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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.

D

Data protection by design

Data protection by design is the obligation in Art. 25(1) GDPR for the controller, both when determining the means of processing and at the time of the processing itself, to implement appropriate technical and organisational measures, such as pseudonymisation, designed to implement the data protection principles effectively and to integrate the necessary safeguards into the processing. The measures must take into account the state of the art, cost of implementation, and the nature, scope, context and purposes of processing, as well as the risks to individuals.

The concept translates the earlier privacy by design framework into a binding legal requirement. Operationally it means involving the DPO at the design stage of a protocol, a data platform or an AI model; carrying out the DPIA before, not after, vendor selection; specifying minimisation, encryption, access control, audit trails and retention in requirements documents; and choosing vendors whose products support these features (by default).

For life sciences, the EDPB Guidelines 4/2019 and the ISO 31700 standard on privacy by design for consumer products offer practical checklists. The EU AI Act extends the logic to high-risk AI systems, requiring risk management and data governance to be embedded throughout the lifecycle. Design decisions that are documented and traceable are also the most convincing evidence of accountability during an inspection or due diligence.