Public Health Impact of Using Biosimilars, Is Automated Follow up Relevant?

Abstract

Biologic reference drugs and their copies, biosimilars, have a complex structure. Biosimilars need to demonstrate their biosimilarity during 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 standardised 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. This article raises the question of relevance of automating real life data processing regarding Biosimilars. The objective is to initiate a discussion about different approaches involving Machine Learning. So, the discussion is established regarding implementation of Neural Network model to ensure safety of biosimilars subject to economic incentives. Nevertheless, the application of Machine Learning in the healthcare field raises ethical, legal and technical issues that require further discussion.

A Regulatory Perspective on Biosimilar Medicines

“…The immunogenicity data required for the approval of a biological medicinal product for approval purposes, as highlighted by the EMA, include the incidence, titration, and persistence of antibodies against the biological medicinal product; neutralisation tests; clinical impact assessment; and measures to manage the risk of immunogenic potential. Nevertheless, these data always depend on the type of biological medicine and its intended use and the characteristics of the product itself, among others [ 1 , 33 ].…”

The Challenges of Implementing Comprehensive Clinical Data Warehouses in Hospitals

“…As the complexity of the data flow increases, greater transparency and standardization of criteria and procedures are required to maintain objective oversight and control. The development of practice-oriented and evidence-based policies in this field is crucial [ 26 , 27 ]. On this point, it is interesting to note the recent initiative of the National Commission for Data Protection and Liberties in France (CNIL).…”