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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With the widespread adoption of technology in the pharmacovigilance space, pharmacovigilance professionals must understand and guide the building, validation, and maintenance of artificial intelligence (AI)-based pharmacovigilance systems. This is essential to ensure inspection readiness as we integrate automation systems across the pharmacovigilance value chain.
Intelligent automation systems can be grouped into three categories: rule-based static systems, AI-based static systems, and AI-based dynamic systems. Validation frameworks currently exist for rule-based static systems but not AI-based systems.
We propose validation considerations for a risk-based approach to compliant GxP AI-based static systems, which can be implemented within the good automated manufacturing practices framework.
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 . It is a discipline that is heavily reliant on data, and benchmark data indicate that most pharmacovigilance departments of pharmaceutical companies dedicate a large proportion of their resources to processing adverse event (AE) cases, and the number of AE cases increases annually [2, 3].
Intelligent automation technologies have strong potential to automate routine work and balance resource use across safety risk management and other pharmacovigilance activities . Intelligent automation can contribute to the quality and consistency of case processing and assessment, leading to a timely assessment of safety signals. When such technology solutions are implemented to assist in processing AE cases, regulations require pharmaceutical companies to validate this software .
Computerized system validation (CSV) is the process of establishing and documenting that the specified requirements of a computerized system are fulfilled consistently from design until decommissioning of the system and/or transition to a new system. The approach to validation should focus on a risk assessment that takes into consideration the intended use of the system and the potential of the system to affect human subject protection and reliability of trial results .
Algorithms and rule-based software, electronic workflows, and pattern matching have been used widely in pharmacovigilance for years . More recently, several companies and vendors have implemented robotic process automation to assist with the management of individual case safety reports (ICSRs). Such technologies follow existing guidance from regulators and industry guidelines such as the International Society for Pharmaceutical Engineering Good Automated Manufacturing Practices Guide (ISPE GAMP® 5) on the validation of computerized systems .
More recently, novel areas of research based on artificial intelligence (AI) technologies have evolved, including machine learning (ML) and natural language processing (NLP) techniques, and are now being adopted to support pharmacovigilance processes [8,9,10]. Although these types of technology show great promise with their capability to learn based on data inputs, existing validation frameworks may need to be augmented to verify intelligent automation systems. While the underlying validation requirements largely remain the same (e.g., such as conformity with user specifications, traceability of decision points with an audit trail, and reliability of algorithm output), additional software development activities tailored to intelligent automation are needed to document evidence that the system is fit for purpose.
This paper presents a proposed classification of automation systems for pharmacovigilance, along with validation considerations for emerging technologies to support whether existing validation frameworks can be used or should be extended to constitute a reasonable/risk-based validation strategy, as has been done for computerized systems elsewhere within the industry [5, 7]. The primary focus of this paper is validation rather than other adoption considerations and potential barriers to implementation. This paper considers the use of intelligent automation solutions for pharmacovigilance, specifically for high-effort activities such as ICSR case processing . The principles proposed may be applied to different areas that are subject to regulatory oversight.
2 Classification of Intelligent Automation Systems in Pharmacovigilance
Because existing validation frameworks may be sufficient for some novel technologies but not for others, there is a need for a classification of intelligent automation systems and the best-suited validation framework for each. Table 1 provides a breakdown of the types of automated pharmacovigilance (sub-) systems, some of the underlying technologies used within them, and examples of their application to pharmacovigilance processes.
In the classification (Table 1), the term “artificial intelligence” or AI is used to describe the simulation of human intelligence processes by computer systems but excludes purely rule-based systems. AI encompasses a wide range of technologies, including ML, text recognition, NLP, and machine translation. The considerations and elements described in this paper apply to supervised learning systems, within which the training data are curated and known by the model developers and subject matter experts .
