Deep Learning-Based Artificial Intelligence to Investigate Targeted Nanoparticles' Uptake in TNBC Cells

Area of Science:

• Biomedical Engineering
• Computational Biology
• Oncology

Background:

• Triple negative breast cancer (TNBC) is an aggressive subtype with limited treatment options.
• Smart nano-based carriers offer targeted delivery but require precise assessment of cellular uptake and drug release.
• Current imaging-based analyses for nanoparticle evaluation are time-consuming and susceptible to human bias.

Purpose of the Study:

• To evaluate the performance of deep learning models for predicting nanoparticle drug uptake and release in TNBC cells.
• To compare the efficacy of custom-trained sequential models against pre-trained convolutional neural network (CNN) models (VGG16, ResNet50, Inception V3).
• To assess the potential of AI in streamlining drug development by accurately quantifying cellular drug interactions.

Main Methods:

• Trained five CNN models (two sequential from scratch, VGG16, ResNet50, Inception V3) using confocal images of TNBC cells treated with fluorescently labeled nanoparticles.
• Utilized comparative and cross-validation analyses to rigorously assess model performance.
• Focused on predicting high or low drug uptake and release dynamics.

Main Results:

• All evaluated deep learning models demonstrated high accuracy in predicting drug uptake and release levels in TNBC cells.
• The models effectively distinguished between high and low cellular drug concentrations.
• Performance metrics indicated strong potential for these AI approaches in quantitative drug assessment.

Conclusions:

• Deep learning models offer a robust and accurate method for evaluating nanoparticle-based drug delivery in TNBC.
• This AI-driven approach can significantly accelerate the early stages of drug development by providing reliable cellular uptake and release data.
• The findings highlight the translational potential of AI in precision medicine and drug discovery for challenging cancers like TNBC.