When to use
- Pre-study QA readiness assessment
- In-life phase audit preparation
- End-of-study data integrity review before report issuance
- Periodic GLP facility self-inspection
ToolsConductScience tool
Biotech / PharmaFree in-browser calculator
GLP Audit Checklist.
Generate an ALCOA+ data integrity checklist for GLP studies with 21 CFR Part 58 requirements mapped to your study scope and records.
PrivateData stays in your browser
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Validated2026-04-08
CitableMethods and citation included
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Load example GLP audit checklist data to see the full workflow
Study Information
Audit Scope
Records to Audit
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Required fields
- Select at least one record category to audit.
- Audit period start date must be YYYY-MM-DD format.
- Audit period end date must be YYYY-MM-DD format.
- Study title is required.
- Study director name is required.
- Facility name is required.
Do not use for
- Non-GLP exploratory or discovery studies
- Clinical trial data integrity (use ICH E6 GCP checklist)
- Manufacturing GMP compliance (use 21 CFR Part 211)
Pearl
Run the checklist BEFORE study initiation to identify gaps in SOPs, training, and systems. Fixing data integrity issues mid-study is far more costly than preventing them.
Pearl
Enable audit trails on ALL electronic systems that generate GLP data — including spreadsheets. A standalone Excel file without version control is an audit finding waiting to happen.
Pitfall
Do not assume "electronic = compliant." An electronic system without validated audit trail, access controls, and backup procedures may be worse than paper from a data integrity perspective.
Pitfall
Corrections that obscure the original entry (white-out, overwriting, deleting electronic records) are among the most common FDA 483 observations. Always preserve the original and document the reason for change.
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Method
The checklist generator applies all nine ALCOA+ principles as universal checks, then filters 21 CFR Part 58 requirements to the specific record categories selected. Requirements are mapped to CFR sections (§58.29 through §58.195) so each check item is traceable to its regulatory source. The gap analysis template includes the most common FDA 483 observations related to data integrity in GLP studies.
2
Validated
Last validated 2026-04-08. Calculations are designed for planning and documentation support; verify procurement decisions against manufacturer specifications or institutional SOPs.
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How to cite
How to Cite
ConductScience GLP Audit Trail & Data Integrity Checklist (v1.26.0). ConductScience. https://conductscience.com/tools/glp-audit-checklist
21 CFR Part 58 — Good Laboratory Practice for Nonclinical Laboratory Studies. U.S. FDA.
MHRA "GxP" Data Integrity Guidance and Definitions (2018).
Understanding ALCOA+ Data Integrity for GLP Studies
Data integrity is the foundation of GLP compliance. Regulatory agencies worldwide use the ALCOA+ framework to evaluate whether study data is trustworthy:
Attributable: Every data point must identify who generated it, when, and on what system. Electronic audit trails must capture user ID and timestamp for every action.
Legible: Data must be readable throughout its retention period. Handwritten data in permanent ink; electronic data in accessible formats. Corrections use single-line strikethrough — the original must remain readable.
Contemporaneous: Data recorded at the time of the activity. FDA inspectors compare instrument timestamps against study schedules to detect backdating.
Original: First recording or a certified true copy. "Transcribing" from unofficial notes to official forms is a common audit finding.
Accurate: Verified calculations, calibrated instruments, and consistent units. Selective reporting (cherry-picking favorable data) is a critical finding.
Complete, Consistent, Enduring, Available (the + in ALCOA+): No deleted data without justification, no contradictions between related records, durable storage media, and accessible archives.
The most common FDA 483 observations related to data integrity involve: missing audit trails on electronic systems, inadequate correction practices, and failure to retain original data.
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