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ENH proofread the background and innovation

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tex/references.bib
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@ -2647,6 +2647,19 @@ CONCLUSIONS: We developed a simplified, semi-closed system for the initial selec
publisher = {Elsevier {BV}},
}
@Article{Rosenberg1988,
author = {Steven A. Rosenberg and Beverly S. Packard and Paul M. Aebersold and Diane Solomon and Suzanne L. Topalian and Stephen T. Toy and Paul Simon and Michael T. Lotze and James C. Yang and Claudia A. Seipp and Colleen Simpson and Charles Carter and Steven Bock and Douglas Schwartzentruber and John P. Wei and Donald E. White},
journal = {New England Journal of Medicine},
title = {Use of Tumor-Infiltrating Lymphocytes and Interleukin-2 in the Immunotherapy of Patients with Metastatic Melanoma},
year = {1988},
month = {dec},
number = {25},
pages = {1676--1680},
volume = {319},
doi = {10.1056/nejm198812223192527},
publisher = {Massachusetts Medical Society},
}
@Comment{jabref-meta: databaseType:bibtex;}
@Comment{jabref-meta: grouping:

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tex/thesis.tex
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@ -84,17 +84,31 @@
% adding as many as possible has the added benefit of making the thesis longer
% and making me sound more sophisticated
% the many flavors of T cells
\newcommand{\tcellacronym}[4]{
\newacronym[shortplural={T\textsubscript{#2}#4
cells}]{#1}{T\textsubscript{#2}#4}{#3 T cell}
}
\renewcommand{\glossarysection}[2][]{} % remove glossary title
\makeglossaries
\tcellacronym{tn}{n}{naive}{}
\tcellacronym{tcm}{cm}{central memory}{}
\tcellacronym{tscm}{scm}{stem-memory}{}
\tcellacronym{tem}{em}{effector-memory}{}
\tcellacronym{teff}{eff}{effector}{}
\tcellacronym{treg}{reg}{regulatory}{}
\tcellacronym{th}{h}{helper}{}
\tcellacronym{tc}{c}{cytotoxic}{}
\tcellacronym{th1}{h}{type 1 helper}{1}
\tcellacronym{th2}{h}{type 2 helper}{2}
% \tcellacronym{th17}{h}{\il{17} helper}{1}
\newacronym{til}{TIL}{tumor infiltrating lymphocyte}
\newacronym{tcr}{TCR}{T cell receptor}
\newacronym{act}{ACT}{adoptive cell therapies}
\newacronym{qc}{QC}{quality control}
\newacronym{tn}{T\textsubscript{n}}{naive T cell}
\newacronym{tcm}{T\textsubscript{cm}}{central memory T cell}
\newacronym{tscm}{T\textsubscript{scm}}{stem-memory T cell}
\newacronym{tem}{T\textsubscript{em}}{effector-memory T cell}
\newacronym{teff}{T\textsubscript{eff}}{effector T cell}
\newacronym{car}{CAR}{chimeric antigen receptor}
\newacronym[longplural={monoclonal antibodies}]{mab}{mAb}{monoclonal antibody}
\newacronym{ecm}{ECM}{extracellular matrix}
@ -190,6 +204,7 @@
\newacronym{cstr}{CSTR}{continuously stirred tank bioreactor}
\newacronym{esc}{ESC}{embryonic stem cell}
\newacronym{msc}{MSC}{mesenchymal stromal cells}
\newacronym{scfv}{scFv}{single-chain fragment variable}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SI units for uber nerds
@ -236,6 +251,9 @@
\newcommand{\invivo}{\textit{in vivo}}
\newcommand{\invitro}{\textit{in vitro}}
\newcommand{\exvivo}{\textit{ex vivo}}
\newcommand{\Invivo}{\textit{In vivo}}
\newcommand{\Invitro}{\textit{In vitro}}
\newcommand{\Exvivo}{\textit{Ex vivo}}
% various CD-whatever crap
\newcommand{\cd}[1]{CD{#1}}
@ -577,39 +595,40 @@ conclusions as well as provide insights for how this work can be extended in the
future.
\chapter{background and significance}\label{background}
\section*{background}
\subsection{quality by design in cell manufacturing}
The challenges facing the cell manufacturing field at large are significant.
Unlike other industries which manufacture inanimate products such as automobiles
and semiconductors, the cell manufacturing industry needs to contend with the
fact that cells are living entities which can change with every process
The challenges for the cell manufacturing field are significant. Unlike other
industries which manufacture inanimate products such as automobiles and
semiconductors, the cell manufacturing industry needs to contend with the fact
that cells are living entities which can change with every process
manipulation\cite{Kirouac2008, Little2006, Pirnay2012, Rousseau2013}. This is
further compounded by the lack of standardization and limited regulation.
In order to overcome these barriers, adopting a systemic approach to cell
manufacturing using \gls{qbd} principles will be extremely
manufacturing using \acrlong{qbd} principles will be extremely
important\cite{Kirouac2008}. In \gls{qbd}, the objective is to reproducibly
manufacturing products which minimizes risk for downstream
stakeholders (in this case, the patient).
manufacturing products which minimizes risk for downstream stakeholders (in this
case, the patient). Broadly, this entails determining \acrlongpl{cqa} and
\acrlongpl{cpp} and incorporating them into models which can explain and predict
the cell manufacturing process.
This requires identification of \glspl{cqa}, which are measurable properties of
the product that can be used to define its functionality and hence quality.
\glspl{cqa} are important for defining the characteristics of a `good' product
(release criteria) but also for ensuring that a process is on track to making
such a product (process control). In the space of cell manufacturing,
examples of \glspl{cqa} include markers on the surface of cells and readouts
from functional assays such as killing assays. In general, these are poorly
understood if they exist at all.
