A Monte Carlo framework for managing biological variability in manufacture of autologous cell therapy from mesenchymal stromal cells therapies.
Manufacturing processes for autologous cell therapy need to reproducibly generate in specification (quality and quantity) clinical product. However, patient variability prevents the level of control of cell input material that could be achieved in a cell line or allogeneic-based process. We have applied literature data on bone marrow-derived mesenchymal stromal cells variability to estimate probability distributions for stem cell yields given underlying truncated normal distributions in total nucleated cell concentration, stem cell percentage and plausible aspirate volumes. Monte Carlo simulation identified potential variability in harvested stem cell number in excess of an order of magnitude. The source material variability was used to identify the proportion of donor manufacturing runs that would achieve a target yield specification of 2E7 cells in a fixed time window with given proliferative rates and different aspirate volumes. A rapid, screening, development approach was undertaken to assess culture materials and process parameters (T-flask surface, medium, feed schedule) to specify a protocol with identified proliferative rate and a consequent model-based target aspirate volume. Finally, four engineering runs of the candidate process were conducted and a range of relevant quality parameters measured including expression of markers CD105, CD73, CD44, CD45, CD34, CD11b, CD19, HLA-DR, CD146 (melanoma cell adhesion molecule), CD106 (vascular cell adhesion molecule) and SSEA-4, specific metabolic activity and vascular endothelial growth factor secretion, and osteogenic differentiation potential. Our approach of using estimated distributions from publicly available information provides a route for data-poor earl- stage developers to plan manufacture with defined risk based on rational assumptions; furthermore, the models produced by such assumptions can be used to evaluate candidate processes, and can be incrementally improved with accumulating distribution understanding or subdivision by new process variables.
['Picken|Andrew|A|', 'Harriman|Jon|J|', 'Iftimia-Mander|Andreea|A|', 'Johnson|Lyndsey|L|', 'Prosser|Amy|A|', 'Quirk|Robin|R|', 'Thomas|Robert|R|']
D000954:Antigens, Surface D000076103:Biological Variation, Individual D002452:Cell Count D018929:Cell Culture Techniques D002454:Cell Differentiation D002470:Cell Survival D064987:Cell- and Tissue-Based Therapy D002478:Cells, Cultured D003470:Culture Media D006801:Humans D045164:Mesenchymal Stem Cell Transplantation D059630:Mesenchymal Stem Cells D009010:Monte Carlo Method D010012:Osteogenesis D014182:Transplantation, Autologous D042461:Vascular Endothelial Growth Factor A
variability Manufacture autologous mesenchymal process model