Ian Foster and Steven Loo-Yong-Kee discuss Artisan Bio's STAR-CRISPR system for optimized gene editing in cell therapy, with a focus on the genetifc modification of T cells for cancer immunotherapy.
Cell therapy is an emerging field with great promise for the treatment of various diseases. One of the most exciting areas of cell therapy is the use of T cells that have been genetically modified to recognize and kill cancer cells. While the use of T cells for cancer immunotherapy has tremendous promise, there is still room for improvement. The efficiency, expansion, and functionality of T cells can be enhanced by genetic modification using the STAR-CRISPR system.
Artisan Bio is a biotechnology company focused on developing a CRISPR-mediated editing platform to improve the efficacy and safety of cell therapy products. In this webinar, we will provide a comprehensive overview of Artisan Bio’s STAR-CRISPR system, which is designed to improve the specificity and efficiency of gene editing for cell therapies. We will explain the system’s key components and how we are using a risk-based approach to optimize and validate the editing platform. The webinar will focus on Artisan Bio’s approach to building T cell OS/APPS through iterative improvements to achieve best-in-class editing capabilities and improved cell health metrics.
Key Topics Include:
- Learn about Artisan Bio’s proprietary high-performance STAR-CRISPR system for improving the specificity and efficiency of gene editing for cell therapies
- Explore Artisan Bio’s risk-based, systems approach to technology development, including how to implement Design of Experiments (DoE) and Quality by Design (QbD) principles to optimize and validate any process
- Case study of the application of QbD to Artisan Bio’s STAR-CRISPR platform to edit T cells for cancer immunotherapy with preliminary data showing improved efficacy, expansion, and functionality
3. Immunology
Pluripotent
Stem Cell Biology
Hematopoietic
Stem Cell Biology
Intestinal Biology
Neural Biology
Endothelial
Cell Biology
Pulmonary Research
Mesenchymal Stromal Cell
Biology
Mammary Research Pancreatic Research
CHO Cells &
Hybridomas
Prostate Research
STEMCELL Technologies
Major Research Areas
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Creating the Next Generation of
CRISPR-edited T cells
Steven Loo-Yong-Kee
6. Copyright 2022 Artisan Bio. All Rights Reserved.
University of Toronto, Chemical Engineering & Applied Chemistry (2014 – 2015)
Bioprocess Engineering Lab and BioZone (Centre for Applied Bioscience and Bioengineering) – MEng
• Scale-up and automation of protein production in from shake flasks to 80-L STR to be used for the desludging of wastewater.
University of Toronto, The Centre for Global Engineering (2015 – 2016)
Re-invent the Toilet Challenge – RA
• Develop a toilet that burns solid waste streams to generate heat and use the heat to dry new incoming solid waste and pasteurize liquid waste.
CCRM/CATCT (2016 – 2020)
The Centre for Advanced Therapeutic Cell Technologies – RA, Snr RA, Scientist
• Leading collaborative immunotherapy and viral vector development efforts between CCRM and GE Healthcare. Non-viral transfection technologies portfolio
manager in cell and gene therapies.
Avectas (2020 – 2021)
Process Development – Scientist II
• Development, optimization, characterization, scale up and tech transfer of SOLUPORE cargo delivery process.
Artisan Bio (2021 – Present)
Cell Biology and Immunology – Scientist, Scientist II
• Development of Artisan’s T cell and iPSC editing platforms, including process development, optimization and tech transfer. Founding member of internal DOE core,
offering insights into QbD methodologies.
Background and Experience
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ARTISAN BIO
DATA
KNOW-HOW
Cas9
Cas12
Off Target Data Packs
Protocols & Builds
Artisan’s team provides:
Platform & Licenses
Guide Sequences
STAR
Toronto, ON
Louisville, CO
A 1-Stop Shop cell therapy powerhouse to drive the cell therapy revolution.
