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The introduction of AI in healthcare promises benefits, but also faces challenges. Currently, one of these challenges is the lack of information on the societal aspects of implementing AI in healthcare. This study aims to: 1) identify which societal factors play a key role in the implementation of AI-driven technology in (smart) hospitals according to different stakeholder groups; 2) examine how these factors play a role within (smart) hospitals by discussing their facilitators, barriers, possibilities, and preconditions; and 3) develop a societal guide to serve as a roadmap for an implementation process of AI in a healthcare setting. A survey was conducted, followed by four focus group interviews (FGIs). In the survey, participants (n = 7) assessed the relevance of factors for inclusion in the FGIs using a rating scale from 1 to 5 (1 = irrelevant, 5 = relevant). In each FGI, 2-3 participants discussed how these societal factors play a role in the implementation of AI technology in (smart) hospitals. By combining and categorizing these insights, a societal guide was set up to provide a structured approach for implementation of AI-driven healthcare innovation. The survey revealed that 9 out of 10 proposed factors were considered relevant (90%). The FGIs demonstrated uncertainty surrounding the (future) use of AI technologies within (smart) hospitals. As this field is still in its early stages, there are limited established methodologies and (regulatory and ethical) frameworks for implementation. While much knowledge exists on different factors concerning AI in (smart) hospitals, this knowledge is often siloed. This knowledge must be integrated across stakeholders to adequately prepare for the deployment of AI technologies. The societal guide developed addresses ethical and regulatory considerations, while also covering important human-centred factors for AI implementation in healthcare. Engaging various stakeholders throughout different phases of AI implementation in (smart) hospitals (i.e., development, implementation, monitoring and evaluation phase) is key for fostering a collaborative approach. Recognizing the interdependence and collective impact of factors is essential for creating a successful implementation trajectory.

CAR-T cell therapy is a promising treatment for acute leukemia and lymphoma. CAR-T cell therapies take a pioneering role in autologous gene therapy with three EMA-approved products. However, the chance of clinical success remains relatively low as the applicability of CAR-T cell therapy suffers from long, labor-intensive manufacturing and a lack of comprehensive insight into the bioprocess. This leads to high manufacturing costs and limited clinical success, preventing the widespread use of CAR-T cell therapies. New manufacturing approaches are needed to lower costs to improve manufacturing capacity and shorten provision times. Semi-automated devices such as the Miltenyi Prodigy ® were developed to reduce hands-on production time. However, these devices are not equipped with the process analytical technology necessary to fully characterize and control the process. An automated AI-driven CAR-T cell manufacturing platform in smart manufacturing hospitals (SMH) is being developed to address these challenges. Automation will increase the cost-effectiveness and robustness of manufacturing. Using Artificial Intelligence (AI) to interpret the data collected on the platform will provide valuable process insights and drive decisions for process optimization. The smart integration of automated CAR-T cell manufacturing platforms into hospitals enables the independent manufacture of autologous CAR-T cell products. In this perspective, we will be discussing current challenges and opportunities of the patient-specific but highly automated, AI-enabled CAR-T cell manufacturing. A first automation concept will be shown, including a system architecture based on current Industry 4.0 approaches for AI integration.

Mesenchymal stem/stromal cells (MSCs) have been identified as a promising therapeutic option for osteoarthritis, graft vs. host disease and cardiovascular diseases, among others. For widespread application of these therapies, robust and scaled manufacturing processes are required that reliably yield high amounts of high quality MSCs. One of the primary challenges in MSC manufacturing is achieving robustness, due to the high donor-to-donor and batch-to-batch variability seen in MSC manufacturing. To achieve more consistent manufacturing, standardization of the manufacturing process and analytical methods to determine cell quality and control process parameters will be needed. Traditionally, MSCs are cultivated in two dimensional (2D) systems, such as flasks or plates. However, these systems are limited in their scalability. To enhance volumetric productivity, upscaling may be achieved using agitated bioreactors where the MSCs are grown on microcarriers or other types of scaffolds. In this article, we have reviewed existing publications on the manufacturing of MSCs in agitated bioreactor systems regarding the process conditions used and the quality parameters measured to define more clearly the most relevant cell quality and process parameters. Key cell quality parameters measured are cell number and viability, immunophenotype and differentiation potential, while key process parameters include the cultivation system (cell source, bioreactor type, media composition), physiochemical properties of the media such as pH and dissolved oxygen (DO), as well as nutrient supply. Defining these parameters more clearly will support the development of robust MSC manufacturing processes at scale using improved process control and facilitate the widespread clinical application of MSC-based cell therapies.

