Neuroimaging Biomarkers · CNS Drug Development

From Imaging-Derived
Signals to Trial-Ready
Endpoints

Independent strategy for neuroimaging-derived digital biomarkers (NdDB) and digital medical device evidence across the full Phase II chain — before protocol commitments, vendor lock-in, or costly trial amendments.

20+

Years CNS
Neuroimaging

5

Steps to
Endpoint-Grade

6

Identified
Failure Modes

Map Your Failure Mode Work With Me

Not ready to reach out? Download the NdDB Readiness Map — useful for sponsors, CROs, core labs, and imaging platforms.

01 — Identify the Gap

Where Does Your Program Stand?

Phase II NdDB adoption most often fails not because the biomarker signal is weak, but because the acquisition-to-output measurement chain is not controlled to the level required for the intended endpoint role.

Most Phase II NdDB efforts do not fail because the biomarker signal was wrong. They fail because no single function owned the full governance chain — from clinical intent through acquisition, QC, and processing to reported outputs. Clinical development defined the endpoint. Imaging managed the measurement. Biostatistics wrote the SAP. Nobody owned the gap between them, and nobody found out until commitments were already locked.

If that sounds familiar, you are not alone. Select the failure mode closest to your situation.

Endpoint Role Not Prespecified

The NdDB's intended trial role has not been translated into a minimum adoption package before protocol and SAP commitments are locked.

Select

Acquisition Chain Underestimated

Multi-site scanner variability, protocol inconsistencies, upgrades, and site drift are not controlled to the level required — making the NdDB vulnerable to noise at the required control level.

Select

Processing Pipeline Not Endpoint-Grade

The computation chain lacks version control, end-to-end QC lineage, and prespecified reprocessing rules needed for the intended decision role — even when the biomarker concept is clinically valid.

Select

Change Control Not Defined Upfront

What counts as a change, who approves it, and how comparability is protected across updates has not been specified before the trial runs — creating late governance amendments and auditability gaps.

Select

Vendor Governance Gaps

Vendor contracts do not embed the required audit access, delivery specifications, or change-control clauses — creating lock-in risk and uncontrolled pipeline drift during the study.

Select

Regulatory Posture Misaligned

FDA clearance or CE marking is assumed to validate the trial claim, but the device intended use does not match the trial role — or SaMD lifecycle controls have not been applied proportionally to the intended endpoint criticality.

Select

02 — Assess the Candidate

The NdDB Readiness Map

Once you've identified your failure mode, the Readiness Map gives you a structured diagnostic to evaluate whether your candidate can support the intended trial role — and at what pipeline grade. A 3-step framework designed to avoid two common traps: overbuilding governance too early and underbuilding it until it is too late.

1

Triage

Eligibility gate & output classification

2

Select

Pipeline maturity grade

3

Apply AI

As a governance multiplier

1

Triage

The Candidate Biomarker

Is the output clearly defined and prespecifiable across sites? Does it map to a credible output class for the intended trial role? Candidates that cannot answer both questions should not proceed to pipeline selection.

Key question

"Does this output unambiguously match the intended clinical variable for the prespecified trial role?"

2

Select

Pipeline Maturity Grade

Research-grade, GCP-grade, and device-grade pipelines enable different trial roles. The maturity grade applies to the delivery system — not to the biomarker concept itself. Each grade has different credible uses.

Key question

"Which trial roles are credible for this output, given the delivery system we can realistically govern?"

3

Apply AI

As a Governance Multiplier

Whether AI outputs can support the intended endpoint role depends on whether the model version is locked, the update policy is defined, and change control is proportionate to trial criticality.

Key question

"Is version locking, update policy, and change control proportionate to the trial criticality of this output?"

Digital biomarkers are constructs.

Regulation applies to the device or software that generates the measurement — not to the signal itself.

Intended use drives governance burden.

Moving from exploratory use to a primary endpoint sharply increases expectations for prespecification, traceability, and change control.

Pipeline maturity is a deliberate decision.

The same candidate biomarker can be delivered research-grade, GCP-grade, or device-grade. Maturity applies to the delivery system.

Self-Assessment Tool

Where does your program stand?

Six dimensions. Eighteen questions. Immediate readiness profile.

Take the Assessment →

PDF

The full methodology is in the white paper

Output classification, maturity grade criteria, and AI governance framework — a practical decision framework for everyone governing neuroimaging-derived biomarkers across the Phase II chain: sponsors, CROs, core labs, and imaging platforms.

Download the Readiness Map ↓

03 — Resolve the Gaps

How I Can Help

Knowing where your program stands is the starting point. The 5-step operating model translates the assessment into governance action — early readiness gates before protocol lock, prespecified change-control rules, and vendor oversight — without replacing core labs or building new internal infrastructure.

