Real‐Time VCC Monitoring and Forecasting in HEK‐Cell‐Based rAAV Vector Production Using Capacitance Spectroscopy
Abstract
Recombinant adeno‐associated virus (rAAV) vector production is a complex process in which the robust cultivation of human embryonic kidney cells (HEK293) plays a critical role in generating high‐quality viral vectors. Tracking the viable cell concentration (VCC) during upstream production is essential for process monitoring and for implementing actions that ensure optimal process management. The advent of inline capacitance probes has introduced a crucial process analytical technology (PAT) tool for real‐time VCC measurement. Here, we present the development and application of a method for real‐time monitoring of VCC in HEK293‐based rAAV vector production. In a first step, BioPAT Viamass probes were used to record capacitance data of individual 10 L rAAV‐8 batches within a frequency range of 50 kHz–20 MHz. Based on the capacitance data, a linear single‐frequency model and an orthogonal partial least square (OPLS) multifrequency model for VCC prediction were developed. Subsequently, these models were deployed inline, and predictions were exposed into BioPAT MFCS bioprocess control software, enabling real‐time VCC monitoring in subsequent rAAV‐8 production batches. In addition, the continuous VCC signal was used as input for an exponential cell growth model that was deployed inline to provide accurate real‐time forecasting of the transfection time point. To the best of our knowledge, this is the first example of inline deployment of VCC and Time‐Till‐Transfection predictive models to the bioprocess control system for real‐time monitoring and forecasting of these parameters in HEK‐cell‐based transient rAAV vector production.
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A comparable integration concept using Node-RED and OPC UA for real-time soft-sensor deployment in an AAV production process has previously been demonstrated by Machleid et al. (2025), establishing the feasibility of this framework for data-driven model execution in bioprocessing environments.
“A comparable integration concept using Node‑RED and OPC UA for real‑time soft‑sensor deployment in an AAV production process has previously been demonstrated by Machleid et al. (2025) establishing the feasibility of this framework for data‑driven model execution in bioprocessing environments [30].”
From Mechanistic model for HEK293 viral vector processes and its application in a digital shadow framework (2026) · cites this paper as [30]
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Kuchemüller, K. B., Austerjost, J., Machleid, R., Roßkamp, A., Niemann, J., & McCready, C. (2026) · Bioprocess and Biosystems Engineering · doi:10.1007/s00449-026-03362-2
Self-citation
- Authors
- Machleid, R., Nunna, S., George, A., Austerjost, J., Tomala, M., & Surowiec, I.
- Affiliations
- Sartorius Stedim Biotech GmbH Advanced Analytics & Spectroscopy, Embedded Hardware Göttingen Germany
- Sartorius Stedim Biotech GmbH Application Testing Separation Consumables – Upstream Göttingen Germany
- Sartorius Stedim Biotech Advanced BioAnalytics, Corporate Research Göttingen Germany
- Sartorius Stedim Data Analytics AB Advanced Analytics & Spectroscopy, Embedded Hardware Umeå Sweden
- Venue
- Engineering in Life Sciences
- Publisher
- Wiley
- Published
- 2025-02
- Volume
- 25
- Issue
- 2
- ISSN
- 1618-0240
- 1618-2863
- PMC
- PMC11802648
- Cited by
- 4
- References
- 0
- Language
- en
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