phd_thesis/tex/thesis.tex

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\DeclareSIUnit\activityunit{U}
\DeclareSIUnit\carrier{carriers}
\DeclareSIUnit\cell{cells}
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\newacronym{act}{ACT}{adoptive cell therapies}
\newacronym{car}{CAR}{chimeric antigen receptor}
\newacronym[longplural={monoclonal antibodies}]{mab}{mAb}{monoclonal antibody}
\newacronym{ecm}{ECM}{extracellular matrix}
\newacronym{cqa}{CQA}{critical quality attribute}
\newacronym{cpp}{CPP}{critical process parameter}
\newacronym{dms}{DMS}{degradable microscaffold}
\newacronym{doe}{DOE}{design of experiments}
\newacronym{gmp}{GMP}{Good Manufacturing Practices}
\newacronym{cho}{CHO}{Chinese hamster ovary}
\newacronym{all}{ALL}{acute lymphoblastic leukemia}
\newacronym{pdms}{PDMS}{polydimethylsiloxane}
\newacronym{dc}{DC}{dendritic cell}
\newacronym{il2}{IL2}{interleukin 2}
\newacronym{apc}{APC}{antigen presenting cell}
\newacronym{mhc}{MHC}{major histocompatibility complex}
\newacronym{elisa}{ELISA}{enzyme-linked immunosorbent assay}
\newacronym{nmr}{NMR}{nuclear magnetic resonance}

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  \Large{
    \textbf{
      Optimizing T Cell Manufacturing and Quality Using Functionalized
      Degradable Microscaffolds
    }
  }
}

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  \begin{flushleft}
    \noindent
    #1 \\
    #2 \\
    \textit{#3}
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\begin{document}

\begin{titlepage}
  \begin{mytitlepage}
    \mytitle{}

    \vfill

    \Large{
      A Dissertation \\
      Presented to \\
      The Academic Faculty \\

      \vspace{1.5em}

      by

      \vspace{1.5em}

      Nathan John Dwarshuis, B.S. \\

      \vfill

      In Partial Fulfillment \\
      of the Requirements for the Degree \\
      Doctor of Philosophy in Biomedical Engineering in the \\
      Wallace H. Coulter Department of Biomedical Engineering

      \vfill

      Georgia Institute of Technology and Emory University \\
      August 2021

      \vfill

      COPYRIGHT \copyright{} BY NATHAN J. DWARSHUIS
    }
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\large{
  \noindent
  Committee Members

  \begin{multicols}{2}
    \begin{singlespace}

      \mycommitteemember{Dr.\ Krishnendu\ Roy\ (Advisor)}
      {Department of Biomedical Engineering}
      {Georgia Institute of Technology and Emory University}

      \vspace{1.5em}

      \mycommitteemember{Dr.\ Madhav\ Dhodapkar}
      {Department of Hematology and Medical Oncology}
      {Emory University}

      \vspace{1.5em}

      \mycommitteemember{Dr.\ Melissa\ Kemp}
      {Department of Biomedical Engineering}
      {Georgia Institute of Technology and Emory University}

      \columnbreak{}
      \null{}
      \vfill

      \mycommitteemember{Dr.\ Wilbur\ Lam}
      {Department of Biomedical Engineering}
      {Georgia Institute of Technology and Emory University}

      \vspace{1.5em}

      \mycommitteemember{Dr.\ Sakis\ Mantalaris}
      {Department of Biomedical Engineering}
      {Georgia Institute of Technology and Emory University}

    \end{singlespace}
  \end{multicols}

  \vspace{1.5em}

  \hfill Date Approved:
}

\clearpage

\chapter*{acknowledgements}
\addcontentsline{toc}{chapter}{acknowledgements} 

Thank you to Lex Fridman and Devin Townsend for being awesome and inspirational.

