{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T19:53:09Z","timestamp":1781639589541,"version":"3.54.5"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:00:00Z","timestamp":1775174400000},"content-version":"vor","delay-in-days":34,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Modeling drug-release kinetics from polysaccharide-coated oral controlled-release formulations remains challenging due to nonlinear diffusion\u2013dissolution behavior, complex polymer\u2013drug interactions, and the limited interpretability of conventional machine-learning approaches. In this study, we develop and validate a predictive framework for targeted colonic delivery of 5-aminosalicylic acid (5-ASA) from polysaccharide-coated solid oral dosage forms using Raman spectroscopy\u2013derived molecular fingerprints and time-resolved dissolution data. The dataset comprises 155 formulation samples, each characterized by more than 1,500 Raman spectral features, categorical formulation variables (polysaccharide type and release medium), and drug-release measurements at 2, 8, and 24\u00a0h collected under simulated physiological conditions. A dual-optimizer, dual-ensemble learning strategy is introduced, integrating the Puma Optimizer Algorithm (POA) and Black-Winged Kite Algorithm (BWKA) within a Damsphere Weighted Ensemble (DWE) of XGBoost regression and AdaBoost models. The complementary exploration\u2013exploitation dynamics of the two optimizers enhance convergence stability and generalization, yielding strong predictive performance under five-fold cross-validation (RMSE\u2009=\u20090.038; R\n                    <jats:sup>2<\/jats:sup>\n                    = 0.991). Feature-level analysis based on F-statistics highlights release time, dissolution medium, and chemically meaningful Raman bands as dominant predictors, consistent with diffusion- and erosion-controlled release mechanisms in polysaccharide-coated systems. From a pharmaceutical perspective, the proposed framework reduces experimental burden while maintaining mechanistic interpretability, supporting Quality by Design (QbD) and green pharmaceutics principles. Owing to its modular architecture, the approach is readily extensible to other polymer-based oral controlled-release formulations and spectroscopic modalities.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-41837-0","type":"journal-article","created":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T12:15:02Z","timestamp":1772280902000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrative ensemble learning framework for forecasting controlled drug release based on Raman spectral signatures"],"prefix":"10.1038","volume":"16","author":[{"given":"Ahmed H.","family":"Albariqi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Awaji Y.","family":"Safhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saad S.","family":"Alqahtani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahad Y.","family":"Sabei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahboubeh","family":"Pishnamazi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,28]]},"reference":[{"key":"41837_CR1","doi-asserted-by":"publisher","first-page":"104817","DOI":"10.1016\/j.rineng.2025.104817","volume":"26","author":"S Amirsaadat","year":"2025","unstructured":"Amirsaadat, S. et al. Design and development of PLGA-hollow mesoporous silica nanoparticles for injectable micro-carriers of metformin based on a microfluidic device: Possible application for drug release in osteoarthritis treatment. Results Eng. 26, 104817 (2025).","journal-title":"Results Eng."},{"key":"41837_CR2","doi-asserted-by":"publisher","first-page":"138327","DOI":"10.1016\/j.ijbiomac.2024.138327","volume":"285","author":"Z Shan","year":"2025","unstructured":"Shan, Z., Jiang, B., Wang, P., Wu, W. & Jin, Y. Sustainable lignin-based composite hydrogels for controlled drug release and self-healing in antimicrobial wound dressing. Int. J. Biol. Macromol. 