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STAR-CRISPR
A novel, high
performance
editing system
with enhanced
safety and FTO
STAR-CRISPR Design
3
Engineered gRNA ensures unique editing system compared to other Cas12a’s
STAR-CRISPR 1.0
Available
AND
Core + Opt +
Mods v2
STAR Nuclease 1.0
STAR gRNA 1.0
STAR-CRISPR 2.X → In Optimization Studies
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Cas9 Cas12a STAR
PAM
Break site
Off-Target (Safety)
On-Target (Performance)
NGG
blunt end
*
***
TTTN
sticky end
***
**
TTTN
sticky end
****
****
STAR-CRISPR OVERVIEW
Table adapted from: Huang et al, 2022. Comparison of DNA targeting CRISPR editors in human cells.
Mechanism
● STAR-guides architecture results in
40% reduction in off-target vs. wild-
type single guide RNA
License availability and
cost
Commercial
* */** ****
● Engineered, high performance
Cas12a-like architecture not found in
nature
● Novel Nuclear Localization tags and
orientation increased editing
efficiency
● Optimized insertions components
(homology arms, template)
● Tuned protocols and experimental
designs
Artisan Engineering Improvements
A high-performance Type 5 nuclease with differentiated IP position and editing performance
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BOTTOM UP
Hypothesis Driven
Solution Space Definition
Deep literature mining, Discovery
analytics, Process flow diagrams
Solution Space Reduction
Risk Assessment, Scale Down Model,
Perturbations, DoE, High-throughput
screening, Model fitting
Design Space Definition
High resolution DoE, Scale Up &
Model validation
TOP DOWN
Data Driven
“The key to the systems approach is a ‘big picture’ view – you need to look at how the components of a system interact
with each other to achieve an overall result, rather than simply optimizing each component.”
Systems Approach
Adapted from Dr. P. Chrysanthopoulos
(weCANdev.ca)
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Quality by Design (QbD): Defining the Levers
“A systematic approach to development that begins with predefined objectives and emphasizes product and process
understanding and process control, based on sound science and quality risk management.” – ICH Q8(R2)
Process
Reagents
Equipment
Materials
Product
INPUTS OUTPUTS
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Quality by Design (QbD): Defining the Levers
By constructing a correlation map that describes how input variations affect the outputs, the approach of utilizing
iterative risk assessments and DoE screening studies allows for a deep process understanding and better control of
quality attributes for the intermediate and drug product state.
INPUTS OUTPUTS
Critical Process
Parameters (CPPs)
Critical Quality
Attributes (CQAs)
Process
Reagents
Equipment
Materials
Quality
Target
Product
Profile
(QTPPs)
Critical Material
Attributes (CMAs)
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QbD in Practice: Allogeneic CAR-T Therapy
High level view is oversimplified, lacking proper process understanding to ensure consistency and reproducibility of the
final product.
Adapted from Dr. P. Chrysanthopoulos
(weCANdev.ca)
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Process Development for Improving Editing Efficiency
INPUTS OUTPUTS
Process Parameters
Intermediate Quality
Attributes
Unit
Operation
Reagents
Equipment
Materials
Analytics
Controlled and
Uncontrolled Variables
With a similar focus on process understanding, taking a DoE-driven risked-based iterative approach to understanding
the correlations between inputs and outputs for Artisan’s STAR-CRISPR editing platform.
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Process Development and Evolution
Knowledge
Space
Solution
Space
Design
Space
Control
Space
Set
Point
Process Understanding
Process Development
Process Characterization
Process
Validation
Input
Output
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HLA-E KI
NK cell
HLA-I KO
HLA-II KO
CD8+
T Cell
CD4+
T Cell
GvHD
Cytokine
Receptor
Solid
Tumor
Site
TCR KO
Dual
CAR
Solid
Tumor
X
X
X
Multiplex
Editing
Precision
Engineering
Larger
Payloads
Patients Need Genome Engineering To Address Medical Needs with Next-Generation Products
Medical Need Artisan Solution
Relapse caused by
“antigen escape” and low
solid tumor antigen
expression
Dual- and Tri-CAR Cell
Therapies
Cost, long vein-to-vein
time, and lack of patient
access
“Off-The-Shelf” and
“FAST” Auto Approaches
Overcoming solid tumor
microenvironment
Tumor homing for
intratumoral CAR-T
infiltration
Low allogeneic
persistence
Immune evasion edits
that knockout HLA-I, HLA-
II, and insert HLA-E for NK
evasion
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MULTIPLEX BUILDS: Solid Tumor Homing CAR-T
ENGINEERED SOLUTIONS
CAR
A target highly expressed on solid tumor and limited
expression on health tissues.
