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

1. AAPS PharmSciTech. 2026 Jun 17;27(5):241. doi: 10.1208/s12249-026-03480-w. In Vitro Release Prediction of Colchicine Transdermal Patch Based on Raman Spectroscopy Imaging and Data-Driven Modeling. Sha X(#)(1)(2)(3), Dong W(#)(1)(2)(3), Zhang L(1)(2)(3), Li L(4), Li W(5)(6)(7). Author information: (1)College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China. (2)Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin, 301617, China. (3)State Key Laboratory of Chinese Medicine Modernization, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China. (4)NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China. lilian@sdu.edu.cn. (5)College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China. wshlwl@tjutcm.edu.cn. (6)Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin, 301617, China. wshlwl@tjutcm.edu.cn. (7)State Key Laboratory of Chinese Medicine Modernization, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China. wshlwl@tjutcm.edu.cn. (#)Contributed equally 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. © 2026. The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists. DOI: 10.1208/s12249-026-03480-w PMID: 42310244 [Indexed for MEDLINE] Conflict of interest statement: Declarations. Competing Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

{
  "language": "lang-en",
  "diagnosis": [
    "ra"
  ],
  "specialty": "rheumatology",
  "study_type": "rct",
  "evidence_level": "level-1",
  "v6_autopublish": true,
  "clinical_impact": "high-impact",
  "practice_recommendation": "practice-change"
}