285, 138327 (2025).","journal-title":"Int. J. Biol. Macromol."},{"issue":"1","key":"41837_CR3","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1186\/s12951-025-03275-4","volume":"23","author":"Y Zhu","year":"2025","unstructured":"Zhu, Y. et al. Injectable hydrogels with ROS-triggered drug release enable the co-delivery of antibacterial agent and anti-inflammatory nanoparticle for periodontitis treatment. J. Nanobiotechnol. 23(1), 205 (2025).","journal-title":"J. Nanobiotechnol."},{"issue":"1","key":"41837_CR4","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1016\/j.xphs.2024.10.032","volume":"114","author":"SS Hate","year":"2025","unstructured":"Hate, S. S., Thompson, S. A. & Singaraju, A. B. Impact of sink conditions on drug release behavior of controlled-release formulations. J. Pharm. Sci. 114(1), 520\u2013529 (2025).","journal-title":"J. Pharm. Sci."},{"key":"41837_CR5","doi-asserted-by":"publisher","first-page":"109756","DOI":"10.1016\/j.compbiomed.2025.109756","volume":"188","author":"S Aghajanpour","year":"2025","unstructured":"Aghajanpour, S. et al. Utilizing machine learning for predicting drug release from polymeric drug delivery systems. Comput. Biol. Med. 188, 109756 (2025).","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"41837_CR6","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1021\/acs.nanolett.4c05153","volume":"25","author":"K Li","year":"2025","unstructured":"Li, K. et al. Li-based nanoprobes with boosted photoluminescence for temperature visualization in NIR imaging-guided drug release. Nano Lett. 25(2), 776\u2013785 (2025).","journal-title":"Nano Lett."},{"issue":"2","key":"41837_CR7","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3390\/jfb16020037","volume":"16","author":"G Onyenso","year":"2025","unstructured":"Onyenso, G., Vakamulla Raghu, S. N., Hartwich, P. & Killian, M. S. Modulated-diameter zirconia nanotubes for controlled drug release\u2014bye to the burst. J. Funct. Biomater. 16(2), 37 (2025).","journal-title":"J. Funct. Biomater."},{"key":"41837_CR8","doi-asserted-by":"crossref","unstructured":"Luo, S., Zhao, C., Wang, R. & Wu, D. Sequential Drug Release Nanocomposites for Diseases Synergistic Therapy. J Mater. Chem. B (2025).","DOI":"10.1039\/D5TB00026B"},{"key":"41837_CR9","doi-asserted-by":"publisher","first-page":"114611","DOI":"10.1016\/j.ejpb.2024.114611","volume":"207","author":"D Kelle","year":"2025","unstructured":"Kelle, D. et al. Effect of protein corona on drug release behavior of PLGA nanoparticles. Eur. J. Pharm. Biopharm. 207, 114611 (2025).","journal-title":"Eur. J. Pharm. Biopharm."},{"issue":"1","key":"41837_CR10","doi-asserted-by":"publisher","first-page":"4218","DOI":"10.1038\/s41598-024-82728-6","volume":"15","author":"Y Sun","year":"2025","unstructured":"Sun, Y. et al. Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles. Sci. Rep. 15(1), 4218 (2025).","journal-title":"Sci. Rep."},{"key":"41837_CR11","doi-asserted-by":"crossref","unstructured":"Wei, X. et al. An artificial cilia-based array system for sound frequency decoding and resonance-responsive drug release. Nat Biomed. Eng 1\u201312 (2025).","DOI":"10.1038\/s41551-025-01505-6"},{"issue":"5","key":"41837_CR12","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1007\/s13346-024-01696-6","volume":"15","author":"R Mousavi","year":"2025","unstructured":"Mousavi, R., Soltani, M. & Souri, M. Microneedle patch capable of dual drug release for drug delivery to brain tumors. Drug Deliv Transl Res. 15(5), 1567\u20131594 (2025).","journal-title":"Drug Deliv Transl Res."},{"issue":"12","key":"41837_CR13","doi-asserted-by":"publisher","first-page":"2415705","DOI":"10.1002\/adma.202415705","volume":"37","author":"Y Meng","year":"2025","unstructured":"Meng, Y. et al. Molecular Trojan Based on Membrane-Mimicking Conjugated Electrolyte for Stimuli\u2010Responsive Drug Release. Adv. Mater. 