Chemokine Homing
Chemokine receptor enables CAR-T to home towards
tumors and serves to increase drug concentration and
ultimately efficacy.
Allo Chassis
Allogeneic edits enable “off-the-shelf” therapeutics that
decrease manufacturing costs and increase drug
availability to patients while preventing GvHD.
HLA-II KO
HLA-I
KO
TCR KO
Chemokine
Homing
CD4+
T Cell
CD8+
T Cell
CAR
Graft vs. Host Disease
X
X
X
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Step 1: Technology transfer of T cell editing process
RNP Delivery
RNP Preparation
STAR Nuclease 1.0
STAR gRNA 1.0
CD3 Isolation
EasySep™ Human T Cell Isolation Kit
Cell Sourcing
Human Peripheral Blood Leukopak, Frozen
Design STAR
gRNA 1.0
Pre-Editing Cell Culture
ImmunoCult™-XF T Cell Expansion Medium
ImmunoCult™ Human CD3/CD28 T Cell Activator
Human Recombinant IL-2
Cryopreservation
CryoStor® CS10
T cell Expansion
ImmunoCult™-XF T Cell Expansion Medium
Human Recombinant IL-2
Downstream Processing / Analysis
Flow Cytometry
(% KO, % KI)
Image-Based Cell Killing Assay
(Cell Potency)
Amplicon-Seq
(% INDELs)
rhAmpSeq
(off-target nomination)
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Step 2: Risk assessment
A risk assessment dictates the potential critical process parameters for the intermediate quality attributes.
Cell Source
Pre-Editing
Culture
Cargo Preparation Cargo Delivery T cell Expansion
Intermediate
Quality Attributes
Parameter Rank
Factor Q 3
Factor R 3
Factor S 3
Factor T 2
Factor U 1
Factor V 2
Factor W 2
Parameter Rank
Factor J 1
Factor K 3
Factor L 3
Factor M 2
Factor N OOS
Factor O 2
Factor P OOS
Parameter Rank
Factor B OOS
Factor C 3
Factor D OOS
Factor E 3
Factor F OOS
Factor G OOS
Factor H OOS
Factor I OOS
Parameter Rank
Factor X OOS
Factor Y OOS
Factor Z OOS
Factor AA OOS
Factor AB OOS
Factor AC OOS
KI Efficiency
KO Efficiency
Parameter Rank
Factor A OOS
Covariates
Scale
Highly Likely 3
Potentially 2
Unlikely/Unknown 1
Ranking of parameters by likely influence on intermediate quality
attributes.
Total Rank = KI Efficiency x KO Efficiency
Parameters with highest Total Rank promoted to DOE.
As a group, decided some factors were out of scope (OOS) for this body of work based on what was being investigated and the focus of the experiment.
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Step 3: DOE Screening Design
Constants Values
Cell Source Single Donor, Frozen Leukopak
Culturing Reagents
Stemcell Technologies
Reagents for T Cell Therapy
Research
Transfection Conditions
Buffer
Method
Levels 3
Factors 6 (5 continuous + 1 discrete)
Resolution IV
Replicates 3
Center Points 0
Blocks 1
Runs/Block 33
Total Runs 99
Definitive screening allows for main linear and quadratic effects, and two-way interactions characterization
Controlled
Factors
Low Mid High
Factor 1 -1 0 1
Factor 2 -1 0 1
Factor 3 -1 0 1
Factor 4 -1 0 1
Factor 5 -1 0 1
Factor 6 -1 - 1
Screening Design Construction
Pre-Editing
Culture
Cargo Preparation Cargo Delivery T cell Expansion
Intermediate
Quality Attributes
VCD/Viability
Phenotype
Cell Cycle Analysis
VCD/Viability
Phenotype
VCD/Viability
Phenotype
Cell Cycle Analysis
VCD/Viability
Phenotype
Editing Efficiency
KI Efficiency
KO Efficiency
Analytics
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Step 4: Model Fit and Critical Factors Identification
CAR
KI
RMSE 0.08
R-Squared 0.89
P-value < 0.001
KI Efficiency
Model Fit
Directionality of critical factors identified, and initial predictive model created
Identified significant main factor for KI efficiency
within the solution space of this DOE.
