A multiscale study on multivalence targeting
Year: 2025
Type: dissertation
Abstract: [eng] Precision drug design utilizes detailed biological information, such as genetic profiles, cellular phenotypes, and molecular expression patterns, to develop therapeutics that are highly specific, effective, and safe. In this work, we demonstrate the potential of nanomedicine in the development of precision drugs. We establish a set of design principles to guide nanoparticle selectivity, grounded in thermodynamics, kinetics, and pharmacokinetics. Nanomedicine can leverage the multivalent effect to enhance binding avidity. To better understand nanoparticle–cell interactions, we developed a thermodynamic model called the phenotypic targeting theory (PAT). Generally, a larger number of low-affinity ligands can effectively increase binding selectivity. We applied PAT to study poly(2-(methacryloyloxy)ethyl phosphorylcholine)-poly(2-(diisopropylamino)ethyl methacrylate) (PMPC-PDPA) nanoparticles with various topologies. The model predicts the binding fraction and association constant for different nanoparticle architectures. Based on PAT, we analyzed the selectivity parameter of spherical nanoparticles as a function of particle radius and the degree of polymerization of a single PMPC chain. Since PMPC targets three distinct receptors, we also explored how changes in receptor density affect the binding fraction. These results provide a quantitative framework for optimizing nanoparticle design for selective targeting. We extended the PAT to include the targeting of large receptors, such as the low-density lipoprotein receptor-related protein 1 (LRP1) and the mannose receptor (MRC1/CD206). When targeting such large receptors, both osmotic steric penalty and compression steric penalty must be taken into account. Additionally, a steric penalty arises from unbound ligands. When an excessive number of functional ligands is used, this steric penalty increases, leading to a reduction in binding strength, ultimately resulting in negligible or no binding. This steric effect is influenced by both the number of functional ligands and an interference parameter. To account for these factors, we redefined the selectivity parameter as a function of the number of ligands rather than the number of receptors. We then analyzed this revised selectivity parameter in relation to binding energy, the interference parameter, the steric penalty per unbound ligand, and the number of available binding sites. After developing the PAT, a thermodynamic model, we extended it to describe the binding kinetics of nanoparticle-cell interactions. We investigated the binding kinetics of PMPC nanoparticles with various topologies, fitting experimental data using a mathematical model. By integrating PAT with a kinetic framework, we clarified key parameters governing multivalent interactions, including the on-rate, dissociation rate, and endocytosis rate. In addition, we explored the intracellular trafficking of PMPC nanoparticles with different topologies to gain deeper insight into their cellular fate. We also applied the model to fit the binding kinetics between mUNO-decorated nanoparticles, functionalized with varying ligand densities, and THP-1-derived macrophages. To expand the applicability of our model to more complex biological scenarios, we developed an advanced framework that accounts for binding kinetics, endocytosis, exocytosis, receptor recycling, and receptor synthesis. Additionally, we introduced a chaotic dynamics model to simulate receptor oscillation behavior observed in certain experimental conditions. We anticipate that this chaos-based model can be further refined and applied to better interpret uptake experiments involving dynamic receptor regulation. To quantify therapeutic efficiency, we developed a physiologically based pharmacokinetic (PBPK) model to describe the absorption, distribution, metabolism, and excretion (ADME) of nanoparticles. We applied this model to two distinct scenarios. The first involved non-specific interactions using PMET (poly(methionine)) nanoparticles, while the second was based on the Phenotypic Targeting Theory (PAT) applied to PMPC nanoparticles. For the PMET nanoparticles, we quantified key pharmacokinetic parameters and analyzed their influence on targeting selectivity. In the case of PMPC nanoparticles, we proposed a strategy to tune nanoparticle design parameters in order to enhance targeting selectivity, ensuring that the nanoparticles preferentially interact with specific cell types. We aim to use in silico methods, including thermodynamic, kinetic, and pharmacokinetic modeling, to assist researchers in designing more selective nanomedicines and gaining deeper insights into nanoparticle-cell interactions.
Author: Xie, Zhendong
Language: English
Subfield: Biomaterials
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7124408708
Cites:
Cited by:
Related to: