{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T16:21:25Z","timestamp":1792167685204,"version":"4.4.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      Predicting how lipophilic pharmaceuticals dissolve in supercritical carbon dioxide (SC-CO\n                      <jats:sub>2<\/jats:sub>\n                      ) remains a central challenge for process development, largely because experimental measurements are time-consuming and traditional modeling approaches often fail to generalize across chemically diverse compounds.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study examines the SC-CO\n                      <jats:sub>2<\/jats:sub>\n                      solubility of four representative drugs Sirolimus, Tacrolimus, Rifampin, and Teriflunomide and introduces an adaptive computational framework designed to improve predictive power across varied molecular structures. The approach combines multiple boosting-based regressors within an ensemble scheme that incorporates a DST-inspired aggregation step and a weighted integration mechanism. Model hyperparameters are optimized using a bio- inspired Electric Eel Foraging strategy. Reliability of the final model was evaluated through repeated cross-validation, comparative statistical tests, and uncertainty estimation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results and discussion<\/jats:title>\n                    <jats:p>\n                      The resulting ensemble achieved high predictive accuracy (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      = 0.99, RMSE = 0.089) with consistent performance across the investigated compounds. By integrating molecular descriptors, thermodynamic features, and process conditions within a unified ensemble framework, the approach provides a practical computational tool for supporting solubility estimation in SC-CO\n                      <jats:sub>2<\/jats:sub>\n                      systems.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1780240","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T05:57:33Z","timestamp":1778651853000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Data-driven prediction of lipophilic drug solubility in supercritical CO2 using an adaptive ensemble learning architecture"],"prefix":"10.3389","volume":"13","author":[{"given":"Arwa Sultan","family":"Alqahtani","sequence":"first","affiliation":[{"name":"Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahboubeh","family":"Pishnamazi","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University","place":["Da Nang, Vietnam"]},{"name":"School of Engineering and Technology, Duy Tan University","place":["Da Nang, Vietnam"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"106605","DOI":"10.1016\/j.supflu.2025.106605","article-title":"Theoretical understanding of pharmaceutics solubility in supercritical CO2: thermodynamic modeling and machine learning study","volume":"223","author":"He","year":"2025","journal-title":"J Supercrit Fluids."},{"key":"B2","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1063\/5.0262557","article-title":"Cloud point pressure estimation of gas-soluble chemicals in carbon dioxide by machine learning paradigms","volume":"37","author":"Vaferi","year":"2025","journal-title":"Phys Fluids."},{"key":"B3","doi-asserted-by":"publisher","first-page":"11256","DOI":"10.1021\/acs.energyfuels.5c01345","article-title":"Predicting CO2 solubility in diverse ionic liquids: a data-driven approach using machine learning algorithms","volume":"39","author":"Bastami","year":"2025","journal-title":"Energy Fuels."},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.2118\/224577-MS","article-title":"Leveraging machine learning to model hydrocarbon-CO2 solubility behavior","author":"Alizadeh","year":"2025","journal-title":"SPE Gas & Oil Technology Showcase and Conference"},{"key":"B5","doi-asserted-by":"publisher","first-page":"26007","DOI":"10.1038\/s41598-025-11823-z","article-title":"Machine learning analysis of drug solubility via green approach to enhance drug solubility for poor soluble medications in continuous manufacturing","volume":"15","author":"Lahiq","year":"2025","journal-title":"Sci Rep"},{"key":"B6","doi-asserted-by":"publisher","first-page":"106601","DOI":"10.1016\/j.supflu.2025.106601","article-title":"A machine learning-based platform for dye solubility in supercritical carbon dioxide: classification optimization and predictive analysis","volume":"222","author":"Wang","year":"2025","journal-title":"J Supercrit Fluids."},{"key":"B7","doi-asserted-by":"publisher","first-page":"114720","DOI":"10.1016\/j.ejpb.2025.114720","article-title":"Predicting the solubility of drugs in supercritical carbon dioxide using machine learning and atomic contribution","volume":"211","author":"Roosta","year":"2025","journal-title":"Eur. 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