Induced pluripotent stem cells (iPSC) open up the unique perspective of manufacturing cell products for drug development and regenerative medicine in tissue-, disease- and patient-specific forms. iPSC can be multiplied almost without restriction and differentiated into cell types of all organs. The basis for clinical use of iPSC is a high number of cells (approximately 7 × 107 cells per treatment), which must be produced cost-effectively while maintaining reproducible and high quality. Compared to manual cell production, the automation of cell production offers a unique chance of reliable reproducibility of cells in addition to cost reduction and increased throughput. StemCellFactory is a prototype for a fully automated production of iPSC. However, in addition to the already tested functionality of the system, it must be shown that this automation brings necessary economic advantages. This paper presents that fully automated stem cell production offers economic advantages in addition to increased throughput and better quality. First, biological and technological basics for a fully automated production of iPSC are presented. Second, the basics for profitability calculation are presented. Third, profitability of both manual and automated production are calculated. Finally, different scenarios effecting the profitability of manual and automated production are compared.

Background Non-invasive online fluorescence monitoring in high-throughput microbioreactors is a well-established method to accelerate early-stage bioprocess development. Recently, single-wavelength fluorescence monitoring in microtiter plates was extended to measurements of highly resolved 2D fluorescence spectra, by introducing charge-coupled device (CCD) detectors. Although introductory experiments demonstrated a high potential of the new monitoring technology, an assessment of the capabilities and limits for practical applications is yet to be provided. Results In this study, three experimental sets introducing secondary substrate limitations of magnesium, potassium, and phosphate to cultivations of a GFP-expressing H. polymorpha strain were conducted. This increased the complexity of the spectral dynamics, which were determined by 2D fluorescence measurements. The metabolic responses upon growth limiting conditions were assessed by monitoring of the oxygen transfer rate and extensive offline sampling. Using only the spectral data, subsequently, partial least-square (PLS) regression models for the key parameters of glycerol, cell dry weight, and pH value were generated. For model calibration, spectral data of only two cultivation conditions were combined with sparse offline sampling data. Applying the models to spectral data of six cultures not used for calibration, resulted in an average relative root-mean-square error (RMSE) of prediction between 6.8 and 6.0%. Thus, while demanding only sparse offline data, the models allowed the estimation of biomass accumulation and glycerol consumption, even in the presence of more or less pronounced secondary substrate limitation. Conclusion For the secondary substrate limitation experiments of this study, the generation of data-driven models allowed a considerable reduction in sampling efforts while also providing process information for unsampled cultures. Therefore, the practical experiments of this study strongly affirm the previously claimed advantages of 2D fluorescence spectroscopy in microtiter plates.

The A-Cell Case Study published by the “Alliance of Regenerative Medicine” illustrates how Quality-by Design can be applied to the manufacturing of Advanced Therapeutical Medicinal Products (ATMPs), using Chimeric Antigen Receptor (CAR)-T cell therapy as a ‘model’ process. However, no emphasis is given to different degrees of automation in this study. CAR-T cell therapies have been developed for various forms of leukemia, such as Acute Lymphoblastic Leukemia (ALL) or Non Hodgkin-Lymphoma (NHL). As more CAR-T cell therapies reach market approval and are being considered as first- or second line treatments, the economic efficiency and scalability of the chosen production modality become increasingly critical. Currently, academic and industrial manufacturers employ a range of approaches, from fully manual and open processing to closed and automated systems. New technologies, investments and cleanroom space requirements must be considered to assess economic and spatial efficiency in cell therapy manufacturing. This study analyses the costs and space requirements of different production modalities for autologous CAR-T cell production. The analysis shows that a higher degree of automation can reduce manufacturing costs by lowering personnel costs, cleanroom grade requirements and spatial footprint. It emphasizes the importance of maximizing cleanroom efficiency to support the scalable production of cell therapies as clinical demand grows. These results underscore the need for both industry and academia to consider automated production as a strategic approach to optimize resource use in CAR-T cell manufacturing.

