Modeling Treatment Response in Tuberculosis Early Bactericidal Activity Trials

Why clinicians should know about it

• Picked for Microbiology (medical) (paper of the day, 9 August 2026): TB early bactericidal activity modeling, not diagnostic method

Abstract

Culture-based monitoring of bacterial load is slow and susceptible to missing data, contributing to the length and cost of TB clinical trials. Non-culture-based alternatives, like the Tuberculosis Molecular Load Bacterial Assay (TB-MBLA), could represent a solution. Our objectives were to evaluate TB-MBLA as a biomarker in early bactericidal activity (EBA) studies and explore whether combining biomarkers with joint modeling could provide insight into underlying biological processes. We generated TB-MBLA (LifeArc) data from sputum samples from all 78 patients from the PanACEA BTZ-043 Phase Ib/IIa trial and derived a summary measure of the joint distribution of the three TB-MBLA, colony forming units (CFU), and time-to-positivity (TTP) biomarkers as the first principal component derived from a probabilistic principal component analysis (pPCA). With TB-MBLA marker and the principal component 1 (PC1) values, we reevaluated the original stage IIa dose-response and stages Ib/IIa pharmacokinetics-pharmacodynamics (PK-PD) exposure-response analyses, applying linear and non-linear mixed models, respectively. For TB-MBLA, we could not detect an exposure-response effect in the PK-PD analysis, in contrast with CFU and TTP. When combining biomarkers, we observed a significant but less pronounced Emax exposure-response between days 0-3 compared with CFU and TTP alone. We also successfully applied pPCA as a modeling framework and show evidence that combining CFU and TTP in a joint latent component can improve detection of treatment effects compared with either biomarker alone. In this study, we present novel EBA data for the first-in-class antimycobacterial compound BTZ-043 and contextualize the value of emerging bacteriological markers within the EBA trial framework. Trial Registration: ClinicalTrials.gov identifier: NCT04044001.

Abstract as published, via PubMed.