Which data are necessary to build a WB-PBPK model that accurately predicts exposure in the lung? A case study using Ethambutol for tuberculosis treatment
Letizia Carrara (1), Paolo Magni (1), Donato Teutonico (2), Oscar Della Pasqua (3), Frank Kloprogge (3)
(1) Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy (2) Clinical Pharmacokinetics and Pharmacometrics division, Servier, France (3) Clinical Pharmacology Group, UCL, London, United Kingdom
Objectives: Characterising exposure profiles in different organs is a key feature of Whole-Body Physiologically–Based Pharmacokinetic (WB-PBPK) models. However, it remains unclear which data are needed to enable accurate predictions. Using a relevant case, ethambutol (EMB) for pulmonary tuberculosis treatment, we investigated this. The aims of this work are: 1) to develop a WB-PBPK model to predict EMB lung concentration; 2) to evaluate the predictive performance of the WB-PBPK framework for prospective evaluation of molecules in first-in-human and in poor data scenarios.
Methods: The model was built in PK-Sim1. Plasma EMB concentration and urinary data2 collected after intravenous infusion were used to estimate clearance parameters. Absorption parameters were estimated from EMB plasma levels following oral administration4. Plasma data were simulated via the SimulX function of the R package mlxR3, according to the models reported in2,4. The observed amount excreted in urine was used. Model-predicted drug profiles in plasma and in the lung at steady state were compared to observed plasma and Alveolar Cells (AC) EMB concentrations5.
The predictive performance of the PBPK framework was evaluated based on what-if scenarios, in which data were added progressively into model development, starting from in vitro and animal experiments, up to human clinical trials.
Results: A PBPK model with hepatic clearance and both passive and active renal elimination was built. The model successfully describes plasma and urinary data after the infusion, and plasma EMB profiles after oral administration. Despite the model was parameterized on mean data, the variability of the biometrics included in the PK-Sim internal database well captures the observed inter-individual variability of the population plasma concentration profiles. Steady state drug levels in plasma and AC were well predicted.
Simulated scenarios showed that for drug mainly excreted via the kidneys, such as EMB, information on the renal elimination is needed to make reasonably accurate predictions. Thus, both animal and human urinary data should be collected. In addition, since the crucial parameter was found to be the intestinal permeability, in vitro experiments with Caco-2 cell line should be performed as well.
Conclusions: The model showed good descriptive and predictive power of data of different studies, also in a population context. Crucial parameters and data to obtain accurate predictions were identified.
References:
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[3] http://simulx.webpopix.org/
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