In Vitro Release Prediction of Colchicine Transdermal Patch Based on Raman Spectroscopy Imaging and Data-Driven Modeling.

This study explored the feasibility of combining Raman spectroscopic imaging with data-driven modeling for estimating colchicine release from transdermal patches under in vitro conditions, aiming to reduce the operational complexity and long testing cycle of the conventional paddle-plate method. Ninety representative patch samples were prepared using a Box-Behnken design, with colchicine content, penetration enhancer content, and evaporation time as key variables. Surface Raman imaging data were collected, while reference release profiles were obtained by the paddle-plate method and fitted using the Weibull equation. Based on these data, three models-partial least squares regression, spectra-based convolutional neural network, and image-based convolutional neural network-were developed under curve-fitting-independent and curve-fitting-dependent strategies. Model performance was evaluated using R2, root mean square error, and similarity factors f1 and f2. The curve-fitting-independent strategy showed better predictive performance than the curve-fitting-dependent strategy, and all three models met the commonly used similarity criteria (f1 < 15 and f2 > 50). The lower performance of the curve-fitting-dependent strategy was mainly related to scale differences among the release-equation parameters. Green analysis further indicated that the proposed method reduced solvent consumption, waste generation, and energy use compared with conventional testing. Overall, Raman spectroscopic imaging combined with data-driven modeling provides a non-destructive, greener, and relatively rapid approach for in vitro release prediction and quality evaluation of transdermal patches.