{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T12:38:03Z","timestamp":1771504683024,"version":"3.50.1"},"reference-count":56,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Br J Clin Pharmacol"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>The study aims to predict and improve the absorption of tyrosine kinase inhibitors (TKIs) in patients with malabsorption issues, particularly those who have undergone bariatric surgery or are using proton\u2010pump inhibitors. The research involves 2 main components: the development of an artificial intelligence (AI) model to identify TKIs that are susceptible to reduced absorption in these patients, and the application of case\u2010specific absorption enhancements based on 2 clinical scenarios.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A fully connected neural network was applied using pH\u2010dependent solubility data from 137\u2009785 compounds to identify TKIs at risk of reduced bioavailability when bypassing the stomach. The clinical impact of gastric acid suppressants on the bioavailability of 71 approved TKIs was evaluated by correlating the AI solubility estimates with the effect of proton\u2010pump inhibitors on clinical exposure. Furthermore, absorption enhancement was applied in 2 clinical scenarios, with therapeutic drug monitoring assessing the effectiveness of the enhancement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Two AI models were developed to predict the difference in molecular aqueous solubility between acid and neutral pH and to capture the concentration available for intestinal absorption in healthy and in patients with malabsorption issues. The AI model capturing the solubility difference between acidic and neutral pH demonstrated predictive capabilities, with a Pearson correlation coefficient of .95 and a coefficient of determination of .90. It predicted that the solubility difference, between acidic and neutral pH, was associated with clinical bioavailability (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u2009.001). Nilotinib, lapatinib and dacomitinib were ranked as TKIs with the highest risk of malabsorption in case of increased stomach pH.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed an AI\u2010based model that predicts TKIs that are at risk of malabsorption when bypassing the stomach or used with gastric acid suppressants. Drugs with a high malabsorption risk can be enhanced by various methods to improve bioavailability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/bcp.70166","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T04:44:35Z","timestamp":1751949875000},"page":"3130-3140","source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence modelling of tyrosine kinase inhibitors at risk of malabsorption and bioavailability\u2010enhancing strategies"],"prefix":"10.1002","volume":"91","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5354-9935","authenticated-orcid":false,"given":"Daan W.","family":"Huntjens","sequence":"first","affiliation":[{"name":"Pharmacy &amp; Clinical Pharmacology Amsterdam University Medical Center  Amsterdam The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7554-9220","authenticated-orcid":false,"given":"Olivier J. M.","family":"B\u00e9quignon","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Amsterdam University Medical Center  Amsterdam The Netherlands"},{"name":"Cancer Center Amsterdam Cancer treatment and Quality of Life  Amsterdam The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4406-7149","authenticated-orcid":false,"given":"Stefanie D.","family":"Krens","sequence":"additional","affiliation":[{"name":"Pharmacy &amp; Clinical Pharmacology Amsterdam University Medical Center  Amsterdam The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4975-9945","authenticated-orcid":false,"given":"Mark","family":"L\u00f6wenberg","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology Amsterdam University Medical Center  The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0123-9182","authenticated-orcid":false,"given":"Martine E. 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