\clearpage

\chapter*{summary}
\addcontentsline{toc}{chapter}{summary} 

\Gls{act} using \gls{car} T cells have shown promise in treating cancer, but
manufacturing large numbers of high quality cells remains challenging. Currently
approved T cell expansion technologies involve anti-CD3 and CD28 \glspl{mab},
usually mounted on magnetic beads. This method fails to recapitulate many key
signals found \invivo{} and is also heavily licensed by a few companies,
limiting its long-term usefulness to manufactures and clinicians. Furthermore,
we understand that highly potent T cells are generally less-differentiated
subtypes such as central memory and stem memory T cells. Despite this
understanding, little has been done to optimize T cell expansion for generating
these subtypes, including measurement and feedback control strategies that are
necessary for any modern manufacturing process.

The goal of this thesis was to develop a microcarrier-based \gls{dms} T cell
expansion system as well as determine biologically-meaningful \glspl{cqa} and
\glspl{cpp} that could be used to optimize for highly-potent T cells. In Aim 1,
we develop and characterized the \gls{dms} system, including quality control
steps. We also demonstrate the feasiblity of expanding highly-potent memory and
CD4+ T cells, and showing compatibility with existing \gls{car} transduction
methods. In aim 2, we use \gls{doe} methodology to optimize the \gls{dms}
platform, and develop a computational pipeline to identify and model the effect
of measurable \glspl{cqa} and \glspl{cpp} on the final product. In aim 3, we
demonstrate the effectiveness of the \gls{dms} platform \invivo{}. This
thesis lays the groundwork for a novel T cell expansion method which can be used
in a clinical setting, and also provides a path toward optimizing for product
quality in an industrial setting.

\clearpage

\tableofcontents

\clearpage

\listoffigures

\clearpage

\listoftables

\clearpage

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\chapter*{acronyms}
\addcontentsline{toc}{chapter}{acronyms}

\printglossary[type=\acronymtype]

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\pagenumbering{arabic}

\clearpage

\chapter{introduction}

\section*{overview}

% TODO this is basically the same as the first part of the backgound, I guess I
% can just trim it down

T cell-based immunotherapies have received great interest from clinicians and
industry due to their potential to treat, and often cure, cancer and other
diseases\cite{Fesnak2016,Rosenberg2015}. In 2017, Novartis and Kite Pharma
received FDA approval for \textit{Kymriah} and \textit{Yescarta} respectively,
two genetically-modified \gls{car} T cell therapies against B cell malignancies.
Despite these successes, \gls{car} T cell therapies are constrained by an
expensive and difficult-to-scale manufacturing process with little control on
cell quality and phenotype3,4. State-of-the-art T cell manufacturing techniques
focus on anti-CD3 and anti-CD28 activation and expansion, typically presented on
superparamagnetic, iron-based microbeads (Invitrogen Dynabead, Miltenyi MACS
beads), on nanobeads (Miltenyi TransACT), or in soluble tetramers
(Expamer)\cite{Roddie2019,Dwarshuis2017,Wang2016, Piscopo2017, Bashour2015}.
These strategies overlook many of the signaling components present in the
secondary lymphoid organs where T cells expand \invivo{}. Typically, T cells are
activated under close cell-cell contact, which allows for efficient
autocrine/paracrine signaling via growth-stimulating cytokines such as
\gls{il2}. Additionally, the lymphoid tissues are comprised of \gls{ecm}
components such as collagen, which provide signals to upregulate proliferation,
cytokine production, and pro-survival pathways\cite{Gendron2003, Ohtani2008,
  Boisvert2007, Ben-Horin2004}. We hypothesized that culture conditions that
better mimic these \invivo{} expansion conditions of T cells, can significantly
improve the quality and quantity of manufactured T cells and provide better
control on the resulting T cell phenotype.

