4.8 Article

Equation learning to identify nano-engineered particle-cell interactions: an interpretable machine learning approach

Journal

NANOSCALE
Volume 14, Issue 44, Pages 16502-16515

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/d2nr04668g

Keywords

-

Funding

  1. Australian Research Council [DE200100988]
  2. Australian Research Council [DE200100988] Funding Source: Australian Research Council

Ask authors/readers for more resources

Designing nano-engineered particles for targeted delivery of therapeutic and diagnostic agents is a challenging task. This study presents a machine learning framework that can interpret particle-cell interactions from experimental data, providing insights into the design choices. The framework reveals consistent models of particle-cell interactions for different particle-cell pairs, highlighting the importance of nonlinear saturation effects. Additionally, the framework facilitates quantitative evaluation of particle design choices by providing robust estimates of particle performance.
Designing nano-engineered particles capable of the delivery of therapeutic and diagnostic agents to a specific target remains a significant challenge. Understanding how interactions between particles and cells are impacted by the physicochemical properties of the particle will help inform rational design choices. Mathematical and computational techniques allow for details regarding particle-cell interactions to be isolated from the interwoven set of biological, chemical, and physical phenomena involved in the particle delivery process. Here we present a machine learning framework capable of elucidating particle-cell interactions from experimental data. This framework employs a data-driven modelling approach, augmented by established biological knowledge. Crucially, the model of particle-cell interactions learned by the framework can be interpreted and analysed, in contrast to the 'black box' models inherent to other machine learning approaches. We apply the framework to association data for thirty different particle-cell pairs. This library of data contains both adherent and suspension cell lines, as well as a diverse collection of particles. We consider hyperbranched polymer and poly(methacrylic acid) particles, from 6 nm to 1032 nm in diameter, with small molecule, monoclonal antibody, and peptide surface functionalisations. Despite the diverse nature of the experiments, the learned models of particle-cell interactions for each particle-cell pair are remarkably consistent: out of 2048 potential models, only four unique models are learned. The models reveal that nonlinear saturation effects are a key feature governing particle-cell interactions. Further, the framework provides robust estimates of particle performance, which facilitates quantitative evaluation of particle design choices.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available