\Glspl{cqa} are measurable properties of the product that can be used to define
its functionality and hence quality. \glspl{cqa} are important for defining the
characteristics of a `good' product (release criteria) but also for ensuring
that a process is on track to making such a product (process control). In the
space of cell manufacturing, examples of \glspl{cqa} include markers on the
surface of cells and readouts from functional assays such as killing assays. In
general, these are poorly understood if they exist at all.
In addition to \glspl{cqa}, the \glspl{cpp} pertinent to the manufacturing
process are poorly understood. \glspl{cpp} are parameters which may be tuned and
varied to control the outcome of process and the quality of the final product.
Examples in the cell manufacturing space include the type of media used and the
amount of \il{2} added. Once \glspl{cpp} are known, they can be optimized to
ensure that costs are minimized and potency of the cellular product is
maximized.
\glspl{cpp} are parameters which may be tuned and varied to control the outcome
of process and the quality of the final product. In cell manufacturing, these
are poorly understood. Examples in the cell manufacturing space include the type
of media used and the amount of \il{2} added. Once \glspl{cpp} are known, they
can be optimized to ensure that costs are minimized and potency of the cellular
product is maximized.
The topic of discovering novel \glspl{cpp} and \glspl{cqa} in the context of
this work are discussed further in \cref{sec:background_doe} and
@ -617,30 +636,39 @@ this work are discussed further in \cref{sec:background_doe} and
\subsection{T cells for immunotherapies}
A variety of T cell therapies have been utilized with varying degrees of
success, and we describe a few of the most prominent below. We should note that
while this work focuses on the application of \gls{car} T cell therapies, in
theory the technology developed in this dissertation could theoretically apply
to any T cell-based therapy with little to no modification.
One of the first successful T cell-based immunotherapies against cancer is
\glspl{til}\cite{Rosenberg2015}. This method works by taking tumor specimens
from a patient, allowing the tumor-reactive lymphocytes to expand \exvivo{}, and
then administered back to the patient along with a high dose of \il{2} [44]. In
particular, \gls{til} therapy has shown robust results in treating melanoma [1],
although \gls{til} have been found in other solid tumors such as
gastointestinal, cervical, lung, and ovarian\cite{Rosenberg2015, Wang2014,
Foppen2015, Solinas2017, June2007, Santoiemma2015}, and their presence is
generally associate with favorable outcomes\cite{Clark1989}. \glspl{til} are
heterogenous cell mixtures and generally are comprised of CD3 T cells and
$\upgamma\updelta$ T cells\cite{Nishimura1999, Cordova2012}. To date, there are
over 250 open clinical trials using \glspl{til}.
then administered back to the patient along with a high dose of
\il{2}\cite{Rosenberg1988}. In particular, \gls{til} therapy has shown robust
results in treating melanoma\cite{Rosenberg2011}, although \glspl{til} have been
found in other solid tumors such as gastointestinal, cervical, lung, and
ovarian\cite{Rosenberg2015, Wang2014, Foppen2015, Solinas2017, June2007,
Santoiemma2015}, and their presence is generally associate with favorable
outcomes\cite{Clark1989}. \glspl{til} are heterogeneous cell mixtures and
generally are comprised of CD3 T cells and $\upgamma\updelta$ T
cells\cite{Nishimura1999, Cordova2012}. To date, there are over 250 open
clinical trials using \glspl{til}.
Besides \gls{til}, the other broad class of T cell immunotherapies that has
Besides \glspl{til}, the other broad class of T cell immunotherapies that has
achieved great success in treating cancer in recent decades are gene-modified T
cells. Rather than expand T cells that are present natively (as is the case with
\gls{til} therapy), gene-modified T cell therapies entail extracting T cells
from either the cancer patient (autologous) or a healthy donor (allogeneic) and
reprogramming them genetically to target a tumor antigen (see
\cref{sec:background_source}). In theory this offers much more
flexibility\cite{Rosenberg2015}.
\cref{sec:background_source} for an overview of how T cells can be sourced).
This approach offers much more flexibility, as the degree of reprogramming is
only limited by the scale and possibilities of gene-editing technology, which
has rapidly accelerated in recent decades\cite{Rosenberg2015}.
T cells with transduced \glspl{tcr} were first designed to overcome the
limitations of \gls{til}\cite{Rosenberg2015, Wang2014}. In this case, T cells
limitations of \glspl{til}\cite{Rosenberg2015, Wang2014}. In this case, T cells
are transduced \exvivo{} with a lentiviral vector to express a \gls{tcr}
targeting a tumor antigen. T cells transduced with \glspl{tcr} have shown robust
results in melanoma patients\cite{Robbins2011}, synovial
@ -649,147 +677,35 @@ sarcoma\cite{Morgan2006}, and others\cite{Ikeda2016}. To date, there are over
While transduced \glspl{tcr} offer some flexibility in retargeting T cells
toward relevant tumor antigens, they are still limited in that they can only
target antigens that are presented via \gls{mhc} complexes. \gls{car} T cells
overcome this limitation by using a the heavy and light chains (scFv) from a
\gls{mab} which can target any antigen recognizable by antibodies. \gls{car} T
cells were first demonstrated in 1989, where the author swapped the
target antigens that are presented via \gls{mhc} complexes. \acrlong{car} T
cells overcome this limitation by using linking a \gls{tcr}-independent antigen
recognition domain with the stimulatory and costimulatory machinery of a T cell
\gls{car} T cells were first demonstrated in 1989, where the authors swapped the
antigen-recognition domains of a native \gls{tcr} with a that of a foreign
\gls{tcr}\cite{Gross1989}. Since then, this method has progressed to using an
scFv with a CD3$\upzeta$ stimulatory domain along with the CD28, OX-40, or 4-1BB
domains for costimulation. Since these can all be expressed with one protein
sequence, \gls{car} T cells are relatively simple to produce and require only a
single genetic transduction step (usually a lentiviral vector) to reprogram a
batch T cells \exvivo{} toward the desired antigen. \gls{car} T cells have
primarily found success in against CD19- and CD20-expressing tumors such as
\gls{all} and \gls{cll} (eg B-cell malignancies)\cite{Kalos2011, Brentjens2011,
Kochenderfer2010, Maude2014, Till2012, Till2008}.
