Lauren Alani, Director of Digital Innovation at Seuss+, on why clinical data integrity is enterprise-level commercial risk and how the same evidence reads differently to inspectors and to partnership diligence teams.
The data behind this perspective
- FDA Data Integrity and CGMP guidance · “in recent years, FDA has increasingly observed CGMP violations involving data integrity during CGMP inspections”
- 71% of FDA inspections produced findings · of 194 domestic drug manufacturing inspections (2018-2024), findings were made in 138; of those 138, 59% (81 inspections) involved data integrity issues. (GMP Platform)
- ALCOA+ principles · nine attributes the data trail must satisfy: Attributable, Legible, Contemporaneous, Original, Accurate, plus Complete, Consistent, Enduring, Available
- Two layers of risk in novel digital endpoints · recruitment risk reduction, which is being assessed; and data integrity complexity from new systems and validation requirements, which is not yet consistently assessed in due diligence
- ~20% likelihood · estimated chance of an FDA Form 483 at any given inspection; multi-vendor sponsors carrying more data integrity risk than is currently visible in due-diligence frameworks face a higher effective rate
“If the data trail is not inspectable at submission, the asset value is not what the board thinks it is.”
Lauren Alani
If a regulatory inspector and a Big-Pharma due-diligence partner walked into the same sponsor on the same day, both would ask the data team for the same set of things. Source documents. Edit-check logic. Audit trails. Data lineage. Evidence of validation. Evidence of oversight. Change control records. Discrepancy logs. Their reasons differ. Their lists do not.
This is the underappreciated truth about clinical data integrity. The same artefacts that make a trial inspection-defensible also make an asset deal-defensible. Sponsors who treat data integrity as a regulatory hygiene exercise produce documents the regulator may grudgingly accept and the investor’s diligence team will not. Sponsors who treat it as commercial risk produce documents that work for both audiences.
The regulatory floor: ALCOA+
The regulatory framing of data integrity is well established. ALCOA, articulated by FDA in the 1990s, sets the principles: Attributable, Legible, Contemporaneous, Original, Accurate. The “+” extension adds Complete, Consistent, Enduring, and Available. EMA’s reflection paper on computerised systems and ICH GCP E6(R3) both anchor expectations to these principles.
ALCOA+ is the floor. It defines what minimally acceptable data evidence looks like. A trial that meets ALCOA+ has data that can be traced to its source, read without ambiguity, recorded at the time of the event, retained in original form, and demonstrated to be accurate. The records are complete, internally consistent, retrievable years after the trial closes, and accessible when an inspector or auditor asks.
Many sponsors treat ALCOA+ as a quality team’s checklist. The data team produces evidence; the quality team verifies it; the inspector accepts it. The framing is operational, narrow, and bounded to the trial. That framing is regulatorily sufficient. It is commercially insufficient.
The commercial ceiling: asset defensibility
Investors and acquirers do not buy clinical trials. They buy assets, defined by their data. The Phase 2 readout. The biomarker analysis. The dose-finding study. The pivotal trial. Each of these is, in commercial-asset terms, a body of data with claims attached. The credibility of the claims rests on the credibility of the data. The credibility of the data rests on its integrity.
The danger of that assumption (that ALCOA+ compliance equals commercial credibility) is that compliance and credibility share evidence but ask different questions of it.
An inspector asks: do these records meet regulatory expectations? An acquirer’s diligence team asks: would I bet a billion dollars on these records? The first question can be answered yes by records that are technically compliant but operationally fragile. The second question is harder, because diligence teams are looking for evidence of structural rigour, not just procedural conformance. They want to see that the sponsor was actively engaged in data quality, not just keeping documents tidy.
How weak data integrity actually destroys value
I am rarely asked to look at an asset that has imploded over a single catastrophic data finding. That happens, occasionally. It is not the typical pattern. The typical pattern is cumulative.
A diligence team reviews the trial dossier. They find a few minor inconsistencies in the audit trail. They find a vendor validation package that does not quite map to the sponsor’s configuration documents. They find oversight records that are present but thin. They find a change control log that has gaps in 2024. None of these is fatal. Together they form a picture: this sponsor’s data integrity discipline was patchy. The diligence team marks down the asset’s data quality risk score. The deal valuation moves. Sometimes by a few percent. Sometimes by enough to break the deal.
This is a quiet failure mode. It rarely makes a press release. It shows up in negotiated terms, in extended diligence periods, in additional conditions precedent, in side letters about indemnities. It also shows up, occasionally, in deals that simply do not happen, with the parties moving on to assets that read more credibly.
