The treatment of life-threatening ailments is being transformed by advanced therapies including gene therapies for inherited diseases, therapeutic cell treatments (e.g., immunotherapies) for cancers and autoimmune disorders, and tissue engineered medical products to restore, maintain, and replace damaged organs.1-4 The outlook for these techniques is promising, with over 900 new investigational drug applications for cell and gene therapy products reported by the FDA as of January 2020. However, even with idealized allogenic models for CAR-T production it is expected that approximately 25% of the cost to manufacture is associated with quality control alone.5Even if allogenic therapies are shown to be viable alternatives to autologous based therapies, batch variability will result in economic burdens associated with quality control. A shortage in suitable real-time quality control methods capable of monitoring cell bioreactors for feedback control results in large batch-to-batch variability andad hoc approaches to cell culturing that make scalable and high yield manufacturing difficult and is largely responsible for the high costs.6 Since advanced therapy workflows depend on the growth of cells in bioreactors, the process analytical technologies (PAT) for real-time monitoring of cell secreted biomarkers are essential for cost effective biomanufacturing of high quality therapeutic products.
As cell cultures mature and go through developmental stages, the biomolecules they secrete as signaling and paracrine factors serve as the critical quality attributes (CQAs) for cell biochemical state and final therapeutic potency.7-9 As shown in figure 1, the concentrations of CQAs vary significantly with both space and time in a cell culture/bioreactor but they are in highest concentration near the cell membrane upon secretion. As the secreted biomolecules diffuse away from the cell surface into the bulk media, they become masked by high concentration media constituents that do not indicate cell state such as serum albumin, growth factors, and salts. Furthermore, the microenvironment represents an instantaneous CQA composition while the bulk provides only a temporal average. Widely employed PATs for biomanufacturing, including both real-time and offline variants, do not characterize either the spatial or temporal variations in CQA content, meaning the product processes cannot be effectively monitored.
Most available real-time PATs, such as those that measure temperature, pH, dissolved oxygen, and glucose, are based on simple analytical outputs and lack the specificity and sensitivity required to discover and detect biochemically complex, low concentration CQAs. These outputs are useful indicators of general culture viability but are not useful for predicting the final products’ quality. This has motivated considerable efforts to develop other approaches to non-invasive, real-time monitoring including volatile species mass spectrometry, Raman spectroscopy, and infrared or near-infrared spectroscopy.10-12 Yet, these approaches are still lacking in their utility for cell bioreactor monitoring, in part due to their poor specificity, limited range of detectable molecules, and low sensitivity for dynamic secretome characterization. A clear and continuing need exists for real-time quality control measurement techniques that are highly sensitive to secreted biomarkers with complex biochemical signatures. An ideal PAT should also be label free or untargeted to enable broad biomolecular detection such that unanticipated or unidentified biomarkers may still be characterized for better process understanding. Electrospray ionization mass spectrometry (ESI-MS) is a particularly promising PAT candidate due to its broad molecular weight coverage, sensitivity, and ability to preserve structure/folding and non-covalent interactions of biomolecular complexes through “soft-ionization”.13-15 Recently, we demonstrated continuous ESI-MS sensing with a ~1 minute response time for detecting the biomolecules serving as proxies to target CQA species.16
The Dynamic Sampling Platform (DSP, Fig. 1) is a multi-functional analytical platform for cell bioreactor characterization that can be integrated directly into therapeutic cell manufacturing quality control approaches. The DSP samples very small volumes (~1 μL) of liquid from the reactor microenvironment and then processes the sample for real-time analytics. The chemicals that cells produce, secrete, and interact with in their growth environment can be identified using a multitude of analytical tools. As a platform technology, DSP can integrate with the optimal analytical tool for the application. Thus, the capability to monitor spatially-resolved (down to the cellular or sub-cellular level) biochemical activity on the time scale of cellular relevant processes with minimum alteration of the cells’ (unobserved) biochemical states, as pursued here, would provide the required level insight for quality control and standardization efforts. In this work, we use DSP in combination with ESI-MS sensing for demonstrating that localized sampling enables in situ fingerprinting of the cell culture over the entire development cycle.
Importantly, using DSP coupled to ESI-MS sensing, we show that CQA heterogeneities exist even within 2D cell cultures. These heterogeneities are important because they impact aspects of cell metabolism, final yield, and product quality. As DSP direct-from-culture sampling does not affect culture sterility or cell growth trajectory during the 3 week culture process, it is found to be suitable for continuous, real-time characterization of CQA content in bioreactors. When incorporated into a workflow with HPLC-MSntechnologies for CQA identification, DSP allows for not only real-time feedback monitoring but also the CQA discovery in biomanufacturing systems. Collectively, these results constitute a vital set of capabilities and biochemical data that i) demonstrate that the local CQA content is critical to detecting cells in their various developmental states (e.g., proliferative, confluent, differentiated) and ii) establish DSP as a viable analytical platform for in situ monitoring of bioreactor state and cell development.