\gls{scfv} with a CD3$\upzeta$ stimulatory domain along with the CD28, OX-40, or
4-1BB domains for costimulation. Since these can all be expressed with one
protein sequence, \gls{car} T cells are relatively simple to produce and require
only a single genetic transduction step (usually a lentiviral vector) to
reprogram a batch T cells \exvivo{} toward the desired antigen. \gls{car} T
cells have primarily found success in against CD19- and CD20-expressing tumors
such as \gls{all} and \gls{cll} (eg B-cell malignancies)\cite{Kalos2011,
Brentjens2011, Kochenderfer2010, Maude2014, Till2012, Till2008}.
% BACKGROUND where else have they been approved?
Out of all the T cell therapies discussed thus far, \gls{car} T cells have
experienced the most commercial success and excitement. In 2017, Novartis and
Kite Pharma acquired FDA approval for \textit{Kymriah} and \textit{Yescarta}
respectively, both of which are \gls{car} T cell therapies against B-cell
malignancies.
% BACKGROUND beef this up, this is a big deal
\gls{car} T cells are under further exploration for use in many other tumors,
including multiple myeloma, mesothelioma, pancreatic cancer, glioblastoma,
neuroblastoma, and prostate cancer, breast cancer, non-small-cell lung cancer,
and others\cite{Rosenberg2015, Wang2014, Fesnak2016, Guo2016}. To date, there
are almost 1000 clinical trials using \gls{car} T cells.
malignancies. \gls{car} T cells are under further exploration for use in many
other tumors, including multiple myeloma, mesothelioma, pancreatic cancer,
glioblastoma, neuroblastoma, and prostate cancer, breast cancer, non-small-cell
lung cancer, and others\cite{Rosenberg2015, Wang2014, Fesnak2016, Guo2016}. To
date, there are almost 1000 clinical trials using \gls{car} T cells.
% TODO there are other T cells like virus-specific T cells and gd T cells, not
% that they matter...
\subsection{cell sources in T cell manufacturing}\label{sec:background_source}
T cells for cell manufacturing can be obtained broadly via two paradigms:
autologous and allogeneic. The former involves obtaining T cells from a patient
and giving them back to the same patient after \exvivo{} expansion and genetic
modification. The latter involves taking T cells from a (usually) healthy donor,
expanding them and manipulating them as desired, storing them long term, and
then giving them to multiple patients. There are advantages and disadvantages to
both, and in some cases such as \gls{til} therapy, the only option is to use
autologous therapy.
Autologous T cells by default are much safer. By definition, they will have no
cross-reactivity with the patient and thus \gls{gvhd} is not a
concern\cite{Decker2012}. However, there are numerous disadvantages. Autologous
therapies are over 20X more costly as the process needs to be repeated for every
patient\cite{Harrison2019}. To highlight how resource-intensive this can be,
many cell products are manufactured at a centralized location, so patient T
cells need to be shipped twice on dry ice from the hospital and back. In
additional to being expensive, this can add days to the process, which is
critical for patients with fast moving diseases. Manufacturing could be done
on-site in a decentralized manner, but this requires more equipment and
personnel overall. Using cells from a diseased patient has many drawbacks in
itself. Cancer patients (especially those with chronic illnesses) often have
exhausted T cells which expand far less readily and are consequently less
potent\cite{Wherry2015, Ando2019, Zheng2017}. Additionally, they may have high
frequencies of T\textsubscript{reg} cells which inhibitory\cite{Decker2012}.
Removing these cells as well as purifying Th1 cells may enhance the potency of
the final product\cite{Goldstein2012, Drela2004, Rankin2003, Luheshi2013,
Grotz2015}; however, this would make the overall process more expensive as an
additional step would be required.
Allogeneic T cell therapies overcome nearly all of these disadvantages. Donor
\glspl{pbmc} are easy to obtain, they can be processed in centralized locations,
they can be stored easily under liquid nitrogen, and donors could be screened to
find those with optimal anti-tumor cells. The key is overcoming \gls{gvhd}.
Obviously this could be done the same way as done for transplants where patients
find a `match' for their \gls{hla} type, but this severally limits options. This
can be overcome by using gene-editing (eg \glspl{zfn}, \glspl{talen}, or
\gls{crispr} to remove the native \gls{tcr} which would prevent the donor T
cells from attacking host tissue\cite{Liu2019, Wiebking2020, Provasi2012,
Berdien2014, Themeli2015}. To date there are about 10 open clinical trials
utilizing allogeneic T cell therapies edited with \gls{crispr} to reduce the
likelihood of \gls{gvhd}.
\subsection{microcarriers in bioprocessing}
Microcarriers have historically been used to grow a number of adherent cell
types for a variety of applications. They were introduced in 1967 as a means to
grow adherent cells `in suspension', effectively turning a 2D flask system into
a 3D culture system\cite{WEZEL1967}. Microcarriers are generally spherical and
are several hundred \si{\um} in diameter, which means they collectively have a
much higher surface area than a traditional flask when matched for volume.
