In GLP contexts, what is the implication of data integrity as highlighted by landmark cases?

• A. It is essential to avoid major lapses in scientific practices and unsupervised operations
• B. It is optional and rarely checked
• C. It only concerns the accuracy of numerical values
• D. It is a local issue for individual labs

Answer: (A)

In GLP contexts, data integrity means data are credible, complete, accurate, traceable, and protected from manipulation at every step of the study. Landmark cases have shown that when data integrity is compromised, the entire scientific record—along with conclusions about safety and efficacy—can be called into question. Small lapses, such as incomplete records, altered entries, or insufficient audit trails, can rapidly escalate into major regulatory actions because they undermine trust in the data and the study’s governance. This underscores that maintaining data integrity is essential to prevent significant lapses in scientific practices and unsupervised operations, ensuring that studies are reliable, auditable, and conducted under proper oversight. Data integrity isn’t just about numbers; it covers all data and documentation throughout a project and involves the whole organization, not just a single lab.