37(12), 2415705 (2025).","journal-title":"Adv. Mater."},{"issue":"15","key":"41837_CR14","doi-asserted-by":"publisher","first-page":"5989","DOI":"10.1021\/acs.nanolett.4c04628","volume":"25","author":"Z Wan","year":"2025","unstructured":"Wan, Z. et al. Metal\u2013Organic Frameworks-Based Microrockets for Controlled and Sustained Drug Release. Nano Lett. 25(15), 5989\u20135996 (2025).","journal-title":"Nano Lett."},{"key":"41837_CR15","doi-asserted-by":"publisher","first-page":"102640","DOI":"10.1016\/j.mtchem.2025.102640","volume":"45","author":"M Lalinia","year":"2025","unstructured":"Lalinia, M., Nemati, N. H., Mofazali, P., Gross, J. D. & Samadi, A. Recent advances in metal-organic framework capabilities with machine learning innovations for enhanced drug release systems. Mater. Today Chem. 45, 102640 (2025).","journal-title":"Mater. Today Chem."},{"key":"41837_CR16","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jconrel.2024.08.010","volume":"374","author":"Y Abdalla","year":"2024","unstructured":"Abdalla, Y. et al. Machine learning of Raman spectra predicts drug release from polysaccharide coatings for targeted colonic delivery. J. Control Release. 374, 103\u2013111 (2024).","journal-title":"J. Control Release"},{"issue":"9","key":"41837_CR17","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.3390\/pharmaceutics16091184","volume":"16","author":"H Lu","year":"2024","unstructured":"Lu, H., Cai, Z. & Hu, P. Recent Advances in Polymeric Delivery Vehicles for Controlled and Sustained Drug Release. Pharmaceutics 16(9), 1184 (2024).","journal-title":"Pharmaceutics"},{"issue":"4","key":"41837_CR18","doi-asserted-by":"publisher","first-page":"102002","DOI":"10.1016\/j.jsps.2024.102002","volume":"32","author":"DU Kapoor","year":"2024","unstructured":"Kapoor, D. U. et al. Pectin hydrogels for controlled drug release: Recent developments and future prospects. Saudi Pharm. J. 32(4), 102002 (2024).","journal-title":"Saudi Pharm. J."},{"issue":"1","key":"41837_CR19","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/s42114-024-01203-y","volume":"8","author":"Q Yu","year":"2025","unstructured":"Yu, Q. et al. Multifunctional composite capsules in drug delivery systems: bridging pharmaceutical and biomedical applications. Adv. Compos. Hybrid. Mater. 8(1), 118 (2025).","journal-title":"Adv. Compos. Hybrid. Mater."},{"issue":"8","key":"41837_CR20","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.1007\/s00289-025-05645-2","volume":"82","author":"MM AL-Rajabi","year":"2025","unstructured":"AL-Rajabi, M. M., Alzyod, S., Patel, A. & Teow, Y. H. A hybrid machine learning framework for predicting drug-release profiles, kinetics, and mechanisms of temperature-responsive hydrogels. Polym. Bull. 82(8), 2911\u20132932 (2025).","journal-title":"Polym. Bull."},{"key":"41837_CR21","doi-asserted-by":"crossref","unstructured":"Kamath, S. et al. Microbiome-active drug delivery systems (MADDS): Leveraging microbial stimuli for controlled drug release. Adv. Drug Deliv. Rev. 115720 (2025).","DOI":"10.1016\/j.addr.2025.115720"},{"key":"41837_CR22","unstructured":"Zia, R. Exploring Antibubbles for Oral Drug Delivery (2025)."},{"issue":"1","key":"41837_CR23","doi-asserted-by":"publisher","first-page":"30","DOI":"10.5530\/ijpi.20250057","volume":"15","author":"FR Salim Altalib","year":"2025","unstructured":"Salim Altalib, F. R. & Hamid, O. A. Three-Dimensional Printing of Colon-Targeting Drug Delivery Systems (CTDDS). Int. J. Pharm. Investig. 15(1), 30\u201348 (2025).","journal-title":"Int. J. Pharm. Investig"},{"key":"41837_CR24","doi-asserted-by":"publisher","first-page":"1658466","DOI":"10.3389\/frsfm.2025.1658466","volume":"5","author":"P Subash","year":"2025","unstructured":"Subash, P. & Khute, S. Recent advances in lyotropic liquid crystal nanoparticle formulations for drug delivery systems. Front. Soft Matter. 