Machine Learning‐Enabled Prediction of pH ‐Responsive Curcumin Release From Self‐Assembled Nanomicelles

This work, leveraging machine‐learning algorithms, focuses on the design and in vitro evaluation of oil‐in‐water surfactant‐based nanomicelles as a novel curcumin formulation with comparable cytotoxic effects against MCF‐7 human breast cancer cells. Nanomicelles were prepared using Pluronic F‐127 as an emulsifying agent and the addition of an ethyl butyrate solution as the organic phase, reaching highly efficient encapsulations of 89.4%, with a particle hydrodynamic size of 534.425 nm. The in vitro drug release studies showed pH‐dependent sustained release, with a faster release at acidic pH 5.4 compared to neutral pH 7.4. The cytotoxicity studies showed that curcumin‐loaded nanomicelles preserved the anticancer activity of free curcumin against MCF‐7 cells while exhibiting a comparable cytotoxicity profile toward normal fibroblast cells, indicating that nanoencapsulation did not compromise efficacy or introduce additional toxicity. Furthermore, this study introduces an ML‐based fitting and interpolation framework for modeling pH‐responsive curcumin release kinetics from nanocarrier systems. The Random Forest regression model demonstrated outstanding goodness‐of‐fit ( R 2 > 0.999), effectively capturing the nonlinear and complex kinetics of curcumin release across the measured time points. These computational insights provide a useful descriptive tool for the rational design of pH‐responsive drug delivery systems with enhanced therapeutic precision.