Consequently, this means that microcarrier-based cultures can operate with much
lower footprints than flask-like systems. Microcarriers also allow cell culture
to operate more like a traditional chemical engineering process, wherein a
\gls{cstr} may be employed to enhance oxygen transfer, maintain pH,
and continuously supply nutrients\cite{Derakhti2019}.
A variety of microcarriers have been designed, primarily differing in their
choice of material and macroporous structure. Key concerns have been cell
attachment at the beginning of culture and cell detachment at the harvesting
step; these have largely driven the nature of the material and structures
employed\cite{Derakhti2019}. Many microcarriers simply use polystyrene (the
material used for tissue culture flasks and dishes in general) with no
modification (SoloHill Plastic, Nunc Biosilon), with a cationic surface charge
(SoloHill Hillex) or coated with an \gls{ecm} protein such as collagen (SoloHill
Fact III). Other base materials have been used such as dextran (GE Healthcare
Cytodex), cellulose (GE Healthcare Cytopore), and glass (\sigald{} G2767),
all with none or similar surface modifications. Additionally, some microcarriers
such as \gls{cus} and \gls{cug} are composed entirely out of protein (in these
cases, porcine collagen) which also allows the microcarriers to be enzymatically
degraded. In the case of non-protein materials, cells may still be detached
using enzymes but these require similar methods to those currently used in
flasks such as trypsin which target the cellular \gls{ecm} directly. Since
trypsin and related enzymes tends to be harsh on cells, an advantage of using
entirely protein-based microcarriers is that they can be degraded using a much
safer enzyme such as collagenase, at the cost of being more expensive and also
being harder to make \gls{gmp}-compliant\cite{Derakhti2019}. Going one step
further, some microcarriers are composed of a naturally degrading scaffold such
as alginate, which do not need an enzyme for degradation but are limited in that
the degradation process is less controllable. Finally, microcarriers can differ
in their overall structure. \gls{cug} and \gls{cus} microcarriers as well as the
Cytopore microcarriers are macroporous, meaning they have a porous network in
which cells can attach throughout their interior. This drastically increases the
effective surface area and consequently the number of cells which may be grown
per unit volume. Other microcarriers are microporous (eg only to small
molecules) or not porous at all (eg polystyrene) in which case the cells can
only grow on the surface.
Microcarriers in general have seen the most use in growing \gls{cho} cells and
hybridomas in the case of protein manufacturing (eg \gls{igg}
production)\cite{Xiao1999, Kim2011} as well as \glspl{esc} and \glspl{msc} more
recently in the case of cell manufacturing\cite{Heathman2015, Sart2011,
Chen2013, Schop2010, Rafiq2016}. Interestingly, some groups have even explored
using biodegradable microcarriers \invivo{} as a delivery vehicle for stem cell
therapies in the context of regenerative medicine\cite{Zhang2016, Saltz2016,
Park2013, Malda2006}. However, the characteristic shared by all the cell types
in this application is the fact that they are adherent. In this work, we explore
the use of microcarrier for T cells, which are naturally non-adherent.
The microcarriers used in this work were \gls{cus} and \gls{cug} (mostly the
former) which are both composed of cross-linked gelatin and have a macroporous
morphology. These specific carriers have been used in the past for pancreatic
islet cells\cite{Guerra2001}, \glspl{esc}\cite{Fernandes2007, Storm_2010}, and
\glspl{msc}\cite{Eibes2010}. Furthermore, they 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)\cite{purcellmain}.
This regulatory history will aid in clinical translation.
\subsection{methods to scale T cells}
\subsection{scaling T cell expansion}
In order to scale T cell therapies to meet clinical demands, automation and
bioreactors will be necessary. To this end, there are several choices that have
@ -799,8 +715,8 @@ The WAVE bioreactor (GE Healthcare) is the choice of expansion for many clinical
applications\cite{Brentjens2011, Hollyman2009, Brentjens2013}. It is part of a
broader class of bioreactors that consist of rocking platforms that agitate a
bag filled with media and cells. Importantly, it has built-in sensors for
measuring media flow rate, carbon dioxide, oxygen, pH, and nutrient consumption
which enables automation. Unfortunately, in some settings this is not considered
measuring media flow rate, \ce{CO2}, \ce{O2}, pH, and nutrient consumption which
enables automation. Unfortunately, in some settings this is not considered
scalable as only one bag per bioreactor is allowed at once\cite{Roddie2019}. The
other disadvantage with the WAVE system is that it keeps cells far apart by
design, which could have negative impact on cross-talk, activation, and
@ -830,6 +746,56 @@ 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}.
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
this work need to consider how it may be used at scale in such a system.
\subsection{cell sources in T cell manufacturing}\label{sec:background_source}
T cells for cell manufacturing can be obtained broadly via two paradigms:
autologous and allogeneic. The former involves obtaining T cells from a patient
and giving them back to the same patient after \exvivo{} expansion and genetic
modification. The latter involves taking T cells from a healthy donor, expanding
them and manipulating them as desired, storing them long term, and then giving
them to multiple patients. There are advantages and disadvantages to both, and
in some cases such as \gls{til} therapy, the only option is to use autologous
therapy.
