Interpretable Two-Stage Machine Learning for Early and Full Drug Release Prediction in PLGA Microspheres
- 1College of Pharmacy, Keimyung University, Daegu 42601, Republic of Korea
- 2Department of Pharmaceutical Engineering, Daegu Catholic University, Gyeongsan 38430, Republic of Korea
- 3College of Pharmacy, Yeungnam University, Gyeongsan 38541, Republic of Korea
Abstract
Background/Objectives: Poly(lactic-co-glycolic acid) (PLGA) microspheres are widely used in long-acting injectable (LAI) formulations because PLGA exhibits well-established biocompatibility and undergoes controlled hydrolytic degradation into metabolizable byproducts. However, optimization of microspheres typically requires time-consuming in vitro testing. Therefore, we developed a predictive machine learning model for early-stage and full time-dependent release profiles of drug-loaded PLGA microspheres. Methods: Using a published dataset comprising 321 release profiles from 89 drugs, we first developed a classification model to identify slow-release behavior (≤20% release within 3 days) and subsequently integrated the predicted early-release probability into a regression model to estimate cumulative release over time. Results: Among tree-based ensemble models, XGBoost achieved the lowest mean absolute error (MAE = 0.126) and highest Pearson correlation coefficient (r = 0.831). SHapley Additive exPlanations (SHAP) analysis revealed that drug and polymer molecular weight, predictive slow-release probability, and polymer concentration substantially influence release behavior. We also assessed this framework with external datasets. Drug release data for olaparib-loaded PLGA microspheres were obtained in-house, whereas those for semaglutide-based microspheres were obtained from the published literature. In both datasets, this framework demonstrated low MAE values (0.096 and 0.068, respectively). Conclusions: This suggests that the proposed framework can predict in vitro drug release and support efficient optimization of PLGA-based LAI formulations.
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BibTeX
@article{song2026interpretable,
title = {{Interpretable Two-Stage Machine Learning for Early and Full Drug Release Prediction in PLGA Microspheres}},
author = {Younghun Song and Saroj Bashyal and Hyuk Jun Cho and Mi Ran Woo and Jong Oh Kim and Duhyeong Hwang},
journal = {Pharmaceuticals},
year = {2026},
month = may,
doi = {10.3390/ph19050767},
url = {https://doi.org/10.3390/ph19050767},
note = {CC BY},
}
RIS
TY - JOUR
TI - Interpretable Two-Stage Machine Learning for Early and Full Drug Release Prediction in PLGA Microspheres
AU - Younghun Song
AU - Saroj Bashyal
AU - Hyuk Jun Cho
AU - Mi Ran Woo
AU - Jong Oh Kim
AU - Duhyeong Hwang
PY - 2026
DA - 2026/05/14
JO - Pharmaceuticals
DO - 10.3390/ph19050767
UR - https://doi.org/10.3390/ph19050767
AB - Background/Objectives: Poly(lactic-co-glycolic acid) (PLGA) microspheres are widely used in long-acting injectable (LAI) formulations because PLGA exhibits well-established biocompatibility and undergoes controlled hydrolytic degradation into metabolizable byproducts. However, optimization of microspheres typically requires time-consuming in vitro testing. Therefore, we developed a predictive machine learning model for early-stage and full time-dependent release profiles of drug-loaded PLGA microspheres. Methods: Using a published dataset comprising 321 release profiles from 89 drugs, we first developed a classification model to identify slow-release behavior (≤20% release within 3 days) and subsequently integrated the predicted early-release probability into a regression model to estimate cumulative release over time. Results: Among tree-based ensemble models, XGBoost achieved the lowest mean absolute error (MAE = 0.126) and highest Pearson correlation coefficient (r = 0.831). SHapley Additive exPlanations (SHAP) analysis revealed that drug and polymer molecular weight, predictive slow-release probability, and polymer concentration substantially influence release behavior. We also assessed this framework with external datasets. Drug release data for olaparib-loaded PLGA microspheres were obtained in-house, whereas those for semaglutide-based microspheres were obtained from the published literature. In both datasets, this framework demonstrated low MAE values (0.096 and 0.068, respectively). Conclusions: This suggests that the proposed framework can predict in vitro drug release and support efficient optimization of PLGA-based LAI formulations.
SN - 1424-8247
ER -