Teicoplanin is an important antimicrobial agent for methicillin-resistant Staphylococcus aureus infections. To enhance its clinical effectiveness while preventing adverse effects, therapeutic drug monitoring (TDM) of teicoplanin trough concentration is recommended. Given the importance of the early achievement of therapeutic concentrations for treatment success, initial dosing regimens are deliberately designed based on patient information. Considerable effort has been dedicated to developing an optimal initial dose plan for specific populations; however, comprehensive strategies for tailoring teicoplanin dosing have not been successfully implemented. The initial dose planning of teicoplanin is conducted at the clinician's discretion and is thus strongly dependent on the clinician's experience and expertise. The present study aimed to use a machine learning (ML) approach to integrate clinicians' knowledge into a predictive model for initial teicoplanin dose planning. We first confirmed that dose planning by pharmacists dedicated to TDM (hereafter TDM pharmacists) significantly improved early therapeutic target attainment for patients without an intensive care unit or high care unit stay, providing the first evidence that dose planning of teicoplanin by experienced clinicians enhances early teicoplanin therapeutic exposure. Next, we used a dataset of teicoplanin initial dose planning by TDM pharmacists to train and implement the model, yielding a model that emulated TDM pharmacists' decision-making for dosing. We further applied ML to cases without TDM pharmacist dose planning and found that the target attainment rate of the initial teicoplanin concentration markedly increased. Our study opens a new avenue for tailoring the initial dosing regimens of teicoplanin using a TDM pharmacist-trained ML system.

The authors have declared no competing interest.

This work was supported by Morinomiyako Medical Research Foundation, the JSPS KAKENHI (Grant Numbers JP 20H03428 and 22K17824), and the Research Funding for Longevity Sciences (22-21) from National Center for Geriatrics and Gerontology (NCGG), Japan.

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This study was conducted with the approval from the Ethic Committee of Nagoya University Hospital (Approval No. 2022-0071).

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