Standardization of Flow Cytometry Workflows for Cell Therapy Applications Novartis Cell and Gene Therapies Unit (CGTU) Sadik H. Kassim, Senior Fellow
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1 Standardization of Flow Cytometry Workflows for Cell Therapy Applications Novartis Cell and Gene Therapies Unit (CGTU) Sadik H. Kassim, Senior Fellow ISCT-Las Vegas (May 27, 2015)
2 Disclaimer The information contained in this document belongs to Novartis and/or its affiliates. Novartis does not make and expressly disclaims: (a) any representation or warranty (express or implied) with respect to the information shown in this presentation; and (b) any liability relating to the accuracy or completeness of the information. 2
3 Flow Cytometry is a Keystone Analytical Platform for Cell Therapy: cgmp Setting D0 Composition Product Release: CoA In Process: Transduction 1. Porter DL, et al. N Engl J Med. 2011;365(8): Porter DL, et al. J Cancer. 2011;2: Kalos M, et al. Sci Transl Med. 2011;3:95ra73. 3 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
4 Flow Cytometry is a Keystone Analytical Platform for Cell Therapy: Research and Development Setting Lead CAR selection involves choosing the optimal ScFv/Signaling Domain Configurations Alfred Garfall et al. Discovery Medicine, 2014 Marc Cartellieri et al. JBB, Phenotype -Function 4 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
5 Flow Cytometry Assays: Cell Based Cancer Immunotherapies 1) Multi-parameter immunophenotyping of cells. 2) Intracellular cytokine staining (ICS). 3) Cytotoxicity assays. 4) Proliferation assays. 5) Cell-free cytokine analysis. 5 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
6 Flow Cytometry Workflows: What Needs to be Standardized? 1. Sample Preparation 4. Data Analysis 2. Instrument Setup 3. Data Acquisition 6
7 Flow Cytometry Workflow Requirements Regulatory and Quality Standards Throughput Drug Discovery Preclinical Clinical Target Identification Target Validation Pharmaceutical Formulation Toxicity Evaluation Clinical Trials 7 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
8 Automating Flow Cytometry Workflows Automation can potentially address the analytical challenges on the path to commercialization. Automation is not just about increasing throughput. Automation means walkaway sample prep, acquisition, and analysis Key benefits: Rigorous management/tracking of multi-patient, multi-assay sample flows Reproducibility and robustness of notoriously variable cell-based assays Ease-of-transfer of analytical methods Applicability across portfolio. 8 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
9 Flow Cytometry Workflow Standardization Solutions: GNF High Throughput Flow Cytometry Industry-first, fully automated screening system dedicated to processing and reading cells using flow cytometry or 384-well plates 50,000 wells per day, <1 FTE Incubators GNF Sampler Beckman Coulter CyAn V-spin / seal / de-seal Chilled incubators Washer/dispenser Bravo Pipettor
10 Flow Cytometry Workflow Standardization Solutions: Commercial Options MSP Flow Cytoprep BD SPAIII Hypercyte-Autosampler IntelliCyt ique Screener Beckman Coulter 10 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
11 CGTU Solution: Automation of Flow Cytometry FlowSPAAA / FlowSPA 3 Flow Cytometry Sample Prep Automation, Acquisition, and Analysis Run Software Output result Sample Prep Automation TRD prototype commissioned Protocol development ongoing Next: Test prototype and optimize workflow Acquisition Assessing multiple vendors Focus on validated BD equip. Next: establish timeline for main & backup prototype paths Analysis TRD leading development Prototype analyses established Next: implement in research workflows in CA and MP Sample Prep Automation Acquisition TRD prototype commissioned Assessing multiple vendors Protocol development ongoing Focus on validated BD equip. Next: Test prototype and Next: establish timeline for 11 optimize ISCT Flow workflow Cytometry Workshop Sadik main Kassim & backup May 27, prototype 2015 paths Analysis TRD leading development Prototype analyses established Next: implement in research workflows in CA and MP
12 Free, Open Source R Based Statistical Programming Aghaeepour et al. Current Topics in Microbiology and Immunology (2014) 12
13 Novartis Custom Solution for Automated Flow Cytometry Analysis Drag and drop data folder Better control over script Select Script from Script folder Progress Indicator 13 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
14 Automated Flow Cytometry Analysis Panel 2 FMO 14 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
15 Automated Flow Cytometry Analysis Combination of data driven gates and FMO-based gates Data Driven FMO Based 15 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
16 Automated Flow Cytometry Analysis Panel 2 Gating Strategy Plots Data split into sections Descriptions Code 16 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
17 Automated Flow Cytometry Analysis Panel 2 Analysis 17 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
18 Typical experimental setup Automated prep and analysis of 10 color panel Samples: Various process development samples (healthy donors) Panel: 10 Color Differentiation/T Cell Subset Panel Panel Composition Lineage Marker #1 Lineage Marker #2 Lineage Marker #3 Product Specific Antibody Differentiation Marker #1 Differentiation Marker #2 Differentiation Marker #3 Activation Marker #1 Activation Marker #2 Viability Dye Software analyzes control samples to set FMObased gates Dynamic gates generated where appropriate 175 gates of data are generated 29 key populations of interest returned in report. 18 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
19 manual Data from two early experiments in aggregate Manual vs Automated sample prep All analysis automated (v1) Each point shows the manual and automated prep values for 1 population from 1 sample Strong correlation across data in aggregate r 2 = automated 19 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
20 manual Looking at Each Population Individually Manual vs Automated sample prep All analysis automated Strong correlations within populations as well 24/29 w r 2 > 0.9 Exceptions under further investigation (small values, etc) automated 20 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
21 manual Manual vs Automated sample prep All analysis automated (v2) Each point shows the manual and automated prep values for 1 population from 1 sample Overall correlation improved r 2 = automated 21 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
22 manual automated rep 2 Manual vs Automated sample prep r 2 = r 2 = automated automated rep 1 Replicate samples processed in same plate present the best case scenario 22 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
23 Automated Flow Cytometry Analysis Manual Analysis Variability 3 FlowJo Users UPCC G Mean +/- SD 40 CV CAR+ CAR+CD4+ CAR+CD8+ CD45RA+CCR7+ CD45RA+CCR7 CD45RA CCR7+ CD45RA CCR7 Naive EMRA Central Memory Effector Memory Easy Messy 23
24 Conclusions and Future Directions -Standardization of flow cytometry workflows, in research, development, and manufacturing/qc will be important for commercialization of cell therapies. -Automation provides a potential cost-effective and reproducible solution. -In house data demonstrates that automated and manual flow cytometry workflows produce similar results. -Instrument qualification and assay validation will be critical for the effective deployment of these approaches. 24 ISCT Flow Cytometry Workshop Sadik Kassim May 27, 2015
25 Acknowledgements Arnaud Colantonio Sean Garrity Viraj Tyagi Brian Gismonde Nebojsa Milovic Mark Dudley Amy Shaw Lana Parent Olja Pulluqi Piotr Pierog Junxia Wang Rod Rietze Joe Chin Ted Manley Brian Duffy Olivier Graf Chelsea Xue Andrew Beavis Joe Sieling Margit Jeschke Marty Giedlin John Scott Jeannine Larrieux Erik Rutjens Poonam Vaidya Dan Sipes Dan Rines John Scott Jen Brogdon Boris Engels Fay Dufort 25
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