Semi-Automated Therapeutic Drug Monitoring as a Pillar toward Personalized Medicine for Tuberculosis Management.
Rannissa Puspita JayantiNguyen Phuoc LongNguyen Ky PhatYong-Soon ChoJae-Gook ShinPublished in: Pharmaceutics (2022)
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.
Keyphrases
- mycobacterium tuberculosis
- drug resistant
- multidrug resistant
- primary care
- pulmonary tuberculosis
- healthcare
- end stage renal disease
- machine learning
- deep learning
- acinetobacter baumannii
- newly diagnosed
- ejection fraction
- high throughput
- quality improvement
- chronic kidney disease
- emergency department
- hiv aids
- adverse drug
- prognostic factors
- drug induced
- peritoneal dialysis
- hiv infected
- combination therapy
- single cell
- pseudomonas aeruginosa
- electronic health record