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AI in pharma: From lab development to patient impact

pharmafile | September 5, 2026 | Feature | | AI, Cancer, Immunology, Merck, Vaccine, Virology, personalised medicine, pharma

In the decade to 2025, AI use in vaccine development has increased markedly but is the boom in research translating into real clinical candidates? Pharmafile catches up with Dr Anton Yuryev, Consulting Director of Bioinformatics and Data Science at Elsevier to find out more about how AI is creating practical change in the pharma sector…

AI has become a major force in vaccine research. Beyond accelerating discovery, where is it having the greatest practical impact on pharmaceutical R&D today?

AI’s biggest practical impact in vaccine development today is in the earliest, most data-intensive stages of R&D. That includes identifying the right antigen, understanding how a pathogen changes over time, and predicting how candidates are likely to perform and behave safely in humans. AI models can help predict how a candidate may interact with the immune system, assess potential safety signals and improve formulation choices early on.

Elsevier’s analysis suggests research activity rises and falls sharply in response to disease outbreaks. What are the risks of this boom-and-bust approach to innovation?

Outbreaks naturally drive a greater volume of research in their wake. The risk is that if research then drops back between outbreaks, the science isn’t ready when the next one hits. Ebola vaccine research is one example of this trend – it is currently more reactive than proactive, rising in the wake of an outbreak and falling once it is out of the news. That’s a problem because a vaccine exists for one Ebola species, Zaire ebolavirus, but other species still pose vaccine and treatment challenges that research struggles to keep pace with.

The pattern is not that clear, though. Low human case numbers do not always mean low research volume; H5N1 is a recent example. And because research takes time to conduct and publish, output peaks often trail the outbreak that prompted them. The key to building stronger disease preparedness is sustained investment in research. This can help scientists build the evidence base between outbreaks by collecting data across strains, tracking virulence, fatality rates, mutations, viral evolution and immune escape. Doing so means new vaccines and diagnostics aren’t starting from scratch each time a new threat surfaces.

One positive sign of progress is that the COVID-19 pandemic showed we can develop a vaccine in a year. Since it is impossible to predict what the next pandemic will be, there remains a challenge in shortening the vaccine development pipeline for all diseases to months and, ideally, weeks.

Your data shows declining publication activity around Ebola but renewed interest in H5N1. What does that tell us about how the industry prioritises emerging threats?

COVID-19 is still a recent event, so there is a heightened awareness of the need for vaccines and improved pandemic preparedness. We’re seeing this reflected now in H5N1 vaccine research as awareness of bird flu as a wider zoonotic threat grows. After a steady output of research over the past decade, H5N1 vaccine publications rose sharply in 2024 and 2025, likely coinciding with concern about H5N1 moving beyond birds into mammals and humans. One likely contributing factor to this awareness is the multistate US dairy cattle outbreak reported in March 2024.

Research output increased from 111 papers in 2023 to 159 in 2024 and 203 in 2025, showing how quickly scientific attention can grow when an emerging threat becomes more visible. The next step is for the industry to work more closely with organisations like the CDC to translate research into real-world vaccine deployments.

AI-generated discoveries are attracting enormous attention, but where do you think we are on the journey from promising research to clinically useful vaccines?

While there has been huge growth in the amount of AI-supported vaccine research, there have only been a few reports so far of candidates progressing beyond the lab. So, the 5,200% growth represents a rapidly expanding research base rather than a proxy for clinical progress. AI-supported vaccine research is still mostly at the exploratory phase in terms of clinical translation. In the event of another pandemic, we could expect clinical translation to accelerate because high case numbers make it possible to test and validate far more candidates in the field.

Personalised cancer vaccines are often described as one of the most exciting areas of development. What obstacles still need to be overcome before they become routine clinical practice?

A central challenge in cancer vaccine development is identifying tumour-specific antigens, particularly neoantigens – mutant proteins unique to cancer cells. Tools such as NetMHCpan, MHCflurry and DeepHLApan use neural networks to predict which peptide fragments will bind to a patient’s specific human leukocyte antigen (HLA) molecules and trigger a robust T-cell response. We’re already beginning to see interesting developments in recent trials, such as Merck’s [1] early findings on a melanoma vaccine given in combination with Keytruda.

As candidates move toward the clinic the question changes from whether a model can identify a biological signal, to is the signal reproducible, safe, clinically meaningful and acceptable to regulators. Human oversight is even more important at this stage. AI can help prioritise candidates and support experimental design, but it cannot replace validation, clinical trials or regulatory review. Patient and clinician trust is another element of adoption, a personalised vaccine can only become routine care once people are confident in how it was designed, not just that it works.

Another barrier to overcome is that developing a cancer vaccine for every patient tumor is very costly. As a result, advances will need to be made in achieving economies of scale. We are also likely to see the development of universal or semi-personalised cancer vaccines that are more affordable for health systems.

Scientific publishing generates an enormous amount of research data. How is large-scale analysis changing the way companies identify opportunities and make R&D decisions?

Some of the biggest gains are in the earlier stages of R&D, where AI can help researchers search and analyse scientific literature, identify promising targets, predict safety and ADME properties and connect biological evidence across literature, pathogen biology and clinical data to generate hypotheses for experimental validation.

Realising those benefits means validating models more rigorously, using high-quality and traceable data, and ensuring expert review is built into decisions about which candidates to progress. For researchers, the test is whether they can trace why a candidate was prioritised, what evidence supports it and what uncertainty remains.

Looking ahead five years, what developments in AI-enabled drug and vaccine discovery are most likely to have a tangible impact on patients and healthcare systems?

Pan-population neoantigen vaccines, which target neoantigens shared across a patient population rather than one individual tumour, are the cutting edge of immunotherapy and likely to be among the most tangible developments. Machine learning models can integrate data from whole-exome sequencing, RNA sequencing and HLA typing to identify the shared neoantigens worth including in a formulation – often delivered as mRNA vaccines, where AI can also support stability and design optimisation.

AI can also help to build a picture of patient-specific immune responses. By analysing data from single-cell sequencing, flow cytometry and clinical records, machine learning models can predict the patients most likely to benefit from a particular vaccine. This stratification can enhance clinical trial design, reduce costs and support the case for regulatory approval.

[1] https://www.bbc.co.uk/news/articles/c79gpv7v190o

Pic: Matnapo. Cottonbro