Madison Parrot (1,2), Venkata Yellepeddi (1,2), Prashant Dogra (3)
(1) Division of Clinical Pharmacology, Department of Pediatrics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA, (2) Department of Molecular Pharmaceutics, Utah Center for Nanomedicine, College of Pharmacy, University of Utah, Salt Lake City, Utah, USA, (3) Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
Introduction: Due to their superior drug loading capacity, mechanical stability, and stimuli-responsive release profile for controlled drug delivery, silica nanoparticles (SiNPs) have entered multiple clinical trials.[1] However, their clinical approval as drug delivery agents in humans is impeded due to off-target effects leading to toxicity, and poor predictability of their systemic pharmacokinetics (PK). To quantitatively establish the influence of the physicochemical characteristics of SiNPs such as size, geometry, porosity, and surface characteristics, on their biodistribution and clearance, and to predict their PK accurately in vivo , we are using a physiologically-based pharmacokinetic (PBPK) modeling approach. Our PBPK model incorporates NP margination, endothelium extravasation, macrophage uptake, chemical degradation, and hepatobiliary/renal excretion. Utilizing empirical and mechanistic parameters, it predicts whole-body biodistribution and clearance of silica NPs, accounting for SiNP properties like size and geometry. The model is calibrated with extracted data from literature involving a biodistribution study of three SiNPs (mesoporous SiNPs with distinct geometrical features (aspect ratio 8 nanorods, AR8), and amine-modified analogs (MA and 8A) in CD-1 mice.[2] Through the integration of experimental data and advanced computational techniques, we explore the fundamental properties, interactions, and potential applications of SiNPs that have lacked clinical translation. This PBPK model was adapted from a rat model of NPs.[3]
Objectives:
- Develop a minimal PBPK model for SiNPs.
- Calibrate and validate the model with in vivo biodistribution kinetics data.
- Identify physicochemical and physiological parameters influencing organ exposure and clearance of SiNPs.
Methods: The PBPK model was formulated as a system of ordinary differential equations to characterize the biodistribution processes and bio-interactions using MATLAB R2023A. The model included the following compartments: plasma, kidney, lung, mononuclear phagocyte system (MPS) (liver and spleen), excretion (urine and feces), and others (brain, heart, small intestine, large intestine, stomach, tail, carcass). The model does not involve absorption processes for the SiNPs. The in vivo PK and biodistribution data of various silica nanoparticles in the compartments included in the PBPK model were obtained from CD-1 mice after intravenous administration.[2] The model was fit to the biodistribution kinetics data by incorporating physiological parameters for CD-1 mouse and SiNP size known a priori, while fitting the unknown model parameters through nonlinear least squares regression. Global sensitivity analysis (GSA) was performed to understand the relative significance of various model parameters on model outputs of interest, i.e., SiNP exposure in various compartments defined by the AUC of the concentration kinetics curve. GSA involved Latin hypercube sampling to effectively sample the vast parameter space of the model, followed by multivariate linear regression analysis, one-way analysis of variance (ANOVA), and Tukey’s test to rank the parameters for their significance.
Results: The model exhibits strong concordance with the observed data, as evidenced by the goodness of fit, showcasing a high level of agreement. Fitted parameters were within physiological parameters reported for CD-1 mice Furthermore, the robust Pearson correlation coefficient, with a value of >0.97, underscores the strength of the association between the model and the empirical data, affirming the reliability and accuracy of the predictive model. GSA was performed on all parameters, and we identified SiNP-related (size, degradation rate) and vasculature-related parameters (porosity, hematocrit) that imparted the greatest effect on the model predictions.
Conclusion: The development of a minimal PBPK model for SiNPs in mice provides a predictive tool for understanding the intricate interactions between SiNPs and biological systems. The model’s simplicity allows for efficient data interpretation and enhances its translational potential to humans. With further refinement and validation, this minimal PBPK model can potentially inform safer and more effective clinical strategies for SiNP-based therapies in humans, facilitating the transition from preclinical studies to clinical applications.
References:
[1] Janjua, T.I., Cao, Y., Yu, C. et al. Clinical translation of silica nanoparticles. Nat Rev Mater 6, 1072–1074 (2021). https://doi.org/10.1038/s41578-021-00385-x
[2] Yu T, Hubbard, D., Ray, A., Ghandehari, H. In vivo biodistribution and pharmacokinetics of silica nanoparticles as a function of geometry, porosity and surface characteristics. Journal of Controlled Release. 2012;163:46-54. doi:https://doi.org/10.1016/j.jconrel.2012.05.046
[3] Dogra P, Butner JD, Ruiz Ramirez J, et al. A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery. Comput Struct Biotechnol J. 2020;18:518-531. doi:10.1016/j.csbj.2020.02.014
Reference: PAGE 32 (2024) Abstr 10811 [www.page-meeting.org/?abstract=10811]
Poster: Drug/Disease Modelling - Absorption & PBPK