ADD conclusion paragraphs
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@ -3403,7 +3403,87 @@ this may explain the modest advantage that the \gls{dms} T cells seemed to have
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in the second experiment in slowing the progression of tumor burden.
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\chapter{conclusions and future work}\label{conclusions}
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\section{conclusions}
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This dissertation describes the development of a novel T cell expansion
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platform, including the fabrication, quality control, and biological validation
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of its performance both \invitro{} and \invivo{}. Development of such a system
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would be meaningful even if it only performed as well as current methods, as
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adding another method to the arsenal of the growing T cell manufacturing
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industry would reduce the reliance on a small number of companies that currently
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license magnetic bead-based T cell expansion technology. However, we
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additionally show that the \gls{dms} platform expands more T cells on average,
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including highly potent \ptmem{} and \pth{} T cells, and produces higher
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percentages of both. If commercialized, this would be a compelling asset the T
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cell manufacturing industry.
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% TODO double check the numbers at the end
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In \cref{aim1}, we develop the \gls{dms} platform and verified its efficacy
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\invitro{}. Importantly, this included \gls{qc} steps at every critical step of
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the fabrication process to ensure that the \gls{dms} can be made within a
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targeted specification. These \gls{qc} steps all rely on common, relatively
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cost-effective assays such as the \gls{haba} assay, \gls{bca} assay, and
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\glspl{elisa}, thus other labs and commercial entities should be able to perform
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them. The microcarriers themselves are an off-the-shelf product available from
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reputable vendors, further enhancing translatability. On average, we
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demonstrated that the \gls{dms} outperforms state-of-the-art bead-based T cell
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expansion technology in terms of total fold expansion, \ptmemp{}, and \pthp{} by
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\SI{143}{\percent}, \SI{2.5}{\percent}, and \SI{9.8}{\percent} controlling for
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donor, operator, and a variety of process conditions.
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In \cref{aim2a}, we developed a modeling pipeline that can be used by commercial
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entities as the scale up this process to identify \glspl{cqa} and \gls{cpp}.
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These are highly important for a variety of reasons. First, understanding
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pertinent \glspl{cpp} allow manufacturers to operate their process at optimal
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conditions. This is important for anti-tumor cell therapies, where the prospects
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of a patient can urgently depend on receiving therapy in a timely manner.
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Optimal process conditions allow T cells to be expanded as quickly as possible
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for the patient, while also minimizing cost for the manufacturer. Second,
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\glspl{cqa} can be used to define process control schemes as well as release
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criteria. Process control, and with it the ability to predict future outcomes
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based on data obtained at the present, is highly important for cell therapies
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given that batch failures are extremely expensive {\#}, and predicting a batch
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failure would allow manufacturers to restart the batch in a timely manner
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without wasting resources. Furthermore, \glspl{cqa} can be used to define what a
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`good' vs `bad' product is, which will important help anticipate dosing and
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followup procedures in the clinic if the T cells are administered. In the aim,
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we cannot claim to have found the ultimate set of \glspl{cqa} and \glspl{cpp},
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as we used tissue culture plates instead of a bioreactor and we only used one
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donor. However, we have indeed outlined a process that others may use to find
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these for their process. In particular, the 2-phase modeling process we used
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(starting with a \gls{doe} and collecting data longitudinally) is a strategy
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that manufacturers can easily implement. Also, collecting secretome and
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metabolome is easily generalized to any setting and to most bioreactors and
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expansion systems, as they can be obtained with relatively inexpensive equipment
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(Luminex assay, benchtop \gls{nmr}, etc) without disturbing the cell culture.
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In \cref{aim2b}, we further explored additional tuning knobs that could be used
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to control and optimize the \gls{dms} system. We determined that altering the
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\gls{dms} concentration temporally has profound effects on the phenotype and
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expansion rate. This agrees with other data we obtained in \cref{aim2a} and with
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what others have generally reported about signal strength and T cell
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differentiation {\#}. We did not find any mechanistic relationship between
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either integrin signaling or \gls{il15} signaling. In the case of the former, it
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may be more likely that the \glspl{dms} surfaces are saturated to the point of
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sterically hindering any integrin interactions with the collagen surface. In the
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case of \gls{il15} more experiments likely need to be done in order to plausibly
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rule out this mechanism and/or determine if it is involved at all.
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% TODO make this tighter and cite paper showing that this makes at least some
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% sense
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In \cref{aim3} we determined that the \glspl{dms} expand T cells that also
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performed better than beads \invivo{}. In the first experiment we performed, the
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results were very clearly in favor of the \glspl{dms}. In the second experiment,
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even the \gls{dms} group failed to fully control the tumor burden, but this is
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not surprising given the low \ptcarp{} across all groups. Also, despite this, the
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\gls{dms} group appeared to control the tumor better on average for early, mid,
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and late T cell harvesting timepoints. It was not clear if this effect was due
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to increased \cdp{} or overall increased fitness of the \gls{dms}-expanded T
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cells given their higher expansion rate. The \ptmemp{} did not seem to be a
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factor given that it was nearly the same in the first experiment between
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\gls{dms} and bead groups despite the clear advantage seen in the \gls{dms} group.
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\section{future work}
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\onecolumn
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