Information Visualization Platform for Postmarket Surveillance Decision Support

Pharmacovigilance in Vaccines: Importance, Main Aspects, Perspectives, and Challenges-A Narrative Review

Pharmacovigilance plays a central role in safeguarding public health by continuously monitoring the safety of vaccines, being critical in a climate of vaccine hesitancy, where public trust is paramount. Pharmacovigilance strategies employed to gather information on adverse events following immunization (AEFIs) include pre-registration data, media reports, clinical trials, and societal reporting. Early detection of AEFIs during clinical trials is crucial for thorough safety analysis and preventing serious reactions once vaccines are deployed. This review highlights the importance of societal reporting, encompassing contributions from community members, healthcare workers, and pharmaceutical companies. Technological advancements such as quick response (QR) codes can facilitate prompt AEFI reporting. While vaccines are demonstrably safe, the possibility of adverse events necessitates continuous post-marketing surveillance. However, underreporting remains a challenge, underscoring the critical role of public engagement in pharmacovigilance. This narrative review comprehensively examines and synthesizes key aspects of virus vaccine pharmacovigilance, with special considerations for specific population groups. We explore applicable legislation, the spectrum of AEFIs associated with major vaccines, and the unique challenges and perspectives surrounding pharmacovigilance in this domain.

Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration

Adverse drug reactions are a common cause of morbidity in health care. The US Food and Drug Administration (FDA) evaluates individual case safety reports of adverse events (AEs) after submission to the FDA Adverse Event Reporting System as part of its surveillance activities. Over the past decade, the FDA has explored the application of artificial intelligence (AI) to evaluate these reports to improve the efficiency and scientific rigor of the process. However, a gap remains between AI algorithm development and deployment. This viewpoint aims to describe the lessons learned from our experience and research needed to address both general issues in case-based reasoning using AI and specific needs for individual case safety report assessment. Beginning with the recognition that the trustworthiness of the AI algorithm is the main determinant of its acceptance by human experts, we apply the Diffusion of Innovations theory to help explain why certain algorithms for evaluating AEs at the FDA were accepted by safety reviewers and others were not. This analysis reveals that the process by which clinicians decide from case reports whether a drug is likely to cause an AE is not well defined beyond general principles. This makes the development of high performing, transparent, and explainable AI algorithms challenging, leading to a lack of trust by the safety reviewers. Even accounting for the introduction of large language models, the pharmacovigilance community needs an improved understanding of causal inference and of the cognitive framework for determining the causal relationship between a drug and an AE. We describe specific future research directions that underpin facilitating implementation and trust in AI for drug safety applications, including improved methods for measuring and controlling of algorithmic uncertainty, computational reproducibility, and clear articulation of a cognitive framework for causal inference in case-based reasoning.

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.

"Artificial Intelligence" for Pharmacovigilance: Ready for Prime Time?

There is great interest in the application of 'artificial intelligence' (AI) to pharmacovigilance (PV). Although US FDA is broadly exploring the use of AI for PV, we focus on the application of AI to the processing and evaluation of Individual Case Safety Reports (ICSRs) submitted to the FDA Adverse Event Reporting System (FAERS). We describe a general framework for considering the readiness of AI for PV, followed by some examples of the application of AI to ICSR processing and evaluation in industry and FDA. We conclude that AI can usefully be applied to some aspects of ICSR processing and evaluation, but the performance of current AI algorithms requires a 'human-in-the-loop' to ensure good quality. We identify outstanding scientific and policy issues to be addressed before the full potential of AI can be exploited for ICSR processing and evaluation, including approaches to quality assurance of 'human-in-the-loop' AI systems, large-scale, publicly available training datasets, a well-defined and computable 'cognitive framework', a formal sociotechnical framework for applying AI to PV, and development of best practices for applying AI to PV. Practical experience with stepwise implementation of AI for ICSR processing and evaluation will likely provide important lessons that will inform the necessary policy and regulatory framework to facilitate widespread adoption and provide a foundation for further development of AI approaches to other aspects of PV.

Feature engineering and machine learning for causality assessment in pharmacovigilance: Lessons learned from application to the FDA Adverse Event Reporting System

BACKGROUND

Our objective was to support the automated classification of Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) reports for their usefulness in assessing the possibility of a causal relationship between a drug product and an adverse event.

METHOD

We used a data set of 326 redacted FAERS reports that was previously annotated using a modified version of the World Health Organization-Uppsala Monitoring Centre criteria for drug causality assessment by a group of SEs at the FDA and supported a similar study on the classification of reports using supervised machine learning and text engineering methods. We explored many potential features, including the incorporation of natural language processing on report text and information from external data sources, for supervised learning and developed models for predicting the classification status of reports. We then evaluated the models on a larger data set of previously unseen reports.

RESULTS

The best-performing models achieved recall and F1 scores on both data sets above 0.80 for the identification of assessable reports (i.e. those containing enough information to make an informed causality assessment) and above 0.75 for the identification of reports meeting at least a Possible causality threshold.

CONCLUSIONS

Causal inference from FAERS reports depends on many components with complex logical relationships that are yet to be made fully computable. Efforts focused on readily addressable tasks, such as quickly eliminating unassessable reports, fit naturally in SE's thought processes to provide real enhancements for FDA workflows.