Emily Behrens (1), Niklas Köhler (2),(3),(4), Max Münchow (1), Christoph Pfaffendorf (1), Nika Zielinski (3),(4),(5), Hans-Peter Grobbel (3),(4),(6), Dagmar Schaub (3),(4),(5), Maja Reimann(3),(4),(5), Laurent A. Decosterd (7), Eva Choong (7), Patricia Maria Sánchez Carballo(3),(4),(5), Rob Aarnoutse (8), Christoph Lange (3),(4),(5),(9), Sebastian G. Wicha (1)
(1) Dept. of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Germany, (2) Division of Infectious Diseases, I. Department of Internal Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, (3) Clinical Infectious Diseases, Research Center Borstel, Leibniz Lung Center, Borstel, Germany, (4) German Center for Infection Research (DZIF), Partner Site Borstel-Hamburg-Lübeck-Riems, Germany, (5) Respiratory Medicine &International Health, University of Lübeck, Lübeck, Germany, (6) University Hospital for Pediatrics and Adolescent Medicine and University of Cologne, Faculty of Medicine, Cologne, Germany, (7) Laboratory of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, (8) Department of Pharmacy Radboud Institute for Medical Innovation, Radboud university medical center, Nijmegen, The Netherlands, (9) Baylor College of Medicine and Texas Childrens´ Hospital, Houston, Texas, USA
Objectives: Personalization of drug therapy can be realized using therapeutic drug monitoring (TDM). A personalized dose for an individual can be calculated using Bayesian simulations/forecasting to maximize PK/PD target attainment. Therefore, prior information about the patient, dosing information, measured plasma concentrations and a population pharmacokinetic (PK) model are needed [1–3].
Moxifloxacin (Mfx) is a fluoroquinolone and was recommended by the WHO in 2022 as part of the BPaLM regimen (bedaquiline, pretomanid, linezolid and Mfx) in treatment of multidrug- or rifampicin-resistant tuberculosis as a 6-month regimen or to be included in longer regimens [4]. Moreover, Mfx is part of treatment regimens in the DECISION and PARADIGM4TB studies within the UNITE4TB clinical trial phase 2B/C program [5].
The aim of this study was the application and evaluation of published population PK models of Mfx with regard to PK prediction of this antitubercular drug based on population data and measured concentrations.
Methods: A literature search was conducted with the keywords ‘pharmacokinetics’, ‘moxifloxacin’, ‘population model’ and ‘tuberculosis’ using PubMed. Six population PK models were recoded from the original publications (Al-Shaer et al. (2019) [6], Chang et al. (2017) [7], Chirehwa et al. (2023) [8], Yun et al. (2022) [9], Zvada et al. (2012) [10], Zvada et al. (2014) [11]). The PK models were processed in NONMEM® 7.5.
The clinical data used in this model evaluation was provided from Research Center Borstel, Germany, and included 16 patients with multi-drug resistant tuberculosis contributing 2996 samples in total. Trough samples without a documented previous dosing event were excluded. Dataset processing was conducted using R (version 4.2.1) [12]. The Bayesian forecasting was performed iteratively such that the previous occasion would inform the forecast of the following occasion stepwise starting from the first occasion and stopping at the seventh occasion. Median percentage error (MPE) and median absolute percentage error (MAPE) were used as metrics for model performance.
Results: 9 IDs receiving 400 mg moxifloxacin once daily were identified in the dataset (223 observations in total). 6 IDs with a total of 199 observations were included in the evaluation of the Bayesian forecasting, while 3 IDs consisting of observations from one occasion were excluded from this part of the evaluation but still used for the analysis of the a priori predictions.
A priori
In the evaluation of the a priori predictions MPE were between -1.8% (Chirehwa et al.) to 181.0% (Zvada et al. (2012)). MAPE values ranged between 34.2% (Chirehwa et al.) and 181.0% (Zvada et al. (2012)).
Bayesian forecasting
MPE resulting from the models of Al-Shaer et al., Chang et al., Chirehwa et al., Yun et al., Zvada et al. (2012) and Zvada et al. (2014) were 7.7%, 14.2%, -1.8%, -1.5%, 103.5% and 6.4% and the calculation of MAPE led to values of 26.2%, 39.5%, 25.7%, 25.3%, 103.5% and 23.8%, respectively.
