The Use of Text Mining to Obtain a Historical Overview of Research on Therapeutic Drug Monitoring

Abstract

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.