Background:
Machine Learning (ML) has experienced an increasing use given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models, capable of predicting biological activity and physicochemical characteristics of new components with high success rates in training and test partitions. Given the current gaps of scientific knowledge and the need of efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in European Union and the role of ML in the authorization process.
Methods:
In terms of methodology, a dogmatic study of the European regulation and the guidances of the European Medicine Agency on the use of predictive models for nanomaterials was carried out. The study has, as the framework of reference, the European Regulation 726/2004 and has focused on the analysis of how ML processes are contemplated in the regulations.
Results:
As result, we present a discussion of the information that must be provided for every case for simulation methods. The results show a favorable and flexible position for the development of the use of predictive models to complement the applicant's information.
Conclusion:
It is concluded that Machine Learning has the capacity to help to improve the application of nanotechnology medicine products regulation. Future regulations should promote this kind of information given the advanced state of art in terms of algorithms that are able to build accurate predictive models. This especially applies to methods such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development (OECD), European Union regulations and European Authority Medicine. To our best knowledge this is the first study focused on nanotechnology medicine products and machine learning use to support technical European public assessment report (EPAR) for complementary information.
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