From 891e82b456d2637b160d620e73ff81229b9f567e Mon Sep 17 00:00:00 2001 From: ndwarshuis Date: Thu, 9 Sep 2021 14:43:32 -0400 Subject: [PATCH] ENH proof conclusions section --- tex/thesis.tex | 573 +++++++++++++++++++++++++------------------------ 1 file changed, 287 insertions(+), 286 deletions(-) diff --git a/tex/thesis.tex b/tex/thesis.tex index 4749004..0d788b7 100644 --- a/tex/thesis.tex +++ b/tex/thesis.tex @@ -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