The single biggest hurdle faced in cell and gene therapy commercialization is technical1. The manufacture of biologics, relies on living systems, which adds a layer of complexity that is absent from other therapies. The number of cell types applicable to cell therapy is vast and their characteristics are easily influenced by the environment. This makes defining and testing the quality of cell therapy manufacturing challenging. Collaboration could be critical for overcoming technical challenges in the industry.
It is crucial that all assays are well characterized and remain valid from preclinical through to the end of clinical trial. Extra time devoted to preclinical development creating robust validated assays that remain consistent across laboratories and potential trial sites in different countries will pay off. It will benefit all parties involved with smooth clinical trial execution, no loss of data due to non-comparability, and a straight-forward review process by the FDA.
Digital and inline/atline strategy combined with machine learning automated prediction will become more and more important in the years to come. Machine learning is particularly well suited to the analysis of images. Multispectral imaging combined with machine learning can, in some cases, efficiently predict the potency of a cell therapy from multispectral images of the final cellular product, provided that a training set of validated potent cells and control non-potent cells are available. Appropriate construction of the training set with a wide array of negative controls is critical to the success of this approach. It is most suitable when genetic engineering is not the primary source of cell potency and biomarkers of potency are unknown.
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October 24-26, 2018
Hilton Washington DC/Rockville Hotel & Executive Meeting Center,
Rockville, MD
October 24-26, 2018
Hilton Washington DC/Rockville Hotel & Executive Meeting Center,
Rockville, MD
Whitepaper Summary
Cell and Gene Therapy: What Are The Analytical Challenges And Most Relevant Bioassays?