Autologous T cells by default are much safer. By definition, they will have no
cross-reactivity with the patient and thus \gls{gvhd} is not a
concern\cite{Decker2012}. However, there are numerous disadvantages. Autologous
therapies are over 20 times more costly as the process needs to be repeated for
every patient\cite{Harrison2019}. Compounding this, many cell products are
manufactured at a centralized location, so patient T cells need to be shipped
twice on dry ice from the hospital and back. This adds days to the process,
which is critical for patients with fast moving diseases. Manufacturing could be
done on-site in a decentralized manner, but this requires more equipment and
personnel overall. Using cells from a diseased patient has many drawbacks in
itself. Cancer patients (especially those with chronic illnesses) often have
exhausted T cells which expand far less readily and are consequently less
potent\cite{Wherry2015, Ando2019, Zheng2017}. Additionally, they may have high
frequencies of \glspl{treg} which inhibitory\cite{Decker2012}. Removing these
cells as well as purifying \glspl{th1} may enhance the potency of the final
product\cite{Goldstein2012, Drela2004, Rankin2003, Luheshi2013, Grotz2015};
however, this would make the overall process more expensive as an additional
separation step would be required.
Allogeneic T cell therapies overcome nearly all of these disadvantages. Donor
\glspl{pbmc} are easy to obtain, they can be processed in centralized locations,
they can be stored easily under liquid nitrogen, and donors could be screened to
find those with optimal anti-tumor cells. The key is overcoming \gls{gvhd}.
Obviously this could be done the same way as done for transplants where patients
find a `match' for their \gls{hla} type, but this severally limits options. This
can be overcome by using advanced gene-editing tools which can both add and
delete genetic material (eg \glspl{zfn}, \glspl{talen}, or \gls{crispr}) to
remove the native \gls{tcr} which would prevent the donor T cells from attacking
host tissue\cite{Liu2019, Wiebking2020, Provasi2012, Berdien2014, Themeli2015}.
This obviously complicates the process, as additional edits besides the
insertion of the \gls{car} would be required, and these technologies are not yet
very efficient. To date there are about 10 open clinical trials utilizing
allogeneic T cell therapies edited with \gls{crispr} to reduce the likelihood of
\gls{gvhd}.
\subsection{overview of T cell quality}\label{sec:background_quality}
T cells are highly heterogeneous and can exist in a variety of states and
@ -840,39 +806,39 @@ criteria, and initial cell source screening.
One of the most important dimensions of T cell quality is that of
differentiation. T cells begin their life in circulation (eg after they exit the
thymus) as naive T cells. When they become activated in the secondary lymph node
organs, they differentiate from \gls{tn} to \gls{tscm}, \gls{tcm}, \gls{tem},
and finally \gls{teff}\cite{Gattinoni2012}. Subtypes earlier in this process are
generally called `memory' or `memory-like' cells (eg \gls{tscm} and \gls{tcm}),
and have been shown to have increased potency toward a variety of tumors,
presumably due to their higher capacity for self-renewal and replication,
enhanced migratory capacity, and/or increased engraftment potential\cite{Xu2014,
Gattinoni2012, Fraietta2018, Gattinoni2011, Turtle2009}. The capacity for
self-renewal is especially important for T cells therapies, as evidenced by the
fact that \gls{til} therapies with longer telomeres tend to work
better\cite{Donia2012}. Additionally, clonal diversity decreases following the
infusion of \gls{car} T cell therapies, which demonstrates that only a few
clones are self-renewing and therefore responsible for the overall
response\cite{Sheih2020}. Memory T cells can be quantified easily using surface
markers such as CD62L, CCR7, CD27, CD45RA, and CD45RO. Furthermore, memory
markers are inversely related to exhaustion markers which are negatively
associated with clinical outcomes\cite{Lee2013}. These cells in particular are
seen in patients with chronic immune activation such as patients with chronic
cancers.
thymus) as \glspl{tn}. When they become activated in the secondary lymph node
organs, they differentiate from \gls{tn} to \glspl{tscm}, \glspl{tcm},
\glspl{tem}, and finally \glspl{teff}\cite{Gattinoni2012}. Subtypes earlier in
this process are generally called `memory' or `memory-like' cells (eg \gls{tscm}
and \gls{tcm}), and have been shown to have increased potency toward a variety
of tumors, presumably due to their higher capacity for self-renewal and
replication, enhanced migratory capacity, and/or increased engraftment
potential\cite{Xu2014, Gattinoni2012, Fraietta2018, Gattinoni2011, Turtle2009}.
The capacity for self-renewal is especially important for T cells therapies, as
evidenced by the fact that \gls{til} therapies with longer telomeres tend to
create more durable responses\cite{Donia2012}. Additionally, clonal diversity
decreases following the infusion of \gls{car} T cell therapies, which
demonstrates that only a few clones are self-renewing and therefore responsible
for the overall response\cite{Sheih2020}. Memory T cells can be quantified
easily using surface markers such as CD62L, CCR7, CD27, CD45RA, and CD45RO.
Furthermore, memory markers are inversely related to exhaustion markers which
are negatively associated with clinical outcomes\cite{Lee2013}. These cells in
particular are seen in patients with chronic immune activation such as patients
with chronic cancers.
In addition to memory, the other major axis by which T cells may be classified
is the CD4/CD8 ratio. CD4 (`helper') T cells are responsible for secreting
cytokines which coordinate the immune response while CD8 (`killer') T cell
responsible for killing tumor or infected cells using specialized lytic enzymes.
