{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T08:04:02Z","timestamp":1789632242632,"version":"build-2803163510"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"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. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Protein kinase inhibitors are central drugs in precision oncology, but cell-line response to these agents is affected by lineage and molecular context rather than target identity alone. Many drug-response models report optimistic internal performance; stricter evaluation requires held-out drugs, independent screens and cross-platform validation. This study evaluates a focused, auditable pharmacogenomic modelling framework for kinase-inhibitor response prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Protein kinase inhibitors were curated from PKIDB and matched to GDSC release 8.5 response data and DepMap 26Q1 molecular profiles. The full matched screens contained GDSC2 (40,403 drug-cell line pairs; 42 drugs; 945 models), GDSC1 (40,900 pairs; 48 drugs; 945 models) and PRISM secondary screen data (7,090 pairs; 23 drugs; 336 models). GDSC2-to-GDSC1 external testing was restricted to compounds shared by both GDSC releases (23,340 GDSC2 training pairs and 22,570 GDSC1 test pairs; 25 drugs). Models were evaluated using cell-line cross-validation, drug cross-validation, independent-screen validation, continuous-response prediction and pathway\/drug subgroup analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the GDSC1 shared-drug external set, molecular-summary models achieved ROC AUC 0.706 for GDSC AUC-defined sensitivity and 0.692 for LN_IC50-defined sensitivity. Continuous-response validation in the same GDSC1 test set produced Spearman correlations of 0.453 for AUC and 0.457 for LN_IC50, with continuous-derived sensitivity ROC AUCs of 0.703 and 0.710. Molecular summaries improved external ROC AUC over drug-lineage context by 0.052 for AUC sensitivity and 0.046 for LN_IC50 sensitivity. ERK\/MAPK signalling showed the strongest pathway-level signal, with ROC AUC 0.825 for AUC sensitivity and 0.780 for LN_IC50 sensitivity. PRISM cross-platform validation was more modest (best ROC AUC 0.597), consistent with assay and drug-overlap differences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>PKIDB-informed molecular profiling improves externally reproducible kinase-inhibitor response modelling across public cancer cell-line screens, especially for ERK\/MAPK inhibitors. These findings support a pharmacogenomic modelling and validation study rather than direct patient-level or experimental validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2026.1923164","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T07:20:46Z","timestamp":1789629646000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PKIDB-informed molecular profiling improves reproducible prediction of cancer kinase-inhibitor response"],"prefix":"10.3389","volume":"17","author":[{"given":"Miao","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China","place":["Mianyang, Sichuan, China"]},{"name":"NHC Key Laboratory of Nuclear Technology Medical Transformation (Mianyang Central Hospital), School of Medicine, University of Electronic Science and Technology of China","place":["Mianyang, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayang","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, Cangxi People\u2019s Hospital","place":["Guangyuan, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianbin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China","place":["Mianyang, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Zhao","sequence":"additional","affiliation":[{"name":"The First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Xiong","sequence":"additional","affiliation":[{"name":"NHC Key Laboratory of Nuclear Technology Medical Transformation (Mianyang Central Hospital), School of Medicine, University of Electronic Science and Technology of China","place":["Mianyang, Sichuan, China"]},{"name":"Department of Pharmacy, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China","place":["Mianyang, Sichuan, China"]},{"name":"Sichuan Provincial Engineering Research Center of Nuclear Medical Equipment Translation and Application, Mianyang Central Hospital","place":["Mianyang, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"102820","DOI":"10.1016\/j.artmed.2024.102820","article-title":"Predicting drug activity against cancer through genomic profiles and SMILES","volume":"151","author":"Abbasi","year":"2024","journal-title":"Artif. 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