| Abstrakt: |
Background: This study aims to facilitate the identification of similar devices for both, the European Medical Device Regulation (MDR) and the US 510(k) equivalence pathway by leveraging existing data. Both are related to the regulatory pathway of read across for chemicals, where toxicological data from a known substance is transferred to one under investigation, as they aim to streamline the accreditation process for new devices and chemicals. Research design and methods: This study employs latent semantic analysis to generate similarity values, harnessing the US Food and Drug Administration 510k-database, utilizing their 'Device Descriptions' and 'Intended Use' statements. Results: For the representative inhaler cluster, similarity values up to 0.999 were generated for devices within a 510(k)-predicate tree, whereas values up to 0.124 were gathered for devices outside this group. Conclusion: Traditionally, MDR equivalence involves manual review of many devices, which is laborious. However, our results suggest that the automated calculation of similarity coefficients streamlines this process, thus reducing regulatory effort, which can be beneficial for patients needing medical devices. Although this study is focused on the European perspective, it can find application within 510(k) equivalence regulation. The conceptual approach is reminiscent of chemical fingerprint similarity analysis employed in read-across. Plain Language Summary: This study addresses improvement of the registration process for medical devices by using automated methods to determine how similar they are to existing devices. Such a process is already used in chemistry for analysis of related substances. In the context of Medical Device Regulation (MDR), which sets standards for these devices, this process might be applicable in device equivalence evaluation. Traditionally, proving equivalence involves manually finding devices that are similar, but this is time-consuming, repetitive and labor-intensive. This study proposes a new approach, using advanced computer methods and a database from the US Food and Drug Administration (FDA) to automatically identify similar devices. This could make the process much quicker and more accurate and furthermore reduce bias. The study suggests that by applying these automated methods, the impact of recent regulatory changes could be reduced. This means that proving equivalence, a critical step to facilitate device accreditation, could be done more efficiently. The study shows potential for a significant transformation in compliance processes within the medical device industry, making them more streamlined and automated. [ABSTRACT FROM AUTHOR] |