Since CD8 T cells actually perform the killing function, it seems intuitive that
CD8 T cells would be most important for anti-tumor immunotherapies. However, in
mouse models with glioblastoma, survival was negatively impacted when CD4 T
cells were removed\cite{Wang2018}. Furthermore, CD4 T cells have been shown to
have cytotoxic properties on their own and also show resistance to T cell
exhaustion compared to CD8 T cells\cite{Yang2017}. While T cell products with a
defined ratio of CD4 and CD8 T cells have been utilized, they are more expensive
than products with undefined ratios as the T cells need to be sorted and
recombined, adding additional complexity\cite{Turtle2016}.
is the CD4/CD8 ratio. \Glspl{th} are CD4+ are responsible for secreting
cytokines which coordinate the immune response while CD8+ \glspl{tc} are
responsible for killing tumors or infected cells using specialized lytic
enzymes. Since \glspl{tc} actually perform the killing function, it seems
intuitive that \glspl{tc} would be most important for anti-tumor
immunotherapies. However, in mouse models with glioblastoma, survival was
negatively impacted when \glspl{th} were removed\cite{Wang2018}. Furthermore,
\glspl{th} have been shown to have cytotoxic properties on their own and also
show resistance to exhaustion compared to \glspl{tc}\cite{Yang2017}. While T
cell products with a defined ratio of CD4 and CD8 T cells have been utilized,
they are more expensive than products with undefined ratios as the T cells need
to be sorted and recombined, adding additional complexity\cite{Turtle2016}.
While less of a focus in this dissertation, other quality markers exists to
assess the overall killing potential and safety of the T cell product. Numerous
@ -887,14 +853,10 @@ using retro- or lentiviral vectors as their means of gene-editing must be tested
for replication competent vectors\cite{Wang2013} and for contamination via
bacteria or other pathogens.
\subsection*{T cell activation}
% Despite these success of T cell therapies (especially \gls{car} T cell
% therapies) they are constrained by an expensive and difficult-to-scale
% manufacturing process\cite{Roddie2019, Dwarshuis2017}.
\subsection*{T cell activation methods}\label{sec:background_activation}
In order for T cells to be expanded \exvivo{} they must be activated with a
stimulatory signal (Signal 1) and a costimulatory signal (Signal 2). \invivo{}
stimulatory signal (Signal 1) and a costimulatory signal (Signal 2). \Invivo{},
Signal 1 is administered via the \gls{tcr} and the CD3 receptor when \glspl{apc}
present a peptide via \gls{mhc} that the T cell in question is able to
recognize. Signal 2 is administered via CD80 and CD86 which are also present on
@ -906,12 +868,13 @@ and \glspl{apc} have corresponding ligands for these depending on the nature of
the pathogen they have detected\cite{Azuma2019}. Furthermore, T cells exist in
high cell density within the secondary lymphoid organs, which allows efficient
cytokine cross-talk in an autocrine and paracrine manner. These cytokines are
responsible for expansion (in the case of \il{2}) and subset differentiation (in
the case of many others)\cite{Luckheeram2012}. By tuning the signal strength and
duration of Signal 1, Signal 2, the various costimulatory signals, and the
cytokine milieu, a variety of T cell phenotypes can be actualized.
responsible for triggering proliferation (in the case of \il{2}) and subset
differentiation (in the case of many others)\cite{Luckheeram2012}. By tuning the
signal strength and duration of Signal 1, Signal 2, the various costimulatory
signals, and the cytokine milieu, a variety of T cell phenotypes can be
actualized.
\invitro{}, T cells can be activated in a number of ways but the simplest and
\Invitro{}, T cells can be activated in a number of ways but the simplest and
most common is to use \glspl{mab} that cross-link the CD3 and CD28 receptors,
which supply Signal 1 and Signal 2 without the need for antigen (which also
means all T cells are activated and not just a few specific clones). Additional
@ -921,21 +884,21 @@ or feeder cells\cite{Forget2014}.
As this is a critical unit operation in the manufacturing of T cell therapies, a
number of commercial technologies exist to activate T cells\cite{Wang2016,
Piscopo2017, Roddie2019, Bashour2015}. The simplest is to use \acd{3} and
\acd{28} \gls{mab} bound to a 2D surface such as a plate, and this can be
ackomplished in a \gls{gmp} manner as soluble \gls{gmp}-grade \glspl{mab} are
commericially available. A similar but distinct method along these lines is to
use multivalent activators such as ImmunoCult (StemCell Technologies) or Expamer
(Juno Therapeutics) which may have increased cross-linking capacity compared to
traditional \glspl{mab}. Beyond soluble protein, \glspl{mab} against CD3 and
CD28 can be mounted on magnetic microbeads (\SIrange{3}{5}{\um} in diameter)
such as DynaBeads (Invitrogen) and MACSbeads (\miltenyi{}), which are easier to
separate using magnetic washing plates. Magnetic nanobeads such as TransAct
(\miltenyi{}) work by a similar principle except they can be removed via
centrifugation rather than a magnetic washing plate. Cloudz (RnD Systems) are
another bead-based T cell expansion which mounts \acd{3} and \acd{28}
\glspl{mab} on alginate microspheres, which are not only easily degradable but
are also softer, which can have a positive impact on T cell activation and
phenotype\cite{Lambert2017, OConnor2012}.
\acd{28} \glspl{mab} bound to a 2D surface such as a plate, and this can be
accomplished in a \gls{gmp} manner (at least from a reagents perspective) as
soluble \gls{gmp}-grade \glspl{mab} are commericially available. A similar but
distinct method along these lines is to use multivalent activators such as
ImmunoCult (StemCell Technologies) or Expamer (Juno Therapeutics) which may have
increased cross-linking capacity compared to traditional \glspl{mab}. Beyond
soluble protein, \glspl{mab} against CD3 and CD28 can be mounted on magnetic
microbeads (\SIrange{3}{5}{\um} in diameter) such as DynaBeads (Invitrogen) and
MACSbeads (\miltenyi{}), which are easier to separate using magnetic washing
plates. Magnetic nanobeads such as TransAct (\miltenyi{}) work by a similar
principle except they can be removed via centrifugation rather than a magnetic
washing plate. Cloudz (RnD Systems) are another bead-based T cell expansion
which mounts \acd{3} and \acd{28} \glspl{mab} on alginate microspheres, which
are not only easily degradable but are also softer, which can have a positive
impact on T cell activation and phenotype\cite{Lambert2017, OConnor2012}.
