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Semi-Automated Therapeutic Drug Monitoring as a Pillar toward Personalized Medicine for Tuberculosis Management.
Jayanti, Rannissa Puspita; Long, Nguyen Phuoc; Phat, Nguyen Ky; Cho, Yong-Soon; Shin, Jae-Gook.
Afiliação
  • Jayanti RP; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea.
  • Long NP; Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea.
  • Phat NK; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea.
  • Cho YS; Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea.
  • Shin JG; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea.
Pharmaceutics ; 14(5)2022 May 05.
Article em En | MEDLINE | ID: mdl-35631576
ABSTRACT
Standard tuberculosis (TB) management has failed to control the growing number of drug-resistant TB cases worldwide. Therefore, innovative approaches are required to eradicate TB. Model-informed precision dosing and therapeutic drug monitoring (TDM) have become promising tools for adjusting anti-TB drug doses corresponding with individual pharmacokinetic profiles. These are crucial to improving the treatment outcome of the patients, particularly for those with complex comorbidity and a high risk of treatment failure. Despite the actual benefits of TDM at the bedside, conventional TDM encounters several hurdles related to laborious, time-consuming, and costly processes. Herein, we review the current practice of TDM and discuss the main obstacles that impede it from successful clinical implementation. Moreover, we propose a semi-automated TDM approach to further enhance precision medicine for TB management.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article