ADD conclusion paragraphs

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Nathan Dwarshuis 2021-07-29 18:44:50 -04:00
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@ -3403,7 +3403,87 @@ this may explain the modest advantage that the \gls{dms} T cells seemed to have
in the second experiment in slowing the progression of tumor burden.
\chapter{conclusions and future work}\label{conclusions}
\section{conclusions}
This dissertation describes the development of a novel T cell expansion
platform, including the fabrication, quality control, 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
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,
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.
% TODO double check the numbers at the end
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, further enhancing 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{143}{\percent}, \SI{2.5}{\percent}, and \SI{9.8}{\percent} controlling for
donor, operator, and a variety of process conditions.
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 {\#}, 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.
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
\gls{dms} concentration temporally has profound effects on the phenotype and
expansion rate. This agrees with other data we obtained in \cref{aim2a} and with
what others have generally reported about signal strength and T cell
differentiation {\#}. 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 experiments likely need to be done in order to plausibly
rule out this mechanism and/or determine if it is involved at all.
% TODO make this tighter and cite paper showing that this makes at least some
% sense
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
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 \cdp{} or overall increased fitness of the \gls{dms}-expanded T
cells given their higher expansion rate. The \ptmemp{} did not seem to be a
factor given that it was nearly the same in the first experiment between
\gls{dms} and bead groups despite the clear advantage seen in the \gls{dms} group.
\section{future work}
\onecolumn