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ENH proof conclusions section

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@ -249,6 +249,7 @@
\newacronym{nhs}{NHS}{N-hydroxysulfosuccinimide}
\newacronym{tocsy}{TOCSY}{total correlation spectroscopy}
\newacronym{hplc}{HPLC}{high-performance liquid chromatography}
\newacronym{grex}{G-Rex}{Gas Permeable Rapid Expansion}
% symbols to make me sound mathier than I really am
@ -862,16 +863,16 @@ time of writing, several clinical trial are underway which use the CliniMACS,
although mostly for stem-cell based cell treatments.
Finally, another option that has been investigated for T cell expansion is the
Grex bioreactor (Wilson Wolf). This is effectively a tall tissue-culture plate
with a porous membrane at the bottom. This allows large volumes of media to be
loaded without suffocating the cells, which can exchange gas through the
\gls{grex} bioreactor (Wilson Wolf). This is effectively a tall tissue-culture
plate with a porous membrane at the bottom. This allows large volumes of media
to be loaded without suffocating the cells, which can exchange gas through the
membrane. While this is quite similar to plates and flasks normally used for
small-scale research, the important difference is that its larger size requires
fewer interactions and keeps the cells at a higher nutrient concentration for
longer periods of time. However, it is still a an open system and requires
manual (by default) interaction from an operator to load, feed, and harvest the
cell product. Grex bioreactors have been using to grow \glspl{til}\cite{Jin2012}
and virus-specific T cells\cite{Gerdemann2011}.
cell product. \gls{grex} bioreactors have been using to grow
\glspl{til}\cite{Jin2012} and virus-specific T cells\cite{Gerdemann2011}.
Much work is still required in the space of bioreactor design for T cell
manufacturing, but novel T cell expansion technologies such as that described in
@ -1395,9 +1396,9 @@ novel considering the state-of-the-art technology for T cell manufacturing:
small scale, where the cost of reagents, cells, and materials often precludes
large sample sizes.
\item The \gls{dms} system is be compatible with static bioreactors such as the
G-Rex which has been adopted throughout the cell therapy industry. Thus this
technology can be easily incorporated into existing cell therapy process that
are performed at scale.
\gls{grex} which has been adopted throughout the cell therapy industry. Thus
this technology can be easily incorporated into existing cell therapy process
that are performed at scale.
\item We analyzed our system using a multiomics approach, which will enable the
discovery of novel biomarkers to be used as \glspl{cqa}. While this approach
has been applied to T cells previously, it has not been done in the context of
@ -1575,8 +1576,9 @@ Cells on the \glspl{dms} were visualized by adding \SI{0.5}{\ul}
\product{\acd{45}-\gls{af647}}{\bl}{368538}, incubating for \SI{1}{\hour}, and
imaging on a spinning disk confocal microscope.
In the case of Grex bioreactors, we either used a \product{24 well plate}{Wilson
Wolf}{P/N 80192M} or a \product{6 well plate}{Wilson Wolf}{P/N 80240M}.
In the case of \gls{grex} bioreactors, we either used a \product{24 well
plate}{Wilson Wolf}{P/N 80192M} or a \product{6 well plate}{Wilson Wolf}{P/N
80240M}.
\subsection{Quantifying Cells on DMS Interior}
@ -2520,7 +2522,7 @@ for bead (\cref{fig:car_bcma_total}).
\label{fig:car_bcma}
\end{figure*}
\subsection{DMSs Efficiently Expand T Cells in Grex Bioreactors}
\subsection{DMSs Efficiently Expand T Cells in G-Rex Bioreactors}
\begin{figure*}[ht!]
\begingroup
@ -2532,8 +2534,8 @@ for bead (\cref{fig:car_bcma_total}).
\phantomsubcaption\label{fig:grex_cd4}
\endgroup
\caption[Grex Expansion]
{\glspl{dms} expand T cells robustly in Grex bioreactors.
\caption[\acrshort{grex} Expansion]
{\glspl{dms} expand T cells robustly in \gls{grex} bioreactors.
\subcap{fig:grex_results_fc}{Fold change of T cells over time.}
\subcap{fig:grex_results_viability}{Viability of T cells over time.}
\subcap{fig:grex_mem}{\ptmemp{}} and
@ -2544,19 +2546,19 @@ for bead (\cref{fig:car_bcma_total}).
\label{fig:grex_results}
\end{figure*}
We also asked if the \gls{dms} platform could expand T cells in a Grex
bioreactor. We incubated T cells in a Grex analogously to plates and found that
T cells in Grex bioreactors expanded as efficiently as beads over \SI{14}{\day}
and had similar viability
We also asked if the \gls{dms} platform could expand T cells in a \gls{grex}
bioreactor. We incubated T cells in a \gls{grex} analogously to plates and found
that T cells in \gls{grex} bioreactors expanded as efficiently as beads over
\SI{14}{\day} with similar viability
(\cref{fig:grex_results_fc,fig:grex_results_viability}). Consistent with past
results, \glspl{dms}-expanded T cells had higher \pthp{} and \ptmemp{} compared
to beads (\cref{fig:grex_mem,fig:grex_cd4}). Overall the \ptmemp{} was lower
than that seen in standard plates (\cref{fig:dms_phenotype_mem}).
These discrepancies might be explained in light of other data as follows. The
Grex bioreactor has higher media capacity relative to its surface area, and we
did not move the T cells to a larger bioreactor as they grew in contrast with
our plate cultures. This means that the cells had higher growth area
\gls{grex} bioreactor has higher media capacity relative to its surface area,
and we did not move the T cells to a larger bioreactor as they grew in contrast
with our plate cultures. This means that the cells had higher growth area
constraints, which may have nullified any advantage to the expansion seen in
standard plates (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth
area could mean increased signaling and \gls{teff} differentiation, which was
@ -2568,12 +2570,12 @@ why the \ptmemp{} was low compared to past data (\cref{fig:dms_phenotype_mem}).