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
Solution Space Definition
Solution Space Reduction
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Step 4: Model Fit and Critical Factors Identification
Triple
KO
RMSE 0.0922
R-Squared 0.91
P-value < 0.0001
KO Efficiency
Model Fit
Directionality of critical factors identified, and initial predictive model created
Preliminary operating range identified with
significant decrease in KO efficiency at low levels
of respective factors.
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
Solution Space Definition
Solution Space Reduction
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Initial data displaying high editing efficiency potential
Non-viral Insertion (3.2 kb) + Triple Knock-Out (3 gRNA)
%
Knock-Out
(flow)
Compan
y
Product
CTX-110
CTX-120
CTX-130
Max
90.8%
Allo
Chassis
%
Knock-In
(flow)
Company
Product
UCAR19
CTX-110
Allo
Chassis
+miniplasmid
ɑβT cells
ɑβT cells
Editing that partners
have indicated they
have obtained
internally with Cas9
after optimization
DOE design identifies significant factors and high-impact interactions, laying the groundwork for efficient on-target
editing in T cells using Artisan’s STAR-CRISPR Platform.
Competitor data references publicly available disclosures from other
CRISPR editing technologies and the companies using their platforms
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Next Steps: Process optimization, characterization and validation
Knowledge
Space
Solution
Space
Design
Space
Control
Space
Set
Point
Process Understanding
Input
Output
Process Development
Process Characterization
Process
Validation
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Systems approach is applicable across cell types: HSC
Fold Expansion and CAR KI
Factor 1 Factor 2 Factor 3
Early data showing improved HSC cell health and insert expression
Response
1
Response
2
Response
3
Response
4
Multiple factors identified that
are critical for the chosen
responses
Single and Triple Knock Out
Flow cytometry
Insertion (3.5 kb)
Flow cytometry
CD34 expression
Flow cytometry
78.5%
19.5%
Max
27.2%
95.4% 97.5%
No loss of CD34 expression post-editing
HSCs edited with CD19-
CAR template into B2M
Singleplex KO: B2M
Multiplex KO: B2M, CIITA, TRAC
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Systems approach is applicable across cell types: iPSC
Preliminary data of iPSC editing showing improved performance compared to alternatives
Factor 1 Factor 2 Factor 3 Factor 4
Factor 5 Factor 6 Factor 3
Viability
Yield
KI
DoE identified multiple factors that
positively impact KI efficiency data to
further improve editing efficiency
3.4%
13.1%
29.3%
PoC DoE 1 DoE 2
%
Knock-In
(flow)
%
Single
HLA-I
Knock-Out
(flow)
Cas9
Company
Nuclease
STAR-
CRISPR
MAD7
Company
Nuclease
HiFi
Cas9
Cas9
MAD7
Cas-Clover
Insert
1
Insert
2
STAR-
CRISPR
%
Knock-In
(flow)
Competitor data references publicly available disclosures from other
CRISPR editing technologies and the companies using their platforms
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Systems
Approach
• QbD principles provide the
framework for DOE deployment for
process development
• Systems approach is iterative
• DOE is the fastest, cheapest (leanest)
way to results
• Deployed with great success within
Artisan enabling accelerated process
understanding to improve editing
efficiency
Know-How
Summary
Protocols & Builds Platform & Licenses
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Process and Analytical
Development
Cell Biology and
Immunology
Synthetic Biology
Acknowledgements
Panos Chrysanthopoulos
Kristina Collavino
Stephie Leung
Hala Abdouni
Tolga Barker
Connor Yanchus
Luca Orlando
Calley Hirsch
Jessica Schwaber
Josh Miller
Javier Hernandes
Alica Luca
Roland Baumgartner
Business Development
Laurén Kinner