For each classification of AI systems, the availability of a validation framework is categorized as established, emergent, or eventual (Table 1). “Established” frameworks are those for which clear guidance exists to support validation efforts. With “emergent” frameworks, a foundation exists but is insufficient for demonstrating fit-for-use systems within today's regulated environment. No validation framework yet exists for “eventual” systems. The focus of our research is to propose an extended validation system for AI-based static systems.
We based our classification of automated pharmacovigilance systems in Table 1 on the main ways people typically interact with data. The primary purpose was to identify considerations for validation of automation systems. We recognize this is an evolving area, and there may be a need to define additional classes in the future.
2.1 Validation of Artificial Intelligence (AI)-Based Systems Require Considerations Beyond Those Needed for Rule-Based Systems
Compared with rule-based systems, AI-based systems do not consist of a set of pre-defined rules but generally include models derived from a training data set (e.g., supervised ML based on "ground truth") or more generalized modeling of a “human-like” capability (e.g., auto-translation, NLP).
The potential advantages of AI-based systems are that more complex associations and patterns sometimes unknown to humans can be considered by a model, resulting in greater flexibility and ability to handle variable input (e.g., NLP for the handling of unstructured text).
However, the use of AI introduces some potential challenges related to the validation of these systems (Sect. 3.2).
2.2 Validation of AI-Based Static vs. AI-Based Dynamic Systems
Dynamic systems are those in which new data (e.g., newly received ICSRs) are used to continually update the model for future data, in contrast to static systems that use batch learning techniques that generate the model by learning on the entire training data set at once. A dynamic system is referred to as “online ML” or an “online adaptive system.”
Such systems will dynamically adapt to new patterns in data and should proactively improve over time; however, they also introduce potential risks in terms of stability and performance of the model over time.
As these systems are inherently dynamic in nature, current validation and change control guidelines are no longer adequate to demonstrate consistent and reliable system performance. Moreover, some AI algorithms do not learn in a deterministic way, which adds additional validation challenges when such algorithms are not restricted to a static state. This reality will again require decisions regarding any re-validation over time.
To date, electronic systems utilized in pharmacovigilance are almost exclusively static. Dynamic systems may be used more frequently in the future when appropriate methods for timely validation of these systems become available and if the benefits of using a dynamic system outweigh the potential risks associated with these systems.
3 Validation Considerations for AI-Based Static Systems
3.1 Validation Frameworks—ISPE GAMP ® 5
Multiple frameworks exist to support CSV. Validation of rule-based static systems is well-established and supported by existing standards and industry guides, as described in ISPE GAMP® 5 . Figure 1 illustrates the ISPE GAMP® 5 general approach to achieving computerized system compliance and fitness for use with any type of AI system. This approach can be applied both for initial system validation and subsequently as part of change control. However, some other considerations are covered in this paper.
The level of detail at each stage should be commensurate with the complexity. The model described in Fig. 1 is not the only potential framework for validation. However, it is the most widely adopted methodology in pharmacovigilance and is based on a scientific, risk-based approach. Therefore, we consider the ISPE GAMP® 5 methodology to be a seminal work and describe best practices for rule-based systems based on the ISPE GAMP® 5 approach. This also allows for AI functionality to be more easily accommodated within existing (validated) systems.
3.2 Proposed ISPE GAMP ® 5 Methodology for AI-Based Static Systems in Pharmacovigilance
To address the best practices as outlined for customized AI systems in pharmacovigilance (Fig. 2), we propose layering the AI approach within the ISPE GAMP® 5 methodology, as shown in Fig. 3. Steps and deliverables are highlighted below and focus on data selection, model training and testing, validation, system testing, and monitoring.
Table 2 describes considerations specific to the validation of AI-based static systems in each of the steps in Fig. 3 and potential techniques and approaches that could be used to address some challenges specific to AI-based static systems. For more general validation considerations, refer to either the ISPE GAMP® 5 guidance or other recommendations for agile models [7, 12]. For AI-based static systems that require initial training, we also consider the “good machine learning practices” framework and terminology introduced by the US FDA to be a seminal work .
