{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:50:00Z","timestamp":1792173000525,"version":"4.4.0"},"reference-count":19,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Translational Sci"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Quantitative prediction of drug removal by hemodialysis is essential for rational dose design in patients with renal failure. We aimed to develop a machine learning\u2010based quantitative structure\u2013pharmacokinetic relationship (QSPKR) model to predict hemodialysis clearance (CL\n                    <jats:sub>HD<\/jats:sub>\n                    ) using molecular descriptors and dialysis conditions. CL\n                    <jats:sub>HD<\/jats:sub>\n                    data were compiled from published clinical studies, yielding a dataset comprising 46 compounds; 457 measurements; 7 dialysis membrane types; and dialysis parameters, including blood flow rate (\n                    <jats:italic>Q<\/jats:italic>\n                    <jats:sub>B<\/jats:sub>\n                    ) and dialysate flow rate (\n                    <jats:italic>Q<\/jats:italic>\n                    <jats:sub>D<\/jats:sub>\n                    ). Recursive feature elimination revealed six key predictors, which were used to construct and compare five models: lasso regression, random forest, support vector machine, averaged neural network, and extreme gradient boosting (XGBoost) models. Feature contributions were interpreted using Shapley additive explanations (SHAP) analysis. Among the models, XGBoost demonstrated the highest predictive performance (root mean square error\u2009=\u200951.71\u2009mL\/min;\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.614). The SHAP analysis revealed that lipophilicity was the most influential predictor. The effects of\n                    <jats:italic>Q<\/jats:italic>\n                    <jats:sub>B<\/jats:sub>\n                    and\n                    <jats:italic>Q<\/jats:italic>\n                    <jats:sub>D<\/jats:sub>\n                    on CL\n                    <jats:sub>HD<\/jats:sub>\n                    were nonlinear and saturable, consistent with the established dialysis clearance theory. CL\n                    <jats:sub>HD<\/jats:sub>\n                    can be predicted from molecular structure and dialysis parameters using a QSPKR\u2010based machine learning approach, providing a practical framework for estimating drug clearance and assisting in the preliminary evaluation of drug dialyzability when empirical data are limited.\n                  <\/jats:p>","DOI":"10.1111\/cts.70746","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T07:29:34Z","timestamp":1791790174000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Translational Prediction of Hemodialysis Clearance Integrating Molecular Structure and Dialysis Conditions"],"prefix":"10.1111","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6111-8537","authenticated-orcid":false,"given":"Takahiko","family":"Aoyama","sequence":"first","affiliation":[{"name":"Laboratory of Clinical Pharmacometrics, School of Pharmacy Nihon University  Chiba Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1798-0063","authenticated-orcid":false,"given":"Tsukasa","family":"Kuwana","sequence":"additional","affiliation":[{"name":"Division of Emergency and Critical Care Medicine, Department of Acute Medicine Nihon University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atsushi","family":"Sato","sequence":"additional","affiliation":[{"name":"Laboratory of Clinical Pharmacometrics, School of Pharmacy Nihon University  Chiba Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haruka","family":"Ohashi","sequence":"additional","affiliation":[{"name":"Laboratory of Clinical Pharmacometrics, School of Pharmacy Nihon University  Chiba Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6131-0665","authenticated-orcid":false,"given":"Junko","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"Division of Emergency and Critical Care Medicine, Department of Acute Medicine Nihon University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toru","family":"Imai","sequence":"additional","affiliation":[{"name":"Department of Pharmacy Nihon University Itabashi Hospital  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9451-9027","authenticated-orcid":false,"given":"Kosaku","family":"Kinoshita","sequence":"additional","affiliation":[{"name":"Division of Emergency and Critical Care Medicine, Department of Acute Medicine Nihon University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7967-2947","authenticated-orcid":false,"given":"Yasuhiro","family":"Tsuji","sequence":"additional","affiliation":[{"name":"Laboratory of Clinical Pharmacometrics, School of Pharmacy Nihon University  Chiba Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,12]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa010877"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1517\/17425255.2014.931371"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.CCM.0000147687.06808.92"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1345\/aph.17105"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.CCM.0000190243.88133.3F"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.02920707"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11030\u2010021\u201010348\u20107"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11030\u2010021\u201010186\u20107"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/1744\u20109987.12675"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1128\/AAC.5.6.611"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1128\/AAC.11.2.214"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1248\/yakushi.127.897"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1177\/0091270005280583"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-0741-0_30"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12872\u2010025\u201004627\u20106"},{"key":"e_1_2_9_17_1","first-page":"387","article-title":"Operating Parameters and Performance Criteria for Hemodialyzers and Other Membrane\u2010Separation Devices","volume":"12","author":"Michaels A. S.","year":"1966","journal-title":"Transactions\u2014American Society for Artificial Internal Organs"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/1744\u20109987.13472"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/cts.13884"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.3109\/15563650.2014.973572"}],"container-title":["Clinical and Translational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ascpt.onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/cts.70746","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ascpt.onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/cts.70746","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ascpt.onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/cts.70746","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:17:47Z","timestamp":1792171067000},"score":1,"resource":{"primary":{"URL":"https:\/\/ascpt.onlinelibrary.wiley.com\/doi\/10.1111\/cts.70746"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":19,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["10.1111\/cts.70746"],"URL":"https:\/\/doi.org\/10.1111\/cts.70746","archive":["Portico"],"relation":{},"ISSN":["1752-8054","1752-8062"],"issn-type":[{"value":"1752-8054","type":"print"},{"value":"1752-8062","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"2026-02-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-10-05","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-10-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70746"}}