Pharmaceuticals and Medical Devices Agency

For pharmacovigilance, the Pharmaceuticals and Medical Devices Agency in Japan has utilized real world data (RWD) from multiple sources, including individual case safety reports, and medical information databases that capture routinely collected data from clinical practice. These RWD have their own characteristics with advantages and disadvantages. In this commentary, we describe current and future direction of post-marketing drug safety assessment in Japan.

There is an unmet need for developing drugs for the treatment of gonorrhea due to rapidly evolving resistance of Neisseria gonorrhoeae against antimicrobial drugs used for empiric therapy, an increase in globally reported multidrug-resistant cases, and the limited available therapeutic options. Furthermore, few drugs are under development. Development of antimicrobials is hampered by challenges in clinical trial design, limitations of available diagnostics, changes in and varying standards of care, lack of robust animal models, and clinically relevant pharmacodynamic targets. On 23 April 2021, the US Food and Drug Administration, Centers for Disease Control and Prevention, and National Institute of Allergy and Infectious Diseases of the National Institutes of Health co-sponsored a workshop with stakeholders from academia, industry, and regulatory agencies to discuss the challenges and strategies, including potential collaborations and incentives, to facilitate the development of drugs for the treatment of gonorrhea. This article provides a summary of that workshop.

Platform trials enable the evaluation of multiple investigational drugs for a single disease and offer flexibility in adding or dropping treatments during the trial. This design would be advantageous for reducing the sample size and drug development time, particularly in contexts such as pandemics. In the platform trials, non-concurrent controls (NCCs) are often used for drug-control comparisons, but temporal shifts in subject characteristics, trial conduct, or standard of care can introduce bias in the estimation of treatment effects and increase the type I error rate. In this study, we develop a new Bayesian power prior to incorporate NCC data in platform trials with binary outcomes. To address temporal shifts, our method adjusts the amount of information borrowed from NCCs using a data-driven similarity index between NCC and concurrent control (CC) data. This index serves as the power parameter in the power prior, enabling adaptive borrowing. We evaluated the proposed method through extensive simulation studies, comparing its operating characteristics with seven alternatives: analysis using only CC data, naïve pooling method, a frequentist linear regression model, and four Bayesian methods designed to address temporal shifts. Across a range of temporal shift scenarios, the proposed method consistently achieved a favorable balance between type I error control and statistical power, maintaining type I error rates below 10% while avoiding the overborrowing seen in more aggressive methods. The practical utility of the proposed method was also examined by applying it to data from a platform trial involving patients with COVID-19.

Background Pharmacovigilance aims to protect patient safety by identifying and managing adverse events associated with pharmaceuticals. Determining the causality of these adverse events is central at both the individual case and population levels; however, it is increasingly challenging as the volume and complexity of safety data grow. Although AI and related technologies have been proposed to support causality assessment, limited research has examined how these methods are used, their information and quality requirements, or how associated risks are addressed. Objective This scoping review aims to determine the available evidence on AI-based methods for causality assessment in pharmacovigilance. The primary objective is to characterize how these methods are applied or proposed with a focus on their functional roles, reported data inputs and information needs, and associated risks. Secondary objectives include comparing applications at the individual case and population levels; describing the types of AI-based techniques and automation tools used in causality assessment workflows; and summarizing reported data quality considerations and governance mechanisms, including risk management approaches. Methods Sources describing or proposing AI-based approaches, including data-driven models (machine learning, natural language processing, knowledge graphs, and causal inference) and knowledge- or rule-based systems implementing causal assessment logic, will be eligible. Searches will be conducted in PubMed, Web of Science Core Collection, ProQuest, EBSCOhost, and Ichushi Web and will be restricted to English- and Japanese-language sources. Two reviewers will independently screen records and full-text articles, with disagreements resolved by a third reviewer. Data will be charted on use cases, information inputs, data quality dimensions, model characteristics, governance mechanisms, and identified risks. Synthesis will follow a reflexive thematic analysis approach and be reported in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines informed by applicable PRISMA-S (Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension) elements. Results This protocol was registered in the Open Science Framework platform on December 23, 2025. The registration was subsequently updated on May 19, 2026, to reflect an extension to the data collection period. A preliminary database search was conducted in December 2025, retrieving a total of 760 records, of which the preliminary title and abstract screening identified 196 (25.8%) articles for full-text review. Database searches are scheduled for July 2026. Data charting is scheduled for August 2026, and synthesis is scheduled for September 2026. Findings are expected to be submitted for publication by the end of December 2026. Conclusions This review is expected to provide a structured map of AI-based applications for causality assessment in pharmacovigilance, clarify reported information inputs and data quality dimensions, and synthesize risk management and governance approaches. The findings are expected to inform methodological development, practical implementation, and the governance of AI-supported causality assessment. Trial Registration Open Science Framework 10.17605/OSF.IO/QVF5C; https://osf.io/qvf5c/overview International Registered Report Identifier (IRRID) DERR1-10.2196/101691