% TODO mention the Cloudz stuff that's in my presentation

A variety of solutions have been proposed to make the T cell expansion process
more physiological. One strategy is to use modified feeder cell cultures to
provide activation signals similar to those of \glspl{dc}\cite{Forget2014}.
While this has the theoretical capacity to mimic many components of the lymph
node, it is hard to reproduce on a large scale due to the complexity and
inherent variability of using cell lines in a fully \gls{gmp}-compliant manner.
Others have proposed biomaterials-based solutions to circumvent this problem,
including lipid-coated microrods\cite{Cheung2018}, 3D-scaffolds via either
Matrigel\cite{Rio2018} or 3d-printed lattices\cite{Delalat2017}, ellipsoid
beads\cite{meyer15_immun}, and \gls{mab}-conjugated \gls{pdms}
beads\cite{Lambert2017} that respectively recapitulate the cellular membrane,
large interfacial contact area, 3D-structure, or soft surfaces T cells normally
experience \invivo{}. While these have been shown to provide superior expansion
compared to traditional microbeads, none of these methods has been able to show
preferential expansion of functional naïve/memory and CD4 T cell populations.
Generally, T cells with a lower differentiation state such as naïve and memory
cells have been shown to provide superior anti-tumor potency, presumably due to
their higher potential to replicate, migrate, and engraft, leading to a
long-term, durable response\cite{Xu2014, Fraietta2018, Gattinoni2011,
  Gattinoni2012}. Likewise, CD4 T cells are similarly important to anti-tumor
potency due to their cytokine release properties and ability to resist
exhaustion\cite{Wang2018, Yang2017}. Therefore, methods to increase naïve/memory
and CD4 T cells in the final product are needed, a critical consideration being
ease of translation to industry and ability to interface with scalable systems
such as bioreactors.

% TODO probably need to address some of the modeling stuff here as well

This thesis describes a novel degradable microscaffold-based method derived from
porous microcarriers functionalized with anti-CD3 and anti-CD28 \glspl{mab} for
use in T cell expansion cultures. Microcarriers have historically been used
throughout the bioprocess industry for adherent cultures such as stem cells and
\gls{cho} cells, but not with suspension cells such as T
cells\cite{Heathman2015, Sart2011}. The microcarriers chosen to make the DMSs in
this study have a microporous structure that allows T cells to grow inside and
along the surface, providing ample cell-cell contact for enhanced autocrine and
paracrine signaling. Furthermore, the carriers are composed of gelatin, which is
a collagen derivative and therefore has adhesion domains that are also present
within the lymph nodes. Finally, the 3D surface of the carriers provides a
larger contact area for T cells to interact with the \glspl{mab} relative to
beads; this may better emulate the large contact surface area that occurs
between T cells and \glspl{dc}. These microcarriers are readily available in
over 30 countries and are used in an FDA fast-track-approved combination retinal
pigment epithelial cell product (Spheramine, Titan Pharmaceuticals) {\#}[Purcell
documentation]. This regulatory history will aid in clinical translation. We
show that compared to traditional microbeads, \gls{dms}-expanded T cells not
only provide superior expansion, but consistently provide a higher frequency of
naïve/memory and CD4 T cells (CCR7+CD62L+) across multiple donors. We also
demonstrate functional cytotoxicity using a CD19 \gls{car} and a superior
performance, even at a lower \gls{car} T cell dose, of \gls{dms}-expanded
\gls{car}-T cells \invivo{} in a mouse xenograft model of human B cell \gls{all}.
Our results indicate that \glspl{dms} provide a robust and scalable platform for
manufacturing therapeutic T cells with higher naïve/memory phenotype and more
balanced CD4+ T cell content.

\section*{hypothesis}

The hypothesis of this dissertation was that using \glspl{dms} created from
off-the-shelf microcarriers and coated with activating \glspl{mab} would lead to
higher quantity and quality T cells as compared to state-of-the-art bead-based
expansion. The objective of this dissertation was to develop this platform, test
its effectiveness both \invivo{} and \invivo{}, and develop computational
pipelines that could be used in a manufacturing environment.

