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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5608_Библиотеки_им_академика_М_И_Перельмана.pdf
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A. K. Sato and S. Rife
system. DNA sequences that are complementary to the spacerRNA will be targeted
by the complex and, once bound, cleaved by the Cas nuclease (see Fig.2.1) [18].
By storing fragments of foreign DNA in CRISPR sequences, these species are
able to recognize and more effectively eliminate pathogens. And, in 2012, researchers showed that this system could be modied to enable highly precise gene editing.
Building on decades of work, Jennifer Doudna at UC Berkeley and Emmanuelle
Charpentier at the University of Sweden collaborated to engineer a synthetic
CRISPR DNA sequence that simplied the CRISPR-Cas complex (by combining
spacerRNA and tracerRNA into a single transcript known as a single-guide RNA;
Fig.2.2a) and enabled it to target non-viral DNA sequences, opening the door to
prescribed gene editing [19]. Further modication to the system by Feng Zhang of
the Broad Institute made the heterologous expression of CRISPR-Cas systems in
eukaryotic cells possible, further expanding its potential for engineering biological
systems through precise gene editing [20].
Though it is not often discussed as such, CRISPR-Cas represents a powerful
product of synthetic biology wherein modied bacterial genes are heterologously
Fig. 2.1 CRISPR-Cas9 adaptive immune system of Streptococcus pyogenes against bacterio-
phages. Diagram showing how the CRISPR-Cas9 complex processes viral DNA within
Streptococcus pyogenes. Upon entry into the cell, cas nucleases digest the DNA and insert it into
the bacterial genome at CRISPR loci. These inserts are referred to as spacer sequences. Expression
of the spacer sequence along with an inborn tracer sequence will generate a guide RNA comprised
of both spacer and tracer RNAs. Together with the Cas9 nuclease, this complex targets and digests
complimentary viral sequences. (Figure adapted from “CRISPR-Cas9 Adaptive Immune System
of Streptococcus pyogenes Against Bacteriophages,” by BioRender.com [2022]. Retrieved from
https://app.biorender.com/biorender- templates)
2 Synthetic Biology inDrug Development andBeyond
31
Fig. 2.2 CRISPR-Cas9in Drug Discovery. (a) In bacteria, CRISPR guide RNAs are comprised of
two distinct RNA sequences, known as the crRNA and tracerRNA.Use of CRISPR gene editing in
drug discovery was enabled by the generation of a synthetic mimic of the natural guide RNA complex. This synthetic version is comprised of a single-guide RNA with sequences corresponding to
the crRNA and tracerRNA. (b) CRISPR screening workow. Large-scale CRISPR screening can
be used to identify potential drug targets by perturbing individual genes and subsequently analyzing for desired effects. (Figure adapted from “CRISPR Screening Protocol,” by BioRender.com
[2022]. Retrieved from https://app.biorender.com/biorender- templates)
expressed in a wide range of hosts for productive means. Since its discovery,
CRISPR-Cas systems have been developed for numerous applications, including
the discovery of therapeutic drug targets [11].
Drug discovery can be carried out in one of two ways: target-forward approaches
or phenotype-forward approaches. In the latter, libraries of compounds are screened
for their ability to elicit a specic phenotype within a model system. This approach
can be useful when the target disease pathology is not well characterized or when
novel drug targets are desired. On the other hand, target-forward approaches involve
the screening of libraries of compounds for their activity against specic drug targets that are known to hold therapeutic value.
32
A. K. Sato and S. Rife
CRISPR-Cas can support both approaches. Target-forward drug discovery starts
with identifying targets of interest, a process greatly improved with CRISPR screening. Through large-scale CRISPR screens, researchers can methodically perturb
gene expression across a large range of targets in an effort to elicit a desired phenotype (such as reduced proliferation). Results from these screens can produce new
therapeutic targets by revealing the proteins and pathways that are critical to disease
pathology. Once identied, libraries of compounds can be screened for their ability
to target these specic proteins.
