On Wednesday Moderna’s share price almost tripled after it announced the first successful Phase 3 trial of a personalised mRNA cancer treatment. Headlines quickly presented the result as an "AI-designed cure". The reality is more complex, but also perhaps just as consequential. Moderna and Merck have reported positive Phase 3 results for intismeran, which is intended to stop melanoma returning after surgery. The trial involved over 1,000 patients and compared the treatment, plus Keytruda, with Keytruda alone. Detailed results have not yet been published, but the combination improved both recurrence-free survival and the time before cancer spread elsewhere.
What is historic here is that each dose of intismeran is made for one patient. Doctors sequence the patient’s tumour and compare it with healthy cells to identify mutations unique to the cancer. This can produce a large number of potential targets. Moderna’s computational system then ranks them according to factors such as whether the mutated gene is active, whether the resulting protein fragment is likely to be displayed by the patient’s immune cells, and whether it is sufficiently different from healthy human proteins.
This is where most of the "AI" sits. The work runs on a specialised bioinformatics and machine-learning pipeline rather than on LLMs. Moderna has discussed using neural networks in vaccine and mRNA design before, and has done so here too. It has not published the architecture, the training data or the current version of the production models behind intismeran.
The software selects up to 34 tumour-specific targets called neoantigens. These are encoded into a single mRNA sequence, manufactured and injected into the patient. Their cells read the mRNA and temporarily produce the selected antigens. This trains the immune system to recognise cancer cells carrying the same mutations. Keytruda then removes one of the controls that can prevent immune cells from attacking the cancer.
The central breakthrough is the programmability of mRNA. Once the delivery and manufacturing platform exists, the informational payload can be changed without developing an entirely new production method. For intismeran, the same platform produces a different molecular instruction for every patient.
The exciting progress here is that molecule design may be starting to resemble software engineering. Researchers define a required function, generate a sequence, predict how it will behave, compile it into a physical product and test the result. The comparison is useful, although biology is much less predictable than a computer. Cells are variable, molecular interactions are difficult to observe, and a sequence that works in one biological environment may fail in another.
A new Anthropic experiment shows where other forms of AI could contribute. Claude was given access to specialist protein-design models, scientific literature and computing resources. It planned design campaigns, selected target sites, generated candidate proteins, evaluated them and chose which should be tested physically. Across the reported work, it produced 1,320 designs and laboratory testing confirmed 354 as binders.
Claude was coordinating the scientific process rather than simulating proteins itself, much as an agentic coding system can plan a software change, use specialist tools, run tests and revise its work. Moderna style predictive models can rank molecular candidates. Generative models can propose new sequences. Reasoning models can increasingly connect those tools into an experimental workflow.
Takeaways: Intismeran should not be reduced to an AI vaccine. Its success combines tumour sequencing, machine learning, mRNA engineering, automated manufacturing and immunotherapy. The deeper development is that medicines are becoming programmable at the level of individual patients. AI will help choose, generate, test and refine those molecular instructions, but mRNA is the wonder technology that allows the programme to be executed inside the body.