Publisher：Cold Spring Harbor Laboratory  

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.

DOI： 10.1101/2023.12.14.23299934

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Cold Spring Harbor Laboratory  

Therapeutic drug monitoring (TDM) is a routine clinical practice used to individualize drug dosing and thereby maintain drug efficacy and minimize the consequences of overexposure. TDM is applied to many drug classes, including immunosuppressants, anticoagulants, antineoplastic agents, and antibiotics. Considerable efforts have been made to establish routine TDM practice for each drug. However, because TDM has been developed within the context of specific drugs, there is insufficient understanding of historical trends in TDM research itself. In this study, we employed text mining approaches to explore trends in the TDM research field. We first performed a PubMed search to determine which drugs and drug classes have been extensively studied in the context of TDM. This investigation revealed that the most commonly studied drugs were warfarin and cyclosporine, followed by tacrolimus, heparin, and vancomycin. In terms of drug classes, most publications focused on antineoplastic agents, anticoagulants, immunosuppressants, and antibiotics. We next used a newly developed python-based module to obtain PubMed records of publications relevant to TDM, and then subjected them to text mining. Our analyses revealed how TDM research has evolved over the years.

DOI： 10.1101/2023.06.22.23291733

Authorship：Last author,　Corresponding author   Publisher：Cold Spring Harbor Laboratory  

Abstract

The budding yeastSaccharomyces cerevisiaeis commonly used as an expression platform to produce valuable compounds. Yeast-based genetics research can uniquely utilize use auxotrophy in transformant selection: auxotrophic complementation by an auxotrophic marker gene on exogenous DNA (such as plasmids). However, the number of auxotrophic nutrients required restricts the number of plasmids maintained by the cells. We therefore developed novel Δ10 strains that are auxotrophic for 10 different nutrients, and new plasmids with two multicloning sites and auxotrophic markers for use in Δ10 strains. We confirmed that Δ10 strains could maintain 10 types of these plasmids. Because each plasmid can express two different genes, this Δ10 strain-based expression system has the potential to co-express a maximum of 20 different proteins.

DOI： 10.1101/2023.05.01.538886

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier {BV}  

Copy-number variations in the ARHGAP10 gene encoding Rho GTPase-activating protein 10 are associated with schizophrenia. Model mice (Arhgap10 S490P/NHEJ mice) that carry "double-hit" mutations in the Arhgap10 gene mimic the schizophrenia in a Japanese patient, exhibiting altered spine density, methamphetamine-induced cognitive dysfunction, and activation of RhoA/Rho-kinase signaling. However, it remains unclear whether the activation of RhoA/Rho-kinase signaling due to schizophrenia-associated Arhgap10 mutations causes the phe-notypes of these model mice. Here, we investigated the effects of fasudil, a brain permeable Rho-kinase inhibitor, on altered spine density in the medial prefrontal cortex (mPFC) and on methamphetamine-induced cognitive impairment in a touchscreen-based visual discrimination task in Arhgap10 S490P/NHEJ mice. Fasudil (20 mg/ kg, intraperitoneal) suppressed the increased phosphorylation of myosin phosphatase-targeting subunit 1, a substrate of Rho-kinase, in the striatum and mPFC of Arhgap10 S490P/NHEJ mice. In addition, daily oral administration of fasudil (20 mg/kg/day) for 7 days ameliorated the reduced spine density of layer 2/3 pyra-midal neurons in the mPFC. Moreover, fasudil (3-20 mg/kg, intraperitoneal) rescued the methamphetamine (0.3 mg/kg)-induced cognitive impairment of visual discrimination in Arhgap10 S490P/NHEJ mice. Our results suggest that Rho-kinase plays significant roles in the neuropathological changes in spine morphology and in the vulnerability of cognition to methamphetamine in mice with schizophrenia-associated Arhgap10 mutations.

DOI： 10.1016/j.phrs.2022.106589

Web of Science

Scopus

PubMed

Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)   Publisher：東京 : 日本病院薬剤師会  

CiNii Books

Other Link： https://ndlsearch.ndl.go.jp/books/R000000004-I033556583

Event date： 2024.3

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2023.11

Presentation type：Oral presentation (general)  

Event date： 2023.7

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2023.5

Language：English   Presentation type：Poster presentation  

Event date： 2023.5

Language：English   Presentation type：Poster presentation