\includegraphics{../figures/grex_luminex.png}
\endgroup
\caption[Grex Luminex Results]
{\gls{dms} lead to higher cytokine production in Grex bioreactors.}
\caption[\acrshort{grex} Luminex Results]
{\gls{dms} lead to higher cytokine production in \gls{grex} bioreactors.}
\label{fig:grex_luminex}
\end{figure*}
We also quantified the cytokines released during the Grex expansion using
We also quantified the cytokines released during the \gls{grex} expansion using
Luminex. We noted that in nearly all cases, the \gls{dms}-expanded T cells
released higher concentrations of cytokines compared to beads
(\cref{fig:grex_luminex}), including higher concentrations of pro-inflammatory
@ -2581,9 +2583,9 @@ cytokines such as GM-CSF, \gls{ifng}, and \gls{tnfa}. This demonstrates that
\glspl{dms} could lead to more robust activation.
Taken together, these data suggest that \gls{dms} also lead to robust expansion
in Grex bioreactors, although more optimization may be necessary to maximize the
media feed rate and growth area to get comparable results to those seen in
tissue-culture plates.
in \gls{grex} bioreactors, although more optimization may be necessary to
maximize the media feed rate and growth area to get comparable results to those
seen in tissue-culture plates.
\subsection{DMSs Do Not Leave Antibodies Attached to Cell Product}
@ -2630,21 +2632,22 @@ possible treatment variables which we controlled when designing the experiments
included in this dataset. Obviously the principle treatment parameter was
``activation method'' which represented the effect of activating T cells with
either beads or \glspl{dms}. We also included ``bioreactor'' which was a
categorical variable for growing the T cells in a Grex bioreactor or polystyrene
plates, ``feed criteria'' which represented the criteria used to feed the cells
(media color or a glucose meter), ``IL2 Feed Conc.'' as a continuous parameter
for the concentration of IL2 added each feed cycle, and ``CD19-CAR Transduced''
representing if the cells were lentivirally transduced or not. Unfortunately,
many of these parameters correlated with each other despite the large size of
our dataset, so the only two parameters for which causal relationships could be
evaluated were ``activation method'' and ``bioreactor''. Note that these were
not the only set of theoretical treatment parameters that we could have used.
For example, media feed rate is an important process parameter, but in our
experiments this was dependent on the feeding criteria and the growth rate of
the cells, which in turn is determined by activation method. Therefore, ``media
feed rate'' (or similar) is a ``post-treatment parameter,'' and including it
would have violated the backdoor criteria and severely biased our estimates of
the treatment parameters themselves.
categorical variable for growing the T cells in a \gls{grex} bioreactor or
polystyrene plates, ``feed criteria'' which represented the criteria used to
feed the cells (media color or a glucose meter), ``IL2 Feed Conc.'' as a
continuous parameter for the concentration of IL2 added each feed cycle, and
``CD19-CAR Transduced'' representing if the cells were lentivirally transduced
or not. Unfortunately, many of these parameters correlated with each other
despite the large size of our dataset, so the only two parameters for which
causal relationships could be evaluated were ``activation method'' and
``bioreactor''. Note that these were not the only set of theoretical treatment
parameters that we could have used. For example, media feed rate is an important
process parameter, but in our experiments this was dependent on the feeding
criteria and the growth rate of the cells, which in turn is determined by
activation method. Therefore, ``media feed rate'' (or similar) is a
``post-treatment parameter,'' and including it would have violated the backdoor
criteria and severely biased our estimates of the treatment parameters
themselves.
In addition to these treatment parameters, we also included covariates to
improve the precision of our model. Among these were donor parameters including
@ -2726,9 +2729,9 @@ harmful to the response, while at the same time it seems to increase the
\dpthp{} response. We should note that this parameter merely represents whether
or not the choice was made experimentally to use a bioreactor or not; it does
not indicate why the bioreactor helped or hurt a certain response. For example,
using a Grex entails changing the cell surface and feeding strategy for the T
cells, and any one of these ``mediating variables'' might actually be the cause
of the responses.
using a \gls{grex} entails changing the cell surface and feeding strategy for
the T cells, and any one of these ``mediating variables'' might actually be the
cause of the responses.
Finally, we stratified on the most common donor (vendor ID 338 from Astarte
Biotech) as accounted for almost half the data (80 runs) and repeated the
@ -2777,29 +2780,28 @@ apoptosis\cite{Yang2017}. Despite evidence for the importance of CD4 T cells,
more work is required to determine the precise ratios of CD4 and CD8 T cell
subsets to be included in CAR T cell therapy given a disease state.
% DISCUSSION this mentions the DOE which is in the next aim
When analyzing all our experiments comprehensively using causal inference, we
found that all three of our responses were significantly increased when
controlling for covariates (\cref{fig:metaanalysis_fx,tab:ci_controlled}). By
extension, this implies that not only will \glspl{dms} lead to higher fold
change overall, but also much higher fold change in absolute numbers of memory
and CD4+ T cells. Furthermore, we found that using a Grex bioreactor is
and CD4+ T cells. Furthermore, we found that using a \gls{grex} bioreactor is
detrimental to fold change and memory percent while helping CD4+. Since there
are multiple consequences to using a Grex compared to tissue-treated plates, we
can only speculate as to why this might be the case. Firstly, when using a Grex
we did not expand the surface area on which the cells were growing in a
comparable way to that of polystyrene plates. One possible explanation is that
the T cells spent longer times in highly activating conditions (since the beads
and DMSs would have been at higher per-area concentrations in the Grex vs
polystyrene plates) which has been shown to skew toward \gls{teff}
populations\cite{Lozza2008}. Furthermore, the simple fact that the T cells spent
more time at high surface densities could simply mean that the T cells didn’t
expand as much due to spacial constraints. This would all be despite the
gas-permeable membrane and tell design of the Grex, which are meant to enhance
growth and not impede it. Given this, our data suggests we were using the
bioreactor sub-optimally, and the hypothesized causes for why our T cells did
not expand could be verified with additional experiments varying the starting
cell density and/or using larger bioreactors.
