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<p>A mechanistic model describing HEK293 cell growth and viral vector production was developed and calibrated for adenoviral processes and subsequently transferred to adeno‑associated and lentiviral cultures. The model represents key cellular states—viable, dead, and lysed cells—linked through substrate consumption, metabolic inhibition, and virus‑specific toxicity terms. By adjusting a limited set of parameters, the same mechanistic structure, combined with vector specific parameter sets, could accurately reproduce distinct infection and transfection dynamics, demonstrating cross‑platform applicability. As a demonstrator for real‑time deployment, the mechanistic framework was combined with an orthogonal projections to latent structures model based on dielectric spectroscopy data to form a Digital Shadow. The soft‑sensor layer provided real‑time viable cell density values that continuously updated the mechanistic simulation, yielding online predictions of culture growth and infection timing. Validation with adenoviral perfusion datasets confirmed high agreement between simulated and measured viable cell concentrations. This study demonstrates that transferable mechanistic models can serve as a robust foundation for hybrid digital systems in bioprocessing. The proposed Digital Shadow exemplifies how such models can be connected to inline monitoring tools for enhanced process understanding and decision support, paving the way toward closed‑loop Digital Twin applications in viral vector manufacturing.</p>

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Mechanistic model for HEK293 viral vector processes and its application in a digital shadow framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

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  • Kim B. Kuchemüller,
  • Jonas Austerjost,
  • Rafael Machleid,
  • Alena Roßkamp,
  • Julia Niemann,
  • Chris McCready

摘要

A mechanistic model describing HEK293 cell growth and viral vector production was developed and calibrated for adenoviral processes and subsequently transferred to adeno‑associated and lentiviral cultures. The model represents key cellular states—viable, dead, and lysed cells—linked through substrate consumption, metabolic inhibition, and virus‑specific toxicity terms. By adjusting a limited set of parameters, the same mechanistic structure, combined with vector specific parameter sets, could accurately reproduce distinct infection and transfection dynamics, demonstrating cross‑platform applicability. As a demonstrator for real‑time deployment, the mechanistic framework was combined with an orthogonal projections to latent structures model based on dielectric spectroscopy data to form a Digital Shadow. The soft‑sensor layer provided real‑time viable cell density values that continuously updated the mechanistic simulation, yielding online predictions of culture growth and infection timing. Validation with adenoviral perfusion datasets confirmed high agreement between simulated and measured viable cell concentrations. This study demonstrates that transferable mechanistic models can serve as a robust foundation for hybrid digital systems in bioprocessing. The proposed Digital Shadow exemplifies how such models can be connected to inline monitoring tools for enhanced process understanding and decision support, paving the way toward closed‑loop Digital Twin applications in viral vector manufacturing.