01

Align
Endpoint

02

Align
Acquisition

03

Change
Control

04

Exception
Oversight

05

Capture
Lessons

Step 01

Align the Endpoint and Decision Role

Before any pipeline, vendor, or protocol decision is made, the NdDB's intended trial role must be explicit — and shared across the functions that will execute it. Without this alignment, different teams hold different assumptions about what "reliable enough" means. Those assumptions collide after commitments are locked, when changes are expensive and options are constrained.

The goal of this step is a shared, documented definition of the endpoint role — and a named integrator accountable for connecting clinical intent to technical and operational constraints before the program is committed to a protocol, a vendor, or a delivery model.

What this step prevents

Late protocol and SAP amendments driven by misaligned endpoint expectations. The most expensive discovery in NdDB adoption is finding out — after protocol lock — that clinical intent, acquisition reality, and processing constraints were never reconciled.

Step 02

Align Acquisition and Processing, Then Lock Requirements

The acquisition-to-output chain is rarely owned end-to-end by a single function. Sites, imaging core labs, and processing vendors each hold part of it — and mismatches between them can silently compromise endpoint stability even when the biomarker signal is clinically robust and the images are technically adequate.

This step confirms that what sites can realistically deliver, what the core lab will QC, and what the processing pipeline requires are mutually consistent — and that this has been verified before the protocol and vendor commitments are locked.

What this step prevents

Reprocessing cycles, unplanned site retraining, and the silent sample-size erosion that follows late QC failures. Particularly critical in psychiatry and rare diseases, where acquisition variability can easily overlap the biological signal.

Step 03

Define Change and Comparability Rules Before the Trial Runs

Minor updates to software versions, QC rules, or processing settings can shift endpoint values. If no one has defined what constitutes a change, who approves it, and how comparability is protected before those changes occur, the endpoint becomes progressively harder to defend — regardless of how well it was designed.

This step establishes a prespecified change-control framework and traceability standard before the trial runs — so every endpoint value can be reconstructed, every change has an approver, and vendor relationships remain auditable throughout the study lifecycle.

What this step prevents

Late governance amendments, vendor lock-in, and unexplained shifts in endpoint values. Sponsors who define change-control upfront resolve processing questions quickly — rather than opening protocol-level investigations mid-study.

Step 04

Run Exception-Driven Oversight During Execution

Operational QC confirms that images were received and processed. It does not confirm that the endpoint remains stable and defensible under the governance standard required for the intended decision role. Drift, scanner upgrades, override patterns, and processing inconsistencies require a separate, sponsor-side view.

During execution, oversight is exception-based: the sponsor is engaged when reliability signals degrade, not as a continuous manual reviewer. Resolutions are documented so the endpoint remains reconstructable and audit-ready throughout the study.

What this step prevents

Incorrect Phase II conclusions driven by avoidable noise, drift, or undetected processing changes. Decision confidence improves because the measurement chain is monitored — not assumed stable. The value is highest in indications with subtle signals or small samples.

Step 05

Capture Lessons and Reuse Across Programs

Each Phase II NdDB program generates hard-won knowledge: what controlled acquisition variability effectively, what governance rules held under pressure, and what the next program should establish earlier. Without a structured capture at key milestones, that knowledge stays informal — and the next program pays the same discovery costs.

This step converts program experience into reusable governance artifacts — reducing the effort and cost of the next engagement, and building portfolio-level readiness that compounds across indications.

What this step prevents

Each program repeating the same governance discovery costs. The operating structure established in one program becomes backbone for the next — applicable across CNS indications and alongside other biomarker modalities with analogous measurement chains.

"The steps are structured. The judgment required to apply them is not."

Before the framework applies, the clinical rationale must hold — whether the NdDB measures something meaningful for this disease and this trial role. That evaluation, and everything that follows it, is where my experience fits.

Neurology Psychiatry Rare Diseases CNS Drug Development SaMD & Regulatory

Structured Readiness Review

Let's Map Your Program Together

A structured readiness review reduces late-rework risk by aligning stakeholders on a shared definition of the NdDB, its trial role, the required pipeline maturity, and a proportionate evidence and governance plan — before commitments are locked.

Request a Readiness Review

Work With Me

Get in Touch

Whether you need a structured readiness review before protocol lock, expert input on a specific governance challenge, or a longer engagement to design and implement the full operating model — I respond within one business day.

Luciana Bonnot, PhD

LuMMiens Consulting

Luciana Bonnot, PhD · France

Scientific consultant in neuroimaging biomarkers and digital medical device evidence strategy. 20+ years coordinating multi-site neuroimaging studies and working with cross-functional teams across neurology and psychiatry programs — translating imaging-derived measures into decision-ready outputs that are feasible across sites, traceable end to end, and aligned with the intended trial role.

Developer of the NdDB Readiness Map, a practical decision framework for Phase II sponsors. Certified Innovation Path on Digital Medical Devices — EIT Health. Full profile →

Neurology Psychiatry CNS Drug Development SaMD & Regulatory
lucianabonnot.github.io — Full Profile linkedin.com/in/bonnot-luciana