\section*{specific aims}

The specific aims of this dissertation are outlined in
\cref{fig:graphical_overview}.

\begin{figure*}[ht!]
  \begingroup

  \includegraphics[width=\textwidth]{example-image-a}

  \endgroup
  \caption[Project Overview]{High-level workflow.}
  \label{fig:graphical_overview}
\end{figure*}

\subsection*{aim 1: develop and optimize a novel T cell expansion process that
  mimics key components of the lymph nodes}

% TODO this might be easier to break apart in separate aims

In this first aim, we demonstrated the process for manufacturing \glspl{dms},
including quality control steps that are necessary for translation of this
platform into a scalable manufacturing setting. We also demonstrate that the
\gls{dms} platform leads to higher overall expansion of T cells and higher
overall fractions of potent memory and CD4+ subtypes desired for T cell
therapies. Finally, we demonstrate \invitro{} that the \gls{dms} platform can be
used to generate functional \gls{car} T cells targeted toward CD19.

\subsection*{aim 2: develop methods to control and predict T cell quality}

For this second aim, we investigated methods to identify and control \glspl{cqa}
and glspl{cpp} for manufacturing T cells using the \gls{dms} platform. This was
accomplished through two sub-aims:

\begin{itemize}
  \item[A --] Develop computational methods to control and predict T cell
    expansion and quality
  \item[B --] Perturb \gls{dms} expansion to identify additional mechanistic
    controls for expansion and quality
\end{itemize}

\subsection*{aim 3: confirm potency of T cells from novel T cell expansion
  process using \invivo{} xenograft mouse model}

In this final aim, we demonstrate the effectiveness of \gls{dms}-expanded T
cells compared to state-of-the-art beads using \invivo{} mouse models for
\gls{all}.

\section*{outline}

In Chapter~\ref{background}, we provide additional background on the current
state of T cell manufacturing and how the work in this dissertation moves the
field forward. In Chapters~\ref{aim1},~\ref{aim2}, and~\ref{aim3} we present the
work pertaining to Aims 1, 2, and 3 respectively. Finally, we present our final
conclusions in Chapter~\ref{conclusions}.

\chapter{background and significance}\label{background}
\section*{background}

% TODO break this apart into mfg tech and T cell phenotypes/quality
% TODO consider adding a separate section on microcarriers and their use in
% bioprocess
% TODO add stuff about T cell licensing
\subsection*{current T cell manufacturing technologies}

\Gls{car} T cell therapy has received great interest from both academia and
industry due to its potential to treat cancer and other
diseases\cite{Fesnak2016, Rosenberg2015}. In 2017, Novartis and Kite Pharma
acquired FDA approval for \textit{Kymriah} and \textit{Yescarta} respectively,
two \gls{car} T cell therapies against B cell malignancies. Despite these
successes, \gls{car} T cell therapies are constrained by an expensive and
difficult-to-scale manufacturing process\cite{Roddie2019, Dwarshuis2017}.

Of critical concern, state-of-the-art manufacturing techniques focus only on
Signal 1 and Signal 2-based activation via anti-CD3 and anti-CD28 \glspl{mab},
typically presented on a microbead (Invitrogen Dynabead, Miltenyi MACS beads) or
nanobead (Miltenyi TransACT), but also in soluble forms in the case of antibody
tetramers (Expamer)\cite{Wang2016, Piscopo2017, Roddie2019, Bashour2015}. These
strategies overlook many of the signaling components present in the secondary
lymphoid organs where T cells normally expand. Typically, T cells are activated
under close cell-cell contact via \glspl{apc} such as \glspl{dc}, which present
peptide-\glspl{mhc} to T cells as well as a variety of other costimulatory
signals. These close quarters allow for efficient autocrine/paracrine signaling
among the expanding T cells, which secrete IL2 and other cytokines to assist
their own growth. Additionally, the lymphoid tissues are comprised of \gls{ecm}
components such as collagen, which provide signals to upregulate proliferation,
cytokine production, and pro-survival pathways\cite{Gendron2003, Ohtani2008,
  Boisvert2007, Ben-Horin2004}.