are multiple consequences to using a \gls{grex} compared to tissue-treated
plates, we can only speculate as to why this might be the case. Firstly, when
using a \gls{grex} we did not expand the surface area on which the cells were
growing in a comparable way to that of polystyrene plates. One possible
explanation is that the T cells spent longer times in highly activating
conditions (since the beads and DMSs would have been at higher per-area
concentrations in the \gls{grex} vs polystyrene plates) which has been shown to
skew toward \gls{teff} populations\cite{Lozza2008}. Furthermore, the simple fact
that the T cells spent more time at high surface densities could simply mean
that the T cells didn’t expand as much due to spacial constraints. This would
all be despite the gas-permeable membrane and tell design of the \gls{grex},
which are meant to enhance growth and not impede it. Given this, our data
suggests we were using the bioreactor sub-optimally, and the hypothesized causes
for why our T cells did not expand could be verified with additional experiments
varying the starting cell density and/or using larger bioreactors.
A key question in the space of cell manufacturing is that of donor variability.
To state this precisely, this is a second order interaction effect that
@ -3403,8 +3405,8 @@ between different timepoints, demonstrating that these could be used to
differentiate between different process conditions qualitatively simply based on
variance (\cref{fig:doe_luminex}). These were also much higher in most cases
that a set of bead based runs which were run in parallel, in agreement with the
luminex data obtained previously in the Grex system (these data were collected
in plates) (\cref{fig:grex_luminex}).
luminex data obtained previously in the \gls{grex} system (these data were
collected in plates) (\cref{fig:grex_luminex}).
\begin{table}[!h] \centering
\caption[Machine Learning Model Results]
@ -4480,69 +4482,67 @@ the precise phenotype responsible for these results.
\section{Conclusions}
This dissertation describes the development of a novel T cell expansion
platform, including the fabrication, \gls{qc}, and biological validation
of its performance both \invitro{} and \invivo{}. Development of such a system
would be meaningful even if it only performed as well as current methods, as
platform, including the fabrication, \gls{qc}, and biological validation of its
performance both \invitro{} and \invivo{}. Development of such a system would
have been meaningful even if it only performed as well as current technology, as
adding another method to the arsenal of the growing T cell manufacturing
industry would reduce the reliance on a small number of companies that currently
license magnetic bead-based T cell expansion technology. However, we
additionally show that the \gls{dms} platform expands more T cells on average,
license magnetic bead-based T cell expansion reagents. However, we additionally
demonstrated that the \gls{dms} platform expands more T cells on average,
including highly potent \ptmem{} and \pth{} T cells, and produces higher
percentages of both. If commercialized, this would be a compelling asset the T
cell manufacturing industry.
In \cref{aim1}, we develop the \gls{dms} platform and verified its efficacy
\invitro{}. Importantly, this included \gls{qc} steps at every critical step of
the fabrication process to ensure that the \gls{dms} can be made within a
targeted specification. These \gls{qc} steps all rely on common, relatively
cost-effective assays such as the \gls{haba} assay, \gls{bca} assay, and
\glspl{elisa}, thus other labs and commercial entities should be able to perform
them. The microcarriers themselves are an off-the-shelf product available from
reputable vendors, and they have a regulatory history in human cell therapies
that will aid in clinical translation\cite{purcellmain}. Both these will help
in translatability. On average, we demonstrated that the \gls{dms} outperforms
state-of-the-art bead-based T cell expansion technology in terms of total fold
expansion, \ptmemp{}, and \pthp{} by \SI{131}{\percent}, \SI{3.5}{\percent}, and
\SI{7.4}{\percent} controlling for donor, operator, and a variety of process
conditions.
In \cref{aim1}, we developed the \gls{dms} platform and verified its efficacy
\invitro{}. Importantly, this included \gls{qc} at every critical step of the
fabrication process to ensure that the \glspl{dms} can be made within a targeted
specification. These \gls{qc} steps all rely on common, cost-effective,
easy-to-use assays such as the \gls{haba} assay, \gls{bca} assay, and
\gls{elisa}. The microcarriers themselves are an off-the-shelf product available
from reputable vendors, and they have a regulatory history in human cell
therapies that will aid in clinical translation\cite{purcellmain}. On average,
we demonstrated that the \glspl{dms} outperforms bead-based technology in terms
of total fold expansion, \ptmemp{}, and \pthp{} by \SI{131}{\percent},
\SI{3.5}{\percent}, and \SI{7.4}{\percent} controlling for donor, operator, and
a variety of process conditions.
In addition to larger numbers of potent T cells, other advantages of our
\gls{dms} approach are that the \glspl{dms} are large enough to be filtered
(approximately \SI{300}{\um}) using standard \SI{40}{\um} cell filters or
similar. If the remaining cells inside that \glspl{dms} are also desired,
digestion with dispase or collagenase may be used. Collagenase D may be
selective enough to dissolve the \gls{dms} yet preserve surface markers which
may be important to measure as critical quality attributes \glspl{cqa}
(\cref{fig:collagenase_fx}). Furthermore, our system should be compatible with
large-scale static culture systems such as the G-Rex bioreactor or perfusion
culture systems, which have been previously shown to work well for T cell
expansion\cite{Forget2014, Gerdemann2011, Jin2012}.
approach are that the \glspl{dms} are large enough to be filtered (approximately
\SI{300}{\um}) using standard \SI{40}{\um} cell strainers or similar. If the
remaining cells inside that \glspl{dms} are also desired, digestion with dispase
or collagenase may be used. \gls{cold} may be selective enough to dissolve the
\gls{dms} yet preserve surface markers which may be important to measure as
critical quality attributes \glspl{cqa} (\cref{fig:collagenase_fx}).
