Simon Koele1, Patrick Phillips2, Veronique De Jager3, Julia Dreisbach4,5, Petra Gross-Demel4,5, Rodney Dawson6, Kim Narusnky6, Leticia Wildner7, Timothy McHugh7, Lindsey te Brake1, Martin Boeree8, Andreas Diacon3, Norbert Heinrich4,5,9, Rob Aarnoutse1, Michael Hoelscher4,5,9,10, Elin M Svensson1,11
1 Department of Pharmacy, Radboud Institute for Medical Innovation (RIMI), Radboud University Medical Center, Nijmegen, the Netherlands 2 UCSF Center for Tuberculosis, University of California San Francisco, San Francisco, United States 3 TASK, Cape Town, South Africa 4 Division of Infectious Diseases and Tropical Medicine, LMU University Hospital, LMU, Munich, Germany 5 German Center for Infection Research (DZIF), Munich Partner Site, Munich, Germany; 6 University of Cape Town Lung Institute, Cape Town, South Africa 7 UCL Centre for Clinical Microbiology, London, UK 8 Department of Pulmonary Diseases, Radboud University Medical Centre, Nijmegen, the Netherlands 9 Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Immunology, Infection and Pandemic Research, Munich, Germany 10 Unit Global Health, Helmholtz Zentrum München, German Research Center for Environmental Health (HMGU), Neuherberg, Germany 11 Department of Pharmacy, Uppsala University, Uppsala, Sweden.
Background: Tuberculosis (TB) remains the most deadly infectious disease in the 21st century. Due to lengthy treatment, adverse effects and resistance developing even to recently approved drugs, there is an urgent need for novel therapeutic options [1]. BTZ-043 is a first-in-class benzothiazinone for the treatment of TB with demonstrated early-bactericidal activity [2]. BTZ-043 is metabolized into two major metabolites: M1 and M2. M2 is unstable, and tends to convert back into the parent in the presence of atmospheric oxygen [3]. It is unknown if M2 contributes to the antimicrobial effect of BTZ-043 or if this is driven solely by the unchanged parent compound (M0).
Objective: To develop a pharmacokinetic-pharmacodynamic (PK-PD) model to characterize the exposure-response relationship for BTZ-043 and its main metabolites.
Methods: We performed a sequential phase 1b/2a, randomized, controlled clinical trial of which the main trial results were previously presented [2]. Participants were administered oral ascending doses of BTZ-043 ranging from 250 mg to 1750 mg, for 14 days using an adaptive continual reassessment method design. Three participants were included in the first six dosing group and six in the 1750mg-group. BTZ-043 was taken in a fasted state during stage one days 1 through 13 and with a high-fat breakfast on day 14. PK-profiling was done on day 1, 12 and 14.
In a second phase participants were randomized into four groups to receive either 250mg, 500mg ,or 1000mg BTZ-043 or the control regimen Rifafour e-275® for 14 days. BTZ-043 was taken 30 minutes after eating, or immediately with food. Intensive plasma PK sampling was performed on day 1 and 14. The bioanalysis was conducted in the GLP-accredited bioanalytical laboratory of Nuvisan (Neu Ulm, Germany).
Overnight sputum samples were collected before treatment start and on days 2, 3, 4, 6, 8, 11, and 14 of treatment. All sputum samples were quantitatively cultured to determine the colony-forming units (CFU) on solid medium, and the time to positivity (TTP) in the MGIT system, as previously described [4]. Linear and bi-linear models were evaluated to describe the change in log10 transformed CFU and TTP over time [5]. The M3 method, incorporating a partial likelihood, was used to account for observations that were below or above a censoring limit [6]. The exposure-response relationship was evaluated based on individual PK-model-derived exposure metrics at steady-state.
Model development was performed in NONMEM 7.5 with FOCE+I estimation for the PK model and LAPLACIAN+I for the PD model [7]. Pirana 2.9.9 was used as graphical interface and PsN 5.3.0 for additional functionalities [8, 9].
