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.