Analytical Quality by Design (QbD) is the science and risk-based approaches for developing and maintaining analytical procedures, those are suitable for the evaluation of the quality of drug substances and drug products. The basic principle of analytical QbD was established and harmonized in ICH Q14 guideline as an enhanced approach, however, the examples for implementation of analytical QbD for biopharmaceuticals are limited. Therefore, as a part of the advancement of quality control through the application of analytical QbD, we conducted case studies focusing on target analytical profile (ATP) setting and risk assessment to show specific examples of analytical procedure development for biopharmaceuticals. Regarding ATP setting, we focused on high molecular weight species/fragment, host cell protein (HCP), and biological activity as representative quality attributes of biopharmaceuticals. As analytical techniques to measure each quality attribute, size exclusion chromatography was selected for high molecular weight/fragment, enzyme-linked immunosorbent assay (ELISA) for the total amount of HCP, and binding ELISA for biological activity were selected. While the examples shown in the ICH Q14 Annex are mainly described assuming the time of application for approval, this case study is characterized by considering issues to be considered from the early stages of drug development. It is expected that analytical QbD will be utilized for biopharmaceuticals, and that appropriate development and life cycle management of analytical procedures will be carried out.

This paper explores the implementation of Artificial Intelligence (AI), especially generative AI, in the pharmaceutical industry amid ongoing digital transformation (DX). AI technologies are being applied more and more across drug development, manufacturing, quality assurance, and supply chain management. These technologies offer improved efficiency and decision-making capabilities. However, these benefits come with challenges related to technical feasibility, ethics, and regulatory compliance. This paper reviews international regulatory initiatives, including FDA, WHO, and ISPE (GAMP 5 Second Edition) guidelines, and highlights key areas such as risk management, explainability, data quality, and model monitoring. It also discusses practical considerations such as data preprocessing, AI model validation, continuous monitoring, and model drift. Furthermore, the paper emphasizes the importance of human-centered design and responsible AI use, with ultimate accountability remaining with human decision-makers. This paper presents strategies for overcoming implementation barriers, such as PoC-driven phased adoption, workforce training, and interdepartmental collaboration. Finally, the paper outlines actionable steps for practitioners to reliably and sustainably integrate AI in compliance with GxP requirements, aiming to guide the pharmaceutical sector toward the responsible and effective utilization of AI.

This article is based on a case study for obtaining the Notified Body Opinion of a staked needle glass syringe pre-filled with a vaccine, produced by an Asian pharmaceutical industry and registered in Europe.

We will follow step by step the filing of the technical dossier, which documents to request from suppliers and which information to provide to the Notified Body.

This document will be a useful guide for the applicant to reduce the time and costs of applying a device constituent of a combined product according to the Art.117 of EU 2017/745.

This study addresses challenges in pharmaceutical manufacturing, including quality assurance, regulatory compliance, rising costs, complex supply chains, and a skilled labor shortage. The increasing demand for advanced knowledge complicates the transfer of skills, while tacit knowledge accumulation remains inadequately formalized and shared. To tackle these issues, we propose the implementation of AI-driven knowledge management tools that enhance knowledge visibility and facilitate effective knowledge transfer. Drawing from the ICH-Q10 definition of knowledge management, we emphasize the importance of not only sharing information but also ensuring its usability by recipients. This research investigates the effectiveness of these digital solutions in improving knowledge management in the pharmaceutical industry.

In order to effectively promote digital transformation (DX) in pharmaceutical manufacturing, we have devised a method using the DX maturity model. This report explains how to promote DX in pharmaceutical manufacturing using this DX maturity model and provides specific examples to raise the level of DX maturity.

Introduce examples of digital transformation (DX) introduction for effective quality assurance and stable supply of drugs, etc. This report shows actual examples of digital tools used at Takeda Pharmaceutical Company Limited. (Hereinafter referred to as Takeda) Hikari Plant and explains how DX can improve the efficiency of operations and investment in the future that enables DX.

