Analyzing Doxorubicin and Cardioprotective PLGA Nanoparticles on Murine Cardiomyocytes using Machine Learning Modeling to Mitigate Cardiotoxicity

Doxorubicin (DOX) is a chemotherapeutic drug used to treat cancerous cells by inducing immunogenic cell death (ICD) and interfering with DNA replication. The viability of cardiomyocytes decreases significantly when treated with DOX and results in a cardiomyopathy mortality rate upwards of 50%, but this is reduced when encapsulated in a Poly-lactic-co-glycolic acid (PLGA) nanoparticle. We collected and treated murine cardiomyocytes in-vitro grouping untreated, DOX, and DOX with PLGA cells, and characterized them utilizing a fluorescence-activated cell sorting technique, cell flow cytometry. Using machine learning, we found which characteristics make PLGA effective at performing drug-carrying capabilities. We created a random forest model for processing the murine cells to predict which of the three treatments it received. We perform feature engineering utilizing correlation matrices between the features, Variation Inflation Factor (VIF) scores, and their Permutation Importance (PI) scores. Our random forest model achieved a 92.63% accuracy in predicting treatments with a dataset composed of highly independent features. By examining the most important features for the model’s accuracy, cell death, DOX, and granularity amount, we discern that because PLGA restores the lysosomal compartment of cells, it allows healthy cells to recycle their cytoplasmic structures. We also discovered that PLGA administers less of the toxic drug when cells are not tumorous, and restores the cellular membranes of cells after they are treated with DOX.