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McClean, M.C.W. ; Koele, S.E.* ; Dreisbach, J.* ; Mirold-Mei, S.* ; Njeleka, F.* ; Mapamba, D.* ; Mtafya, B.* ; Phillips, P.P.J.* ; De Jager, V.R.* ; Dawson, R.* ; Narunsky, K.* ; Diacon, A.H.* ; Svensson, E.M.* ; Heinrich, N.* ; Casale, F.P. ; Hoelscher, M.

Modeling treatment response in tuberculosis early bactericidal activity trials.

CPT: Pharmacomet. Syst. Pharmacol. 15:e70311 (2026)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Btz‐043 ; Eba Study ; Biomarkers ; Machine Learning ; Tuberculosis; Mycobacterium-tuberculosis; Time; Load; Positivity; Impact
ISSN (print) / ISBN 2163-8306
e-ISSN 2163-8306
Journal CPT: Pharmacometrics and Systems Pharmacology
Quellenangaben Volume: 15, Issue: 8, Pages: , Article Number: e70311 Supplement: ,
Publisher American Society for Clinical Pharmacology and Therapeutics
Publishing Place 111 River St, Hoboken 07030-5774, Nj Usa
Reviewing status Peer reviewed
Institute(s) Research Unit Global Health (UGH)
Institute of AI for Health (AIH)
Helmholtz Pioneer Campus (HPC)
Grants Nederlandse Organisatie voor Wetenschappelijk onderzoek
German Ministry of Education and Research (BMBF)
German Center for Infection Research (DZIF)
Bavarian Ministry to their institutions
LigaChem Biosciences
Janssen Pharmaceuticals
TB Alliance
Free State of Bavaria's Hightech Agenda
European Union's Horizon 2020
EFPIA
Deutsches Zentrum für Infektionsforschung e. V. (DZIF)
Ludwig-Maximilians-Universität München (LMU)
German Federal Ministry of Education and Research
EDCTP2 programme
German Ministry for Education and Research
InfectControl
Bavarian Ministry for Science and the Arts
Swiss State Secretariat for Education, Research, and Innovation
European and Developing Countries Clinical Trials Partnership (EDCTP)