A recent example of this came from the Bassik lab at Stanford University, where
a team of researchers performed a large-scale CRISPR screen to identify synthetic
lethal drug combinations in a BCR-ABL expressing chronic myeloid leukemia cell
line [21]. The team utilized a dual-guide approach so that each cell in the study
would receive two sgRNAs, and thus have two genes simultaneously knocked out.
Approximately 490,000 sgRNA pairs were created, enabling the team to simulate
21,321 drug combinations and identify numerous synthetic lethal combinations.
In addition to driving target identication and validation, CRISPR can also aid in
the development of screening models. A signicant challenge in drug discovery is
ensuring that efcacy in the laboratory setting translates to the bedside. As many as
90% of therapeutics entering clinical trials fail due to a lack of efcacy or unforeseen toxicities [22]. Quantitative decision-theoretic modeling of the drug discovery
pipeline suggests that better preclinical models, ones that more accurately reect
human physiology, could substantially improve drug development productivity [23].
To this end, CRISPR-Cas has been used to engineer both cell lines and animal
models that better emulate human disease [24]. Having the ability to introduce
disease- specic mutations into the desired model—be it human cells or rodents—
enables the ne-scale study of disease pathogenesis, rapid screening of compounds
in a disease-relevant context, and provides an opportunity for early go/no-go
decision- making. All of this builds toward a more productive drug development
pipeline.
Beyond its use as a tool in drug development, CRISPR-Cas is also being explored
as a therapeutic in and of itself, largely for the treatment of monogenic disorders. A
clinical trial initiated in 2019 by pharmaceutical company Editas Medicine is studying the ability of a CRISPR-Cas system to improve vision for patients harboring
heterozygous or homozygous point mutations in the Centrosomal Protein 290
(CEP290) gene, which leads to Leber Congenital Amaurosis Type 10 [25, 26]. As of
the writing of this book, the trial is ongoing and isexpected to conclude in May of
2025. And, in December of 2023, the US FDA approved two of the rst ever
CRISPR-based therapeutics (Casgevy and Lyfgenia) forthe treatment of sickle cell
anemia [27, 28].
While CRISPR-Cas technology is proving to be an invaluable tool for drug
development, there remain signicant challenges that limit its application. Practical
challenges include difculties with effectively delivering multiple macromolecules
(such as the vectors encoding both Cas nucleases and gRNAs) to primary cell lines
and living tissues. More difcult is the challenge of anticipating and preventing offtarget editing. Though the CRISPR-Cas system is capable of highly specic editing,
2 Synthetic Biology inDrug Development andBeyond
promiscuous nuclease activity as well as imperfect gRNA design can lead to unintended editing. Here, imperfect gRNA designs are those that target non-unique
DNA sequences that may be identical—in whole or in part—to other loci within the
genome. These redundant loci can attract CRISPR-Cas activity. The many challenges associated with CRISPR-Cas systems and the ongoing efforts to overcome
those challenges are covered in depth by Liu et al., in Precision Clinical
Medicine [29].
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2.4 Engineering Biosynthetic Gene Clusters
forDrug Discovery
An analysis of 1881 approved drugs in the United States between January 01, 1981,
and September 30, 2019, found that only 463 (24.6%) were produced through
entirely synthetic means, meaning without inspiration from natural products [30].
Stated another way, nature remains a signicant source of medicinal compounds,
from biologics to small molecules.
Nature-derived therapeutics can often be traced back to secondary metabolites—
biologically active small molecules whose activity is not critical to the growth of an
organism but may contribute to signaling or defense systems. Secondary metabolites can be structurally complex and difcult, if not impossible, to synthesize in the
laboratory. Therefore, extraction has been the primary approach to accessing secondary metabolites, which may then serve as the chemical skeleton for further modication and development [31].
