Web-based automated therapeutic drug monitoring application for precision medicine in tuberculosis management

Tuberculosis (TB) remains one of the leading causes of infectious disease-related deaths worldwide. Model-informed precision dosing-based therapeutic drug monitoring (TDM) is a promising strategy to optimize anti-TB drugs doses based on pharmacokinetic (PK) profiles of patients. However, this approach requires significant time and trained personnel to interpret the results. To address this limitation, we developed and utilized an automated, web-based TDM platform that simplifies implementation and enhances accessibility, ultimately aiming to improve treatment outcomes. The system incorporates population PK models for both first- and second-line anti-TB drugs, integrating clinical data including demographics, NAT2 genotype and drug concentrations from limited sampling strategies. Bayesian forecasting is used to estimate individual PK parameters and simulate optimized dosing regimens. Clinicians can use the platform to automatically generate the individual concentration-time curve plot that compares a patient's exposure with population level references, along with a table displaying the estimated individual PK parameters. If the dose adjustment is needed, users may input alternative regimens and run the simulation to predict the corresponding PK metrics. These features enable users to visualize predicted outcomes, compare exposures against therapeutic targets, and support optimal dose selection. The system produces downloadable reports containing patient specific data, PK parameter values, graphical PK profiles, and pharmacogenomic interpretations with minimal user input. This automated web-based platform enhances the time-efficiency and accessibility of TDM, making it a practical tool for personalized TB therapy. It is especially valuable in resource-limited settings where expert support is limited, by supporting clinical decision making and improving patient outcomes.