% \documentclass[twocolumn]{article} \documentclass{report} \usepackage[top=1in,left=1.5in,right=1in,bottom=1in]{geometry} \usepackage{siunitx} \usepackage{multicol} \setlength{\columnsep}{1cm} \usepackage[acronym]{glossaries} \usepackage[T1]{fontenc} \usepackage{enumitem} \usepackage{titlesec} \usepackage{titlecaps} \usepackage{upgreek} \usepackage{graphicx} \usepackage{subcaption} \usepackage{nth} \usepackage[capitalize]{cleveref} \usepackage[version=4]{mhchem} \usepackage{pgfgantt} \usepackage{setspace} \doublespacing{} \titleformat{\chapter}[block]{\filcenter\bfseries\large} {\MakeUppercase{\chaptertitlename} \thechapter: }{0pt}{\uppercase} % \titleformat{\chapter}[block]{\filcenter\bfseries\large}{}{0pt}{\uppercase} \titleformat{\section}[block]{\bfseries\large}{}{0pt}{\titlecap} \titleformat{\subsection}[block]{\itshape\large}{}{0pt}{\titlecap} \titleformat{\subsubsection}[runin]{\bfseries\itshape\/}{}{0pt}{\titlecap} \setlist[description]{font=$\bullet$~\textbf\normalfont} \sisetup{per-mode=symbol,list-units=single} \DeclareSIUnit\activityunit{U} \DeclareSIUnit\carrier{carriers} \DeclareSIUnit\cell{cells} \DeclareSIUnit\ab{mAbs} \DeclareSIUnit\molar{M} \DeclareSIUnit\gforce{\times{} g} % add acronyms here \renewcommand{\glossarysection}[2][]{} % remove glossary title \makeglossaries \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} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % my commands \newcommand{\mytitle}{ \Large{ \textbf{ Optimizing T Cell Manufacturing and Quality Using Functionalized Degradable Microscaffolds } } } \newcommand{\mycommitteemember}[3]{ \begin{flushleft} \noindent #1 \\ #2 \\ \textit{#3} \end{flushleft} } \newcommand{\invivo}{\textit{in vivo}} \newcommand{\invitro}{\textit{in vitro}} \newcommand{\exvivo}{\textit{ex vivo}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % my environments \newenvironment{mytitlepage}{ \begin{singlespace} \begin{center} } { \end{center} \end{singlespace} } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % document \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 } \end{mytitlepage} \end{titlepage} \onecolumn \pagenumbering{roman} \clearpage \begin{mytitlepage} \mytitle{} \end{mytitlepage} \vfill \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 % \twocolumn \chapter*{acronyms} \addcontentsline{toc}{chapter}{acronyms} \printglossary[type=\acronymtype] \clearpage \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} \onecolumn \clearpage % 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}