{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T05:16:59Z","timestamp":1777526219389,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:00:00Z","timestamp":1766707200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["ASSAY and Drug Development Technologies"],"published-print":{"date-parts":[[2026,4,1]]},"abstract":"<jats:p>\n                    <jats:italic toggle=\"yes\">\n                      Automation plays a crucial role in enhancing efficiency and increasing capacity for\n                      <jats:italic toggle=\"yes\">in vitro<\/jats:italic>\n                      absorption, distribution, metabolism, and excretion profiling in early drug discovery. Building an automation platform requires a careful balance of innovation and practical considerations to align assay needs with technological capabilities. In this study, we present a state-of-the-art automation system designed to support the miniaturization of the Caco-2 permeability assay from the 24-well to the 96-well format. This platform integrates advanced infrastructure for cell culture and assay execution, along with several key features, including innovative cleaning protocols, cutting-edge plate tracking, and dynamic scheduling capabilities. The fully automated 96-well platform delivers significant efficiency gains, increased capacity, and faster turnaround for permeability assay support while maintaining high predictive accuracy. It correctly classified 94% of 50 literature compounds, demonstrating strong concordance with the established 24-well format. Moreover, the platform enables large-scale permeability data generation, advancing our \u201cpredict-first\u201d modeling paradigm, in which predictive models guide experimental design and compound prioritization\n                    <\/jats:italic>\n                    .\n                  <\/jats:p>","DOI":"10.1177\/1540658x251408742","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T17:09:05Z","timestamp":1767114545000},"page":"228-236","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Building a State-of-the-Art Automation Platform to Support High-Throughput 96-Well Caco-2 Permeability Assay"],"prefix":"10.1177","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9210-681X","authenticated-orcid":false,"given":"Xianmei","family":"Cai","sequence":"first","affiliation":[{"name":"Lead Discovery and Optimization, Bristol Myers Squibb. Co., Princeton, New Jersey, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregory","family":"Barker","sequence":"additional","affiliation":[{"name":"Lead Discovery and Optimization, Bristol Myers Squibb. Co., Princeton, New Jersey, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shivani","family":"Patel","sequence":"additional","affiliation":[{"name":"Lead Discovery and Optimization, Bristol Myers Squibb. Co., Princeton, New Jersey, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anthony","family":"Paiva","sequence":"additional","affiliation":[{"name":"Lead Discovery and Optimization, Bristol Myers Squibb. Co., Princeton, New Jersey, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongnian","family":"Sun","sequence":"additional","affiliation":[{"name":"Lead Discovery and Optimization, Bristol Myers Squibb. Co., Princeton, New Jersey, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunli","family":"Huang","sequence":"additional","affiliation":[{"name":"Lead Discovery and Optimization, Bristol Myers Squibb. Co., Princeton, New Jersey, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Connors","sequence":"additional","affiliation":[{"name":"Lead Discovery and Optimization, Bristol Myers Squibb. Co., Princeton, New Jersey, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Chase","sequence":"additional","affiliation":[{"name":"Lead Discovery and Optimization, Bristol Myers Squibb. Co., Princeton, New Jersey, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicole","family":"Diaz","sequence":"additional","affiliation":[{"name":"Spectrix Analytical Services, North Haven, Connecticut, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary Ellen","family":"Cvijic","sequence":"additional","affiliation":[{"name":"Lead Discovery and Optimization, Bristol Myers Squibb. Co., Princeton, New Jersey, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wilson","family":"Shou","sequence":"additional","affiliation":[{"name":"Lead Discovery and Optimization, Bristol Myers Squibb. 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