Current digital twin models for mammalian cell culture lack broad validation because they are trained on limited and narrowly varied datasets, and they do not fully capture key metabolic and glycosylation mechanisms. In addition, industry lacks the deep, high‑dimensional, longitudinal datasets needed to link media composition, metabolite dynamics, and product quality attributes, making it difficult to verify and refine these models under real manufacturing conditions. As a result, manufacturers need more predictive, reliable tools that can reduce experimental workload, enable scenario analysis, and accelerate process development timelines.
The project strengthens and validates UD’s existing metabolic and glycosylation digital‑twin models by expanding their mechanistic scope and improving predictive accuracy, leveraging Waters’ rich, high‑dimensional datasets that include extensive metabolite profiles, CQAs, and bioreactor analytics. It then uses closed‑loop MBDOE experiments to test and refine model‑derived predictions, ensuring real industrial applicability. The final outcome is a deployment‑ready digital‑twin framework supported by standardized data formats, workflows, and a controlled‑access NIIMBL repository for industry use.
Strong predictive tools will reduce experimentation cycles and enable risk-informed decision making
Improved prediction of glycosylation outcomes leads to more consistent biologic drug quality
The project delivers a predictive digital‑twin platform that reduces experimental burden, improves product quality, and accelerates bioprocess development through mechanistic modeling and partner‑validated workflows. By providing reproducible tools, standardized data, and demonstrated predictive accuracy, it strengthens industry adoption and enhances NIIMBL member competitiveness.
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University of Delaware
Sanofi
Waters Technologies Corporation