A problem with all of these commercial solutions is that they only focus on
Signal 1 and Signal 2 and ignore the many other physiological cues present in
@ -952,9 +915,78 @@ microrods\cite{Cheung2018}, 3D-scaffolds via either Matrigel\cite{Rio2018} or
\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 are in theory much easier to produce and \gls{qc} compared to feeder
cells, none have been demonstrated to demonstrably expand high quality T cells
as outlined in \cref{sec:background_quality}.
None have been demonstrated to demonstrably expand high quality T cells as
outlined in \cref{sec:background_quality}.
\subsection{microcarriers in bioprocessing}
In this work, we explored microcarriers as the basis for an alternative to the
methods described in \cref{sec:background_activation}.
Microcarriers have historically been used to grow a number of adherent cell
types for a variety of applications. They were introduced in 1967 as a means to
grow adherent cells `in suspension', effectively turning a 2D flask system into
a 3D culture system\cite{WEZEL1967}. Microcarriers are generally spherical and
are several hundred \si{\um} in diameter, which means they collectively have a
much higher surface area than a traditional flask when matched for volume.
Consequently, this means that microcarrier-based cultures can operate with much
lower footprints than flask-like systems. Microcarriers also allow cell culture
to operate more like a traditional chemical engineering process, wherein a
\gls{cstr} may be employed to enhance oxygen transfer, maintain pH,
and continuously supply nutrients\cite{Derakhti2019}.
A variety of microcarriers have been designed, primarily differing in their
choice of material and macroporous structure. Key concerns driving these
choiceshave been cell attachment at the beginning of culture and cell detachment
at the harvesting step\cite{Derakhti2019}. Many microcarriers simply use
polystyrene (the material used for tissue culture flasks and dishes in general)
with no modification (SoloHill Plastic, Nunc Biosilon), with a cationic surface
charge (SoloHill Hillex) or coated with an \gls{ecm} protein such as collagen
(SoloHill Fact III). Other base materials have been used such as dextran (GE
Healthcare Cytodex), cellulose (GE Healthcare Cytopore), and glass (\sigald{}
G2767), all with none or similar surface modifications. Additionally, some
microcarriers such as \gls{cus} and \gls{cug} are composed entirely out of
protein (in these cases, porcine collagen) which also allows the microcarriers
to be enzymatically degraded. In the case of non-protein materials, cells may
still be detached using enzymes but these require similar methods to those
currently used in flasks such as trypsin which target the cellular \gls{ecm}
directly. Since trypsin and related enzymes tends to be harsh on cells, an
advantage of using entirely protein-based microcarriers is that they can be
degraded using a much safer enzyme such as collagenase, at the cost of being
more expensive and also being harder to make
\gls{gmp}-compliant\cite{Derakhti2019}. Going one step further, some
microcarriers are composed of a naturally degrading scaffold such as alginate,
which do not need an enzyme for degradation. Finally, microcarriers can differ
in their overall structure. \gls{cug} and \gls{cus} microcarriers as well as the
Cytopore microcarriers are macroporous, meaning they have a porous network in
which cells can attach throughout their interior. This drastically increases the
effective surface area and consequently the number of cells which may be grown
per unit volume. Other microcarriers are microporous (eg only to small
molecules) or not porous at all (eg polystyrene); in either case the cells can
only grow on the surface.
Microcarriers in general have seen the most use in growing \gls{cho} cells and
hybridomas in the case of protein manufacturing (eg \gls{igg}
production)\cite{Xiao1999, Kim2011} as well as \glspl{esc} and \glspl{msc} more
recently in the case of cell manufacturing\cite{Heathman2015, Sart2011,
Chen2013, Schop2010, Rafiq2016}. Interestingly, some groups have even explored
using biodegradable microcarriers \invivo{} as a delivery vehicle for stem cell
therapies in the context of regenerative medicine\cite{Zhang2016, Saltz2016,
Park2013, Malda2006}. However, the characteristic shared by all the cell types
in this application is the fact that they are adherent. In this work, we explore
the use of microcarrier for T cells, which are naturally non-adherent.
The microcarriers used in this work were \gls{cus} and \gls{cug} (mostly the
former) which are both composed of cross-linked gelatin and have a macroporous
morphology. Their protein-based composition makes functionalization easy; the
surface is rich in lysine residues which can be easily bonded with a
base-reactive linker such as \gls{snb}. These specific carriers have been used
in the past for pancreatic islet cells\cite{Guerra2001},
\glspl{esc}\cite{Fernandes2007, Storm_2010}, and \glspl{msc}\cite{Eibes2010}.
Furthermore, they 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)\cite{purcellmain}. This regulatory history
will aid in clinical translation.
\subsection*{integrins and T cell signaling}
@ -1007,22 +1039,23 @@ components\cite{MirandaCarus2005}. Additionally, blocking \il{15} itself or
\il{15R$\upalpha$} \invitro{} has been shown to inhibit homeostatic
proliferation of resting human T cells\cite{MirandaCarus2005}.