Results: In total, 78 participants were included. For the PK model, 1808 plasma concentrations were available for M0, and M1, and 1793 for M2. The PK were best described by a 2-compartment disposition model for M0 and M2, and a 1-compartment disposition model for M1. The parent and metabolite models were estimated simultaneously. All disposition parameters were allometrically scaled based on total body weight. M0 absorption was best described by three transit compartments, and a secondary delayed absorption when BTZ-043 was administered with food. Administration together with high-fat food increased the bioavailability by 41% (95% CI: 5.0%- 78%), and no food decreased the bioavailability by 54% (95% CI: 43%- 65%) over administration with standard food. BTZ-043 bioavailability decreased by 29% (95% CI: 2.0%- 49%) for doses over 1250mg QD. The M2 clearance decreased over time on treatment by 27% (95% CI: 24%- 29%).
For the PD model, 921 CFU and 1113 TTP culture results were available. A bi-linear model with the node estimated at 48 hours described the decrease in bacterial load over time best. The exposure-response relationship for BTZ-043 was best described by an Emax relationship of M0+M2 AUC0-24 on the decrease in bacterial load over the first two days on treatment. The EC50 was estimated to be 19600 (mg/L*h) (95% CI: 5930- 49100), comparable to the exposure reached by a 500 mg QD dose together with a standard breakfast.
Conclusion: A PK-PD model of BTZ-043 and its main metabolites was developed. An exposure-response relationship was identified for the first two days on treatment. The combined M0 and M2 exposure seems to drive the antimicrobial effect. These population PK and PK-PD models are valuable tools for further clinical trial design and analysis.
Funding: This project was part of the PanACEA-TB consortium (EDCTP2 program grant number TRIA2015-1102) and was partially funded by a Veni project (EM Svensson, project number 09150161910052) financed by the Dutch Research Council (NWO), the German Ministry for Education and Research (BMBF; 01KA1701), the German Center for Infection Research (DZIF); InfectControl (03ZZ0803A, 03ZZ0835A and 03ZZ0826A); Bavarian Ministry for Science and the Arts; Swiss State Secretariat for Education, Research, and Innovation (SERI); Dutch Research Council (NWO).
References:
- Global tuberculosis report 2022. Geneva: World Health Organization.
- Hoelscher, M, et al., BTZ-043 shows good safety and strong bactericidal activity in a seamless phase 1b/2a study in patients with pulmonary tuberculosis, in Union conference. 2023: Paris.
- Kloss, F, et al., In Vivo Dearomatization of the Potent Antituberculosis Agent BTZ043 via Meisenheimer Complex Formation. Angewandte Chemie International Edition, 2017. 56(8): p. 2187-2191.
- Diacon, AH, et al., Time to liquid culture positivity can substitute for colony counting on agar plates in early bactericidal activity studies of antituberculosis agents. Clin Microbiol Infect, 2012. 18(7): p. 711-7.
- Burger, DA and R Schall, A Bayesian Nonlinear Mixed-Effects Regression Model for the Characterization of Early Bactericidal Activity of Tuberculosis Drugs. J Biopharm Stat, 2015. 25(6): p. 1247-1271.
- Beal, SL, Ways to fit a PK model with some data below the quantification limit. J Pharmacokinet Pharmacodyn, 2001. 28(5): p. 481-504.
- Beal, S, Sheiner, LB , Boeckmann, A. & Bauer, R.J., NONMEM user’s guides. 1989–2013, Icon Development Solutions: Ellicott City, MD.
- Keizer, RJ, MO Karlsson, and A Hooker, Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose. CPT Pharmacometrics Syst Pharmacol, 2013. 2(6): p. e50.
- Lindbom, L, Pihlgren P, and EN Jonsson, PsN-Toolkit–a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Programs Biomed, 2005. 79(3): p. 241-57.
Reference: PAGE 32 (2024) Abstr 11187 [www.page-meeting.org/?abstract=11187]
Poster: Drug/Disease Modelling - Infection