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Formulating and Characterizing Lipid Nanoparticles for Gene Delivery using a Microfluidic Mixing Platform

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On predicting heterogeneity in nanoparticle dosage.

Celia V Dowling¹, Paula M Cevaal², Matthew Faria³

¹School of Mathematics and Statistics, The University of Melbourne, Australia.

Mathematical Biosciences

|November 5, 2022

Summary

This summary is machine-generated.

This study introduces a new mathematical model for nanoparticle-cell interactions, improving understanding of targeted drug delivery heterogeneity. The model reveals how biological factors influence nanoparticle dosage at the individual cell level.

Keywords:

Cell division Dosage distribution Heterogeneity Nanoparticles Nanoparticle–cell interactions Stochastic modelling

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Area of Science:

Biomedical Engineering
Mathematical Biology
Nanotechnology

Background:

Targeted drug delivery using nanoparticles faces challenges due to limited understanding of nanoparticle-cell interactions.
Heterogeneity in experimental data complicates distinguishing stochastic nanoparticle-cell interactions from cell population variability.
Existing mathematical models for nanoparticle internalization lack biological complexity.

Purpose of the Study:

To develop a stochastic mathematical model of nanoparticle internalization that incorporates key biological phenomena.
To investigate the impact of biological factors on nanoparticle dosage heterogeneity at the single-cell level.
To derive and validate analytic approximations for nanoparticle dosage distributions.

Main Methods:

Developed a stochastic mathematical model for nanoparticle internalization.
Incorporated multistage internalization, cell division, asymmetric inheritance, and saturation.
Performed model simulations to analyze heterogeneity sources.
Derived and validated analytic approximations for dosage distributions.

Main Results:

The model quantifies nanoparticle dosage at the individual cell level.
Biological phenomena significantly influence nanoparticle dosage heterogeneity.
Analytic approximations accurately describe nanoparticle dosage distributions.
Dosage follows a Poisson mixture distribution with Beta-distributed rates.

Conclusions:

The developed model offers a more biologically realistic approach to studying nanoparticle-cell interactions.
Analytic results provide insights into parameter estimation and model identifiability.
This work advances the understanding of nanoparticle delivery systems for therapeutic applications.