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Evaluation of Polymeric Gene Delivery Nanoparticles by Nanoparticle Tracking Analysis and High-throughput Flow Cytometry
Published on: March 1, 2013
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A machine learning approach to predict cellular uptake of pBAE polyplexes.
Aparna Loecher1, Michael Bruyns-Haylett1, Pedro J Ballester1
1Department of Bioengineering, Imperial College London, SW7 2AZ London, UK. nuria.oliva@iqs.url.edu.
Biomaterials Science
|July 4, 2023
Summary
Machine learning (ML) models predict poly β-amino ester (pBAE) nanoparticle uptake for efficient gene delivery. This approach reduces trial-and-error, optimizing formulations for new cell types and accelerating therapeutic development.
Area of Science:
- Biomaterials Science
- Nanotechnology
- Gene Therapy
Background:
- Gene delivery systems are crucial for treating diseases but face limitations in cellular uptake efficiency.
- Poly β-amino esters (pBAEs) are effective polymer vectors forming polyplexes for oligonucleotide delivery.
- Optimizing pBAE formulations for specific cell types currently relies on time-consuming trial-and-error methods.
Purpose of the Study:
- To develop and validate machine learning (ML) models for predicting poly β-amino ester (pBAE) polyplex cellular internalisation.
- To identify key features influencing pBAE nanoparticle uptake and transfection efficiency across different cell lines.
- To establish an in silico screening tool for accelerating the optimization of gene delivery formulations.
Main Methods:
- Fabrication of a library of poly β-amino ester (pBAE) nanoparticles.
- Assessment of nanoparticle uptake in four distinct cell lines.
- Training and evaluation of various machine learning models, including gradient-boosted trees and neural networks.
- Interpretation of the best-performing model using SHapley Additive exPlanations (SHAP).
Main Results:
- Machine learning models, particularly gradient-boosted trees and neural networks, were successfully trained to predict pBAE polyplex uptake.
- The developed ML models demonstrated effectiveness in learning complex, non-linear relationships within the data.
- SHapley Additive exPlanations provided insights into the critical features driving cellular internalisation predictions.
Conclusions:
- Machine learning offers a powerful in silico tool to predict and optimize poly β-amino ester (pBAE) nanoparticle delivery systems.
- This predictive capability can significantly reduce the experimental burden associated with finding optimal gene delivery formulations for new cell types.
- The findings pave the way for accelerated development of efficient and targeted gene therapies.