Furthermore, our system should be compatible with large-scale static culture
systems such as the \gls{grex} bioreactor or perfusion culture systems, which
have been previously shown to work well for T cell expansion\cite{Forget2014,
Gerdemann2011, Jin2012}.
In \cref{aim2a}, we developed a modeling pipeline that can be used by commercial
entities as the scale up this process to identify \glspl{cqa} and \gls{cpp}.
These are highly important for a variety of reasons. First, understanding
pertinent \glspl{cpp} allow manufacturers to operate their process at optimal
conditions. This is important for anti-tumor cell therapies, where the prospects
of a patient can urgently depend on receiving therapy in a timely manner.
Optimal process conditions allow T cells to be expanded as quickly as possible
for the patient, while also minimizing cost for the manufacturer. Second,
\glspl{cqa} can be used to define process control schemes as well as release
criteria. Process control, and with it the ability to predict future outcomes
based on data obtained at the present, is highly important for cell therapies
given that batch failures are extremely expensive\cite{Harrison2019}, and
predicting a batch failure would allow manufacturers to restart the batch in a
timely manner without wasting resources. Furthermore, \glspl{cqa} can be used to
define what a `good' vs `bad' product is, which will important help anticipate
dosing and followup procedures in the clinic if the T cells are administered. In
the aim, we cannot claim to have found the ultimate set of \glspl{cqa} and
\glspl{cpp}, as we used tissue culture plates instead of a bioreactor and we
only used one donor. However, we have indeed outlined a process that others may
use to find these for their process. In particular, the 2-phase modeling process
we used (starting with a \gls{doe} and collecting data longitudinally) is a
strategy that manufacturers can easily implement. Also, collecting secretome and
metabolome is easily generalized to any setting and to most bioreactors and
expansion systems, as they can be obtained with relatively inexpensive equipment
(Luminex assay, benchtop \gls{nmr}, etc) without disturbing the cell culture.
entities to identify \glspl{cqa} and \gls{cpp} during scale-up. These are highly
important for a variety of reasons. First, understanding pertinent \glspl{cpp}
allow manufacturers to operate their process at optimal conditions. This is
important for anti-tumor cell therapies, where the prospects of a patient can
urgently depend on receiving therapy in a timely manner. Optimal process
conditions allow T cells to be expanded as quickly as possible for the patient,
while also minimizing cost for the manufacturer. Second, \glspl{cqa} can be used
to define process control schemes as well as release criteria. Process control,
and with it the ability to predict future outcomes based on data obtained at the
present, is highly important for cell therapies given that batch failures are
extremely expensive\cite{Harrison2019}, and predicting a batch failure would
allow manufacturers to restart the batch in a timely manner without wasting
resources. Furthermore, \glspl{cqa} can be used to define what a ``good'' vs
``bad'' product is, from which dosing and followup procedures in the clinic can
be planned more accurately. In the aim, we cannot claim to have found the
universal set of \glspl{cqa} and \glspl{cpp}, as we used tissue culture plates
instead of a bioreactor and we only used one donor. However, we have indeed
outlined a method that others may use to find \glspl{cqa} and \glspl{cpp} for
their process. In particular, the 2-phase modeling approach we used (starting
with a \gls{doe} and collecting data longitudinally) is a strategy that
manufacturers can easily implement. Also, collecting secretome and metabolome is
generalizable to most bioreactors and expansion systems, as they can be obtained
with relatively inexpensive equipment (Luminex assay, benchtop \gls{nmr}, etc)
without disturbing the cell culture.
In \cref{aim2b}, we further explored additional tuning knobs that could be used
to control and optimize the \gls{dms} system. We determined that altering the
@ -4553,22 +4553,22 @@ differentiation\cite{Gattinoni2012, Lozza2008, Lanzavecchia2005, Corse2011}. We
did not find any mechanistic relationship between either integrin signaling or
\gls{il15} signaling. In the case of the former, it may be more likely that the
\glspl{dms} surfaces are saturated to the point of sterically hindering any
integrin interactions with the collagen surface. In the case of \gls{il15} more
integrin interactions with the collagen surface. In the case of \gls{il15}, more
experiments likely need to be done in order to plausibly rule out this mechanism
and/or determine if it is involved at all.
In \cref{aim3} we determined that the \glspl{dms} expand T cells that also
performed better than beads \invivo{}. In the first experiment we performed, the
results were very clearly in favor of the \glspl{dms}. In the second experiment,
even the \gls{dms} group failed to fully control the tumor burden, but this is
In \cref{aim3} we determined that \gls{dms}-expanded T cells that also performed
better than beads \invivo{}. In the first experiment we performed, the results
were clearly in favor of the \glspl{dms}. In the second experiment, even the
\gls{dms}-expanded cells failed to fully control the tumor burden, but this is
not surprising given the low \ptcarp{} across all groups. Also, despite this,
the \gls{dms} group appeared to control the tumor better on average for early,
mid, and late T cell harvesting timepoints. It was not clear if this effect was
due to increased \pthp{}, \ptmemp{}, or fitness of the \gls{dms}-expanded T
cells given their higher expansion rate. More data is needed to establish which
phenotype is responsible for the results we observed, as we did not include the
phenotype is responsible for the results we observed. We did not include the
\gls{car} in the same panel as the other phenotype surface markers, making it
difficult to reliably say the identity of the \ptcar{} cells.
difficult to reliably assess the identity of the \ptcar{} cells.
