2134/17982 Qasim Rafiq Qasim Rafiq Karen Coopman Karen Coopman Alvin W. Nienow Alvin W. Nienow Christopher Hewitt Christopher Hewitt A quantitative approach for understanding small-scale human mesenchymal stem cell culture implications for large-scale bioprocess development Loughborough University 2015 Bioprocessing Human mesenchymal stem cells Hypoxia Normoxia Regenerative medicine Chemical Engineering not elsewhere classified 2015-06-23 10:28:24 Journal contribution https://repository.lboro.ac.uk/articles/journal_contribution/A_quantitative_approach_for_understanding_small-scale_human_mesenchymal_stem_cell_culture_implications_for_large-scale_bioprocess_development/9241835 Human mesenchymal stem cell (hMSC) therapies have the potential to revolutionise the healthcare industry and replicate the success of the therapeutic protein industry; however, for this to be achieved there is a need to apply key bioprocessing engineering principles and adopt a quantitative approach for large-scale reproducible hMSC bioprocess development. Here we provide a quantitative analysis of the changes in concentration of glucose, lactate and ammonium with time during hMSC monolayer culture over 4 passages, under 100% and 20% dissolved oxgen (dO2), where either a 100%, 50% or 0% growth medium exchange was performed after 72h in culture. Yield coefficients, specific growth rates (h-1) and doubling times (h) were calculated for all cases. The 100% dO2 flasks outperformed the 20% dO2 flasks with respect to cumulative cell number, with the latter consuming more glucose and producing more lactate and ammonium. Furthermore, the 100% and 50% medium exchange conditions resulted in similar cumulative cell numbers, whilst the 0% conditions were significantly lower. Cell immunophenotype and multipotency were not affected by the experimental culture conditions. This study demonstrates the importance of determining optimal culture conditions for hMSC expansion and highlights a potential cost savings from only making a 50% medium exchange, which may prove significant for large-scale bioprocessing.