Multipath Adaptive Gated Bottleneck Latent ODE with Raman Data Fusion for Cell Culture Process Forecasting
arXiv:2606.26520v1 Announce Type: new Abstract: Mammalian cell-culture processes underpin the manufacture of many biopharmaceuticals, yet keeping a run on track is hard: critical process parameters drift over days, and an off-specification trend is often confirmed too late to intervene. Early-stage, multi-day forecasts could enable timely adjustment of feeding, sampling, and control, but bioprocess forecasting is challenging because measurements are sparse and irregularly sampled, operating conditions are heterogeneous across cell lines and media, and runs with near-identical early behaviour c
The increasing complexity and demand for biopharmaceuticals are driving innovation in process monitoring and control, making AI-driven solutions crucial for efficiency and quality in cell culture.
This development can significantly improve the predictability and control of biomanufacturing, reducing costs and accelerating drug development for sophisticated readers in the pharmaceutical and biotech sectors.
Traditional reactive monitoring is replaced by proactive, multi-day forecasting capabilities in biopharmaceutical production, enabled by advanced AI and multimodal data fusion.
- · Biopharmaceutical companies
- · Biotech AI/ML startups
- · Patients needing biopharmaceuticals
- · Contract Development and Manufacturing Organizations
- · Companies relying on outdated bioprocess monitoring
- · Inefficient biomanufacturing processes
More efficient and cost-effective production of biopharmaceuticals becomes possible.
Accelerated development and market entry for new biological drugs due to improved R&D and manufacturing pipelines.
The democratization of advanced biomanufacturing leads to a broader availability of complex therapies globally.
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