What “data integrity as commercial risk” looks like in practice
Reframing data integrity as commercial risk does not mean spending more, although it sometimes does. It means deciding earlier, asking harder questions of vendors and CROs, and building evidence trails that anticipate diligence as well as inspection.
Practical posture shifts I see in sponsors who do this well:
- Earlier oversight investment. Quality and data leadership engaged at protocol authoring, not just at trial start.
- Vendor selection that prioritises evidence quality. Not just feature parity. Validation evidence, audit trail granularity, and integration lineage are weighted heavily.
- Documented oversight cadence. Sponsor-led review of the data lifecycle on a regular schedule, with records that survive personnel changes.
- Risk-based escalation. Discrepancies are graded, escalated, and resolved in writing. Resolution evidence sits in the sponsor’s records, not the CRO’s.
- Submission readiness as continuous discipline. The trial dossier is structured to support both regulatory submission and commercial diligence from the day data starts flowing.
None of these are technical changes. They are leadership posture changes that produce technical evidence as a downstream consequence.
What this looks like for early-stage biotechs
Clinical-stage biotechs frequently object that this framing is for big pharma, not for them. They have lean teams, fast timelines, capital constraints. They cannot match Tier 1 pharma on data integrity infrastructure.
The inverse is closer to true. A clinical-stage biotech is, almost by definition, an asset-on-the-table for an eventual deal. The data is the asset. Diligence will, at some point, scrutinise it. Lean does not exempt; it focuses. The smaller the team, the more important it is that the few oversight artefacts produced are the right ones, structured to scale into a diligence-grade dossier without rework.
Pragmatically, this means a small sponsor needs fewer documents but demands more from each one. The data flow map needs to be accurate, signed off, and maintained. The vendor oversight log needs to be present and thin rather than absent and fat. The validation acceptance records need to be structured to be audit-and-diligence ready from the start. The cost is small at the start. The cost of the alternative compounds.
The closing question
If your most active trial were the subject of an unscheduled regulatory inspection on Monday and a partnership diligence call on Wednesday, would you produce the same set of evidence for both? If the answer is yes, your data integrity discipline is doing both jobs at once. If the answer is no, the gap is the work.
Continue reading: Accountability Cannot Be Delegated for the underlying principle that anchors all four perspectives. Requirements Before Selection for the procurement-stage discipline. The Regulator-Vendor Gap for the structural context. All four perspectives in the insights archive. To bring this perspective to a board, an investor briefing, or a partnership conversation, see media and booking.
Frequently asked
How does ALCOA+ relate to commercial diligence standards?
ALCOA+ sets the regulatory floor for data integrity. Commercial diligence standards are typically informal but draw on the same evidence: audit trails, validation packages, oversight records, change control logs, discrepancy resolution. The diligence team will read your ALCOA+ evidence and ask whether it is structurally rigorous beyond procedural compliance. Sponsors that treat ALCOA+ as a checkbox tend to fail the structural-rigour question.
What’s the most common data integrity issue that surfaces in commercial diligence?
Inconsistencies between the sponsor’s configuration documentation and the vendor’s validation evidence. The two often grow out of sync over the course of a multi-year trial as configurations are tweaked, vendor versions update, and oversight cadence slips. Diligence teams routinely sample-test these alignments; sponsors who treated them as procedural compliance frequently find the gaps mid-diligence.
How early should an early-stage biotech invest in data integrity infrastructure?
The investment is not infrastructure-heavy. It is discipline-heavy. The earliest material moment is at first IND-enabling study: data flow maps, vendor selection rigour, oversight cadence. The cost of authoring those artefacts at trial start is small. The cost of reconstructing them under partnership diligence pressure two years later is substantial. The decision is not capital, it is timing.
Does this framing change with patient-level data and real-world data sources?
Yes, in scope, not in principle. RWD and direct-to-patient data sources expand the data lifecycle that the sponsor must oversee, including data not generated under the sponsor’s quality system. The principle (sponsor accountability for data integrity end-to-end) is unchanged. The execution becomes more complex, requires more upfront specification work, and produces more vendor-side risk if not led from the sponsor’s specification document.
If we are pre-commercial, do we still need to think about commercial diligence framing?
Especially so. Pre-commercial sponsors are pre-deal, which means the diligence event is in the future, the data being generated now is what the diligence team will eventually examine, and the cost of restructuring evidence after the fact is highest. Building data integrity discipline at trial start, with diligence in mind, is the cheapest version of this work. Doing it later is more expensive and more limited.
