Optimization of Infectious Disease Treatment via Clinical Practice-Based Personalized Therapy and Big Data Analysis.

Authors

Yuichi Muraki

Published in

Biological & pharmaceutical bulletin. Volume 49. Issue 7. Pages 1043-1048.

Abstract

The optimization of infectious disease treatment requires a multilevel perspective linking individualized pharmacotherapy with population-level evaluation. Clinical questions arising from bedside practices provide a starting point to improve treatment via therapeutic drug monitoring, pharmacogenomics, and pathophysiology-based dose optimization. For example, patient-specific factors, such as hepatic dysfunction and CYP3A5 genotype, substantially influence drug exposure and infection risk in transplant recipients. However, patient-level optimization alone is insufficient to determine whether antimicrobial therapy is practiced appropriately across institutions, regions, or healthcare systems. Therefore, antimicrobial surveillance systems are critical for quantifying antimicrobial use using standardized metrics and providing a measurable foundation for stewardship. In Japan, nationwide surveillance studies have enabled quantitative assessment of antimicrobial consumption and resistance patterns at both hospital and national levels. Furthermore, the increasing availability of large healthcare databases, including administrative claims data and pharmacy dispensing information, has expanded the scope of evaluation to prescribing behavior, guideline adherence, stewardship interventions, and impacts of healthcare policies. These developments allow antimicrobial stewardship to be assessed at both individual institution and national healthcare system levels. In this evolving landscape, clinical pharmacists are uniquely positioned to contribute to both individualized therapy and broader evidence generation. This review builds on our accumulated research findings and discusses how bedside clinical questions can be extended to surveillance infrastructure and big data analysis, thereby linking patient care to health policies and public health strategies against antimicrobial resistance.

PMID:
42386538
Bibliographic data and abstract were imported from PubMed on 02 Jul 2026.

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