Machine Learning Assisted Prediction of Degradation Behavior of Poly(Lactic-Co-Glycolic Acid)

Abstract

Poly(lactic-co-glycolic acid) (PLGA) is widely used in biomedical implants due to its excellent biocompatibility and nontoxic degradation by products. Accurately predicting its degradation is crucial for implant stability. Traditional hydrolytic and autocatalytic models offer valuable insights but have limitations in describing real-world degradation kinetics. To address this problem, machine learning (ML) is applied to predict PLGA degradation under physiological conditions. Eight features, including molecular weight, lactide/glycolide ratio, pH, temperature, surface-area-to-volume ratio, geometry shape, degradation time, and lactide stereochemistry type, were used to predict degradation behavior. A dataset with 484 data points from 30 studies trained six ML models: k-nearest neighbors (KNN), random forest regression (RFR), eXtreme Gradient Boosting Regression (XGBR), AdaBoost regression (ABR), Cat Boosting regression (CBR), and gradient boosting regression (GBR). GBR was identified as the optimized model, with a determination coefficient of 0.90. SHapley Additive exPlanations (SHAP) analysis identified degradation time, molecular weight, and lactide/glycolide ratio as the most influential features. This study demonstrates the potential of ML in predicting PLGA degradation behavior, reducing the need for extensive lab tests and providing data support for accurate predictions of in vivo implant degradation.

Keywords

biodegradable polymers / machine learning / prediction

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