Conclusions: Overall, the a priori predictions led to higher values of the chosen metrics in comparison to Bayesian forecasting. The model from Zvada et al. (2014) performed best – closely followed by the models from Yun et al., Chirehwa et al. and Al-Shaer et al. – and the model from Zvada et al. (2012) shows the least favorable predictive performance guided by MAPE. Limitations of our study are the very small number of patients and observations in the investigated dataset on the one hand and the different composition of the observed occasions on the other hand as not all observed occasions contained the same number or type of samples (e.g. trough samples).
References:
[1] Sturkenboom MGG, Märtson AG, Svensson EM, et al (2021) Population Pharmacokinetics and Bayesian Dose Adjustment to Advance TDM of Anti-TB Drugs. Clinical Pharmacokinetics 60:685–710. https://doi.org/10.1007/s40262-021-00997-0
[2] Abrantes JA, Jönsson S, Karlsson MO, Nielsen EI (2019) Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data. British Journal of Clinical Pharmacology 85:1326–1336. https://doi.org/10.1111/bcp.13901
[3] Broeker A, Nardecchia M, Klinker KP, et al (2019) Towards precision dosing of vancomycin: a systematic evaluation of pharmacometric models for Bayesian forecasting. Clinical Microbiology and Infection 25:1286.e1-1286.e7. https://doi.org/10.1016/j.cmi.2019.02.029
[4] World Health Organization (2022): WHO consolidated guidelines on tuberculosis: Module 4: Treatment – Drug-susceptible tuberculosis treatment (2022 update). World Health Organization, Geneva. https://www.who.int/publications/i/item/9789240063129. Accessed February 29, 2024
[5] UNITE4TB (2023) A new dawn in the fight against Tuberculosis. UNITE4TB, the largest public-private collaboration in tuberculosis drug development, announces start of clinical trials. https://www.unite4tb.org/sites/unite4tb/files/2023-11/UNITE4TBPressRelease_8November2023.pdf (UNITE4TB website published by Lygature). Accessed March 1, 2024
[6] Al-Shaer MH, Alghamdi WA, Alsultan A, et al (2019) Fluoroquinolones in Drug-Resistant Tuberculosis: Culture Conversion and Pharmacokinetic/Pharmacodynamic Target Attainment To Guide Dose Selection. Antimicrob Agents Chemother 63:e00279-19. https://doi.org/10.1128/AAC.00279-19
[7] Chang MJ, Jin B, Chae J woo, et al (2017) Population pharmacokinetics of moxifloxacin, cycloserine, p-aminosalicylic acid and kanamycin for the treatment of multi-drug-resistant tuberculosis. International Journal of Antimicrobial Agents 49:677–687. https://doi.org/10.1016/j.ijantimicag.2017.01.024
[8] Chirehwa MT, Resendiz-Galvan JE, Court R, et al (2023) Optimizing Moxifloxacin Dose in MDR-TB Participants with or without Efavirenz Coadministration Using Population Pharmacokinetic Modeling. Antimicrobial Agents and Chemotherapy 67:e01426-22. https://doi.org/10.1128/aac.01426-22
[9] Yun H-Y, Chang V, Radtke KK, et al (2021) Model-Based Efficacy and Toxicity Comparisons of Moxifloxacin for Multidrug-Resistant Tuberculosis. Open Forum Infect Dis 9:ofab660. https://doi.org/10.1093/ofid/ofab660
[10] Zvada SP, Denti P, Geldenhuys H, et al (2012) Moxifloxacin Population Pharmacokinetics in Patients with Pulmonary Tuberculosis and the Effect of Intermittent High-Dose Rifapentine. Antimicrob Agents Chemother 56:4471–4473. https://doi.org/10.1128/AAC.00404-12
[11] Zvada SP, Denti P, Sirgel FA, et al (2014) Moxifloxacin Population Pharmacokinetics and Model-Based Comparison of Efficacy between Moxifloxacin and Ofloxacin in African Patients. Antimicrob Agents Chemother 58:503–510. https://doi.org/10.1128/AAC.01478-13
[12] R Core Team (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Acknowledgements: This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 101007873. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA, Deutsches Zentrum für Infektionsforschung e. V. (DZIF), and Ludwig-Maximilians-Universität München (LMU). EFPIA/AP contribute to 50% of funding, whereas the contribution of DZIF and the LMU University Hospital Munich has been granted by the German Federal Ministry of Education and Research.
Reference: PAGE 32 (2024) Abstr 10984 [www.page-meeting.org/?abstract=10984]
Poster: Clinical Applications