Séverine Urdy1, Chiara Zunino1, Sichen Wang2, Yanyan Zhang2, Wilhelmus E.A. de Witte3, Xavier Declèves4, Alicja Puszkiel4, Nassim Djebli5
1Phinc Development, 2Jiangsu Hengrui Pharmaceutical, 3ESQlabs GmbH, 4Université Paris Cité, Inserm UMRS1144, Cochin Hospital, 5Luzsana Biotechnology
Introduction: Antibody-drug conjugates (ADCs) represent a promising anticancer approach [1]. Although physiologically based pharmacokinetics (PBPK) modeling became important in the drug development process [2], very few PBPK models have been published for ADCs [3], none within an open-source software. Objectives: -Build a PBPK model in PK-Sim® and MoBi® [4] of an anti-Claudin 18.2 ADC -Compare the variability range in population simulations to 3 phase I clinical studies after intravenous (IV) administration in 109 patients with gastric, pancreatic and lung cancers Methods: In PK-Sim®, we defined one PBPK model comprising three compounds (ADC, toxophore, and naked antibody), which were mechanistically linked. This model captured the ADC PK profile. However, additional clearance mechanisms were essential to improve the fit of the ADC elimination phase. After integration of target-mediated drug disposition (TMDD) and deconjugation of the toxophore in MoBi®, 6 parameters were optimized (reference concentration of the target, degradation rate constant and deconjugation rate constant for the ADC; lipophilicity, nonspecific hepatic clearance rate constant and passive renal clearance for the toxophore). The derived PK parameters were considered inaccurate if the predicted error ratios were outside the two-fold error range (0.5-2) [5]. Results: The model described ADC administration and disposition, binding to the target in healthy tissues, and intracellular processing of ADC and toxophore within the endosomal compartment leading to ADC degradation, toxophore release, and target recycling. In addition, the model described the competition between the ADC and the naked antibody for binding to the target. The PK data was adequately captured for both observed compounds, with a predicted error ratio within the two-fold range: Cmax_ADC (1.07-1.50), Cmax_Toxophore (0.69-1.44), AUC0-504h_ADC (0.59-0.98) and AUC0-504h_toxophore (0.82-1.38). Population simulations enabled the comparison of the observed inter-patient variability at each dose to 100 virtual individuals. The predicted and observed geometric standard deviations for Cmax and AUC0-504h of the ADC and the toxophore were within the observations range, showing that the PBPK model was able to predict those plasma concentrations in an Asian population aged between 27 and 84 years old, although the population simulations underpredicted the inter-individual variability in most of the dose groups. Conclusion: The calibration of 6 parameters allowed us to accurately capture the observed data for both ADC and toxophore in a reference patient for an anti-Claudin 18.2 ADC. The deconjugation rate constant appeared to be the most sensitive parameter among those which were calibrated. The under-prediction of inter-patient variability suggests the potential need to include variability in target-related or endosomal clearance-related parameters. This analysis paves the way for PBPK modeling of other ADCs currently in development.
[1] Mahmood, I. (2021) ‘Clinical Pharmacology of Antibody-Drug Conjugates’, Antibodies, 10(2), p. 20. Available at: https://doi.org/10.3390/antib10020020. [2] Shebley, M. et al. (2018) ‘Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective’, Clinical Pharmacology & Therapeutics, 104(1), pp. 88–110. Available at: https://doi.org/10.1002/cpt.1013. [3] Li, C. et al. (2020) ‘Impact of Physiologically Based Pharmacokinetics, Population Pharmacokinetics and Pharmacokinetics/Pharmacodynamics in the Development of Antibody-Drug Conjugates’, The Journal of Clinical Pharmacology, 60(S1). Available at: https://doi.org/10.1002/jcph.1720. [4] Niederalt, C. et al. (2018) ‘A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim’, Journal of Pharmacokinetics and Pharmacodynamics, 45(2), pp. 235–257. Available at: https://doi.org/10.1007/s10928-017-9559-4. [5] Abduljalil, K. et al. (2014) ‘Deciding on Success Criteria for Predictability of Pharmacokinetic Parameters from In Vitro Studies: An Analysis Based on In Vivo Observations’, Drug Metabolism and Disposition, 42(9), pp. 1478–1484. Available at: https://doi.org/10.1124/dmd.114.058099.
Reference: PAGE 33 (2025) Abstr 11715 [www.page-meeting.org/?abstract=11715]
Poster: Methodology - New Modelling Approaches