Advanced Therapy Medicinal Products (ATMP) provide promising treatment options particularly for unmet clinical needs, such as progressive and chronic diseases where currently no satisfying treatment exists. Especially from the ATMP subclass of Tissue Engineered Products (TEPs), only a few have yet been translated from an academic setting to clinic and beyond. A reason for low numbers of TEPs in current clinical trials and one main key hurdle for TEPs is the cost and labor-intensive manufacturing process. Manual production steps require experienced personnel, are challenging to standardize and to scale up. Automated manufacturing has the potential to overcome these challenges, toward an increasing cost-effectiveness. One major obstacle for automation is the control and risk prevention of cross contaminations, especially when handling parallel production lines of different patient material. These critical steps necessitate validated effective and efficient cleaning procedures in an automated system. In this perspective, possible technologies, concepts and solutions to existing ATMP manufacturing hurdles are discussed on the example of a late clinical phase II trial TEP. In compliance to Good Manufacturing Practice (GMP) guidelines, we propose a dual arm robot based isolator approach. Our novel concept enables complete process automation for adherent cell culture, and the translation of all manual process steps with standard laboratory equipment. Moreover, we discuss novel solutions for automated cleaning, without the need for human intervention. Consequently, our automation concept offers the unique chance to scale up production while becoming more cost-effective, which will ultimately increase TEP availability to a broader number of patients.

The introduction of AI in healthcare promises benefits, but also faces challenges. Currently, one of these challenges is the lack of information on the societal aspects of implementing AI in healthcare. This study aims to: 1) identify which societal factors play a key role in the implementation of AI-driven technology in (smart) hospitals according to different stakeholder groups; 2) examine how these factors play a role within (smart) hospitals by discussing their facilitators, barriers, possibilities, and preconditions; and 3) develop a societal guide to serve as a roadmap for an implementation process of AI in a healthcare setting. A survey was conducted, followed by four focus group interviews (FGIs). In the survey, participants (n = 7) assessed the relevance of factors for inclusion in the FGIs using a rating scale from 1 to 5 (1 = irrelevant, 5 = relevant). In each FGI, 2-3 participants discussed how these societal factors play a role in the implementation of AI technology in (smart) hospitals. By combining and categorizing these insights, a societal guide was set up to provide a structured approach for implementation of AI-driven healthcare innovation. The survey revealed that 9 out of 10 proposed factors were considered relevant (90%). The FGIs demonstrated uncertainty surrounding the (future) use of AI technologies within (smart) hospitals. As this field is still in its early stages, there are limited established methodologies and (regulatory and ethical) frameworks for implementation. While much knowledge exists on different factors concerning AI in (smart) hospitals, this knowledge is often siloed. This knowledge must be integrated across stakeholders to adequately prepare for the deployment of AI technologies. The societal guide developed addresses ethical and regulatory considerations, while also covering important human-centred factors for AI implementation in healthcare. Engaging various stakeholders throughout different phases of AI implementation in (smart) hospitals (i.e., development, implementation, monitoring and evaluation phase) is key for fostering a collaborative approach. Recognizing the interdependence and collective impact of factors is essential for creating a successful implementation trajectory.

With the utilization of small-scale and highly parallelized cultivation platforms embedded in laboratory robotics, microbial phenotyping and bioprocess development have been substantially accelerated, thus generating a bottleneck in bioanalytical bioprocess sample analytics. While microscale cultivation platforms allow the monitoring of typical process parameters, only limited information about product and by-product formation is provided without comprehensive analytics. The use of liquid chromatography mass spectrometry can provide such a comprehensive and quantitative insight, but is often limited by analysis runtime and throughput. In this study, we developed and evaluated six methods for amino acid quantification based on two strong cation exchanger columns and a dilute and shoot approach in hyphenation with either a triple-quadrupole or a quadrupole time-of-flight mass spectrometer. Isotope dilution mass spectrometry with 13C15N labeled amino acids was used to correct for matrix effects. The versatility of the methods for metabolite profiling studies of microbial cultivation supernatants is confirmed by a detailed method validation study. The methods using chromatography columns showed a linear range of approx. 4 orders of magnitude, sufficient response factors, and low quantification limits (7–443 nM) for single analytes. Overall, relative standard deviation was comparable for all analytes, with < 8% and < 11% for unbuffered and buffered media, respectively. The dilute and shoot methods with an analysis time of 1 min provided similar performance but showed a factor of up to 35 times higher throughput. The performance and applicability of the dilute and shoot method are demonstrated using a library of Corynebacterium glutamicum strains producing l-histidine, obtained from random mutagenesis, which were cultivated in a microscale cultivation platform.