Transcript 2016 Peachree WIL RTC Flow Cytometry Presentation 14Mar2016

Flow Cytometry: Applications and
Specialized Method Development
in Preclinical Toxicology
Vanessa L. Peachee, MS, PhD
Director, Immunotoxicology
March 14, 2016
Flow Cytometry in Immunotoxicology
 Immunotoxicology is a rapidly expanding field with new methodologies
constantly being developed in drug discovery.
 Flow cytometry is a powerful analytical tool used at different stages in drug
development.
 Validation basics
 Overview of importance and challenges of flow cytometry in preclinical
toxicology
Validation Basics in Flow Cytometry
 Quantitative: LC-MS/MS
 Absolute quantification of unknown samples: calibration curves
 Reference standard: well defined & output is representative of endogenous analyte
 Relative Quantitative: Cytokine immunoassays (ELISA or flow cytometry)
 Temporal changes in concentrations rather than absolutes: calibration curves
 Reference standard: not well defined & output is not representative of endogenous analyte
 Quasi-Quantitative: Flow cytometry (immunophenotyping)
 No reference standards or calibration curves
 Quantitative: Genetic marker
 Lacks proportionality to the amount of analyte
 Results are non-numeric:+, ++, yes/no or positive/negative
Lee JW, 2005. Pharmaceutical Research, Vol. 22, No. 4
Critical Parameters in Validation
• Accuracy
High accuracy
High precision
• Closeness to target
• Precision
• Repeatability
• Specificity
Low accuracy
High precision
• Sensitivity
• Stability
High accuracy
Low precision
Low accuracy
Low precision
Viginia Litwin and Philip Marder, Flow Cytometry in drug development and discovery, Edition 2011
Critical Parameters in Validation
• Accuracy
High accuracy
High precision
• Closeness to target
Assay Design
Development
• Precision
• Repeatability
• Specificity
Low accuracy
High precision
• Sensitivity
• Stability
Validation
Implementation
High accuracy
Low precision
Re-Validation
Low accuracy
Low precision
Viginia Litwin and Philip Marder, Flow Cytometry in drug development and discovery, Edition 2011
Critical Parameters in Validation
Precision



Closeness of agreement between independent assay results
Expressed as co-efficient of variation (%CV)
Acceptance criteria

<20% CV for cell frequencies >10% of parent population
<30% CV for cell frequencies <10% of parent population (rare populations)
Thymus
Types
Replicates reading
%CV
• Intra-assay precision
1
2
3
4
5
• Between replicates
•
•
•
•
•
•
Inter-assay precision
• Between experiments
• Requirement: Stability
Between analysts
Between instruments
Between laboratories
Myeloid cells
B cells
0.47 0.43 0.49 0.52 0.37
0.34 0.38 0.31 0.41 0.3
14.59
15.39
CD4 T cells
DP T cells
CD8 T cells
DN T cells
9.87 9.94 10 9.72 10.2
84 83.8 83.7 84 83.5
3.92 4.05
4 3.93 4.02
2.16 2.2 2.24 2.38 2.29
1.986
0.248
1.28
3.444
T cells
αβ T cells
γδ T cells
16.8 16.5 16.1 17.1 16.2
90.1 89.1 89.7 90.4 90.9
0.89 1.05 0.91 0.97 0.98
2.721
0.876
5.976
Critical Parameters in Validation
Specificity
The ability to correctly identify the target in the
presence of other substances
• Marker, antibody and fluorochrome selection
• Gating strategy and compensation
• Backgating
•
•
•
Fluorescence Minus One (FMO) controls
Differential gating
Peer reviewed literature helpful
Assay Design
Development
Validation
Implementation
Re-Validation
Critical Parameters in Validation
Specificity: Backgating
Myeloid or Lymphoid (blood)
T cells
B cells
Myeloid cells
T cells
B cells
FSC
Myeloid cells
FSC
SSC
CD45
CD3
•
•
•
•
T cells
B cells
B220
B220
Myeloid
CD11b/c
Critical Parameters in Validation
Specificity: viability stain
700C for 30min
Thymus
Live cells without
L/D stain
Live cells with
L/D stain
Dead cells with
L/D stain
Dead cells
Live cells
95.3
Live dead
Live cells
99.3
FSC
Non-specific
staining
Live cells
1.39
Critical Parameters in Validation
Specificity: FMO control
L/D stain CD45
CD4
CD8
CD45
CD4
CD8
CD4
CD8
L/D stain
L/D stain CD45
L/D stain CD45
CD4
Thymus
Gated on L/D-CD45+ leukocytes
CD8
CD8
CD4
Critical Parameters in Validation
Thymus
CD4
CD4
Stability
CD8
Acceptance criteria : 20% from base line
Day 7
Day 14
Day 21
Animal ID
39009 39012 39014 39015 39016 39009 39012 39014 39015 39016 39009 39012 39014 39015 39016
Myeloid cells 119
203
182
223
71
296
284
296
365
368
136
143
57
129
63
B cells
395
374
414
298
171
305
196
276
288
273
406
329
211
218
201
CD4 T cells
DP T cells
CD8 T cells
DN T cells
67
-22
114
85
60
-22
176
174
40
-20
152
148
89
-20
187
265
62
-15
135
66
62
-23
87
177
59
-20
95
275
31
-19
92
263
56
-16
141
270
56
-19
122
284
79
-26
105
149
77
-21
130
144
37
-18
105
160
86
-17
140
185
76
-17
106
120
T cells
αβ T cells
γδ T cells
59
3
-54
46
4
-55
28
3
-56
56
6
-28
74
9
-72
84
2
-36
95
2
-45
53
1
-37
83
6
-66
101
5
-48
83
0
0
99
1
-35
50
1
-33
79
5
-48
93
6
-49
Freeze thaw caused detection of higher number mature thymic T cell phenotype
Critical Parameters in Validation
Sensitivity




