Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency

An examination of process models and model risk frameworks for pharmaceutical manufacturing

Process models are a growing tool for pharmaceutical manufacturing process design and control. The Industry 4.0 paradigm promises to increase the amount of data available to understand manufacturing processes. Tools such as Artificial Intelligence (AI) might accelerate process development and allow better predictions of process trajectories. Several examples of process improvements realized through the application of process models have been shown in lyophilization, chromatography, fluid bed drying, bioreactor control, continuous direct compression, and wet granulation. An important consideration of implementing a process model is determining the impact of the model on the quality of the product and the risks associated with model maintenance over the product lifecycle. Several regulatory documents address risk-based considerations for process models. This work discusses existing risk-based frameworks for model validation and lifecycle maintenance that could aid the adoption of process models in pharmaceutical manufacturing. Hypothetical case studies illustrate the implications of applying a model risk framework to facilitate model validation and lifecycle maintenance in the manufacture of pharmaceuticals and biological products.

Artificial Intelligence (AI): A Potential Game Changer in Regenerative Orthopedics-A Scoping Review

Background and Aims

Regenerative orthopedics involves approaches like stem cell therapy, platelet-rich plasma (PRP) therapy, the use of biological scaffold implants, tissue engineering, etc. We aim to present a scoping review of the role of artificial intelligence (AI) in different treatment approaches of regenerative orthopedics.

Methods

Using the PRISMA guidelines, a search for articles for the last ten years (2013-2024) on PubMed was done, using several keywords. We have discussed the state-of-the-art, strengths/benefits, and limitations of the published research, and provide a useful resource for the way ahead in future for researchers working in this area.

Results

Using the eligibility criteria out of 82 initially screened publications, we included 18 studies for this review. We noticed that the treatment responses to regenerative treatments depend on several factors; hence, to facilitate better comprehensive and patient-specific treatments, AI technology is very useful. Machine learning (ML) and deep learning (DL) are a few of the most frequently used AI techniques. They use a data-driven approach for training models to make human-like decisions. Data are fed to the ML/DL algorithm and the trained model makes classifications or predictions based on its learning.

Conclusion

The area of regenerative orthopedics is highly sophisticated and significantly aids in providing cost-effective and non-invasive treatments to patients suffering from orthopedic ailments and injuries. Due to its promising future, the use of AI in regenerative orthopedics is an emerging and promising research field; however, its universal clinical applications are associated with some ethical considerations, which need addressing.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s43465-024-01189-1.

Investigation of MSC potency metrics via integration of imaging modalities with lipidomic characterization

Mesenchymal stem/stromal cell (MSC) therapies have had limited success so far in clinical trials due in part to heterogeneity in immune-responsive phenotypes. Therefore, techniques to characterize these properties of MSCs are needed during biomanufacturing. Imaging cell shape, or morphology, has been found to be associated with MSC immune responsivity-but a direct relationship between single-cell morphology and function has not been established. We used label-free differential phase contrast imaging and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to evaluate single-cell morphology and explore relationships with lipid metabolic immune response. In interferon gamma (IFN-γ)-stimulated MSCs, we found higher lipid abundances from the ceramide-1-phosphate (C1P), phosphatidylcholine (PC), LysoPC, and triglyceride (TAG) families that are involved in cell immune function. Furthermore, we identified differences in lipid signatures in morphologically defined MSC subpopulations. The use of single-cell optical imaging coupled with single-cell spatial lipidomics could assist in optimizing the MSC production process and improve mechanistic understanding of manufacturing process effects on MSC immune activity and heterogeneity.

Current status of stem cell therapy for type 1 diabetes: a critique and a prospective consideration

Over the past decade, there had been progress in the development of cell therapy for insulin-dependent diabetes. Nevertheless, important hurdles that need to be overcome still remain. Protocols for the differentiation of pluripotent stem cells into pancreatic progenitors or fully differentiated β-cells have been developed. The resulting insulin-producing cells can control chemically induced diabetes in rodents and were the subject of several clinical trials. However, these cells are immunogenic and possibly teratogenic for their transplantation, and an immunoisolation device and/or immunosuppression is needed. A growing number of studies have utilized genetic manipulations to produce immune evasive cells. Evidence must be provided that in addition to the expected benefit, gene manipulations should not lead to any unforeseen complications. Mesenchymal stem/stromal cells (MSCs) can provide a viable alternative. MSCs are widely available from many tissues. They can form insulin-producing cells by directed differentiation. Experimentally, evidence has shown that the transplantation of allogenic insulin-producing cells derived from MSCs is associated with a muted allogeneic response that does not interfere with their functionality. This can be explained by the immunomodulatory functions of the MSC subpopulation that did not differentiate into insulin-producing cells. Recently, exosomes derived from naive MSCs have been used in the experimental domain to treat diabetes in rodents with varying degrees of success. Several mechanisms for their beneficial functions were proposed including a reduction in insulin resistance, the promotion of autophagy, and an increase in the T regulatory population. However, euglycemia was not achieved in any of these experiments. We suggest that exosomes derived from β-cells or insulin-producing cells (educated) can provide a better therapeutic effect than those derived from undifferentiated cells.