Finally, while we have demonstrated the \gls{dms} system in the context of
\gls{car} T cells, this method can theoretically be applied to any T cell
@ -4592,34 +4592,33 @@ will be relevent to using this technology in a clinical trial:
\subsection{Using GMP Materials}
While this work was done with translatability and \gls{qc} in mind, an important
feature that is missing from the process currently is the use of \gls{gmp}
materials. The microcarriers themselves are made from porcine-derived collagen,
which itself is not \gls{gmp}-compliant due to its non-human animal origins.
However, using any other source of collagen should work so long as the structure
of the microcarriers remains relatively similar and it has lysine groups that
can react with the \gls{snb} to attach \gls{stp} and \glspl{mab}. Obviously
these would need to be tested and verified, but these should not be
insurmountable. Furthermore, the \gls{mab} binding step requires \gls{bsa} to
prevent adsorption to the non-polar polymer walls of the reaction tubes. A human
carrier protein such as \gls{hsa} could be used in its place to eliminate the
non-human animal origin material, but this could be much more expensive.
Alternatively, the use of protein could be replaced altogether by a non-ionic
detergent such as Tween-20 or Tween-80, which are already used for commercial
\gls{mab} formulations for precisely this purpose\cite{Kerwin2008}. Validating
the process with Tween would be the best next step to eliminate \gls{bsa} from
the process. The \gls{stp} and \glspl{mab} in this work were not
\gls{gmp}-grade; however, they are commonly used in clinical technology such as
dynabeads and thus the research-grade proteins used here could be easily
replaced. The \gls{snb} is a synthetic small molecule and thus does not have any
animal-origin concerns.
While this work was done with translatability and \gls{qc} in mind, \gls{gmp}
are still absent from the fabrication process. The microcarriers themselves are
made from porcine-derived collagen, which itself is not \gls{gmp}-compliant due
to its non-human animal origins. However, using any other source of collagen
should work so long as the structure of the microcarriers remains relatively
similar and it has lysine groups that can react with the \gls{snb} to attach
\gls{stp} and \glspl{mab}. Obviously these would need to be tested and verified,
but these should not be insurmountable. Furthermore, the \gls{mab} binding step
requires \gls{bsa} to prevent adsorption to the non-polar polymer walls of the
reaction tubes. A human carrier protein such as \gls{hsa} could be used in its
place to eliminate the non-human animal origin material, but this could be much
more expensive. Alternatively, the use of protein could be replaced altogether
by a non-ionic detergent such as Tween-20 or Tween-80, which are already used
for commercial \gls{mab} formulations for precisely this
purpose\cite{Kerwin2008}. Validating the process with Tween would be the best
next step to eliminate \gls{bsa} from the process. The \gls{stp} and \glspl{mab}
in this work were not \gls{gmp}-grade; however, they are commonly used in
clinical technology such as dynabeads and thus the research-grade proteins used
here could be easily replaced. The \gls{snb} is a synthetic small molecule and
thus does not have any animal-origin concerns.
\subsection{Mechanistic Investigation}
Despite the improved outcomes in terms of expansion and phenotype relative to
beads, we don't have a good understanding of why they \gls{dms} platform works
as well as it does. The following are several plausible hypotheses and a
proposed experiment for testing them:
beads, we don't have a good understanding of why the \gls{dms} platform works as
well as it does. The following are several plausible hypotheses and testing
strategies:
\subsubsection{Cytokine Cross-talk}
@ -4640,144 +4639,146 @@ added, while the \gls{dms} will have better expansion and phenotype when the
cocktail is not added. If this experiment shows any effects, the cytokines
responsible can be resolved by testing individually (or in small pools).
One caveat with this approach is that it assumes that the \gls{mab} cocktail
will completely quench their target cytokines between each feed cycle. This assumption
can be tested by running luminex with each cocktail addition. If a given
cytokine is undetectable, this indicates that the blocking \gls{mab} completely
quenched all target cytokine at the time of addition and in the time between
feeding cycles.
One caveat with this approach is that it assumes that each \gls{mab} in the
cocktail is in sufficient quantity to quench their target cytokine between each
feed cycle. This assumption can be tested by running Luminex with each cocktail
addition. If a given cytokine is undetectable, this indicates that the blocking
\gls{mab} completely quenched all target cytokine at the time of addition and in
the time between feeding cycles.
\subsubsection{Interior Cell Phenotype}
Unlike the beads, the \glspl{dms} have interior and exterior surfaces. We
demonstrated that some T cell expand on the interior of the \glspl{dms}, and is
plausible that these cells are phenotypically different than those growing on
the exterior or completely detached from the microcarriers, and that this leads
to an asymmetric cytokine cross-talk which accounts for the population-level
differences seen in comparison to the beads.
demonstrated that some T cell expand on the interior of the \glspl{dms}, and
these cells may be phenotypically different than those growing on the exterior.
This could lead to an asymmetric cytokine cross-talk which accounts for the
population-level differences seen in comparison to the beads.
Experimentally, the first step involves separating the \glspl{dms} from the
loosely or non-adhered T cells and digesting the \glspl{dms} with \gls{cold}
(concentrations of \SI{10}{\ug\per\ml} will completely the \glspl{dms} within
\SIrange{30}{45}{\min}) isolate the interior T cells. Unfortunately, only
\SIrange{10}{20}{\percent} of all cells will be on the interior, so the interior
group may only have cells on the order of \num{1e3} to \num{1e4} for analysis. A
good first pass experiment would be to analyze both populations with a T cell
differentiation/activation state flow panel first (since flow cytometry is
relatively cheap and doesn't require a large number of cells) to simply
establish if the two groups are different phenotypes or are in a different state
of activation. From there, more in-depth analysis using \gls{cytof} or another
high-dimensionality method may be used to evaluate differential cytokine
expression.
