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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

Processing

Processing means any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction (Art. 4(2) GDPR).

The list is illustrative, not exhaustive, and the concept is as broad as that of personal data itself: simply viewing a record, holding it in an archive, pseudonymising it, anonymising it, transferring it to a vendor or deleting it are all processing operations, each of which needs a legal basis and must respect the principles. Automated processing includes AI model training and inference; manual processing is covered when the data forms part of a filing system.

For life sciences organisations, the width of the definition means that the record of processing activities must capture the full lifecycle of trial, patient and employee data, not only the initial collection. It also means that seemingly technical steps (a CRO running a query, a central lab storing residual samples with identifiers, a statistician pooling datasets, a cloud provider taking backups) are processing operations that must be allocated to a controller or processor and reflected in contracts. Each purpose of processing, rather than each system, is the unit of analysis in a DPIA.