Ferdinand Biermann

Induced pluripotent stem cell (iPSC)-derived mesenchymal stem cells (iMSCs) are amenable for use in a clinical setting for treatment of osteoarthritis (OA), which remains one of the major illnesses worldwide. Aside from iPSC-derived iMSCs, chondrocytes (iCHO) and extracellular vesicles (EV) are also promising candidates for treatment of OA. Manufacturing and quality control of iPSC-derived therapies is mainly manual and thus highly time consuming and susceptible to human error. A major challenge in translating iPSC-based treatments more widely is the lack of sufficiently scaled production technologies from seeding to fill-and-finish. Formerly, the Autostem platform was developed for the expansion of tissue-derived MSCs at scale in stirred tank bioreactors and subsequent fill-and-finish. Additionally, the StemCellDiscovery platform was developed to handle plate-based cultivation of adherent cells including their microscopic analysis. By combining the existing automation technology of both platforms, all required procedures can be integrated in the AutoCRAT system, designed to handle iPSC expansion, differentiation to iMSCs and iCHOs, pilot scale expansion, and formulation of iMSCs as well as extracellular vesicles and their purification. Furthermore, the platform is equipped with several in-line and at-line assays to determine product quality, purity, and safety. This paper highlights the need for adaptable and modular automation concepts. It also stresses the importance of ensuring safety of generated therapies by incorporating automated release testing and cleaning solutions in automated systems. The adapted platform concepts presented here will help translate these technologies for clinical production at the necessary scale.

The automation of cell production processes demands strict requirements with regard to sterility, reliability, and flexibility. Robots work in such environments as transporting devices for a huge variety of disposables, e.g., cell plates, tubes, cassettes, and other objects. Therefore, the blades of their grippers must be designed to hold all of these different materials in a stable, gentle manner, and in defined positions, which means that the blades require complex geometries. Furthermore, they should have as few edges as possible, so as to be easy to clean. In this report, we demonstrate how these requirements can be met by producing stainless steel robot grippers by additive manufacturing.

The constantly growing interest and range of applications of advanced cell, gene and regenerative therapies raise the need for efficient production of biological material and novel treatment technologies. Many of the production and manipulation processes of such materials are still manual and, therefore, need to be transferred to a fully automated execution. Developers of such systems face several challenges, one of which is mechanical and communication interfaces in biotechnological devices. In the present state, many devices are still designed for manual use and rarely provide a connection to external software for receiving commands and sending data. However, a trend towards automation on the device market is clearly visible, and the communication protocol, Open Platform Communications Data Access (OPC DA), seems to become established as a standard in biotech devices. A rising number of vendors offer software for device control and automated processing, some of which even allow the integration of devices from multiple manufacturers. The high, application-specific need in functionalities, flexibility and adaptivity makes it difficult to find the best solution and, in many cases, leads to the creation of new custom-designed software. This report shall give an overview of existing technologies, devices and software for laboratory automation of biotechnological processes. Furthermore, it presents an outlook for possible future developments and standardizations.

Laboratory automation is a key driver in biotechnology and an enabler for powerful new technologies and applications. In particular, in the field of personalized therapies, automation in research and production is a prerequisite for achieving cost efficiency and broad availability of tailored treatments. For this reason, we present the StemCellDiscovery, a fully automated robotic laboratory for the cultivation of human mesenchymal stem cells (hMSCs) in small scale and in parallel. While the system can handle different kinds of adherent cells, here, we focus on the cultivation of adipose-derived hMSCs. The StemCellDiscovery provides an in-line visual quality control for automated confluence estimation, which is realized by combining high-speed microscopy with deep learning-based image processing. We demonstrate the feasibility of the algorithm to detect hMSCs in culture at different densities and calculate confluences based on the resulting image. Furthermore, we show that the StemCellDiscovery is capable of expanding adipose-derived hMSCs in a fully automated manner using the confluence estimation algorithm. In order to estimate the system capacity under high-throughput conditions, we modeled the production environment in a simulation software. The simulations of the production process indicate that the robotic laboratory is capable of handling more than 95 cell culture plates per day.