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. 2021 Mar;44(3):261-272.
doi: 10.1007/s40264-020-01030-2. Epub 2021 Feb 1.

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

Kristof Huysentruyt  1 , Oeystein Kjoersvik  2 , Pawel Dobracki  3 , Elizabeth Savage  4 , Ellen Mishalov  5 , Mark Cherry  6 , Eileen Leonard  7 , Robert Taylor  8 , Bhavin Patel  9 , Danielle Abatemarco  7
Affiliations

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

Kristof Huysentruyt et al. Drug Saf. 2021 Mar.

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.

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Conflict of interest statement

Kristof Huysentruyt is an employee and shareholder of UCB; Oeystein Kjoersvik is an employee of MSD, Czech Republic; Pawel Dobracki is an employee of Roche Products Limited; Elizabeth Savage is an employee of Janssen, the Pharmaceutical Companies of Johnson & Johnson; Ellen Mishalov is an employee of Astellas; Mark Cherry is an employee of AstraZeneca; Eileen Leonard is an employee of Bristol Myers Squibb; Robert Taylor is an employee of Merck & Co., Inc.; Bhavin Patel is an employee of Pfizer; and Danielle Abatemarco is an employee and shareholder of Bristol Myers Squibb.

Figures

Fig. 1
Fig. 1
A General Approach for Achieving Compliance and Fitness for Intended Use (ISPE). Source: Figure 4.1, GAMP 5: A Risk-Based Approach to Compliant GxP Computerized Systems, © Copyright ISPE 2008 [7]. All rights reserved. https://www.ISPE.org. Used with permission from ISPE (ISPE GAMP® 5, Figure 4.1)
Fig. 2
Fig. 2
Overlay of the US FDA's total product lifecycle approach on artificial intelligence/machine learning workflow. Source: US FDA Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)—based software as a Medical Device (SaMD), Discussion Paper and Request for Feedback, 2019 [13]
Fig. 3
Fig. 3
Proposed ISPE GAMP® 5 methodology for validating artificial intelligence (AI)-based static systems in pharmacovigilance
See this image and copyright information in PMC

References

    1. World Health Organization. Pharmacovigilance. 2020. https://www.who.int/medicines/areas/quality_safety/safety_efficacy/pharm.... Accessed 4 Sep 2020.
    1. Stergiopoulos S, Fehrle M, Caubel P, et al. Adverse drug reaction case safety practices in large biopharmaceutical organizations from 2007 to 2017: an industry survey. Pharm Med. 2019;33:499–510. doi: 10.1007/s40290-019-00307-x. - DOI - PubMed
    1. Wynn M, Fauber J. Analysis: reports of drug side effects increase fivefold in 12 years. Milwaukee J Sentin. 2017. https://www.jsonline.com/story/news/investigations/2017/03/17/analysis-r.... Accessed 4 Sep 2020
    1. Lewis JL, McCallum JF. Utilizing advanced technologies to augment pharmacovigilance systems: challenges and opportunities. Ther Innov Regul Sci. 2020;54:889–899. doi: 10.1007/s43441-019-00023-3. - DOI - PMC - PubMed
    1. European Commission. Commission Implementing Regulation (EU) No 520/2012 of 19 June 2012 on the performance of pharmacovigilance activities provided for in Regulation (EC) No 726/2004 of the European Parliament and of the Council and Directive 2001/83/EC of the European Parliament and of the Council. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2012:159:000.... Accessed 20 Nov 2020.

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