A natural language processing approach towards harmonisation of European medicinal product information.

Product information (PI) is a vital part of any medicinal product approved for use within the European Union and consists of a summary of products characteristics (SmPC) for healthcare professionals and package leaflet (PL) for patients, together with the product packaging. In this study, based on the English corpus of the EMA product information documents for all centrally approved medicinal products within the EU, a BERT sentence embedding model was used together with clustering and dimensional reduction techniques to identify sentence similarity clusters that could be candidates for standardization. A total of 1258 medicinal products were included in the study. From these, a total of 783 K sentences were extracted from SmPC and PL documents which were aggregated into a total of 284 and 129 semantic similarity clusters, respectively. The spread distribution among clusters shows separation into different cluster types. Examples of clusters with low spread include those with identical word embeddings due to current standardization, such as section headings and standard phrases. Others show minor linguistic variations, while the group with the largest variability contains variable wording but with significant semantic overlap. The sentence clusters identified could serve as candidates for further standardization of the PI. Moving from free text human wording to auto-generated text elements based on multiple-choice input for appropriate parts of the package leaflet and summary of product characteristics, could reduce both time and complexity for applicants as well as regulators, and ultimately provide patients and prescribers with documents that are easier to understand and better adapted for search availabilities.