\SIrange{30}{45}{\min}) to isolate the interior T cells. Unfortunately, only
\SIrange{10}{20}{\percent} of all cells will be on the interior, so this
population may only have cells on the order of \num{1e3} to \num{1e4} for
analysis. A good first pass experiment would be to analyze both populations with
flow cytometry (since flow cytometry is relatively cheap and doesn't require a
large number of cells) to simply establish if the two groups are different
phenotypes or are in a different state of activation. From there, more in-depth
analysis using \gls{cytof} or another high-dimensionality method may be used to
evaluate differential cytokine expression.
\subsubsection{Antibody Surface Density}
While our \gls{doe} experiments showed a relationship between activating
\gls{mab} density and number of cells, we don't know how the \gls{mab} surface
density of the \gls{dms} compares to that of the beads. In all likelihood, the
\gls{mab} density on the \gls{dms} surface is lower (given the number of total
binding sites on \gls{stp} and the number of \glspl{mab} that actually bind)
which may lead to differences in performance\cite{Lozza2008}.
\gls{mab} density and number of cells, we don't know how the \gls{dms} \gls{mab}
surface density compares to that of the beads. The \gls{mab} surface density on
the \glspl{dms} is likely lower given the number of total binding sites on
\gls{stp} and the number of \glspl{mab} that actually bind, which may lead to
differences in performance\cite{Lozza2008}.
Before attempting this experiment, it will be vital to improve the \gls{dms}
manufacturing process such that \gls{mab} binding is predictable and
reproducible (see below). Once this is established, we can then determine the
amount of \glspl{mab} that bind to the beads, which could be performed much like
the \gls{mab} binding step is quantified in the \gls{dms} process (eg with
ELISA, \cref{fig:dms_flowchart}). Knowing this, we can vary the
\gls{mab} surface density for both the bead and the \glspl{dms} using a dummy
\gls{mab} as done previously with the \gls{doe} experiments in \cref{aim2a}.
Using varying surface densities that are matched per-area between the beads and
\glspl{dms} we can then activate T cells and assess their growth/phenotype as a
function of surface density and the presentation method.
amount of \glspl{mab} that bind to the beads, which could be quantified much
like the \gls{mab} binding step in the \gls{dms} process (eg with ELISA,
\cref{fig:dms_flowchart}). Knowing this, we can vary the \gls{mab} surface
density for both the bead and the \glspl{dms} using a dummy \gls{mab} as done
previously with the \gls{doe} experiments in \cref{aim2a}. Using varying surface
densities that are matched per-area between the beads and \glspl{dms} we can
then activate T cells and assess their growth/phenotype as a function of surface
density and the presentation method.
\subsection{Reducing Ligand Variance}
While we have robust \gls{qc} steps to quantify each step of the
\gls{dms} coating process, we still see high variance across time and personnel
(\cref{fig:dms_coating}). This is less than ideal for translation.
While we have robust \gls{qc} for each step of the \gls{dms} coating process, we
still see high variance across time and personnel (\cref{fig:dms_coating}). This
is less than ideal for translation. The following are a list of variance sources
and potential mitigation strategies:
When investigating the \gls{mab} and \gls{stp} binding, it appears that there is
a significant variance both between and within different experiments (even
within the same operator). The following are a list of variance sources and
potential mitigation strategies:
\subsubsection{Mass loss during autoclaving}
\begin{description}
\item[Mass loss during autoclaving --] In order to ensure a consistent reaction
volume, we mass the tube after adding carriers and \gls{pbs} prior to
autoclaving. Autoclaving and washing will cause variations in the liquid
level, and these are corrected using the pre-recorded tube mass. However, this
assumes that the mass of the tube never changes, which may or may not be true
in an autoclave where the temperature easily causes deformation of the plastic
tube material. This can easily be tested by autoclaving empty tubes and
observing a mass change. If there is a mass change, it may be mitigated by
pre-autoclaving tubes (assuming that autoclaving is idempotent with respect to
mass loss), or alternatively we could estimate the bias by autoclaving a
set of tubes, recording the mean mass loss, and using this to correct the tube
mass for downstream calculations.
\item[Errors in initial microcarrier massing --] The massing of microcarriers at
the very beginning of the process requires care due to the low target mass and
the propensity for both the plastic tubes and microcarriers to accumulate
static. Oddly, the biotin attachment readout does not seem to be much affected
by the mass of carriers (\cref{fig:dms_qc_doe}); however, this merely means
that errors in carrier mass lead to different biotin surface densities, which
downstream causes different ratios of \gls{stp} and \gls{mab} attachment since
these relationships are non-linear with respect to biotin surface density
(\cref{fig:stp_coating,fig:mab_coating}) (this is in addition to the fact that
having more or less carriers will bias the total amount of \gls{stp} and
\gls{mab} able to bind). A quick survey of operators revealed that acceptable
margins for error in mass range from \SIrange{2.5}{5.0}{\percent} (eg, a
target value $X$ \si{\mg} will be accepted as $X$ at plus or minus these
margins). These could easily be reduced and standardized via protocol.
Additionally, we do not currently record the exact mass of microcarriers
weighed for each batch. Knowing this would allow us to pinpoint how much of
this variance is due to our acceptable measurement margins and what errors may
arise from static and other instrument noise.