A variety of solutions have been proposed to make the T cell expansion process
more physiological. One strategy is to use modified feeder cell cultures to
provide activation signals similar to those of \glspl{dc}\cite{Forget2014}.
While this has the theoretical capacity to mimic several key components of the
lymph node, it is hard to reproduce on a large scale due to the complexity and
inherent variability of using cell lines in a fully \gls{gmp}-compliant manner.
Others have proposed biomaterials-based solutions to circumvent this problem,
including lipid-coated microrods\cite{Cheung2018}, 3D-scaffolds via either
Matrigel\cite{Rio2018} or 3d-printed lattices\cite{Delalat2017}, ellipsoid
beads\cite{meyer15_immun}, and \gls{mab}-conjugated \gls{pdms}
beads\cite{Lambert2017} that respectively recapitulate the cellular membrane,
large interfacial contact area, 3D-structure, or soft surfaces T cells normally
experience \textit{in vivo}. While these have been shown to provide superior
expansion compared to traditional microbeads, no method has been able to show
preferential expansion of functional memory and CD4 T cell populations.
Generally, T cells with a lower differentiation state such as memory cells have
been shown to provide superior anti-tumor potency, presumably due to their
higher potential to replicate, migrate, and engraft, leading to a long-term,
durable response\cite{Xu2014, Gattinoni2012, Fraietta2018, Gattinoni2011}.
Likewise, CD4 T cells are similarly important to anti-tumor potency due to their
cytokine release properties and ability to resist exhaustion\cite{Wang2018,
  Yang2017}, and no method exists to preferentially expand the CD4 population
compared to state-of-the-art systems.

Here we propose a method using microcarriers functionalized with anti-CD3 and
anti-CD28 \glspl{mab} for use in T cell expansion cultures. Microcarriers have
historically been used throughout the bioprocess industry for adherent cultures
such as stem cells and \gls{cho} cells, but not with suspension cells such as T
cells\cite{Heathman2015, Sart2011}. The carriers have a macroporous structure
that allows T cells to grow inside and along the surface, providing ample
cell-cell contact for enhanced autocrine and paracrine signaling. Furthermore,
the carriers are composed of gelatin, which is a collagen derivative and
therefore has adhesion domains that are also present within the lymph nodes.
Finally, the 3D surface of the carriers provides a larger contact area for T
cells to interact with the \glspl{mab} relative to beads; this may better
emulate the large contact surface area that occurs between T cells and
\glspl{dc}.

\subsection*{strategies to optimize cell manufacturing}

The \gls{dms} system has a number of parameters that can be optimized, and a
\gls{doe} is an ideal framework to test multiple parameters simultaneously. The
goal of \gls{doe} is to answer a data-driven question with the least number of
resources. It was developed in many non-biological industries throughout the
\nth{20} century such as the automotive and semiconductor industries where
engineers needed to minimize downtime and resource consumption on full-scale
production lines.

% TODO add a bit more about the math of a DOE here

\Glspl{doe} served three purposes in this dissertation. First, we used them as
screening tools, which allowed us to test many input parameters and filter out
the few that likely have the greatest effect on the response. Second, they were
used to make a robust response surface model to predict optimums using
relatively few resources, especially compared to full factorial or
one-factor-at-a-time approaches. Third, we used \glspl{doe} to discover novel
effects and interactions that generated hypotheses that could influence the
directions for future work.

