Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Pharmaceutical Society of Japan  

<p>Therapeutic drug monitoring (TDM) is a routine clinical practice used to individualize drug dosing to maintain drug efficacy and minimize the consequences of overexposure. TDM is applied to many drug classes, including immunosuppressants, antineoplastic agents, and antibiotics. Considerable effort has been made to establish routine TDM practices for each drug. However, because TDM has been developed within the context of specific drugs, there is insufficient understanding of historical trends within the field of TDM research as a whole. 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 are tacrolimus, followed by cyclosporine and vancomycin. With regard to drug classes, most studies focused on immunosuppressants, antibiotics, and antineoplastic agents. We also subjected PubMed records of TDM-related studies to a series of text-mining pipelines. Our analyses revealed how TDM research has evolved over the years, thereby serving as a cornerstone for forecasting future research trends.</p>

DOI： 10.1248/bpb.b24-00319

Open Access

Web of Science

Scopus

PubMed

CiNii Research

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

The budding yeast Saccharomyces cerevisiae is commonly used as an expression platform for the production of valuable compounds. Yeast‐based genetic research can uniquely utilize auxotrophy in transformant selection: auxotrophic complementation by an auxotrophic marker gene on exogenous DNA (such as plasmids). However, the number of required auxotrophic nutrients 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 multiple cloning sites and auxotrophic markers for use in Δ10 strains. We confirmed that Δ10 strains were able to maintain 10 types of plasmids. Using plasmids encoding model proteins, we detected the co‐expression of 17 different genes in Δ10 cell lines. We also constructed Δ9 strains that exhibited auxotrophy for nine nutrients and increased growth compared to Δ10. This study opens a new avenue for the co‐expression of a large number of genes in eukaryotic cells.

DOI： 10.1002/2211-5463.13893

Open Access

Web of Science

Scopus

PubMed

Language：English   Publisher：Cold Spring Harbor Laboratory  

1Abstract

Copy number variations in theARHGAP10gene encoding Rho GTPase–activating protein 10 are significantly associated with schizophrenia. ARHGAP10 negatively regulates RhoA/Rho-kinase (ROCK) signaling. We previously demonstrated that fasudil, a non-selective ROCK inhibitor, exhibited antipsychotic-like effects in several mouse models of schizophrenia. ROCK has two subtypes, ROCK1 and ROCK2. ROCK1 is mainly expressed in the thymus and blood, while ROCK2 is predominantly expressed in the brain. Therefore, it is expected that like fasudil, selective ROCK2 inhibitors will exhibit antipsychotic-like effects, accompanied by a lower incidence of adverse effects due to ROCK1 inhibition. Here, we used genetic and pharmacological models of schizophrenia to investigate whether the selective ROCK2 inhibitor KD025 would show antipsychotic-like effects with a favorable adverse effect profile. Oral administration of KD025 suppressed the abnormal increase in the phosphorylation level of myosin phosphatase–targeting subunit 1, a substrate of ROCK, and ameliorated the decreased spine density of layer 2/3 pyramidal neurons in the medial prefrontal cortex ofArhgap10S490P/NHEJ mice. Furthermore, KD025 mitigated the methamphetamine-induced impairment of visual discrimination (VD) inArhgap10S490P/NHEJ and wild-type mice. KD025 also reduced MK-801–induced impairments of VD, novel object recognition, and hyperlocomotion. Regarding side effects that are commonly seen with typical antipsychotics, KD025 did not affect systolic blood pressure and did not induce extrapyramidal symptoms, hyperprolactinemia, or hyperglycemia at the effective dosage in naïve wild-type mice. Taken together, KD025 shows antipsychotic-like effects with a favorable adverse effect profile in genetic and pharmacological mouse models of schizophrenia.

DOI： 10.1101/2024.09.16.613372

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：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： 2024.12

Presentation type：Oral presentation (invited, special)  

Event date： 2024.10

Presentation type：Oral presentation (general)  

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)