Assay sensitivity is the measure of assay performance with known
negative samples used in determining the lowest limit of
measurement in known positive samples.
Assay setup requires target cells at very low frequencies
 Difficult under general immunophenotyping
Reagent sensitivity is important in developing a flow cytometric assay
to find the minimum staining intensity above background
fluorescence with acceptable precision.
Sensitivity assessment should determine both the maximum
fluorescence intensity of a positive population and the maximum
separation between the positive and negative populations.
Critical Parameters in Validation
for flow cytometry
for quantification
antibody
isotype
isotype
autofluorescence
Antibody Concentration
Fluorescence
Antibody concentration in flow cytometry
Antibody dilution
High concentration of antibody preferred for flow cytometry
Fluorescence
Complexity in Tissue Flow Cytometry
Challenges in immunophenotyping data Interpretation
 Variation: inter- and intra- animals
 Broad range in historical control animal data: Example NHPs
 Frequency versus absolutes
 Varying levels of information and data in peer reviewed publications
Flow Cytometry Data Interpretation – Case study 1
Challenges in data interpretation
Thymus
 Frequency versus absolutes
Gated on CD3+ cells
B cells
γδ T
Myeloid
αβ T
T cells
Normal
CD4
CD4 T
DN
CD8
Abnormal
DP
CD8 T
CD4 T
DN
DP
CD8 T
Absolutes gave more
abnormal cell types than
frequency
Antibody Drug Conjugates

ADCs are a new class of biopharmaceuticals designed as a targeted
therapy for the treatment of cancer.

Complex molecules composed of monoclonal antibodies conjugated to
cytotoxic drugs through chemical linkers with labile bonds.

Internalization of ADC bound to specific cell leads to release of the
cytotoxic drug and death of target cells.

Receptor Occupancy: Requires a specialized approach that addresses the
biologic antibody, the drug and the linker.
Fab
CH3
CH2
Drug
Linker
Fc
Antibody drug conjugate
Death of target cell
ADC
Receptor Occupancy
in NHPs – Case Study 2
Gated on mononuclear leukocytes
A
Monocytes
B cells
CD45
T cells
CD20
SSC
CD14
Granulocytes
Fab
CD45
CD3
B ADC
binding
leukocytes
ADC
binding
totoleukocytes
cells
TTcells
cells
BBcells
Monocytes
Granulocytes
Granulocytes
Monocytes
Isotype
ADC
ADC
isotype
Isotype
ADC
Ab
ADC
Ab
IGN786
ADC ADC
Antibody
Naked
ADC
Naked
ADCAb
Drug
Fc
Linker
binding
neutralization by by
specific
peptidepeptide
C ADC
ADC
binding
neutralization
specific
Isotype
ADC
+ Peptide
Isotype
ADC
isotype
ADC
+ Peptide
Ab
+ Peptide
Events
Antibody
ADC
Ab +
Peptide
+ Peptide
IGN786
ADC
+ ADC
Peptide
ADC fluorescence
Examination of antibody binding to leukocytes within 2-4 hrs of blood collection
ADC Receptor Occupancy - NHPs
300
% T c e lls in b lo o d
T cells
Vehicle
Low dose
Medium dose
High dose
200
100
0
D ay 2
D ay 7
D ay 21
B cells
300
200
100
300
% B c e lls in b lo o d
CD4 T cells
% C D 4 T c e lls in b lo o d
D a y -2 3
0
D ay 7
D ay 21
300
Monocytes
200
100
% M o n o c y te s in b lo o d
% C D 8 T c e lls in b lo o d
D ay 2
D a y -2 3
D ay 2
D a y -2 3
D ay 2
D ay 7
D ay 21
500
400
300
200
100
0
0


100
0
D a y -2 3
CD8 T cells
200
D a y -2 3
D ay 2
D ay 7
D ay 21
D ay 7
D ay 21
Dose dependent ablation (day 2 and day 7) and gradual replenishment (day 21) of T and B cells
Monocytes increased subsequent to ADC treatment.
Summary
 Critical to develop an implementation plan for assay qualification, validation,
and sample analysis. Prior to validation the assay should be evaluated and
qualified.
 Specificity, precision and stability are critical in flow cytometry method
validation.
 Immunotoxicity of ADCs requires a specialized approach that addresses the
binding of antibody (Fc or Fab), the drug and the linker to the target cell.
Acknowledgements
 WIL Research Pathology and Immunology Services:
 George Parker, DVM, PhD, DACVP, DABT, Vice President,
Global Pathology
 Norbert Makori, PhD, Director, General Toxicology
 Josely Figueiredo, DVM, MS, PhD, DACVP, Staff
Pathologist
 Tracey Papenfuss, DVM, PhD, DACVP, Staff Pathologist
 Raghu Tadagavdi, BVSc, MVSc, PhD, DABT, RAC,
Research Scientist, Immmunotoxicology
 Gary Coleman, DVM, PhD, DACVP, DACVPM, Director,
Pathology
 Technical personnel
 Julia England, MS, Flow Cytometry/Project Specialist
 Lisa Manson, MT, MLT, Group Supervisor, Clinical Pathology
THANK YOU.