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Biological reference drugs and biosimilars have highly complex structures. Biosimilars need to demonstrate their biosimilarity during their development but unpredictable variations can remain, such as micro-heterogeneity. The healthcare community may raise questions regarding the clinical outcomes induced by this micro-heterogeneity. Indeed, unwanted immune reactions may be induced for numerous reasons, including product variations. However, it is challenging to assess these unwanted immune reactions because of the multiplicity of causes and potential delays before any reaction. Moreover, safety assessments, as part of preclinical studies and clinical trials, may be of limited value with respect to immunogenicity assessments because they are performed on a standardized population during a limited period. Real-life data could therefore supplement the assessments of clinical trials by including data on the real-life use of biosimilars, such as switches. Furthermore, real-life data also include any economic incentives to prescribe or use biosimilars.

A review carried out by French researchers raises the question of relevance of automating real-life data processing regarding biosimilars. The objective of the review was to initiate a discussion about different approaches involving machine learning.

Such technology includes models that enable identification of patterns and classification. Processing data in this way could directly apply to biosimilars. First, searching for patterns in biosimilar use could emphasize risk factors that are related to immune reactions. This application of machine learning could rely on developing relevant categories used by a neural network. So, the model would be looking for interactions in Big Data* to identify parameters playing an important role in the immune reaction. Medical expertise would then be required to identify risk factors considering the parameters identified. The pattern identification could be replicated over time to cover evolutions such as approval of an additional biosimilar and new medical practices allowed by regulation.

Then, a similarly trained neural network could benefit from the instructions and categories developed to focus on classification. The processing purposes would proactively identify situations with high likelihood of immune reactions. Therefore, a clinical background associated with specific medical concerns could be associated with a level of immune reaction risk according to the risk factors identified. In this way medical decision-making could be supported by the classification provided by the neural network.

This discussion suggests that both data processing purposes could be applied to the specificities of biosimilars in real life. Identification of patterns could provide useful insights for further classification. In that way, an automated follow-up could support medical decision-making for drugs associated with economic incentives.

Nevertheless, the application of machine learning in the healthcare field raises ethical, legal and technical issues that require further discussion.

*Big data is a field that treats ways to analyse, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Conflict of interest
The authors of the research paper [1] declared that there was no conflict of interest.

Abstracted by François Bocquet, Law and Social Change Laboratory, Faculty of Law and Political Sciences, University of Nantes, CNRS UMR6297, 44300 Nantes, France; Oncology Data Factory and Analytics Department, Institut de Cancérologie de l’Ouest, FR-44800 Nantes-Angers, France

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Reference
1. Perpoil A, Grimandi G, Birklé S, et al. Public health impact of using biosimilars, is automated follow up relevant? Int J Environ Res Public Health. 2020;18(1):186.

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