Tatiana Zasedateleva 1, Stephan Schaller 1, Wilhelmus E. A. de Witte 1
1 ESQlabs GmbH (Saterland, Germany)
Introduction:
Physiologically based pharmacokinetic (PBPK) modeling has become an essential tool in modern drug development, enabling a mechanistic characterization of drug disposition by integrating system-specific physiology with compound-specific properties. PBPK models can be coupled with pharmacodynamic (PD) models to describe and explore drug effects in a quantitative and mechanistic manner [1]. Such integrated frameworks are particularly valuable for compounds exhibiting target-mediated drug disposition (TMDD), where high-affinity drug–target interactions significantly influence both pharmacokinetics (PK) and pharmacodynamics, often resulting in nonlinear PK behavior [2,3]. Large molecule therapeutics, including monoclonal antibodies and antibody-drug conjugates, are particularly prone to TMDD due to their properties. Mechanistic modeling approaches are therefore well suited to describe and investigate the complex behavior of these molecules. In this study, a monoclonal antibody designed to induce B-cell depletion for the treatment of autoimmune diseases such as systemic lupus erythematosus and rheumatoid arthritis was explored within a PBPK–PD modeling framework.
Objectives:
This study aimed to develop a mechanistic PBPK–PD model for a monoclonal antibody targeting B-cell depletion for the treatment of autoimmune diseases. The objective was to quantitatively describe the antibody’s pharmacokinetics, characterize nonlinear disposition driven by TMDD, and mechanistically link systemic exposure to downstream immune cell effects.
Methods:
A large-molecule PBPK model was built using PK-Sim® and MoBi® v12.0, leveraging a modular workflow to ensure reusability, scalability, and efficient model development. The base module PBPK structure incorporated physiological distribution processes typical for large molecules and recirculation from organ interstitial space to venous blood via lymphatic flow as described by the two-pore formalism, as well as endosomal uptake and FcRn-mediated salvage from endosomes. The model was extended with a TMDD module describing target turnover, drug–target binding, complex internalization and target-mediated elimination. Therefore, antibody clearance was governed by both nonspecific endosomal degradation and saturable target-mediated pathways. The PD module described target-driven expansion of B and T follicular helper cells, with target turnover regulated by B cells through a positive feedback mechanism.
Pharmacokinetic and cell depletion data following single ascending intravenous doses of the antibody were used for model development and evaluation.
Results:
The mechanistic structure of the model enabled dynamic interaction between drug exposure, target suppression, and immune cell responses. Drug-mediated inhibition of target synthesis reduced target abundance, leading to suppression of immune cell expansion and reduction of TMDD. As target levels declined, the contribution of target-mediated clearance decreased, leading to an increase in the drug’s half-life.
The model adequately captured the drug’s nonlinear pharmacokinetics and the associated immune cell depletion dynamics. PD effects were driven by systemic exposure, with cell-depleting effects resolving after drug clearance, while higher doses resulted in reduced clearance, more pronounced and prolonged effects.
Conclusions:
Overall, this PBPK–PD framework provides a mechanistic tool to characterize and explore exposure–response relationships for biologics exhibiting drug effect–dependent target-mediated drug disposition. The model enhances understanding of nonlinear antibody pharmacokinetics and immune cell modulation of the monoclonal antibody developed for the treatment of autoimmune diseases. Modular structure of the model ensures efficient model reusability.
References:
References:
[1] H. Jones, Y. Chen, C. Gibson, T. Heimbach, N. Parrott, S. Peters, J. Snoeys, V. Upreti, M. Zheng, S. Hall, Physiologically based pharmacokinetic modeling in drug discovery and development: A pharmaceutical industry perspective, Clin. Pharmacol. Ther. 97 (2015) 247–262. https://doi.org/10.1002/cpt.37.
[2] L.A. Peletier, J. Gabrielsson, Dynamics of target-mediated drug disposition: characteristic profiles and parameter identification, J. Pharmacokinet. Pharmacodyn. 39 (2012) 429–451. https://doi.org/10.1007/s10928-012-9260-6.
[3] T. Zasedateleva, S. Schaller, E.C.M. de Lange, W.E.A. de Witte, Local depletion of large molecule drugs due to target binding in tissue interstitial space, CPT Pharmacomet. Syst. Pharmacol. 13 (2024) 2068–2086. https://doi.org/10.1002/psp4.13262.
Reference: PAGE 34 (2026) Abstr 12172 [www.page-meeting.org/?abstract=12172]
Poster: Drug/Disease Modelling - Other Topics