\subsection*{strategies to characterize cell manufacturing}

A number of multiomics strategies exist which can generate rich datasets for T
cells. We will consider several multiomics strategies within this proposal:

\begin{description}
\item[Luminex:] A multiplexed bead-based \gls{elisa} that can measure
  many bulk (not single cell) cytokine concentrations simultaneously
  in a media sample. Since this only requires media (as opposed to
  destructively measuring cells) we will use this as a longitudinal
  measurement.
\item[Metabolomics:] It is well known that T cells of different
  lineages have different metabolic profiles; for instance memory T
  cells have larger aerobic capacity and fatty acid
  oxidation\cite{Buck2016, van_der_Windt_2012}. We will interrogate
  key metabolic species using \gls{nmr} in collaboration with the
  Edison Lab at the University of Georgia. This will be both a
  longitudinal assay using media samples (since some metabolites may
  be expelled from cells that are indicative of their phenotype) and
  at endpoint where we will lyse the cells and interogate their entire
  metabolome.
\item[Flow and Mass Cytometry:] Flow cytometry using fluorophores has been used
  extensively for immune cell analysis, but has a practical limit of
  approximately 18 colors\cite{Spitzer2016}. Mass cytometry is analogous to
  traditional flow cytometry except that it uses heavy-metal \gls{mab}
  conjugates, which has a practical limit of over 50 markers. This will be
  useful in determining precise subpopulations and phenotypes that may be
  influencing responses, especially when one considers that many cell types can
  be defined by more than one marker combination. We will perform this at
  endpoint. While mass cytometry is less practical than simple flow cytometers
  such as the BD Accuri, we may find that only a few markers are required to
  accurately predict performance, and thus this could easily translate to
  industry using relatively cost-effective equipment.
\end{description}

% TODO add a computational section

% TODO add a section explaining causal inference since this is a big part of
% the end of aim 1

\section{Innovation}

\subsection{Innovation}

Several aspects of this work are novel considering the state-of-the-art
technology for T cell manufacturing:

\begin{itemize}
\item \Glspl{dms} offers a compelling alternative to state-of-the-art magnetic
  bead technologies (e.g. DynaBeads, MACS-Beads), which is noteworthy because
  the licenses for these techniques is controlled by only a few companies
  (Invitrogen and Miltenyi respectively). Because of this, bead-based expansion
  is more expensive to implement and therefore hinders companies from entering
  the rapidly growing T cell manufacturing arena. Providing an alternative as we
  are doing will add more options, increase competition among both raw material
  and T cell manufacturers, and consequently drive down cell therapy market
  prices and increase innovation throughout the industry.
\item This is the first technology for T cell immunotherapies that selectively
  expands memory T cell populations with greater efficiency relative to
  bead-based expansion Others have demonstrated methods that can achieve greater
  expansion of T cells, but not necessarily specific populations that are known
  to be potent.
\item We propose to optimize our systems using \gls{doe} methodology, which is a
  strategy commonly used in other industries and disciplines but has yet to gain
  wide usage in the development of cell therapies. \Glspl{doe} are advantageous
  as they allow the inspection of multiple parameters simultaneously, allowing
  efficient and comprehensive analysis of the system vs a one-factor-at-a-time
  approach. We believe this method is highly relevant to the development of cell
  therapies, not only for process optimization but also hypotheses generation.
  Of further note, most \textit{in vivo} experiments are not done using a
  \gls{doe}-based approach; however, a \gls{doe} is perfectly natural for a
  large mouse study where one naturally desires to use as few animals as
  possible.
\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.
\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
  a large \gls{doe}-based model. This approach is aware of the whole design
  space, and thus enables greater understanding of process parameters and their
  effect on cell phenotype.
\end{itemize}

\chapter{aim 1}\label{aim1}

\section{introduction}
\section{methods}
\section{results}
\section{discussion}

\chapter{Aim 2}\label{aim2}

\section{introduction}
\section{methods}
\section{results}
\section{discussion}

\chapter{Aim 3}\label{aim3}

\section{introduction}
\section{methods}
\section{results}
\section{discussion}

\chapter{conclusions and future work}\label{conclusions}
\section{conclusions}
\section{future work}

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% TODO some people put appendices here....not sure if I need to

\chapter{References}
\renewcommand{\section}[2]{} % noop the original bib section header

\bibliography{references}

\bibliographystyle{naturemag}

\end{document}