Data about drug regulation are of great value to various stakeholders such as drug developers, regulatory scientists, healthcare professionals, patients, as well as regulators themselves – to aid consistent decision-making. Data science methods, including artificial intelligence, provide new opportunities to make these data increasingly findable, accessible, interoperable and reusable (FAIR) for drug regulatory science and regulatory intelligence. This may positively impact drug development strategies and facilitate regulatory innovation. Here, three data-science projects are highlighted, which facilitate 1) access to data about medicines, 2) harmonized communication on benefits and risks and 3) insights into the interplay between EMA scientific guidelines and the assessment made during regulatory review.
1) Utrecht University (UU) together with the Dutch Medicines Evaluation Board (MEB) have developed the European Medicines Regulatory Database (EMRD), a free-to-access interactive dashboard that centralizes and contextualizes data about medicines authorized and orphan designations (ODs) granted by the European Medicines Agency (EMA) since its establishment in 1995. EMRD 1.0 will be made openly available in 2024 to enhance accessibility, understanding and consistent use of European regulatory data.
2) To harmonize regulatory communication of important remaining uncertainties of authorized medicines a natural language processing approach was taken (Verweij et al., 2023). In collaboration with the Swedish Medical Products Agency (MPA), UU and MEB retrieved 553 European Public Assessment Reports (EPARs) from 1,648 authorized medicines between 1995 and 2022, and extracted 13,105 sentences from the sections describing uncertainties. Subsequently, 26 clusters were generated using a Sentence BERT Language model (sBERT), of which 24 could be linked to an overarching theme. The results can be used as a steppingstone towards harmonized communication about uncertainties.
3) The third project from the MPA and MEB focuses on the interplay between EMA scientific guidelines and the assessment made during the regulatory review (Bergman et al., 2023). Using sBERT the semantic similarity between EPARs and EMA guidelines was studied. The results can assist in identifying therapeutic areas in need of further support by regulatory guidance.
Based on these three examples the additional value and impact of data science methods is highlighted for drug regulatory science, drug development and regulatory innovation in a multi-stakeholder fashion.

Data science and artificial intelligence to study the drug regulatory system: current and future avenues

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