This report explores the potential applications and challenges of generative AI in the pharmaceutical industry from the perspective of its users. It begins by outlining the fundamental concepts of prompt engineering and Retrieval-Augmented Generation (RAG) tuning, presenting methods for optimizing AI-generated outputs. Additionally, using the generative AI application development platform “Dify,” a demonstration was conducted based on the FDA’s concept of Computer Software Assurance (CSA), in which the effects of prompt design and the presence or absence of RAG tuning on AI responses were compared and analyzed. The results suggest that prompt engineering and knowledge enhancement through RAG tuning are effective for generating contextually appropriate responses. On the other hand, challenges such as accuracy, reproducibility, and ethical risks were also identified. To ensure the appropriate use of generative AI, it is important not only to promote user understanding of the technology, but also to design operational workflows that incorporate the Human-in-the-Loop approach.

Our group has studied the ideal state of deviation control and CAPA handling at the pharmaceutical manufacturing. It is important to detect minor deviations and incidents earlier and respond promptly, which promotes “Speak-up” culture and allocating resources according to the risk of the deviations.

This paper proposes a Double-Loop Method for managing incidents according to the procedure that investigation, disposition whether it is an incident or not, PDCA cycle will be implemented with record of incidents, list as deviation, and observing incidents.

Two examples of incidents are introduced for deeper understanding. One is packaging abnormalities of raw material, and the other is data loss in environmental monitoring systems.

Digital technologies and artificial intelligence (AI) are rapidly advancing in the field of pharmaceutical manufacturing, bringing about dramatic transformations within the industry. Member companies of the Japan PDA Pharmaceutical Association have also been actively adopting digital transformation (DX), and lively discussions on DX were held during individual committee sessions at the 2023 Annual Meeting. Based on this experience, at the 2024 Annual Meeting, the Association consolidated these previously separate committee discussions into a unified, three-and-a-half-hour joint symposium titled “Transforming Pharmaceutical Manufacturing through Digital Technology and AI.” This paper reports on the Committee Joint Symposium held on Thursday, December 5, 2024, during the Japan PDA Pharmaceutical Association 2024 Annual Meeting.

In recent years, Advanced Therapy Medicinal Products (ATMPs) have been adopted. One of the major differences from the conventional sterile pharmaceuticals is the very short time to administration; in some cases, it is only 2–3 days. For such products, rapid microbiological testing is necessary, and conventional culture-based sterility testing, which takes 14 days to obtain results, is a particularly significant challenge from the perspective of patient safety.

In this paper, the regulatory trends regarding microbial control for ATMPs by PIC/S, Japan, the U.S. and Europe are discussed.

Moreover, the application of advanced Rapid Microbiological Method (RMM：Rapid Microbiological Method) to the sterility testing for ATMPs is discussed. In this study, “High Sensitivity ATP Method,” which can directly measure ATP levels as low as those of single viable bacterial cells is used. The challenge with the ATP method is background ATP noise caused by mammalian cell-derived ATP. However, by combining suitable sample preparation methods such as the “pre-culture method” and the “cell-elimination method”, it is suggested that sterility test results can be obtained within a few hours to a few days.

In recent years, the advancement of digital technologies such as digital twins, metaverse, and CPS (Cyber-Physical Systems) has begun to influence the pharmaceutical industry. This report presents case studies from various sectors, including a virtual hospital developed by Juntendo University, a digital twin-based manufacturing approach by Toyota Production Engineering, and xR-based inspection technologies from Hitachi Solutions. These cases illustrate the potential for enhancing communication, optimizing production environments, and ensuring operational accuracy through digital integration. Furthermore, this paper discusses the expected evolution from the current Digital Island status of pharmaceutical plants toward a more connected and predictive model, based on the Digital Plant Maturity Model (BioPhorum). The insights aim to serve as a reference for the pharmaceutical industry's future digital transformation.