Vasileios Chronopoulos 1,2, Periklis Tsiros 1,2, Aristeidis Dokoumetzidis 1,2
1 National and Kapodistrian University of Athens (Athens, Greece), 2 Athena Research and Innovation Center (Athens, Greece)
Objectives
Tuberculosis (TB) treatment requires prolonged multidrug therapy and exhibits substantial variability in response across patients. Treatment outcomes are determined by complex interactions between drug exposure at the site of infection, heterogeneous bacterial subpopulations with differing susceptibility, and host immune responses contributing to bacterial clearance. Model-informed multiscale approaches integrating these processes provide a quantitative framework for understanding treatment response and supporting translational regimen optimization. The objective of this study was to develop an integrated multiscale population model linking human lung pharmacokinetics, bacterial dynamics, drug pharmacodynamics, and host immune response, and to evaluate its ability to quantitatively reproduce early bactericidal activity (EBA) observed across clinically used TB regimens.
Methods
A modular mechanistic framework was constructed by integrating three established components: a translational bacterial dynamics and in vitro-to-clinical pharmacodynamic model describing fast-, slow-, and non-replicating bacterial subpopulations and drug-specific killing mechanisms [1], a host immune response model capturing macrophage- and lymphocyte-mediated bacterial control [2], and physiologically based pharmacokinetic models predicting antitubercular drug exposure in human lung tissue [3]. The integrated system was implemented in a unified population simulation framework and used to simulate standard oral dosing regimens of isoniazid (INH, 300 mg once daily), rifampicin (RIF, 600 mg once daily), and combination HRZE therapy consisting of rifampicin (600 mg), isoniazid (300 mg), pyrazinamide (1500 mg), and ethambutol (1200 mg) administered once daily. Population simulations (n=500) were performed, and EBA was calculated as the decline in log10 CFU/day over clinically relevant intervals (0–2, 2–14, and 0–14 days). Model predictions were evaluated by comparison with independent clinical EBA cohorts, including INH monotherapy [4–10,14], RIF monotherapy [4,5,8,12-14], and HRZE combination therapy [15].
Results
The model reproduced distinct regimen-specific bactericidal dynamics observed clinically. For INH 300 mg, simulations (n=500) predicted rapid early killing with EBA0-2 of 0.639 log10 CFU/day (95% CI: 0.626–0.652), followed by substantially reduced activity during days 2–14 (0.038 log10 CFU/day, 0.037–0.040), compared with reported clinical values of 0.27–0.77 and 0.113 log10 CFU/day, respectively [4–10,14]. For RIF 600 mg, the model predicted lower initial activity (EBA0-2: 0.062 log10 CFU/day, 0.059–0.065) and sustained bactericidal effects during days 2–14 (0.205 log10 CFU/day, 0.198–0.212), compared with clinical values of 0.028–0.631 and 0.096 log10 CFU/day [4,5,8,12–14]. For HRZE combination therapy, the model predicted greater and sustained bacterial killing, with EBA0-2 of 0.373 log10 CFU/day (0.363–0.384), EBA2-14 of 0.321 log10 CFU/day (0.310–0.331), and EBA0-14 of 0.328 log10 CFU/day (0.319–0.338), compared with reported clinical values of 0.413, 0.046, and 0.112 log10 CFU/day, respectively [15].
Conclusions
This study presents an integrated multiscale population model linking lung pharmacokinetics, immune-mediated bacterial control, bacterial subpopulation dynamics, and drug pharmacodynamics within a unified translational framework. The model reproduced characteristic regimen-specific bactericidal activity profiles and generated quantitative predictions consistent with clinical EBA observations across multiple independent cohorts [4–15]. These results demonstrate the ability of the framework to mechanistically describe early TB treatment response and support its application as a model-informed platform for quantitative evaluation and translational optimization of antitubercular regimens in early drug development.
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Reference: PAGE 34 (2026) Abstr 11909 [www.page-meeting.org/?abstract=11909]
Poster: Drug/Disease Modelling - Infection