Jorin Diemer 1, Venetia Karamitsou 1, Vanessa Baier 1, Alexander Kulesza 1, Stephan Schaller 1
1 ESQlabs GmbH (Saterland, Germany)
Objectives
Mechanistic modeling offers a unique opportunity to integrate complex renal and cardiovascular physiology with pharmacological interventions. Dogs have historically played a central role in advancing the understanding of renal physiology and blood pressure regulation, serving as critical experimental models of renal injury [1] with implications for both veterinary medicine and preclinical toxicity testing. In dogs, chronic cardiovascular and kidney diseases are associated with activation of the renin–angiotensin–aldosterone system (RAAS), leading to maladaptive myocardial and renal remodeling and progressive renal injury, resulting in increased glomerular capillary pressure. ACE inhibitors (ACEis) and mineralocorticoid receptor antagonists (MRAs) are standard therapies in treating chronic cardiovascular disease by suppressing RAAS [2]. However, optimizing dosing regimens and predicting individual biomarker responses remain challenging due to the complex interplay between renal hemodynamics, systemic blood pressure, and drug pharmacokinetics. To address this, we developed a scalable, modular renal–cardiovascular Quantitative Systems Pharmacology (QSP) platform within an end-to-end Model-Informed Drug Development (MIDD) ecosystem, designed to seamlessly couple with Physiologically Based Pharmacokinetic (PBPK) models to predict efficacy and safety outcomes.
Methods
We implemented an existing physiology-based renal–cardiovascular model [3] within the Open Systems Pharmacology (OSP) Suite (MoBi®). Crucially, the model was constructed using the MoBi® V12 modular architecture, making it a plug-and-play component within our broader MIDD ecosystem. This architecture enables flexible mechanistic extensions and straightforward, automated coupling with PBPK models. The core framework explicitly accounts for renal hemodynamics, sodium handling, and systemic cardiovascular regulation. To assess safety, the framework was expanded with a mechanistic module for drug-induced renal injury (DIRI), capturing proximal tubule epithelial cell damage and its cascading impact on overall renal function [4]. Model assembly, simulation execution, and visualization were performed programmatically using the open-source {ospsuiteR} and {esqlabsR} R packages, which have been developed to enable a fully automated, database-driven model generation and qualification workflow. This workflow allows for the rapid assessment of how different indication-specific drug classes, dosing schedules, or combination therapies impact renal and cardiovascular outcomes.
Results
The modular renal–cardiovascular platform was successfully scaled and parametrized to canine physiology and quantitatively validated against reference physiological measures, accurately reproducing canine glomerular filtration rate (GFR), mean arterial pressure (MAP), renal vascular resistance (RVR), renal blood flow (RBF), and fraction of excreted sodium (FENa). Demonstrating the scalability of the ecosystem approach, the QSP platform was coupled to two-compartment pharmacokinetic (PK) models for the ACEi benazepril and the MRA spironolactone, informed by publicly available data [5].
Simulations of this integrated PK-QSP model successfully predicted dynamic renal and cardiovascular responses in dogs. The model confirmed that RAAS suppression effectively reduces glomerular capillary pressure, thereby mitigating downstream glomerular injury. Furthermore, the platform was used to systematically explore dosing schedules. Consistent with ACVIM consensus guidelines [6], the simulations demonstrated that twice-daily dosing regimens more effectively sustained reductions in glomerular capillary pressure over a 24-hour period compared with once-daily administration, providing a mechanistic rationale for clinical dosing recommendations.
Conclusion
We present a flexible, modular PBPK-QSP modeling platform for exploring cardiovascular and renal diseases, evaluating antihypertensive therapies, and assessing drug-induced kidney injury in dogs. By integrating physiology, pharmacokinetics, and mechanistic injury pathways into a standardized, programmatic framework, the platform provides a highly scalable, predictive tool for simulating treatment effects across multiple biomarkers. This work demonstrates how an end-to-end, mechanistic modular MIDD ecosystem can support the rational design of RAAS-targeted interventions, optimize dosing regimens, and accelerate translational research in both veterinary and human renal–cardiovascular indications.
References:
[1] Brown, Scott A. “Renal Pathophysiology: Lessons Learned from the Canine Remnant Kidney Model.” Journal of Veterinary Emergency and Critical Care 23, no. 2 (2013): 115–21. https://doi.org/10.1111/vec.12030.
[2] Ames, Marisa K., Clarke E. Atkins, and Bertram Pitt. “The Renin-Angiotensin-Aldosterone System and Its Suppression.” Journal of Veterinary Internal Medicine 33, no. 2 (2019): 363–82. https://doi.org/10.1111/jvim.15454.
[3] Hallow, Km, and Y Gebremichael. “A Quantitative Systems Physiology Model of Renal Function and Blood Pressure Regulation: Model Description.” CPT: Pharmacometrics & Systems Pharmacology 6, no. 6 (2017): 383–92. https://doi.org/10.1002/psp4.12178.
[4] Gebremichael, Yeshitila, James Lu, Harish Shankaran, Gabriel Helmlinger, Jerome Mettetal, and K Melissa Hallow. “Multiscale Mathematical Model of Drug-Induced Proximal Tubule Injury: Linking Urinary Biomarkers to Epithelial Cell Injury and Renal Dysfunction.” Toxicological Sciences 162, no. 1 (2018): 200–211. https://doi.org/10.1093/toxsci/kfx239.
[5] Manson, Elizabeth, Jessica L. Ward, Maria Merodio, et al. “Dose-Exposure-Response of CARDALIS® (Benazepril/Spironolactone) on the Classical and Alternative Arms of the Renin-Angiotensin-Aldosterone System in Healthy Dogs.” Journal of Veterinary Internal Medicine 39, no. 1 (2025): e17255. https://doi.org/10.1111/jvim.17255.
[6] Keene, Bruce W., Clarke E. Atkins, John D. Bonagura, et al. “ACVIM Consensus Guidelines for the Diagnosis and Treatment of Myxomatous Mitral Valve Disease in Dogs.” Journal of Veterinary Internal Medicine 33, no. 3 (2019): 1127–40. https://doi.org/10.1111/jvim.15488.
Reference: PAGE 34 (2026) Abstr 12282 [www.page-meeting.org/?abstract=12282]
Poster: Drug/Disease Modelling - Other Topics