{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T06:03:18Z","timestamp":1789538598815,"version":"build-2803163510"},"reference-count":48,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100004319","name":"Pfizer","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004319","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aiche.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biotechnology Progress"],"abstract":"Abstract<\/jats:title>\n The deployment of advanced modeling and machine learning in biopharmaceutical process control has been limited by legacy fragmented automation infrastructure and a lack of model lifecycle governance. We present BioOps, a modular automation framework for biopharmaceutical advanced process control (APC). The framework incorporates established Machine Learning Operations (MLOps) principles, including version\u2010controlled model registries, containerized deployment, and continuous integration\/continuous delivery (CI\/CD) pipelines. BioOps explicitly decouples model development from model execution within the control architecture. This decoupling enables process scientists to deploy, test, and iteratively refine mathematical models and control strategies in a flexible manner. These workflows can be applied across heterogeneous bioreactor platforms and are executed entirely in\u2010house, without reliance on vendor\u2010specific solutions or custom system integration. In addition, BioOps embeds traceability, auditability, and model lifecycle management directly into the control stack. As a result, the framework aligns with regulatory expectations while continuing to support agile, in\u2010house experimentation and development. Case studies across a geographically distributed set of bioreactors demonstrate BioOps' versatility in PAT\u2010driven feedback control, hybrid model\u2010based nutrient and glucose feeding, adaptive phase transitions, and cross\u2010scale deployment. By operationalizing model\u2010centric control through an MLOps\u2010inspired architecture, BioOps provides a practical foundation for scalable, reproducible, and future\u2010ready biomanufacturing.<\/jats:p>","DOI":"10.1002\/btpr.88556","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:16:56Z","timestamp":1789535816000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BioOps<\/scp>\n : Enabling\n MLOps<\/scp>\n for scalable and flexible model\u2010centric process control in biopharmaceutical development"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4630-9287","authenticated-orcid":false,"given":"Shu","family":"Yang","sequence":"first","affiliation":[{"name":"Manufacturing Intelligence & Technology Transformation Pfizer Inc Parsippany New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edwin 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