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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. [ABSTRACT FROM AUTHOR]

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.