% ACRO fix the il2R and IL15R stuff
\il{15} has been puzzling historically because it shares almost the same pathway
as \il{2} yet has different functions\cite{Stonier2010, Osinalde2014, Giri1994,
Giri1995}. In particular, both cytokines share the common gamma subchain
Giri1995}. In particular, both cytokines share the common $\upgamma$ subchain
(CD132) as well as the \il{2} $\upbeta$ receptor (CD122). The main difference in
the heterodimeric receptors for \il{2} and \il{15} is the \il{2} $\upalpha$
chain (CD25) and the \il{15} $\upalpha$ chain respectively, both of which have
high affinity for their respective ligands. The \il{2R$\upalpha$} chain itself
does not have any signaling capacity, and therefore all the signaling resulting
from \il{2} is mediated thought the $\upbeta$ and $\upgamma$ chains, namely via
JAK1 and JAK3 leading to STAT5 activation consequently T cell activation.
\il{15R$\upalpha$} itself has some innate signaling capacity, but this is poorly
characterized in lymphocytes. Thus there is a significant overlap between the
functions of \il{2} and \il{15} due to their receptors sharing the $\upbeta$ and
$\upgamma$ chains in their heterodimeric receptors, and perhaps the main driver
of their differential functions it the half life of each respective
receptor\cite{Osinalde2014}.
receptor (CD25) and the \il{15} $\upalpha$ chain respectively, both of which
have high affinity for their respective ligands. The \il{2R$\upalpha$} chain
itself does not have any signaling capacity, and therefore all the signaling
resulting from \il{2} is mediated thought the $\upbeta$ and $\upgamma$ chains,
namely via JAK1 and JAK3 leading to STAT5 activation consequently T cell
activation. \il{15R$\upalpha$} itself has some innate signaling capacity, but
this is poorly characterized in lymphocytes\cite{Stonier2010}. Thus there is a
significant overlap between the functions of \il{2} and \il{15} due to their
receptors sharing the $\upbeta$ and $\upgamma$ chains in their heterodimeric
receptors, and perhaps the main driver of their differential functions it the
half life of each respective receptor\cite{Osinalde2014}.
Where \il{15} is unique is that many (or possibly most) of its functions derive
from being membrane-bound to its receptor\cite{Stonier2010}. Particularly,
@ -1045,16 +1078,19 @@ described\cite{Budagian2004}.
\subsection*{overview of design of experiments}\label{sec:background_doe}
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\cite{Wu2009}. It was developed in many non-biological industries
The \gls{dms} system described in this dissertation has a number of parameters
that can be optimized and controlled (eg \glspl{cpp}). A \gls{doe} is an ideal
framework to test multiple parameters simultaneously and determine which are
relevant \glspl{cpp}.
The goal of \gls{doe} is to answer a data-driven question with the least number
of resources\cite{Wu2009}. 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.
At its core, a \gls{doe} is simply a matrix of conditions to test where each row
is usually called a `run' and corresponds to one experimental unit to which the
(usually called a `run') corresponds to one experimental unit for which the
conditions are applied, and each column represents a parameter of concern to be
tested. The values in each cell represent the level at which each parameter is
to be tested. When the experiment is performed using this matrix of conditions,
@ -1073,11 +1109,11 @@ principles:
\begin{description}
\item [randomization --] The order in which the runs are performed should
ideally be as random as possible. This is to mitigate against any confounding
factors that may be present which depend on the order or position of the
experimental runs. For an example in context, the evaporation rate of media in
a tissue culture plate will be much faster at the perimeter of the plate vs
the center. While randomization does not eliminate this bias, it will ensure
the bias is `spread' evenly across all runs in an unbiased manner.
factors that may be present which depend on the order or position of the runs.
For an example in context, the evaporation rate of media in a tissue culture
plate will be much faster at the perimeter of the plate vs the center. While
randomization does not eliminate this error, it will ensure the error is
`spread' evenly across all runs in an unbiased manner.
\item [replication --] Since the analysis of a \gls{doe} is inherently
statistical, replicates should be used to ensure that the underlying
distribution of errors can be estimated. While this is not strictly necessary
@ -1108,14 +1144,14 @@ principles:
\end{description}
\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. To this end, the types of \glspl{doe} we generally
used in this work were fractional factorial designs with three levels, which
screening tools for potential \glspl{cpp}, 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. To this end, the types of \glspl{doe}
we generally used were fractional factorial designs with three levels, which
enable the estimation of both main effects and second order quadratic effects.
\subsection*{identification and standardization of CPPs and
@ -1175,7 +1211,7 @@ interesting cell types and the markers that define them.
Ultimately, identifying \glspl{cqa} will likely be an iterative process, wherein
putative \glspl{cqa} will be identified, the corresponding \glspl{cpp} will be
set and optimized to maximize products with these \glspl{cpp} and then
set and optimized to maximize products with these \glspl{cpp}, and then
additional data will be collected in the clinic as the product is tested on
various patients with different indications. Additional \glspl{cqa} may be
identified which better predict specific clinical outcomes, which can be fed
@ -1183,10 +1219,8 @@ back into the process model and optimized again.
\section{Innovation}
\subsection{Innovation}
Several aspects of this work are novel considering the state-of-the-art
technology for T cell manufacturing:
Several aspects of the \gls{dms} platform described in this dissertation 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
@ -1194,26 +1228,22 @@ technology for T cell manufacturing:
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.
the rapidly growing T cell manufacturing arena. Providing an alternative will
provide more options for manufacturers, leading to increased competition,
lower costs, and higher innovation in the T cell manufacturing space.
\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 We used \glspl{doe} to discover and validate novel \glspl{cpp}, which is a
strategy commonly used in non-biological industries but has yet to gain wide
usage in the development of cell therapies where research often employs 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. Furthermore, it is a perfectly natural strategy to use
even at 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