Why a $10M Full-Service CRO POC May Be the Wrong Investment for Early & Mid-Stage Teams

Alzheimer’s proof-of-concept (POC) trials rarely end with a clean answer. Instead, they end with language that sounds careful but unresolved: directionally positive, high variability, hard to interpret.

For sponsors, that ambiguity is expensive.

For patients, it’s something more personal.

For someone like John, 68, time is already narrowed. He joined a POC study not expecting certainty, but hoping for clarity, whether something meaningful was changing while there was still time to act.

When clinical trials struggle to deliver learning, patients feel the cost before it shows up in a budget line.

That gap raises an uncomfortable question: If the job of a POC is learning, why do so many default to delivery models built for scale?

This is where the full-service CRO POC becomes a trap. It can purchase infrastructure and process when what’s needed most is architecture that protects signal.

Let’s look at a case.

Case: Project Lumen

Project Lumen involved a midsize pharma company with an Alzheimer’s asset that had a plausible mechanistic story, early human safety, and a biomarker hypothesis expected to show movement within months.

The goal wasn’t to win Phase III yet. It was to answer a single question: is there enough signal, clinical, functional, biomarker, or some combination, to justify the next major investment?

For patients like John, this kind of trial carries quiet weight. It represents a chance to learn something meaningful before progression makes learning harder.

Constraints

The company faced familiar pressures:

• a tight portfolio decision window

• internal pressure for speed (“get into clinic”)

• executive desire for simplicity (“one vendor, one throat to choke”)

• limited in-house bandwidth to run a complex neurology study

Leadership wanted speed and simplicity. One vendor. One plan. Fewer moving parts.

The Decision

The team chose a full-service CRO for a Phase 2a/POC study. The rationale sounded responsible:

• “They do Alzheimer’s all the time.”

• “They have sites.”

• “They can manage imaging, labs, central raters.”

• “Turnkey will be faster.”

The expected outcome was also reasonable:

• one contract

• one project plan

• clean execution

• decision-grade readout

From the outside, it looked like certainty. From the inside, it introduced distance, from the science, from adaptation, and from what patients were actually experiencing.

What they planned to run

On paper, the design looked straightforward:

• Early symptomatic Alzheimer’s (e.g., MCI due to AD / mild AD)

• Amyloid-positive confirmation (PET or CSF; sometimes plasma screening as a pre-screen layer)

• ~24–36 weeks duration

• ~10–20 sites (with central rating and imaging vendors)

• A familiar stack of endpoints:

  • clinical/cognitive (e.g., CDR-SB, ADAS-Cog, composites)

  • functional/PRO elements

  • biomarker readouts (fluid + imaging, depending on mechanism)

  • safety, tolerability

• The stated goal was decision-grade signal, not label-grade proof

In practice, it behaved like an expensive, slow-moving machine.

So, what happened?

Why Alzheimer’s POC is uniquely vulnerable to the full-service mismatch

A full-service CRO model isn’t inherently wrong. It’s optimized for scale, standardization, and consistency. The problem is that Alzheimer’s POC trials demand something very different.

The real challenge isn’t scale, it’s measurement

Alzheimer’s POC is a measurement challenge before it’s a sample-size challenge. Cognitive and functional endpoints carry natural variability. Rater effects compound across sites. Changes unfold slowly and subtly, making it difficult to distinguish real signal from noise over short timeframes. Placebo and practice effects further blur interpretation, while visit burden and missingness quietly erode data quality.

More biomarkers don’t mean more clarity

In this environment, architecture matters more than volume. Early learning depends less on how much data you collect and more on how precisely the study is built to detect change.

That challenge is magnified by the complexity of Alzheimer’s biology. Even relatively lean POCs often include amyloid confirmation, imaging cores, fluid biomarkers, central raters, and digital assessments. When these elements are bundled under a full-service umbrella, the system can appear simpler on paper, while becoming harder to tune in practice.

When learning lags, patients pay the price first

Small delays have outsized consequences. If rater drift, site variability, or burden-related missingness isn’t detected early, sponsors don’t just lose time. They lose confidence in the endpoint movement they do observe.

The economic mismatch from a patient’s POV

From John’s side of the trial, the value equation is simple. Every visit, every assessment, every hour rearranged around the study should move understanding forward. A proof-of-concept trial, at its core, is an exchange: patients give time, effort, and trust in return for learning that can guide what happens next.

That’s why a POC should maximize learning per dollar. Not because budgets matter less to patients, but because time does.

In a full-service model, much of the spend goes into managing the system rather than sharpening what the system can detect. Layers of project management, default-heavy monitoring, bundled vendors, and slow change processes add structure and cost, but they don’t necessarily help the study listen better. From a patient’s perspective, the machinery grows while insight lags.

The result is a clinical trial that costs more, moves slower, and protects less signal. Patients like John complete visits and assessments believing they are contributing to clarity, only to learn later that the data was difficult to interpret. The money was spent. The effort was real. The learning was not.

In Alzheimer’s POC, that mismatch isn’t just economic. It’s personal.

This is how a “manageable” POC quietly becomes Phase III-level operating spend, without Phase III-level certainty.

What should have been done differently

The sponsor didn’t struggle because they hired a CRO.

They struggled because they outsourced trial architecture decisions that are non-delegable in Alzheimer’s POC.

To generate decision-grade data, sponsors must retain ownership of the elements that protect signal: a clear signal map with explicit success criteria, disciplined endpoint selection, a cohort strategy that reduces heterogeneity, and a rater approach treated as a core design feature, not an operational afterthought.

Just as important is cadence. Alzheimer’s POC requires real-time visibility into recruitment friction, missingness, site variability, and early endpoint movement, with pre-defined triggers for adaptation. “Wait and see” is rarely a viable strategy when learning windows are narrow.

The alternative: sponsor-led architecture with modular execution

For many early and mid-stage Alzheimer’s teams, the answer isn’t DIY clinical operations. It’s sponsor-led architecture paired with modular execution partners.

This approach keeps strategic control close to the science and the patient, while allowing sponsors to plug in specialized services including imaging, central rating, enrichment workflows, analytics, or right-sized monitoring, only where they create real lift.

That’s the Rubix LS thesis: remove Phase III-grade overhead from Phase II questions, accelerate learning loops, and design POC systems that protect signal rather than simply deliver tasks.

The point isn’t fewer vendors. The point is a system you can steer.

The goal isn’t simply to spend less. It’s to spend toward clarity.

When full-service does make sense

To keep this grounded: there are cases where full-service is exactly right, especially when you’re truly scaling:

• large global programs

• complex supply/device logistics

• late-stage regulatory complexity across regions

• programs where operational consistency at scale is the dominant risk

But for many Alzheimer’s POCs, where learning and signal detection are the dominant risks, the full-service model can be an expensive default.

Closing: The real ROI metric is decision clarity

A $10M Alzheimer’s POC isn’t too expensive if it delivers a clean, credible signal that justifies the next stage. It’s a poor investment when it buys infrastructure over insight, cadence over learning, and standardization over signal protection.

Rubix LS helps teams design sponsor-led POC systems built for what Alzheimer’s actually demands: lean execution, faster learning, and data that supports real decisions.

If a full-service Alzheimer’s POC budget feels like Phase III overhead applied to a Phase II question, the right question isn’t whether it can be afforded. It’s whether the design will produce decision-grade clarity or expensive ambiguity.

If this feels familiar, contact Rubix LS to discuss how a sponsor-led, modular POC approach could improve signal, speed, and decision clarity.