\item[Centrifugation after washing --] After coating the \gls{dms} with \gls{snb},
\gls{stp}, or \glspl{mab}, they must be washed. After washing, they must be
massed in order to ensure the reaction volume is consistent. Ideally, the
tubes are centrifuged after washing to ensure that all liquid is at the bottom
prior to beginning the next coating step. Upon survey, not all operators
follow this protocol, and the protocols are not written such to make this
obvious. Therefore, protocols will be revised followed by additional training.
\item[Accidental microcarrier removal --] When washing the microcarriers after a
coating step, liquid is aspirated using a stripette. The carriers should be at
the bottom of the tube during this aspiration step. Depending on the skill and
care of the operator, carriers may be aspirated with the liquid during this
step. If this happens, downstream \gls{qc} assays will not reflect the true
binding magnitude, as these assays assume the number of carriers is constant.
\item[\gls{bsa} binding kinetics --] Prior to \gls{mab} addition, \gls{bsa} is
added to the \gls{mab} to block binding to the tubes. \glspl{mab} are added
immediately after adding the \gls{bsa}, which means the \gls{bsa} has almost
no time to mix completely and thus the \gls{mab} could come into contact with
the sides of the tube unshielded. In theory this could cause the \gls{mab}
reading to be lower on the \gls{elisa} during \gls{qc}. This problem may be
minor since significant binding would only occur if the \gls{mab}/plastic
adhesion was quite fast and happened in the seconds prior to beginning
agitation. However, this problem is easily mitigated by agitating the tubes
with \gls{bsa} for several minutes prior to adding \gls{mab} to ensure even
mixing.
\item[Improving protein detection --] While the \gls{bca} assay and \gls{elisa}
are quite precise, they both have problems that could lead to systemic bias as
well as increases in random noise. The \gls{bca} assay is non-specific. All
our data shows consistent small (\SI{0.5}{\ug}) but negative readings when
adding zero \gls{snb}, which indicates that some background protein (or
something that behaves like a protein) may be present that the \gls{bca} assay
is detecting. The \gls{elisa} is specific to \gls{mab}; however, in our case
we need to run a blank (just \gls{pbs}, \gls{bsa}, and \glspl{mab} without
carriers) and subtract this from the reading, effectively doubling the assay
variance. Using \gls{hplc} would mitigate both of these issues. \gls{hplc} can
specifically detect species based on differences in charge and size, so it
will likely be able to resolve \gls{stp} without the extraneous bias
introduced via the \gls{bca} assay. In the case of \gls{elisa} it will not
have remove the need to run a blank, but it likely will have lower variance
due to its automated nature.
\end{description}
In order to ensure a consistent reaction volume, we mass the tube after adding
carriers and \gls{pbs} prior to autoclaving. Autoclaving and washing will cause
variations in the liquid level, and these are corrected using the pre-recorded
tube mass. However, this assumes that the mass of the tube never changes, which
may or may not be true in an autoclave where the temperature easily causes
deformation of the plastic tube material. This can easily be tested by
autoclaving empty tubes and observing a mass change. If there is a mass change,
it may be mitigated by pre-autoclaving (assuming that autoclaving is idempotent
with respect to mass loss), or by statistically estimating the bias by recording
the mean mass loss for a set of tubes and using this as a correction factor.
\subsubsection{Surface Stiffness}
\subsubsection{Errors in initial microcarrier massing}
The beads and \gls{dms} are composed of different materials: iron/polymer in the
former case and cross-linked gelatin in the latter. These materials likely have
The massing of microcarriers at the very beginning of the process requires care
due to the low target mass and the propensity for both the plastic tubes and
microcarriers to accumulate static. Oddly, the biotin attachment readout does
not seem to be much affected by the mass of carriers (\cref{fig:dms_qc_doe});
however, this merely means that errors in carrier mass lead to different biotin
surface densities, which downstream causes different ratios of \gls{stp} and
\gls{mab} attachment since these relationships are non-linear with respect to
biotin surface density (\cref{fig:stp_coating,fig:mab_coating}) (this is in
addition to the fact that having more or less carriers will bias the total
amount of \gls{stp} and \gls{mab} able to bind). A quick survey showed that
operators had acceptable margins for error from
\SIrange{2.5}{5.0}{\percent} (eg, a target value $X$ \si{\mg} will be accepted
as $X$ at plus or minus these margins). These could easily be reduced and
standardized via protocol. Additionally, we do not currently record the exact
mass of microcarriers weighed for each batch. Knowing this would allow us to
pinpoint how much of this variance is due to our acceptable measurement margins
and what errors may arise from static and other instrument noise.
\subsubsection{Centrifugation after washing}
After coating the \glspl{dms} with \gls{snb}, \gls{stp}, or \glspl{mab}, they
must be washed. After washing, they must be massed in order to ensure the
reaction volume is consistent. Ideally, the tubes are centrifuged after washing
to ensure that all liquid is at the bottom prior to beginning the next coating
step. Upon survey, not all operators do this, and the protocol is not written to
make this obvious. This protocol can be revised followed by additional training.
\subsubsection{Accidental microcarrier removal}
When washing the microcarriers after a coating step, liquid is aspirated using a
stripette. The carriers should be at the bottom of the tube during this
aspiration step. Depending on the skill and care of the operator, carriers may
be aspirated with the liquid during this step. If this happens, downstream
\gls{qc} assays will not reflect the true binding magnitude, as these assays
assume the number of carriers is constant. Equipment can be modified (such as
aspirators with guides to ensure fixed depth of suction) to mitigate this issue.
\subsubsection{BSA binding kinetics}
Prior to \gls{mab} addition, \gls{bsa} is added to the reaction volume to block
binding to the tubes. \glspl{mab} are added immediately after adding the
\gls{bsa}, which means the \gls{bsa} has almost no time to mix completely and
thus the \gls{mab} could come into contact with the sides of the tube without
competition. This could cause the \gls{mab} \gls{elisa} reading to be lower.
