Abstract
Cell therapy manufacturing is limited by lack of online tools capable of realtime in-process monitoring, particularly of simultaneous changes in multiple orthogonal (mutually independent) parameters. Here, we studied changes in CD36 expression, number density and size (area) of erythroblasts through different stages of erythropoiesis in vitro using a quartz crystal resonator (QCR), integrated with a microscope, and flow cytometry in parallel. An analytical model was developed extending the Kanazawa-Gordon theory. Based on this model, independent correlations were established between changes in each QCR parameter, dissipation (ΔΓ) and resonance frequency (−Δf0), and CD36 expression (from flow cytometry) and cell area (from microscope). The correlation functions were used to derive an acoustic signature (−ΔΓ/Δf0) of the differentiation process that uniquely mapped the relative changes in CD36 expression and late-stage enucleation-related deviations. A method to quantify relative changes in cell area purely from the acoustic parameters was also proposed. This work demonstrated for the first time the potential of an electromechanical tool for online monitoring of concurrently varying orthogonal phenotypic parameters in cell therapy manufacturing.
Highlights
In view of the need to understand and control pharmaceutical manufacturing processes and ensure final product quality, the U.S Food and Drug Administration published a guidance on process analytical technology (PAT) in 2004 (FDA, 2004)
Supported by experiments and modelling, this study described for the first time an entirely electrome chanical method that is potentially implementable for online realtime monitoring of simultaneously varying orthogonal phenotypic parameters in cell therapy manufacturing without the need for flow cytometry or microscopy
The cell size and internal complexity of erythroprogenitor (CD34+) cells were interpreted from the intensity of forward scatter (FSC) and side or orthogonal scatter (SSC) light in flow cytometry
Summary
In view of the need to understand and control pharmaceutical manufacturing processes and ensure final product quality, the U.S Food and Drug Administration published a guidance on process analytical technology (PAT) in 2004 (FDA, 2004). Conventional process control is adopted in most cases, where manual ‘invasive’ sampling is followed by ex-situ laboratory analysis, contributing to delayed feedback and contamination risks. Analytical methods interrogating the contents of the membrane proteome often rely on label-based signal transduction, such as fluorescence-activated cell sorting (FACS) and enzyme-linked immunosorbent assay (ELISA) (Piyasena and Graves, 2014). Label-based methods require time and resource-intensive sample preparation procedures that are largely unsuitable for high-throughput screening and automated appli cations outside a laboratory environment, such as online process monitoring of cell-based therapies (Hur et al, 2011; Di Carlo, 2012)
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