Life Sciences: evidence expectations in reality

In Life Sciences, “evidence” isn’t a box-ticking exercise — it’s the connective tissue between your technical uncertainty, your experimental plan, and what you actually did when reality didn’t behave. The strongest claims feel like a short, well-edited lab story: what you attempted, why it was uncertain, how you tested it, what failed, what changed, and what was learned.

1) What HMRC actually wants to see in Life Sciences

Life Sciences R&D typically involves genuine scientific and technical uncertainty — but HMRC will still test whether the uncertainty is real (not just effort), whether the approach is systematic, and whether the narrative is coherent. In practice, good evidence answers:

• What was unknown? (and why competent professionals couldn’t readily resolve it at the outset)
• What hypotheses/methods were tested? (and why those approaches were chosen)
• What iterations occurred? (changes to parameters, materials, protocols, designs, assay conditions, etc.)
• What failed — and what was learned? (negative results are often the most persuasive)
• What was achieved? (even if only partial success, performance thresholds, reproducibility, stability)

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2) “Strong” evidence isn’t more — it’s cleaner

The common failure mode is not lack of work — it’s lack of a clean evidence trail. In Life Sciences, the documents usually exist (ELNs, protocols, assay runs, batch records, QC reports), but they’re scattered and not translated into a short, understandable thread. A strong approach is to build a small evidence pack per project.

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3) The boundaries that trip Life Sciences claims up

Most Life Sciences businesses do a mix of R&D and operational work. The boundary issues that create friction are usually:

• Routine testing vs experimental testing: QC to confirm specifications is usually BAU; experimental runs to establish feasibility/performance are different.
• Scale-up and tech transfer: sometimes qualifying (where uncertainty remains); sometimes operational (where it’s execution against known parameters).
• Regulatory documentation: can support the story, but the claim must still be anchored in uncertainty + experimentation, not paperwork volume.
• Manufacturing & batch work: production batches are typically non-qualifying unless they are demonstrably part of experimental development.

A simple way to describe “why it’s R&D” in Life Sciences

A reliable pattern is: uncertainty → hypothesis → method → iterations → results → learning. Even when outcomes are negative, the learning is often exactly what shows systematic resolution attempts.

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4) What “good” looks like by evidence type

ELN entries

HMRC won’t read every ELN entry — but selected extracts can be powerful if they show intent, parameters, and change rationale. Choose entries that clearly show decision points (why you changed a variable, why you dropped an approach, why you adopted a new method).

Protocols / method development

“Protocol v1 → v2 → v3” is often a strong narrative backbone. Include the delta (what changed), and tie changes to measured outcomes.

Assay runs / validation extracts

Pick a small number of runs that show exploration (ranges, controls, sensitivity, repeatability) rather than routine acceptance tests. The aim is to show that outcomes were uncertain and were resolved through structured experimentation.

Batch records / scale-up notes

Use batch evidence only where it demonstrates uncertainty (e.g., stability issues, yield variance, reproducibility breakdown) and the responses to it. Otherwise it can dilute the claim by looking like production.

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5) Turning evidence into a defensible narrative

The highest-impact improvement most Life Sciences claimants can make is editorial: reduce complexity and make the story easy to follow. A concise structure per project works well:

• Objective: what you were trying to achieve (in measurable terms where possible)
• Baseline: what was known/available and why it didn’t solve your problem
• Uncertainty: the specific unknowns and why they weren’t readily resolvable
• Approach: your hypothesis/method and why you chose it
• Iterations: changes made and evidence of why (results/observations)
• Outcome: what you achieved/learned (including partial success)
• Link to costs: who did what and how time/cost is allocated

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6) Cost logic: make it explainable to a third party

Even if your science is strong, poor cost logic can undermine credibility. In Life Sciences, common cost risks are:

• Mixed roles: staff spanning R&D and BAU without a consistent allocation rationale
• Routine testing included by default: QC/acceptance activity pulled into R&D buckets
• Consumables: unclear linkage to experimental work vs production or operational use
• Third parties: unclear contracts / unclear distinction between subcontracted R&D and outsourced services

A good standard is to be able to answer: “If an officer asked why this person/cost is in the claim, could we show the work and the rationale in 60 seconds?” If not, tighten the story and/or adjust allocations.

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7) The “no drama” way to stay consistent year-round

You don’t need heavyweight governance. A lightweight monthly rhythm can keep evidence and narratives tidy:

• Maintain a simple project log (objective, uncertainty, key iterations, outcomes)
• Save 3–5 key artefacts per project as the year evolves (not at year-end)
• Keep a short note on role allocations and any changes in responsibilities