Astrid Dürauer

Recombinant adeno‐associated virus (rAAV) vectors have become the leading platform for in vivo gene delivery, yet their industrial‐scale production remains inefficient and costly. Current manufacturing, which primarily relies on transient triple‐plasmid transfection in HEK293‐derived cells, is limited by low vector yields, high empty‐to‐full capsid ratios, and pronounced batch‐to‐batch variability. To contribute to the understanding of underlying host factors, we compared transcriptomic profiles of five HEK293‐derived producer lines with varying rAAV yields over a 72‐hour production time course. The low‐producing BalCD line showed poor transfection efficiency, reduced plasmid‐derived transcript levels, exclusive interferon expression and a rapid activation of interferon‐stimulated genes (ISG), indicating strong antiviral restriction for vector productivity. In contrast, high‐producing lines mounted a reduced inflammatory and innate immune response with limited induction of antiviral genes, supporting sustained vector production. Expression patterns of high‐producing cell lines further exhibited transcriptional signatures resembling those seen for producer cells arrested in the G0/G1 cell cycle phase, which potentially favors efficient vector genome replication and capsid assembly. The observed differences in the regulation of antiviral and cell cycle–associated pathways highlight key molecular determinants of rAAV productivity and provide a foundation for targeted host engineering strategies aimed at enhancing rAAV manufacturing efficiency.

Stable cell lines have recently achieved recombinant adeno-associated virus (rAAV) titers comparable to the standard triple transfection approach, making them a promising alternative to plasmid-based production systems. However, whether integration of the rAAV transgene into the host genome influences packaging efficiency and vector quality remains unclear. In this study, we generated stable HEK293 cell lines carrying the rAAV transgene in their genome. rAAV production was enabled by supplying the rep/cap and helper genes on two plasmids, rendering vector genome generation dependent on the chromosomally integrated transgene. Although the stable cell lines produced a 4.5-fold lower titer of viral genomes (VGs) compared to the standard triple transfection method, VG-normalized potency was four times higher. Detailed particle characterization further revealed 3-fold lower plasmid backbone DNA packaging in rAAVs produced by stable cell lines relative to triple transfection. Consistent results were obtained from mass photometry and ELISA/ddPCR analyses for the double transfection condition, while discrepancies emerged under triple transfection. These findings emphasize the importance of functional and qualitative assessments for evaluating different rAAV production approaches.

Human embryonic kidney cells HEK293 are widely used in biopharmaceutical manufacturing, with a recent surge particularly in recombinant adeno‐associated virus production. Despite their industrial relevance, comprehensive data on their genomic background and stability remains limited. Here, we systematically analyze the genetic landscape of various HEK293 cell lines in response to cultivation conditions, clonal selection, genetic manipulation and over time in culture. Adherent HEK293 were adapted to suspension growth using different serum‐free media. Whole genome sequences from these cell lines were analyzed together with previously published data from additional variants in common use. All data sets were aligned against the human reference genome, enabling the assessment of genome stability by evaluation of variants and revealing a conserved genetic core across all lines, regardless of cultivation history or phenotypic divergence. Evaluation of the functional implications of conserved core mutations identified an enrichment in genes related to cellular structure, morphology and cellular connectivity. The distribution of structural variants and single nucleotide polymorphisms indicated a gradual accumulation of mutations over time in culture rather than abrupt shifts in response to environmental changes. Notably, the integrated adenoviral genes remained highly conserved with respect to copy number, integration site and sequence integrity. These findings provide insight into the genomic evolution of HEK293 cells and offer a foundation for further multi‐omics studies aimed at optimizing HEK293 cells for applications in biopharmaceutical production.

BACKGROUND: Adeno‐Associated Virus vectors are widely used in gene therapy due to their safety and efficacy. Despite the interest in their therapeutical application, AAV purification remains a significant challenge. Density gradient ultracentrifugation using cesium chloride, sucrose, or iodixanol gradients are common methods for AAV purification and polishing. However, when conventional ultracentrifuges are used, density gradient ultracentrifugation is not scalable and time‐consuming. To overcome these limitations, this study applies density gradient ultracentrifugation using a continuous flow ultracentrifuge which allows the development of scalable and robust AAV purification methods. RESULTS: Our results showed that continuous flow ultracentrifugation with both iodixanol and sucrose density gradients is a suitable method for AAV capture. Sucrose density gradient allowed for the recovery of 72% full AAV particles, however, poor resolution of full and empty AAV particles and co‐purification of host cell derived impurities were observed. Iodixanol density gradient allowed for the recovery of 68% full AAV particles as well as enrichment of full AAV particles in the higher density fractions. Moreover, separation of AAV particles from host cell derived impurities was achieved with the iodixanol density gradient. Nevertheless, chromatin fragments were still present. CONCLUSIONS: The findings of this study highlight the possibility of development of scalable AAV capture methods based on density gradient ultracentrifugation using continuous flow ultracentrifuges. © 2025 The Author(s). Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).