However, extraction processes can put signicant strain on natural resources and
may be prohibitively expensive to operationalize at scale. Additionally, natural concentrations of these compounds can be very low, necessitating large quantities of
starting material. Owing to these limitations, the sourcing of secondary metabolites
has largely been focused on organisms that can be cultured in the laboratory setting
or else farmed in signicant quantities. Alternatively, some laboratories are attempting to synthesize chemical pathways that generate secondary metabolites invitro.
The advent of both next-generation sequencing (NGS) and synthetic biology has
greatly expanded opportunities for new drug discovery by enabling the production
of secondary metabolites in chassis organisms [31–35].
This is done through the identication, synthesis, and cloning of biosynthetic
gene clusters (BGCs). Secondary metabolites are frequently produced through an
enzymatic relay that enables the formation of complex and pharmacologically
active structures. Importantly, the DNA encoding these enzymes is often grouped
together into clusters, or BGCs, that can range from several kilobases to >100kb in
length. These clusters can be identied by mining next-generation sequencing
(NGS) data using advanced bioinformatics [31–36].
Though the specic enzymes used in this process can vary, enzymes like
polyketide synthases (PKSs) and non-ribosomal peptide synthetases (NRPs)
34
frequently appear in the production pathways for toxins, antibiotics, cytostatics, and
immunosuppressants [36, 37]. Therefore, bioinformatic tools can be used to mine
genomic databases for potential BGCs by searching for PKSs, NRPs, and their constituent elements. Once identied, these gene clusters can be synthesized and cloned
into chassis organisms.
This approach has several advantages. Porting BGCs into chassis organisms
obviates the need for the original host organism to be involved in compound screening, reducing ethical, nancial, and operational concerns. Additionally, BGCs in
chassis organisms can be optimized to produce large quantities of the candidate
compounds, enabling testing of compounds that may have otherwise been overlooked due to low expression levels in their native host [38]. Further, the synthetic
nature of this approach allows for combinatorial experimentation such that novel
variants and unique gene combinations can be tested en masse.
A recent preprint from Liu etal. highlights this potential. Using metagenomic
data from soil samples, Liu etal. analyzed >6.8 billion BGCs for the potential to
produce inhibitors of human methionine aminopeptidase-1 (HsMetAP1)—a validated oncology target [39]. This search yielded 35 BGCs with enzymes that are
likely to produce secondary metabolites against HsMetAP1. Heterologous expression of two of these BGCs in a bacterial chassis resulted in the discovery of several
novel HsMetAP1 inhibitors, one of which demonstrated sub-micromolar potency
and high selectivity in an invitro enzymatic assay (specically a methionine aminopeptidase colorimetric activity assay based on an R&D Systems commercial kit).
This example demonstrates how heterologous expression of BGCs can be used to
discover new small molecules with therapeutic potential.
Heterologous expression of BGCs is far from trivial, though. Gene regulation
and cellular context can be important factors in determining the catalytic efciency
and end products of a BGC.When these enzymatic networks are ported into chassis
organisms, the costly and time-consuming process of directed evolution may be
required to optimize secondary metabolite production. Herein, directed evolution
describes the creation of libraries in which BGC elements will undergo an iterative
process that involves random mutation, screening of mutants for desired properties,
and further mutation of selected mutants until optimal performance is attained.
Such a process can be time and resource intensive for individual genes, much less
gene clusters.
A. K. Sato and S. Rife
2.5 Unprecedented Antibody Discovery andDevelopment
Through Synthetic Biology
Over the past two decades, biologics have been an increasingly important source of
new molecular entities [40]. Between the years 2000 and 2021, 723 new drugs were
approved in the United States, 158 (21.9%) of which were biologics [41]. Within
this category, monoclonal antibodies are by far the most common.
2 Synthetic Biology inDrug Development andBeyond
Owing to size and physiochemical differences, small molecules and antibodies
display substantially different pharmacokinetics, which can be both benecial and
detrimental. For example, antibodies generally have longer half-lives in the body,
enabling longer exposure to the therapeutic and intermittent dosing. However, antibodies also struggle to penetrate beyond tissue barriers (such as the blood-brain
barrier), which can greatly limit their distribution [42]. As polymeric proteins, antibodies are also susceptible to deamidation, glycosylation, and oxidation events that
can alter their biological properties.
Therefore, the development of antibodies and other biologics often includes
extensive rounds of iterative optimization in an effort to tailor the antibody’s pharmacological and pharmacokinetic properties. This process is widely referred to as a
design, build, test, learn (DBTL) cycle. During the design phase, libraries of antibodies can be computationally designed such that each antibody clone contains a
combination of missense mutations that are expected to affect the antibody’s properties, such as binding afnity or stability.
Synthetic biology intersects with and drives this process in many ways. Much of
this work relies on the same tools and high-level workows already discussed:
Synthetic DNA sequences representing whole or partial antibodies are heterologously expressed in a chassis organism—be it a bacteria cell, mammalian cell, or a
transgenic mouse—in order to screen, optimize, and manufacture novel biologics.
In the following sections, we will briey describe two of the primary approaches
in biologics development—one based on B-cell technology, and one based on phage
display—as well as the impact of synthetic biology in enabling these approaches.
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2.5.1 B-Cells andHybridoma Technology forAntibody
Discovery andDevelopment
Hybridoma technology advanced antibody discovery and development efforts considerably by enabling the long-term culture of B-cells collected from immunized
animals. First described in 1975, hybridoma technology fuses mouse myeloma cells
with antibody-producing B-cells to form an immortalized B-cell line from which
antibodies can be collected [43]. Clonal expansion of B-cells, each expressing a different antibody variant, can then be used to produce antibody libraries (Fig.2.3).
Hybridoma technology enables robust production of monoclonal antibodies in
the laboratory and has been used to develop many currently approved biologics [43,
44]. However, antibodies derived from non-human species are very likely to be
recognized as foreign proteins by the human immune system and elicit a lifethreatening response. To mitigate this risk, therapeutic antibodies must be made to
resemble human antibodies using tools from synthetic biology.
Antibodies are hetero-tetrameric proteins consisting of two identical heavy
chains and two identical light chains joined together through disulde bonds. At one
end of the protein complex can be found variable regions for both the heavy and
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Fig. 2.3 B-cell hybridoma library creation. Workow for the creation of antibody libraries using
hybridoma technology. (Figure adapted from “Monoclonal Antibodies Production,” by BioRender.
com [2022]. Retrieved from https://app.biorender.com/biorender- templates)
A. K. Sato and S. Rife
light chains. As their name suggests, variable regions have highly varied amino acid
sequences whose unique composition determines the antibody’s unique antigen
specicity and afnity. Within these variable regions, three amino acid loops (known
as complementarity-determining regions, or CDRs) are critically inuential in
determining antigen recognition. The rest of the antibody complex consists of
frameworks and constant regions whose amino acid sequences are largely conserved within species [45].
Reducing immunogenic potential requires careful modication of the candidate
antibody to preserve antigen specicity while incorporating human frameworks
wherever possible. Multiple approaches have been developed, all of which rely on
creating novel antibody genes in which elements of the original antibody’s DNA
sequence are replaced with human antibody sequences [44].
For example, chimeric antibodies commonly fuse promising mouse antibodies
with human antibody frameworks. In these synthetic antibodies, the DNA encoding
the human antibody variable regions are replaced by the corresponding variable
region in the mouse therapeutic antibody, such that the nal product contains human
constant regions and non-human variable regions. This is also sometimes referred to
as CDR grafting because the CDR from one animal is transplanted onto a human
antibody framework [44].
2 Synthetic Biology inDrug Development andBeyond
37
Novel chimeric antibodies are easily produced through the use of de novo DNA
synthesis (discussed in more detail below), homology-based cloning methods, and
chassis organisms. Though the majority of therapeutic antibodies used to treat
humans are chimeras, the development of chimeric antibodies remains relatively
risky compared to fully human antibodies [46].