5, 1658466 (2025).","journal-title":"Front. Soft Matter"},{"issue":"10","key":"41837_CR25","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.3390\/pharmaceutics17101264","volume":"17","author":"P Drapi\u0144ska","year":"2025","unstructured":"Drapi\u0144ska, P., Skulmowska-Polok, K., Cha\u0142upka, J. & Sikora, A. Sustained-Release Oral Delivery of NSAIDs and Acetaminophen: Advances and Recent Formulation Strategies\u2014A Systematic Review. Pharmaceutics 17(10), 1264 (2025).","journal-title":"Pharmaceutics"},{"issue":"1","key":"41837_CR26","doi-asserted-by":"publisher","first-page":"25139","DOI":"10.1038\/s41598-025-10417-z","volume":"15","author":"WA Mahdi","year":"2025","unstructured":"Mahdi, W. A., Alhowyan, A. & Obaidullah, A. J. Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations. Sci. Rep. 15(1), 25139 (2025).","journal-title":"Sci. Rep."},{"issue":"1","key":"41837_CR27","doi-asserted-by":"publisher","first-page":"103206","DOI":"10.1016\/j.asej.2024.103206","volume":"16","author":"Z Qi","year":"2025","unstructured":"Qi, Z., Feng, Y., Wang, S. & Li, C. Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniques. Ain Shams Eng. J. 16(1), 103206 (2025).","journal-title":"Ain Shams Eng. J."},{"key":"41837_CR28","doi-asserted-by":"publisher","first-page":"100722","DOI":"10.1016\/j.rinma.2025.100722","volume":"27","author":"B Yarahmadi","year":"2025","unstructured":"Yarahmadi, B. Determining the quality of imprinted polymers using diverse feature selections methods, Ada Boost and Gradient Boosting Algorithms. Results Mater. 27, 100722 (2025).","journal-title":"Results Mater."},{"key":"41837_CR29","doi-asserted-by":"crossref","unstructured":"Gupta, S., Gupta, S., Sunil, G., Kant, V. & Bharany, S. Path planning optimization for four-wheeled mobile robots using the Black-Winged Kite Algorithm. in 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 1\u20136 (IEEE, 2025).","DOI":"10.1109\/OTCON65728.2025.11070707"},{"key":"41837_CR30","doi-asserted-by":"crossref","unstructured":"Akbulut, H., Atasever, S. & S\u0131ramkaya, E. Machine learning-based orange quality classification: A hyperparameter optimization approach through puma optimizer. in 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) 1\u20135 (IEEE, 2025).","DOI":"10.1109\/ICHORA65333.2025.11017028"},{"key":"41837_CR31","first-page":"e04205","volume":"22","author":"SS Regalla","year":"2025","unstructured":"Regalla, S. S. & Kumar, N. S. Optimizing ultra high-performance concrete (UHPC) mix proportions using technique for order preference by similarity to ideal solution (TOPSIS) for enhanced performance: Precision towards optimal concrete engineering. Case Stud. Constr. Mater. 22, e04205 (2025).","journal-title":"Case Stud. Constr. Mater."},{"issue":"2","key":"41837_CR32","doi-asserted-by":"publisher","first-page":"38","DOI":"10.3390\/beverages11020038","volume":"11","author":"T Javanbakht","year":"2025","unstructured":"Javanbakht, T. Optimization of Beverage Formulation with Technique for Order of Preference by Similarity to Ideal Solution. Beverages 11(2), 38 (2025).","journal-title":"Beverages"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41837-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41837-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41837-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T19:06:04Z","timestamp":1781636764000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41837-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,28]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["41837"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-41837-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,28]]},"assertion":[{"value":"15 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"11343"}}