Objective: Therapeutic antibodies have few varieties of side effects due to their high specificity; however, many therapeutic antibodies have serious side effects. A thorough understanding of the side effects is crucial for early recognition and optimal management. To facilitate the understanding of the side effects of therapeutic antibodies, this study attempted to classify therapeutic antibodies based on their side effects using principal component analysis (PCA) and cluster analysis.
Method: We collected data on the serious side effects of therapeutic antibodies from package inserts and created a therapeutic antibody-side effect matrix, with therapeutic antibodies as indices and side effects as columns. PCA was performed on the therapeutic antibody-side effect matrix, and hierarchical cluster analysis was performed using principal components.
Results: The therapeutic antibodies were classified into four clusters. Cluster 1 included immune checkpoint inhibitors, and featured type 1 diabetes, thyroid disorder, and myasthenia gravis. Cluster 2 included antibodies that inhibit the vascular endothelial growth factor pathway, and featured impaired wound healing, nephrotic syndrome, and thrombosis. Cluster 3 included anti-epidermal growth factor receptor antibodies, and featured diarrhea, hypomagnesemia, and skin disorders. Cluster 4 included other therapeutic antibodies, and featured infection, bone marrow suppression, and hypersensitivity.
Conclusion: Therapeutic antibodies can be classified based on their side effects. The results of this study make it easier to understand the side effects of therapeutic antibodies with complex profiles. A better understanding facilitates early detection of side effects and enables high-quality management of side effects.

Objective: To determine the extent of pharmacists’ burden of inquiries from healthcare professionals in community pharmacies.
Design: A descriptive cross-sectional study.
Methods: A web-based survey was administered to pharmacists affiliated with Medical System Network Group, Inc.’s community pharmacies in Japan. The survey was conducted from February 15 to March 31, 2022. The primary outcome was the burden of inquiries from healthcare professionals and the secondary outcome was the level of burden by job category. Healthcare professionals were defined as physicians, nurses, pharmacists, administrators, care managers, and nursing home staff. To assess the degree of burden, participants were asked “Do you feel burdened by inquiries from health care professionals ? ” and their response was rated on a 5-point Likert scale (not at all, slightly, a little, a lot, and very much). The responses “a lot” and “very much” were combined and defined as “burdened.” To identify the causes of burden, factors of burden and inquiries were investigated.
Results: Totally, 1,667 participants were recruited, of which 915 (54.9%) were included in the analysis (women: 62.6%). The participants had a median age of 38 years (interquartile range 31, 48), and worked as pharmacists for 12 years (interquartile range 5, 20). Nearly 13.5% of the respondents felt burdened by inquiries from healthcare professionals and 34.7% reported that physician inquiries were burdensome. The most common reason for feeling burdened was a lack of knowledge about the inquiries (77.3%).
Conclusion: It was found that 13.5% of respondents felt burdened when dealing with inquiries from healthcare professionals to pharmacists. In particular, a high percentage of respondents felt burdened by inquiries from physicians. Further research is needed to clarify whether the introduction of a tool that matches the results of this study will reduce the burden of responding to inquiries.

Background: Imprinting tablet bodies is an essential element for the safe use of pharmaceuticals. It has been observed that reports on tablet imprint design that incorporate pharmacists’ perspectives are scarce.
Objectives: The aim of this study was to provide a better understanding of the concept of universal design in tablet body identification and differentiation, specifically in two scenarios: normal handling of tablets and encountering tablets with similar appearances.
Methods: A survey was conducted among pharmacists registered with the Yamaguchi Prefecture Pharmaceutical Association to collect data on optimal tablet imprint designs from a dispensing perspective.
Results: Analysis of the survey results indicated that: 1. In normal use of tablets, a simple design with “horizontal Kana notation without emphasis on one character” was most preferred. 2. In cases where tablets were similar in appearance, designs such as “horizontal Kana notation with an underline on one character” and “horizontal Kana notation with emphasis on one character” were preferred.
Conclusions: The study may indicate that certain tablet imprint designs may enhance the ability to differentiate and recognize pharmaceuticals, particularly in cases where tablets have similar appearances. To aid in distinguishing tablets with similar appearances, it is suggested that the design should incorporate ‘horizontal Kana notation’ and give emphasis to one character. This design has been shown to result in a clear improvement in identification. It is recommended that tablet imprint designs prioritize high readability for pharmacists and provide appropriate pharmaceutical information. When considering similarity with other tablets, it may be advisable to use ‘emphasis on one character’ as a universal design for differentiating and identifying tablet bodies.