3.3 Documentation Considerations for AI-Based Static Systems
Confidence in the output of AI-based static systems requires insight into why decisions are made within the model. Documentation regarding how the model was developed is a good way to provide insight into which considerations were taken during the development process.
We are proposing more specific documentation to be considered. This does not mean all forms of documentations are recommended for every model but rather for the documentation to be applied where it is appropriate, as documentation requirements also depend on the complexity of the system. The last column in Table 2 includes a proposal of the specific documentation that could be considered for AI-based static systems.
New and existing intelligent automation systems have great promise for enhancing existing processes in pharmacovigilance. In this article, we introduced a categorization of automation subsystems. Depending on the category, validation methodology may already be well-understood (e.g., rule-based static systems) or may pose significant unknowns (e.g., AI-based dynamic systems).
Traditional validation methodologies can be extended to cover AI-based static systems. The main points to highlight for AI-based static systems are as follows:
Risk assessment within an overall risk-based approach will be crucial to defining the level of validation activities needed.
New deliverables are being introduced to cover ML (e.g., data acquisition plan and model test plan).
Data quality is a key consideration.
Monitoring after deployment in production is critical and should be an essential consideration of the validation approach.
Terminology alignment is necessary for a good understanding of the meaning between CSV specialists, business users, and ML experts.
Close collaboration amongst pharmacovigilance professionals and technical experts is required.
As this technology area is still evolving quickly, best practices for validation of these systems will continue to emerge and evolve. AI-based static systems may be implemented more frequently in the future, including, perhaps, even some dynamic elements. Our validation considerations applied to the GAMP validation framework and support opportunities for increasing the adoption of intelligent automation technologies within pharmacovigilance by offering the industry a framework for developing these technologies. In time, best validation practices for these technologies can be identified and evaluated against the goal of enhanced patient safety. Future research will focus on the development of AI-based dynamic systems, for which the considerations must address the continuous learning nature of these systems.
Alignment with regulators on validation is critical to the implementation of AI-based systems within the highly regulated pharmacovigilance industry. As part of this alignment process, industry should engage regulators actively in discussions since agreement on high-level performance measures with a clear interpretation and verifiable measurement processes will be essential.
Pharmacovigilance professionals must learn new skills and acquire technical knowledge relating to system implementation and CSV to remain successful in this rapidly advancing sector . A once-siloed working paradigm is shifting, requiring pharmacovigilance professionals to understand business and technical requirements, to select representative data to adequately train and approve AI models, and to understand how, when, and why to retrain their models over time. A deep understanding of AI is becoming more closely intertwined with pharmacovigilance operations and will dictate which pharmacovigilance functions will successfully leverage assistive technology to further consistency in data collection and evaluation, ultimately improving compliance and positive outcomes for our patients globally.
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The authors worked as part of TransCelerate Biopharma Inc., which is funded by 20 organizations dedicated to making research and development, including pharmacovigilance monitoring and reporting, more efficient and effective in the pharmaceutical industry. We acknowledge contributions from AbbVie, Amgen, Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Merck & Co., Inc., GlaxoSmithKline, Johnson & Johnson, Eli Lilly and Company, MSD, Novo Nordisk, Novartis, Pfizer, Regeneron, Roche, Sanofi, Shionogi, and UCB for their support of contributing members and their review of earlier drafts of this manuscript. We thank Neal Grabowski from AbbVie for his leadership support and John Roth from Sanofi for supporting Health Authority engagement and helping summarize main points. Finally, we thank the TransCelerate team of Jeneen Donadeo for portfolio direction support, Patrice Wright for regulatory direction support, Hal Ward for program management support, and Clint Craun for project management support.
This work was supported by TransCelerate BioPharma Inc.
Conflicts of interest
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.
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Huysentruyt, K., Kjoersvik, O., Dobracki, P. et al. Validating Intelligent Automation Systems in Pharmacovigilance: Insights from Good Manufacturing Practices. Drug Saf 44, 261–272 (2021). https://doi.org/10.1007/s40264-020-01030-2