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

Author

Listed:
  • Antoine Perpoil

    (Compliance Department, Amgen SAS, 92100 Boulogne-Billancourt, France)

  • Gael Grimandi

    (Faculty of Pharmaceutical and Biological Sciences, University of Nantes, 44035 Nantes, France
    University of Nantes, INSERM UMR1229, RMeS, Regenerative Medicine and Skeleton, ONIRIS, 44322 Nantes, France
    Central Pharmacy, University Public Hospitals of Nantes, 44093 Nantes, France)

  • Stéphane Birklé

    (Faculty of Pharmaceutical and Biological Sciences, University of Nantes, 44035 Nantes, France
    Université de Nantes, CRCINA, F-44000 Nantes, France)

  • Jean-François Simonet

    (Compliance Department, Amgen SAS, 92100 Boulogne-Billancourt, France)

  • Anne Chiffoleau

    (Sponsor Department, University Public Hospitals of Nantes, 44093 Nantes, France)

  • 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, 44800 Nantes-Angers, France)

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.

Suggested Citation

  • Antoine Perpoil & Gael Grimandi & Stéphane Birklé & Jean-François Simonet & Anne Chiffoleau & François Bocquet, 2020. "Public Health Impact of Using Biosimilars, Is Automated Follow up Relevant?," IJERPH, MDPI, vol. 18(1), pages 1-13, December.
  • Handle: RePEc:gam:jijerp:v:18:y:2020:i:1:p:186-:d:470067
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    Citations

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    Cited by:

    1. François Bocquet & Mario Campone & Marc Cuggia, 2022. "The Challenges of Implementing Comprehensive Clinical Data Warehouses in Hospitals," IJERPH, MDPI, vol. 19(12), pages 1-6, June.

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