Objective: Research and development for drug discovery is time-consuming and expensive. Artificial intelligence (AI) technologies, such as machine learning are attracting the attention of researchers as tools for efficiently advancing drug discovery. However, the use of AI technology requires a high amount of data, and the scope of application and accuracy of prediction depend on data quality. Therefore, the development of technology for efficiently collecting drug information data is required. The present examined an interactive AI system for extracting absorption, distribution, metabolism, and excretion (ADME) data from clinical practice documents.
Methods: Attachments for five drugs were collected from the Pharmaceuticals and Medical Devices Agency (PMDA) for properties influencing pharmacokinetics, including dosage, maximum concentration (C_max), half-life (T_1/2), time to peak drug concentration (T_max), area under the curve (AUC), and clearance (CL). Data were collected directly from PDFs using ChatGPT Plus, SciSpace, and ChatPDF as interactive AI systems capable of performing this task, and variations in these properties were compared. In addition, we compared the variations in the prompting outputs.
Results: ChatGPT Plus was able to retrieve some pharmacokinetic properties including the values in the tables, whereas SciSpace and ChatPDF were unable to retrieve pharmacokinetic information. In addition, the ChatGPT Plus output changed depending on the prompt, whereas the results obtained using SciSpace and ChatPDF did not change significantly based on the prompt. Therefore, ChatGPT Plus was the most appropriate system for collecting ADME data.
Conclusion: Based on the results of collection of ADME characteristics from documents using the three interactive AI systems, ChatGPT Plus is the most effective method for obtaining the desired characteristics, although several issues need to be addressed. Interactive AI will be an indispensable technology for data collection in drug research, and could contribute significantly to drug discovery in the future.

Collaboration between medical institutions and community pharmacies is crucial for sharing patient’s treatment status and chemotherapy regimens in order to give safe and effective cancer chemotherapy for outpatient. In Kagoshima Medical Association Hospital, we established a framework for sharing this information with community pharmacies through a multidisciplinary approach. We conducted a questionnaire survey among community pharmacists to evaluate the usefulness of patient’s treatment status and chemotherapy regimens available on our hospital’s website. Most respondents found these resources “very useful.” Furthermore, respondents expressed a desire for additional information on chemotherapy regimen that was not currently available on our website but accessible from other institutions. This survey helped us understand the specific information required by community pharmacists regarding patient treatment status and chemotherapy regimens from medical institutions.

Objective: This study evaluates the potential for automated data linkages between survey items collected during Japanese postmarketing surveillance (PMS) of pharmaceuticals and the medical health data stored in hospital information systems (HIS) by automatically relating the PMS survey items to the structured data (SD) in HIS. This relationship has not been explored previously; therefore, our findings offer fundamental insights for exploring automated data linkages between PMS survey items and medical health data.
Methods: The PMS survey items from 107 case report forms (CRFs) currently used at Kagawa University Hospital as of April 1, 2022, were analyzed. The survey items were categorized into major and detailed items based on the CRF units, and the frequency of occurrence for each detailed item was calculated. We considered the SD used in MID-NET^®, which is a Japanese national medical information database, as being analogous to the SD used in HIS and determined whether each detailed item had a one-to-one relationship with the SD in HIS.
Results: Twelve major items were identified, including a total of 83 detailed items. Among them, 25 items (30%) showed a one-to-one simple relationship with SD in HIS, while 28 items (33%) did not show a relationship. The remaining 30 items (37%) did not show any correspondence to the SD in HIS.
Conclusion: The results demonstrate that approximately 60% of the detailed survey items could be collected from SD. However, a physician’s medical judgment was needed for approximately half of the items. These findings will contribute to the realization of automated data linkages between the PMS survey items and the medical health data in HIS, thereby improving the efficiency of information acquisition required for PMS of pharmaceuticals.