Alternatively, transgenic mouse models, such as HuMabMouse and XenoMouse,
have been created by replacing mouse immunoglobulin genes for those derived
from the human genome [44]. Rather than expressing murine antibodies, these mice
produce human antibodies and can thus be used to create fully human hybridoma
cells. While undoubtedly useful, the application of transgenic mice is limited by
their costly and slow nature, two attributes that make it difcult to apply to emergent
cases, such as infectious diseases.
Rather than humanizing antibodies, it is also possible to start with human antibodies. Human B-cell screening is an approach whereby antibody libraries are
sourced from the human B-cell repertoire of resear ch participants [44, 47].
Heterogeneous populations of plasma and memory B-lymphocytes can be collected
from either lymphatic tissue or peripheral bone marrow cell populations and subsequently screened in cell-based or bead-based assays. As these cells naturally excrete
antibodies, individual B-cells can be isolated and assessed for antigen-binding
activity. Those that show binding activity can then be sequenced to identify the corresponding DNA sequence.
Such an approach provides several benets. It is estimated that at least 107 antibody variants can be found among circulating human immune cells at any given
time. Based on the mathematical possibility of different V(D)J recombinations, it is
estimated that the true human B-cell repertoire size is between 1012 and 1018 distinct
variants [47]. However, the human body has natural maturation processes that prevent B-cells containing autoreactive, truncated, or otherwise non-functional antibodies from entering circulation. This maturation process is advantageous for
antibody development.
Additionally, B-cell screening can be a valuable tool when combating infectious
diseases. Patients who have previously been infected with the target pathogen are
very likely to have memory B-cells that produce high-afnity antibodies. Collection
and screening of B-cells from these patients can help researchers rapidly develop
therapeutic antibodies. This was made evident early in the SARS-CoV-2 pandemic
when B-cells collected from previously infected patients were used to rapidly identify 49 different human anti-SARS-CoV-2 antibodies within a matter of weeks [48].
For a more in-depth review of B-cell technology, we refer you to Pedrioli and
Oxenius’ recent review in Cell [47].
Though a powerful method for antibody discovery, human B-cell screening has
notable limitations related to throughput and scale. But perhaps the biggest limitation is that B-cell screening can only survey antibodies that nature has already produced. Sometimes, it is advantageous to go beyond what already exists and develop
a de novo antibody sequence. For this, researchers turn to synthetic libraries.
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A. K. Sato and S. Rife
2.5.2 Synthetic Libraries andDisplay Systems
As with small molecules, the discovery and development of novel biologics often
begins with the screening of large libraries in which millions of antibody (or antibody fragment) sequences are represented. When these libraries are diverse, with
uniform representation among variants, and are free of liability motifs that can lead
to poor pharmacokinetics, hundreds of promising candidates can be found and carried forward for further characterization and optimization.
Historically, antibody libraries have been sourced from the serum of animals
previously infected with target antigen [44]. With a natural, pre-existing repertoire
of diverse antibodies, the animal’s immune system is often readily able to produce
polyclonal antibodies against a wide range of targets. However, this approach has
many drawbacks related to variability, a general inability to scale, and ethical concerns surrounding animal welfare that preclude its use in the development of
therapeutics.
Whereas hybridoma technology relies on animal immune systems to surface
candidate antibodies, recent advances in both DNA synthesis technology and synthetic biology have given researchers the power to design, produce, and screen custom antibody libraries. Specically, DNA synthesis can now be reliably performed
on a large scale [9], such that millions of custom oligonucleotides can be generated
per run on platforms such as Twist Bioscience’s silicon-based DNA synthesis platform. This makes it possible to synthesize large libraries of deliberately designed
antibodies.
