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21 CFR Part 11
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Term of the Day

Natural history study

A natural history study is a preplanned observational study intended to track the course of a disease over time, identifying demographic, genetic, environmental and other variables that correlate with its development and outcomes in the absence of intervention, or under standard of care. Designs may be retrospective (chart review of existing records) or prospective (longitudinal follow-up of a cohort or registry).

Natural history data is particularly important in rare and paediatric diseases, where randomised placebo-controlled trials may be infeasible or unethical. The FDA (guidance on rare disease natural history studies, 2019) and the EMA accept well-designed natural history studies to define endpoints and biomarkers, identify patient subgroups, estimate sample sizes and, in some cases, serve as external or historical control arms for single-arm trials supporting orphan products.

Because they are non-interventional, natural history studies fall outside the CTR and are governed by national law (for example France's MR-003 or MR-004 reference methodologies) and by the GDPR. They typically involve secondary use of medical records, long-term follow-up, genetic data and small populations in which anonymisation is rarely achievable, so pseudonymisation, a DPIA and a robust research legal basis under Art. 9(2)(j) are essential. Registries maintained by patient organisations or academic consortia raise additional questions of joint controllership and data access governance.

P

Privacy by design

Privacy by design is an approach to systems engineering and organisational design, developed by Ann Cavoukian in the 1990s and endorsed by the International Conference of Data Protection Commissioners in 2010, which holds that privacy must be built into technologies, business practices and infrastructures from the outset rather than added afterwards. Its seven foundational principles are: proactive not reactive; privacy as the default setting; privacy embedded into design; full functionality (positive-sum, not zero-sum); end-to-end security; visibility and transparency; and respect for user privacy.

The GDPR turned the concept into a binding obligation in Art. 25, split into data protection by design and data protection by default. ISO 31700 (2023) provides a standard for privacy by design in consumer goods and services, and the framework informs the EDPB Guidelines 4/2019. The EU AI Act applies the same lifecycle logic to high-risk AI systems through risk management and data governance requirements.

In life sciences, privacy by design means involving the DPO when the protocol and case report form are drafted, choosing pseudonymisation architectures and key custody before selecting vendors, specifying minimisation, encryption and retention in requirements for EDC, eCOA and health apps, and running the DPIA early enough to change the design. The evidence of these choices is also the best demonstration of accountability.

Is privacy by design just a task for the IT team?

No. It also concerns protocol choices, vendor selection, responsibilities and working practices. Technical controls cannot fix unnecessary collection or unclear purposes on their own. Involve the people responsible for research operations and data protection early, so privacy requirements shape the workflow rather than being added as a final system check.