{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T11:39:44Z","timestamp":1792669184060,"version":"4.5.0"},"reference-count":34,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biotech &amp;amp; Bioengineering"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Mechanistic modeling is important in biopharmaceutical manufacturing by enabling in\u2010silico experimentation. However, developing a robust mechanistic model for mammalian cell culture remains challenging due to the complexity of the underlying bioprocesses. Existing data\u2010driven models often suffer from limited extrapolation capability and require extensive datasets. Not all modeling approaches can accurately represent differing metabolic scenarios such as lactate consumption and production. To address these limitations, this study proposes a hybrid mechanistic model for mammalian cell culture to improve predictive accuracy and process understanding. Our model integrates high\u2010frequency pH data with low\u2010frequency measurements of viable cell density (VCD), glucose, lactate, monoclonal antibody (mAb) concentrations from 10 runs (7\u2009L). Novel kinetic equations for cell growth and death, and lactate metabolism considering both scenarios were developed. Additionally, our model enables automatic lactate switch detection based on time\u2010series pH and feeding data. It demonstrated high predictive capability with a mean\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    of 0.88 for VCD, mAb, glucose, and lactate concentrations across training runs. Real\u2010time fine\u2010tuning further improved accuracy, reducing the root mean squared error (RMSE) for glucose by ~50% and for lactate by ~30%. The results indicate that our model provides accurate real\u2010time predictions of VCD, mAb, glucose, and lactate concentrations under variable bioprocessing conditions.\n                  <\/jats:p>","DOI":"10.1002\/bit.70417","type":"journal-article","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T10:54:18Z","timestamp":1792666458000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Mechanistic Model Capturing Lactate Run\u2010Away and Back\u2010Metabolism in Mammalian Cell Culture"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0448-5397","authenticated-orcid":false,"given":"Li","family":"Qin","sequence":"first","affiliation":[{"name":"Department of Biochemical Engineering University College London London 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