There are many approaches to designing such a library that go beyond the scope
of this chapter. However, whether starting with a known antibody sequence or taking a broader approach, computational tools can be used to identify key loci in an
antibody sequence where missense mutations are likely to impact antigen recognition [49]. The library would subsequently consist of antibody variants containing
combinations of missense mutations (a so-called combinatorial variant library).
Such libraries may contain as many as 1010 unique antibodies for screening [50].
Once designed, antibody libraries may then be constructed using DNA synthesis
technology. Modern synthesis platforms leverage a myriad of approaches to create
variant libraries that can generally be divided into direct and degenerate approaches.
Degenerate approaches rely on randomly incorporating nucleotides at specied
locations within the growing oligonucleotide. Most common among these methods
is the NNK approach, wherein the rst and second positions of a codon may be
occupied by any of the four nucleotide possibilities (A, T, G, C). The nal position
denoted by a K, the so-called wobble position, is limited to just two potential nucleotides (G, T).
NNK synthesis can be extremely effective for directed evolution; however, its
inefciencies and bias-prone nature greatly limit its application in antibody development. Consider the redundancy of the genetic code, wherein multiple codons may
apply to the same amino acid. Using NNK synthesis, you are likely to generate
multiple distinct codons that correspond to the same amino acid, resulting in
2 Synthetic Biology inDrug Development andBeyond
39
redundant variants that may not provide additional value to your study. This also
means it is difcult to create uniform libraries—libraries where every variant antibody is equally represented—using NNK.
For example, because six different codons code for the amino acid arginine, it is
far more likely to occur relative to amino acids like phenylalanine that only have
two corresponding codons. Using NNK, then, a mutation in any given loci is more
likely to substitute an arginine than phenylalanine, biasing the test pool away from
antibody variants containing less common amino acids.
Finally, NNK synthesis can produce stop codons. When creating large libraries
with multiple variants per oligo, premature stop codons are likely to affect a signicant portion of the library, resulting in many truncated and non-functional antibodies [51].
Collectively, these drawbacks mean that creating combinatorial variant libraries
with NNK synthesis is prohibitively inefcient. The combination of redundant antibody clones, non-uniform antibody representation, and truncated antibodies (produced by premature stop codons) generates an expensive library with few unique,
viable antibody clones to screen. Put another way, NNK approaches reduce the
functional diversity in antibody libraries and may require costly, time-consuming
efforts to compensate for this, ultimately increasing the researcher’s screening burden.
The use of libraries synthesized with trinucleotide phosphoramidites (trimers)
can be a good alternative to NNK libraries because it gives the user more control.
Unlike NNK libraries, which randomly incorporate individual nucleotides, trimer
approaches incorporate blocks of three nucleotides at a time. The trinucleotide
blocks can be synthesized through various means and are designed such that every
desired codon is represented, whereas undesired codons—such as stop codons or
those coding for cysteine—can be avoided.
While trimer synthesis affords the user with more control, it is still libel to produce biased oligonucleotide libraries where certain amino acids are over- represented.
This is because each trimer phosphoramidite has a different reaction factor that
dictates the rate of the coupling reaction during synthesis. For example, the trimer
AAC (encoding asparagine) couples with the greatest efciency—meaning it is
very likely to be incorporated whenever given the opportunity—and thus has a reaction factor of 1.0. In contrast, the trimer CAG (coding for glutamine) has a lower
coupling efciency and a reaction factor of 2.0. In order to prevent over- representation
of asparagine in the nal product, the oligonucleotide synthesis reaction mixture
will need to have two CAG trimers for every one AAC trimer [52, 53].
The coupling efciency of trimers is also lower than individual nucleotides
because three nucleotides are being coupled instead of one, which increases steric
hindrance. If the coupling efciency is high, we can expect to see fewer truncated
oligos and a higher percentage of full-length material. Truncated material in the
pool will impact downstream processes such as cloning because shorter fragments
clone preferentially. For the same reasons as above, if trimers are used to construct
a long domain, such as an antibody’s third heavy chain complementarity- determining
region (CDRH3), the percent full length can be very low.