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Validating Intelligent Automation Systems in Pharmacovigilance: Insights from Good Manufacturing Practices

Author

Listed:
  • Kristof Huysentruyt

    (Patient Safety, UCB)

  • Oeystein Kjoersvik

    (R&D IT, MSD)

  • Pawel Dobracki

    (Roche Polska Sp. z o.o.)

  • Elizabeth Savage

    (Global Medical Organization, Janssen Research & Development, LLC a division of Johnson & Johnson)

  • Ellen Mishalov

    (Astellas)

  • Mark Cherry

    (Information Technology, AstraZeneca)

  • Eileen Leonard

    (Bristol-Myers Squibb Company)

  • Robert Taylor

    (Global Regulatory Affairs and, Merck & Co., Inc.)

  • Bhavin Patel

    (Pfizer Inc)

  • Danielle Abatemarco

    (Bristol-Myers Squibb Company)

Abstract

Pharmacovigilance is the science of monitoring the effects of medicinal products to identify and evaluate potential adverse reactions and provide necessary and timely risk mitigation measures. Intelligent automation technologies have a strong potential to automate routine work and to balance resource use across safety risk management and other pharmacovigilance activities. While emerging technologies such as artificial intelligence (AI) show great promise for improving pharmacovigilance with their capability to learn based on data inputs, existing validation guidelines should be augmented to verify intelligent automation systems. While the underlying validation requirements largely remain the same, additional activities tailored to intelligent automation are needed to document evidence that the system is fit for purpose. We propose three categories of intelligent automation systems, ranging from rule-based systems to dynamic AI-based systems, and each category needs a unique validation approach. We expand on the existing good automated manufacturing practices, which outline a risk-based approach to artificially intelligent static systems. Our framework provides pharmacovigilance professionals with the knowledge to lead technology implementations within their organizations with considerations given to the building, implementation, validation, and maintenance of assistive technology systems. Successful pharmacovigilance professionals will play an increasingly active role in bridging the gap between business operations and technical advancements to ensure inspection readiness and compliance with global regulatory authorities.

Suggested Citation

  • Kristof Huysentruyt & Oeystein Kjoersvik & Pawel Dobracki & Elizabeth Savage & Ellen Mishalov & Mark Cherry & Eileen Leonard & Robert Taylor & Bhavin Patel & Danielle Abatemarco, 2021. "Validating Intelligent Automation Systems in Pharmacovigilance: Insights from Good Manufacturing Practices," Drug Safety, Springer, vol. 44(3), pages 261-272, March.
    • Handle: RePEc:spr:drugsa:v:44:y:2021:i:3:d:10.1007_s40264-020-01030-2
    DOI: 10.1007/s40264-020-01030-2
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    References listed on IDEAS

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    1. Karolina Danysz & Salvatore Cicirello & Edward Mingle & Bruno Assuncao & Niki Tetarenko & Ruta Mockute & Danielle Abatemarco & Mark Widdowson & Sameen Desai, 2019. "Artificial Intelligence and the Future of the Drug Safety Professional," Drug Safety, Springer, vol. 42(4), pages 491-497, April.
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    Cited by:

    1. Oeystein Kjoersvik & Andrew Bate, 2022. "Black Swan Events and Intelligent Automation for Routine Safety Surveillance," Drug Safety, Springer, vol. 45(5), pages 419-427, May.
    2. Barenji, Reza Vatankhah & Hariry, Reza Ebrahimi & Demirkol, Denizhan & Daim, Tugrul U., 2024. "Research landscape analysis for quality in Pharma 4.0 era," Technology in Society, Elsevier, vol. 76(C).
    3. Ghobakhloo, Morteza & Asadi, Shahla & Iranmanesh, Mohammad & Foroughi, Behzad & Mubarak, Muhammad Faraz & Yadegaridehkordi, Elaheh, 2023. "Intelligent automation implementation and corporate sustainability performance: The enabling role of corporate social responsibility strategy," Technology in Society, Elsevier, vol. 74(C).

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    1. Oeystein Kjoersvik & Andrew Bate, 2022. "Black Swan Events and Intelligent Automation for Routine Safety Surveillance," Drug Safety, Springer, vol. 45(5), pages 419-427, May.
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