Sebastian Polak1,2, Seweryn Ulaszek1, Barbara Wisniowska1, Bartek Lisowski1
1Jagiellonian University Medical College, 2Certara UK
Introduction: Destabilized transthyretin (TTR – transport protein involved in thyroxine and retinol circulation) can misfold and form amyloid fibrils with ATTR cardiomyopathy and neuropathy being the most prominent clinical effects. TTR tetramer stabilizers, tafamidis and acoramidis, prevent tetramer dissociation and clinical data show indeed increase in TTR levels after treatment. However, mechanism of this effect is still not known and an effect our study seeks to explain the mechanism of this effect. Objectives: • utilize combination of PBPK (Physiologically Based Pharmacokinetics) model describing tafamidis disposition in human body after oral application and phenomenological QSP (Quantitative Systems Pharmacology) model describing TTR dynamics to simulate tafamidis disposition and effect • simulate virtual clinical scenarios for various doses and dosing regimens • analyse influence of dosing parameters on clinical effect, expressed as TTR plasma level modification after tafamidis application • define, based on the simulation results, potentially effective dose of tafamidis Methods: Simcyp Simulator (V23, Certara, Sheffield, UK) was used to develop a PBPK-QSP (Physiologically Based Pharmacokinetic – Quantitative Systems Pharmacology) model by combining drug, system (population), and trial related data [1]. The Simcyp embedded tool for custom models’ development, which employs LUA programming language, was used. Model development included the following steps: 1) parametrisation, verification and validation (with the use of experimental clinical data of plasma tafamidis concentration after oral application [2-4]) of PBPK model; 2) addition of age dependent TTR baseline concentration to the target population (based on the available literature data – age and sex dependent TTR plasma concentrations come from population data collected from 67000 U.S. citizens, as published by Inglenbleek et al. [5]); 3) application of a QSP ligand-protein binding model accounting for drug presence-dependent effects rates-dependent concentration transthyretin levels; the model has been described in publications [6-8]. Drug related data were collected from publications and US Food and Drug Administration Clinical Pharmacology and Biopharmaceutics Review documents [9]. In order to obtain numerical values of the concentration time profiles a digitizing tool, namely WebPlotDigitizer v.4.7, was used. Simulation results were analysed for correlation between tafamidis plasma concentration and clinical effect expressed e.g. as total TTR plasma level. Results: The developed PBPK-QSP model describes overall drug effect on TTR and successfully replicates tafamidis pharmacokinetic outcomes observed in multiple-dose studies conducted in a healthy population. The suggested standard tafamidis dose of 50 mg allows to increase the total average TTR plasma steady state concentration from baseline by about 34%, after multiple doses. This stays in agreement with the published data (32-34% increase). Further increase of the dose up to 250 mg does not bring effect as the total average TTR concentration increase stabilizes at 35%. Interestingly dose reduction to 25 mg allows to reach in average 30% increase of total plasma TTR. Similar effect can be observed for other biomarkers namely average plasma concentration of bound TTR and TTR monomers. Results analysis also suggested that albumin plays a crucial role in modulating tafamidis pharmacokinetics by competing for the second binding site on TTR, thereby reducing the proportion of double-bound complexes and stabilizing the fraction of unbound drug in plasma. Conclusion: These findings suggest that PBPK-QSP model can be utilized to critically assess biomarkers of TTR amyloidosis treatment. This is not limited to total TTR plasma concentration but also other potential biomarkers including TTR monomers plasma concentration.
[1] https://www.certara.com/software/simcyp-pbpk/ [2] P. A. Lockwood et al., “The Bioequivalence of Tafamidis 61-mg Free Acid Capsules and Tafamidis Meglumine 4 × 20-mg Capsules in Healthy Volunteers,” Clin. Pharmacol. Drug Dev., vol. 9, no. 7, pp. 849–854, Oct. 2020, doi: 10.1002/cpdd.789. [3] K. J. Klamerus, E. Watsky, R. Moller, R. Wang, and S. Riley, “The effect of tafamidis on the QTc interval in healthy subjects,” Br. J. Clin. Pharmacol., vol. 79, no. 6, pp. 918–925, Jun. 2015, doi: 10.1111/bcp.12561. [4] Center for drug evaluation and research, “Application number 211996Orig1s000; 212161Orig1s000.” Nov. 02, 2018. [5] Y. Ingenbleek, “Plasma Transthyretin Reflects the Fluctuations of Lean Body Mass in Health and Disease,” in Recent Advances in Transthyretin Evolution, Structure and Biological Functions, S. J. Richardson and V. Cody, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 329–357. doi: 10.1007/978-3-642-00646-3_20. [6] Ulaszek, S., Wisniowska, B., & Lisowski, B. (2024). No body fits in the test tube – the case of transthyretin. Amyloid, 31(4), 347–349. [7] B. Lisowski et al. “Phenomenological model of transthyretin stabilization”, submitted for publication. [8] S. Ulaszek et al. “Exploring Tafamidis Effects Through PBPK-QSP Modelling”, submitted for publication. [9] “VYNDAQEL PRODUCT MONOGRAPH,” Pfizer, Oct. 2022. [Online]. Available: https://pdf.hres.ca/dpd_pm/00067760.PDF
Reference: PAGE 33 (2025) Abstr 11634 [www.page-meeting.org/?abstract=11634]
Poster: Drug/Disease Modelling - Other Topics