This problem may be minor since significant binding would only occur if the
\gls{mab}/plastic adhesion was fast and happened in the seconds prior to
beginning agitation. We can mitigate this by agitating the tubes with \gls{bsa}
for several minutes prior to adding \gls{mab} to ensure mixing.
\subsubsection{Improving protein detection}
While the \gls{bca} assay and \gls{elisa} are relatively precise, they both have
problems that could lead to systemic bias or excess random noise. The \gls{bca}
assay is non-specific. All our data shows consistent small (\SI{0.5}{\ug}) but
negative readings for blank carriers, which indicates that some background
protein (or something that behaves like a protein) may be present that the
\gls{bca} assay is detecting. The \gls{elisa} is specific to \glspl{mab};
however, in our case we need to run a blank (just \gls{pbs}, \gls{bsa}, and
\glspl{mab} without carriers) and subtract this from the reading, effectively
doubling the assay variance. Using \gls{hplc} would mitigate both issues.
\gls{hplc} can specifically detect species based on differences in charge and
size, so it should be able to quantify \gls{stp} without the extraneous bias of
the \gls{bca} assay. In the case of \gls{elisa} it will not remove the need to
run a blank, but it should lower variance due to its automated nature.
\subsection{Surface Stiffness}
The beads and \glspl{dms} are composed of different materials: iron/polymer for
the former and cross-linked gelatin for the latter. These materials likely have
different stiffnesses, and stiffness could play a role in T cell
activation\cite{Lambert2017}.
@ -4792,8 +4793,8 @@ cross-linked gelatin\cite{Wang1984}.
\subsection{Additional Ligands and Signals on the DMSs}
In this work we only explored the use of \acd{3} and \acd{28} \glspl{mab} coated
on the surface of the \gls{dms}. The chemistry used for the \gls{dms} is very
general, and any molecule or protein that could be engineered with a biotin
on the surface of the \glspl{dms}. The chemistry used for the \glspl{dms} is
very general, and any molecule or protein that could be engineered with a biotin
ligand could be attached without any further modification. There are many other
ligands (in addition to integrin-binding domains and \il{15} complexes as
described at the end of \cref{aim2b}) that could have profound effects on the
@ -4804,7 +4805,7 @@ mimic \textit{trans} presentation from other cell types\cite{Stonier2010}. Other
adhesion ligands or peptides such as GFOGER could be used to stimulate T cells
and provide more motility on the \glspl{dms}\cite{Stephan2014}. Finally, viral
delivery systems could theoretically be attached to the \gls{dms}, greatly
simplifying the transduction step.
simplifying transduction.
\subsection{Assessing Performance Using Unhealthy Donors}
@ -4812,31 +4813,31 @@ All the work presented in this dissertation was performed using healthy donors.
This was mostly due to the fact that it was much easier to obtain healthy donor
cells and was much easier to control. However, it is indisputable that the most
relevant test cases of the \glspl{dms} will be for unhealthy patient T cells, at
least in the case of autologous therapies. In particular, it will be interesting
to see how the \gls{dms} performs when assessed head-to-head with bead-based
expansion technology given that even in healthy donors, we observed the
\gls{dms} platform to work where the beads failed
(\cref{fig:dms_exp_fold_change}).
least for autologous therapies. In particular, it will be interesting to see how
the \gls{dms} performs when assessed head-to-head with bead-based expansion
technology given that even in healthy donors, the \gls{dms} platform worked
where the beads failed (\cref{fig:dms_exp_fold_change}).
\subsection{Translation to Bioreactors}
In this work we performed some preliminary experiments demonstrating that the
\gls{dms} platform can work in a Grex bioreactor. While an important first step,
more work needs to be done to optimize how this system will or can work in a
scalable environment using bioreactors. There are several paths to explore.
Firstly, the Grex itself has additional automation accessories which could be
tested, which would allow continuous media exchange and cytokine
administration. While this is an improvement from the work done here, it is
still a Grex and has all the disadvantages of an open system. Secondly, other
static bioreactors such as the Quantum hollow fiber bioreactor (Terumo) could be
explored. Essentially the \gls{dms} would be an additional matrix that could be
supplied to this system which would enhance its compatibility with T cells.
Finally, suspension bioreactors such as the classic \gls{cstr} or WAVE
bioreactors could be tried. The caveat with these is that the T cells only seem
to be loosely attached to the \gls{dms} throughout culture, so an initial
activation/transduction step in static culture might be necessary before moving
to a suspension system (alternatively the \gls{dms} could be coated with
additional adhesion ligands to make the T cells attach more strongly).
\gls{dms} platform can work in a \gls{grex} bioreactor. While an important first
step, more work needs to be done to optimize how the \gls{dms} system will or
can function in a scalable environment using bioreactors. There are several
paths to explore. Firstly, the \gls{grex} itself has additional automation
accessories which could be tested, which would allow continuous media exchange
and cytokine administration. While this is an improvement from the work done
here, it is still a \gls{grex} and has all the disadvantages of an open system.
Secondly, other static bioreactors such as the Quantum hollow fiber bioreactor
(Terumo) could be explored. Essentially the \gls{dms} would be an additional
matrix that could be supplied to this system which would enhance its
compatibility with T cells. Finally, suspension bioreactors such as the classic
\gls{cstr} or WAVE bioreactors could be tried. The caveat with these is that the
T cells only seem to be loosely attached to the \gls{dms} throughout culture, so
an initial activation/transduction step in static culture might be necessary
before moving to a suspension system (alternatively the \gls{dms} could be
coated with additional adhesion ligands to make the T cells attach more
strongly).
\onecolumn
\clearpage