Decision-ready Data: Make the Call with Confidence, Not Crossed Fingers
In high-stakes drug discovery, teams are often asked to commit millions of dollars, months of work, and organisational credibility based on data generated early in the process.
The science may look solid. The models may behave as expected. And yet, when programmes fail later, the question is always the same: why didn’t we see this sooner?
Decision-ready data exists to close that gap. It gives teams the evidence they need to make – and defend – critical calls with confidence.
The real cost of late-stage failure
Many late-stage failures can be traced back to the same root cause: early data that didn’t reflect how compounds behave in real biological systems.
In early discovery, biochemical assays and engineered cell models are essential for narrowing chemical space and generating early signals of activity. But they also simplify biology and remove much of the complexity that ultimately determines clinical success.
Proteins exist in networks, not isolation. Cells impose barriers, compartments, and competing interactions. When early decisions fail to account for this complexity, teams are left inferring what might be happening rather than measuring what is.
As programmes progress, questions that once felt theoretical become practical and urgent. Does the compound engage its target in a physiological context? Does it behave the same way in complex systems as it does in simplified ones? Are we advancing based on evidence, or inference?
When those questions remain unanswered, risk accumulates quietly. By the time uncertainty turns into failure, the cost of correcting course is high – often too high to justify. Measuring target engagement in intact cells, tissues, or lysates earlier preserves the biological relevance that can prevent those failures from happening in the first place.
What makes data “decision-ready”?
Decision-ready data isn’t about generating more information. It’s about generating the right information at the right time.
At its core, decision-ready data does three things well.
First, it reflects biology as it actually exists, not simplified for convenience. Data generated in relevant cellular or tissue contexts is more likely to predict downstream behaviour than data produced in isolated systems.
Second, it arrives early enough to influence strategic direction. Evidence that emerges after major commitments have been made may explain disappointing outcomes, but it rarely changes them.
Third, it is interpretable beyond the lab. Decision-ready data supports clear narratives: why a programme should move forward, why it should pause, or why a pivot is justified. It gives teams confidence that their recommendations will stand up to scrutiny.
This is especially important at go / no-go decision points. An early, evidence-backed “no” can save years of work and significant investment. It also builds trust that decisions are being made deliberately, not reactively.
De-risking earlier doesn’t mean slowing down
There is a persistent concern that adding biological validation early will slow discovery. In practice, the opposite is often true.
When teams can confirm – or rule out – target engagement in relevant systems early on, they avoid investing in programmes that are unlikely to translate into clinical success.
Resources are focused more effectively, discussions with leadership become clearer, and decisions are made with intent, rather than hesitation.
The goal isn’t to eliminate risk entirely – an unrealistic dream in the world of drug discovery! – but to reduce avoidable uncertainty with the right evidence, generated at the right moment.
CETSA®: A confidence tool, not just another assay
CETSA provides biologically relevant target engagement data by measuring compound–target interactions directly within intact cells, tissues, or lysates under physiologically meaningful conditions.
Unlike traditional biochemical assays that rely on engineered proteins, tags, or artificial buffers, CETSA:
- Measures binding in a natural, unmodified environment – preserving native protein interactions.
- Is label-free and tag-free, meaning neither the target nor the compound needs modification.
- Provides data that correlates more closely with what you would see in real biology.
For teams facing critical decisions, this kind of evidence can be transformative.
CETSA data helps answer questions that traditional assays often leave open. Does the compound reach the target in complex biology? Is the mechanism real, or assumed? Are we seeing true engagement, or artefacts of the system?
Crucially, CETSA delivers these insights early enough to influence key decisions, not just explain them after the fact.
Make the call with confidence
CETSA is used across the industry to support translational decisions, from early discovery through to preclinical development. That adoption reflects a broader shift in how teams think about risk, moving away from wishful thinking and towards decisions grounded in evidence.
Every drug discovery programme reaches moments where the data looks promising, but uncertainty remains. Decision-ready data helps teams move from “We think” to “We know” at exactly those moments. It supports decisions leaders can stand behind, whether the answer is go, stop, or change course.
Because the most expensive failures in drug discovery are the ones that could have been avoided.
Decisions this big deserve data this good.
Want to explore what decision-ready data looks like in practice?
See how teams like yours use biologically relevant target engagement data to support real decisions.
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