it’s transformative.”
Roche and its biotechnology partner Genentech are currently applying Machine Learning across disease areas and therapeutic modalities, with the goal of creating better models for drug discovery that are predictive, generative and interpretable. This trifecta of model characteristics could be used to predict whether a specific molecule can access a target; generate a molecule to bind to that target: and explain how the target and molecule will then go on to interact with each other.
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