Techniques to provide in vitro tissue culture have undergone significant changes during the last decades, and current applications involve interactions of cells and organoids, three-dimensional cell co-cultures, and organ/body-on-chip tools. Efficient computer-aided and mathematical model-based methods are required for efficient and knowledge-driven characterization, optimization, and routine manufacturing of tissue culture systems. As an alternative to purely experimental-driven research, the usage of comprehensive mathematical models as a virtual in silico representation of the tissue culture, namely a digital twin, can be advantageous. Digital twins include the mechanistic of the biological system in the form of diverse mathematical models, which describe the interaction between tissue culture techniques and cell growth, metabolism, and the quality of the tissue. In this review, current concepts, expectations, and the state of the art of digital twins for tissue culture concepts will be highlighted. In general, DT’s can be applied along the full process chain and along the product life cycle. Due to the complexity, the focus of this review will be especially on the design, characterization, and operation of the tissue culture techniques.

Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four‐dimensional antibody‐producing CHO fed‐batch process are compared. Overall, 33 experiments are performed in the fractional factorial four‐dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling.

The human skin is involved in protecting the inner body from constant exposure to outer environmental stimuli. There is an evident need to screen for toxicity and the efficacy of drugs and cosmetics applied to the skin. To date, animal studies are still the standard method for substance testing, although they are currently controversially discussed Therefore, the multi-organ chip is an attractive alternative to replace animal testing. The two-organ chip is designed to hold 96-well cell culture inserts (CCIs). Small-sized skin equivalents are needed for this. In this study, full-thickness skin equivalents (ftSEs) were generated successfully inside 96-well CCIs. These skin equivalents developed with in vivo-like histological architecture, with normal differentiation marker expressions and proliferation rates. The 96-well CCI-based ftSEs were successfully integrated into the two-organ chip. The permeation of fluorescein sodium salt through the ftSEs was monitored during the culture. The results show a decreasing value for the permeation over time, which seems a promising method to track the development of the ftSEs. Additionally, the permeation was implemented in a computational fluid dynamics simulation, as a tool to predict results in long-term experiments. The advantage of these ftSEs is the reduced need for cells and substances, which makes them more suitable for high throughput assays.