Tamara Ray 1, James Millard 2,3,4, Lufina Tsirizani 1, Geraint Davies 4, Elin Svensson 5,6, Paolo Denti 1
1 Division of Clinical Pharmacology, Department of Medicine, University of Cape Town (, South Africa), 2 Imperial NHS Foundation Trust (, UK), 3 Africa Health Research Institute, Durban (, South Africa), 4 Department of Clinical Infection, Microbiology and Immunology, University of Liverpool (, UK), 5 Department of Pharmacy, Uppsala University (, Sweden), 6 Department of Pharmacy, Pharmacology and Toxicology, Radboudumc (, Netherlands )
Objective: Pyrazinamide is a key sterilizing agent in first- and second-line tuberculosis (TB) treatment [1]. Current WHO guidelines recommend weight-band dosing, with each weight-band receiving on average the same mg/kg dose [2]. South African national guidelines operationalize this at approximately 30–40 mg/kg in multidrug-resistant TB regimens [3].
A recent study [4] reported no significant effect of body size on pyrazinamide pharmacokinetics (PK), but observed higher bioavailability in women, lower bioavailability at higher doses, and recommended flat dosing.
We aimed to characterize pyrazinamide PK in a South African rifampicin resistant TB population and evaluate the effect of body size using allometric scaling to explain interindividual variability including dose and sex effects. Simulations were performed to assess optimized dosing strategies.
Methods: A prospective clinical trial was conducted at Don Mackenzie hospital, Kwa-Zulu Natal, South Africa, in treatment naïve rifampicin resistant-TB patients. Pyrazinamide was administered as part of the standard multidrug regimen at the time, using weight-based dosing according to 2016 WHO dosing guidelines [5]. Intensive PK sampling was performed, and blood samples were collected at pre-dose, 1, 2, 4, 6 and 8 hours post dose. Plasma concentrations were determined using validated High-Performance Liquid Chromatography-Tandem Mass Spectrometry assay (Lower Limit of Quantification = 0.2 mg/L).
Population PK analysis was conducted using NONMEM (v7.5.1, FOCE-I). A one-compartment model with first-order elimination with varied absorption models was evaluated. Standard allometric scaling was applied (3/4 exponent for clearances and 1 for volume), testing total body weight (TBW) and fat-free mass (FFM) as size descriptors. Covariate effects of sex and dose on bioavailability, as identified by Xu et al.[4], were assessed. Simulations compared: (i) flat dosing, with the same dose for all patients; (ii) WHO weight-band dosing, and (iii) optimized weight-band dosing, derived from model-predicted AUCs to achieve balanced exposure.
Results: A total of 662 plasma samples were analysed from 112 patients. Median (range) weight was 54.3 (30.0-86.7) kg, age 35 (20-68) years, and fat free mass 45 (24.2- 61.8) kg. PK was best described by a one-compartment disposition model with first-order elimination and sequential zero- and first-order absorption with a lag time. Clearance and volume of distribution were estimated at 2.97 L/h and 38.6 L, respectively.
Disposition parameters were best described using allometric scaling with TBW (∆OFV=-30.2) compared with FFM (∆OFV=-21.1). Concomitant lopinavir/ritonavir (n=27) was associated with ~23% higher clearance (∆OFV =-11.0).
Without allometry, females had 4% higher bioavailability, and doses ≥1750 mg showed 20% lower bioavailability. Despite estimating two additional parameters, the improvement in fit was not as good as when allometry with fixed exponents was included (ΔOFV = -16.5). With allometry included, sex and dose effects were no longer significant (ΔOFV = -14.0).
Simulations demonstrated that WHO weight-band dosing tended to achieve lower exposure in patients in the lower weight-bands, whereas flat dosing led to larger exposures in smaller patients. Optimized weight-band dosing produced consistent balanced concentrations across body sizes [6].
Conclusions: Our population PK model structure and parameter estimates are consistent with previous reports [7] and confirmed the effect of body size on pyrazinamide PK. TBW performed better than FFM, but both improved the model fit and explained variability in clearance. The previously reported effect of sex and dose on bioavailability were only significant when allometry was excluded and did not perform as well as allometry. Females generally have lower body weight (including in our study), while heavier patients receive higher nominal doses as per WHO weight-band dosing guidelines. With oral data only, the model estimates CL/F and V/F, thus reduced bioavailability in larger individuals produces similar effect as increased CL/F and V/F, causing sex and dose to act as surrogates for body size. Consistent with the principles of parsimony and recent consensus recommendations [6], allometry should be included before testing empirical covariates that may act as proxies for body size. We also report faster clearance with concomitant lopinavir/ritonavir, though the mechanism is unclear given pyrazinamide’s metabolism is not CYP-mediated. Further investigation would be required. Dosing simulations suggests WHO weight band dosing underexposed smaller individuals reflecting linear mg/kg assumptions while flat dosing was found to overexpose patients. Optimized weight band dosing, which accounts for nonlinear scaling of clearance with body weight, can be used to achieve balanced exposure across the weight range.
References:
[1] J. D. Millard, E. A. Mackay, L. J. Bonnett, and G. R. Davies, “The impact of inclusion, dose and duration of pyrazinamide (PZA) on efficacy and safety outcomes in tuberculosis: systematic review and meta-analysis protocol,” Syst. Rev., vol. 8, no. 1, p. 329, Dec. 2019, doi: 10.1186/s13643-019-1231-1.
[2] Global Programme on Tuberculosis and Lung Health (GTB), WHO operational handbook on tuberculosis: Module 4: Treatment Drug-resistant tuberculosis treatment 2022 update. Geneva: World Health Organization, 2022.
[3] R. of S. A. Department of Health, “Interim clinical guidance for the implementation of injectable-free regimens for rifampicin-resistant tuberculosis in adults, adolescents and children,” South Africa, 2018.
[4] A. Y. Xu et al., “Pyrazinamide Safety, Efficacy, and Dosing for Treating Drug-Susceptible Pulmonary Tuberculosis: A Phase 3, Randomized Controlled Clinical Trial,” Am. J. Respir. Crit. Care Med., vol. 210, no. 11, pp. 1358–1369, Dec. 2024, doi: 10.1164/rccm.202401-0165OC.
[5] World Health Organization, “WHO treatment guidelines for drug-resistant tuberculosis, 2016 update,” Geneva, 2016. Accessed: Feb. 28, 2026. [Online]. Available: https://www.who.int/publications/i/item/9789241549639
[6] P. Denti et al., “One dose does not fit all: revising the WHO paediatric dosing tool to include the non-linear effect of body size and maturation,” Lancet Child Adolesc. Health, vol. 6, no. 1, pp. 9–10, Jan. 2022, doi: 10.1016/S2352-4642(21)00302-3.
[7] H. Zhang et al., “Model-informed precision pyrazinamide dosing: The establishment of a population pharmacokinetic model repository for clinical decision support,” Int. J. Antimicrob. Agents, vol. 67, no. 1, p. 107658, Jan. 2026, doi: 10.1016/j.ijantimicag.2025.107658.
[8] K. Sanghavi et al., “Covariate modeling in pharmacometrics: General points for consideration,” CPT Pharmacometrics Syst. Pharmacol., vol. 13, no. 5, pp. 710–728, May 2024, doi: 10.1002/psp4.13115.
Reference: PAGE 34 (2026) Abstr 12073 [www.page-meeting.org/?abstract=12073]
Poster: Drug/Disease Modelling - Infection