{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:00:20Z","timestamp":1790830820540,"version":"4.1.0"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but traditional pharmacovigilance lacks a translational computational framework capable of resolving target-specific nonlinear temporal risks of adverse events such as hypokalemia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We integrated 3,243 ICI-related hypokalemia reports from the FDA Adverse Event Reporting System (FAERS) and VigiBase (2014\u20132024). An ensemble signal-detection strategy cross-validated four disproportionality algorithms (Reporting Odds Ratio [ROR], Proportional Reporting Ratio [PRR], Empirical Bayesian Geometric Mean [EBGM], and Information Component [IC]). Flexible nonlinear survival analysis was performed using restricted cubic spline (RCS) Cox regression to model time-to-event dynamics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Programmed Death-Ligand 1 (PD-L1) inhibitors triggered the strongest signal (ROR = 3.20, 95% CI: 2.95\u20133.48), with ultra-early onset (median 23 days) and the highest case fatality rate (23.7%). An asymmetric biphasic mortality pattern showed a nadir at 60 days: early-onset (\u226430 days) hypokalemia peaked at a hazard ratio (HR) of \u223c1.9, whereas late-onset (&amp;gt;90 days) risk gradually rose to HR \u223c1.8 within 350 days. Programmed Death-1 (PD-1) inhibitors significantly increased severe hypokalemia risk (odds ratio [OR] = 28.32), with male predominance across PD-1 and CTLA-4 subgroups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This interpretable ensemble framework reveals target-specific temporal toxicity signatures from multi-source real-world data, providing a computationally grounded translational pharmacology foundation for precision electrolyte monitoring and mechanism-informed safety prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2026.1960328","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T04:45:22Z","timestamp":1790829922000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Ensemble signal detection reveals target-specific temporal risk of hypokalemia after immune checkpoint inhibition: a translational pharmacology framework"],"prefix":"10.3389","volume":"17","author":[{"given":"Shengwang","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, The Third Hospital of Xiamen","place":["Xiamen City, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Dai","sequence":"additional","affiliation":[{"name":"The Second Clinical College of China Medical University","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihan","family":"Guo","sequence":"additional","affiliation":[{"name":"Dalian Medical University","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Li","sequence":"additional","affiliation":[{"name":"The Second Clinical College of China Medical University","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiqi","family":"Liu","sequence":"additional","affiliation":[{"name":"The First Clinical College of China Medical University","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuewen","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Xiamen Xinglin Hospital","place":["Xiamen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1007\/s11096-025-01975-3","article-title":"Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined Bayesian network tool","volume":"47","author":"Algarvio","year":"2025","journal-title":"Int. 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