(1) Recombinant protein production in mammalian cells is either based on transient transfection processes, often inefficient and underlying high batch-to-batch variability, or on laborious generation of stable cell lines. Alternatively, BacMam, a transduction process using the baculovirus, can be employed. (2) Six transfecting agents were compared to baculovirus transduction in terms of transient and stable protein expression characteristics of the model protein ACE2-eGFP using HEK293-6E, CHO-K1, and Vero cell lines. Furthermore, process optimization such as expression enhancement using sodium butyrate and TSA or baculovirus purification was assessed. (3) Baculovirus transduction efficiency was superior to all transfection agents for all cell lines. Transduced protein expression was moderate, but an 18-fold expression increase was achieved using the enhancer sodium butyrate. Ultracentrifugation of baculovirus from a 3.5 L bioreactor significantly improved the transduction efficiency and protein expression. Stable cell lines were obtained with each baculovirus transduction, yet stable cell line generation after transfection was highly unreliable. (4) This study demonstrated the superiority of the BacMam platform to standard transfections. The baculovirus efficiently transduced an array of cell lines both transiently and stably and achieved the highest efficiency for all tested cell lines. The feasibility of the scale-up of baculovirus production was demonstrated and the possibility of baculovirus purification was successfully explored.

The application of model‐based real‐time monitoring in biopharmaceutical production is a major step toward quality‐by‐design and the fundament for model predictive control. Data‐driven models have proven to be a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness, and reliability. That is only possible when (i) the data used for modeling is of high quality and sufficient size, (ii) state‐of‐the‐art modeling algorithms are employed, and (iii) the input‐output mapping of the model has been characterized. In this study, we evaluate the accuracy of multiple data‐driven models in predicting the monoclonal antibody (mAb) concentration, double stranded DNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. The models achieved high‐quality predictions with a normalized root mean squared error of <4% for the mAb concentration and of ≈10% for the other process variables. Furthermore, we demonstrate how permutation/occlusion‐based methods can be used to gain an understanding of dependencies learned by one of the most complex data‐driven models, convolutional neural network ensembles. We observed that the models generally exhibited dependencies on correlations that agreed with first principles knowledge, thereby bolstering confidence in model reliability. Finally, we present a workflow to assess the model behavior in case of systematic measurement errors that may result from sensor fouling or failure. This study represents a major step toward improved viability of data‐driven models in biopharmaceutical manufacturing.

The biopharmaceutical industry is still running in batch mode, mostly because it is highly regulated. In the past, sensors were not readily available and in‐process control was mainly executed offline. The most important product parameters are quantity, purity, and potency, in addition to adventitious agents and bioburden. New concepts using disposable single‐use technologies and integrated bioprocessing for manufacturing will dominate the future of bioprocessing. To ensure the quality of pharmaceuticals, initiatives such as Process Analytical Technologies, Quality by Design, and Continuous Integrated Manufacturing have been established. The aim is that these initiatives, together with technology development, will pave the way for process automation and autonomous bioprocessing without any human intervention. Then, real‐time release would be realized, leading to a highly predictive and robust biomanufacturing system. The steps toward such automated and autonomous bioprocessing are reviewed in the context of monitoring and control. It is possible to integrate real‐time monitoring gradually, and it should be considered from a soft sensor perspective. This concept has already been successfully implemented in other industries and requires relatively simple model training and the use of established statistical tools, such as multivariate statistics or neural networks. This review describes a scenario for integrating soft sensors and predictive chemometrics into modern process control. This is exemplified by selective downstream processing steps, such as chromatography and membrane filtration, the most common unit operations for separation of biopharmaceuticals.

Model-based real-time monitoring of biopharmaceutical production is a major step towards Quality-by-Design in this field and the fundament for model predictive control. Data-driven models have been proven a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness and explainability. That is only possible when (i) the data used for modeling is of high quality, (ii) state-of-the-art modeling algorithms are employed and (iii) the input-output mapping of the model has been characterized. In this study we evaluate the accuracy of multiple data-driven models in predicting the monoclonal antibody concentration, dsDNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. We demonstrate how permutation/occlusion-based methods can be used to gain understanding on dependencies learned by of one of the most complex data-driven models; convolutional neural network ensembles. Finally, we present a workflow to test the model behavior in case of simulated sensor fouling and failure. This study represents a major step towards improved viability of data-driven models in biopharmaceutical manufacturing.