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Assay Design Using Process Optimization Ideology: A Case ... · 1 Ideal assay 1 > Z ≥ 0.5...
Transcript of Assay Design Using Process Optimization Ideology: A Case ... · 1 Ideal assay 1 > Z ≥ 0.5...
Assay Design Using Process
Optimization Ideology:
A Case For Coupling Assay
Development And Liquid Handler
Qualification
Sponsored by Artel, presented by
Nathaniel Hentz, Ph.D. and Rebecca Kitchener, Ph.D.
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Outline
• Assay development: current practices
• Liquid handler optimization: current practices
• Process optimization – What? Why? How?
• Example: model assay & findings
• Conclusions
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OVERVIEW:
ASSAY DEVELOPMENT
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Foundations Of Assay Development
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Select MethodDefine Assay
Purpose
Sample MatrixIdentify
Process Steps
Assay
Components
Risk
AssessmentCharacterize Validate
TrainSet
SpecificationsDocument
High Throughput Screening Assay Validation
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Reagent
Stability
Signal
Variability
Plate
Uniformity
DMSO
Compatibility
Reaction
Stability
Assay
So, What Does A “Good” Assay Look Like?
Let’s define a couple of parameters:
• First, let’s consider the assay variability
• Second, let’s consider the assay window
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minmax
minmax 331
Z
Z-Factor
ValueAssay Quality
1 Ideal assay
1 > Z ≥ 0.5 Excellent assay
0.5 > Z > 0 Marginal assay
0 Yes/no assay
<0 Assay not useful
The Result
Z’ = 0.74
0 1 2 2 4 3 6 4 8 6 0 7 2 8 4 9 6
0
2 0 0 0
4 0 0 0
6 0 0 0
8 0 0 0
1 0 0 0 0
W e ll ID
Sig
na
l (R
FU
)
Z’ = 0.94
0 1 2 2 4 3 6 4 8 6 0 7 2 8 4 9 60
2 0 0 0
4 0 0 0
6 0 0 0
8 0 0 0
1 0 0 0 0
W e ll IDS
ign
al
(RF
U)
OVERVIEW:
LIQUID HANDLER OPTIMIZATION
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Liquid Dispense Technologies
Dispense Principle Attributes
Air displacement• Problematic for volatile liquids
• Possible cross contamination
• Wide range of volumes
Positive displacement• Useful for volatile solvents
• Cross contamination possible
• Wide range of volumes
Droplet (acoustic)• Non-contact
• Useful for small volumes (pL – nL)
Droplet (solenoid/inkjet)• Useful for small volumes (nL)
• Sensitive to fluid types
Capillary (pintool)• Useful for small volumes
• Direct contact with sample (contamination)
• Sensitive to fluid type
Peristaltic• Useful for bulk dispense;
• More frequent calibrations needed
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Parameters that Effect Volume Transfer
• Hardware/labware – plate format, tip types, tubing type
• ALH settings – target (or off-set) volume, single/multi dispense,
aspirate/dispense rate, aspirate/dispense height, liquid class, pre and
post air gaps, accuracy/precision of volume transfer, transfer
speed/time delays, on-board mixing
• Assay – reagent mixing, incubation, centrifugation, number of liquid
transfers, wash steps, dilution steps, serial dilutions
• Sample – viscosity, density, surface tension, temperature, volatility
• Environment – temperature, humidity, light, vibration
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Typical Liquid Handler Optimization:
• Usually performed as a stand-alone activity
– Precision is always checked, but accuracy is not as easy
• Several methods for volume verification
– In-house (e.g., fluorescence, gravimetric, absorbance, etc.)
– Commercial (e.g., dual-dye spectrophotometry)
• Volume verification is typically performed with ideal solutions
• Liquid handler is certified, calibrated, or repaired (if
necessary)
• Then….someone programs ALH for assay use
– Default method
– Specific to basic assay requirements11
ADOPTING A PROCESS
OPTIMIZATION APPROACH
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Sources Of Assay Variability
Biology
• Diffusion
• Binding equilibrium
• Steric hindrance
• Cell population diversity
• Protein activity
Environment
• Temperature
• Light
• Humidity
• People
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Instrumentation
• Liquid handlers
• Detectors
• Mixers
• Incubators
• Centrifuges
Consumables
• Reagents
• Tips
• Plate type
• Plate seals
What can
we control
or optimize?
What Do We Mean By Process Optimization?
Two levels of HTS assay optimization:
• Level 1
– performed by development scientist
– basis: biology (mostly)
• Level 2
– performed by (or with) the automation scientist
– basis: meeting predefined assay specifications
• Process Optimization minimizes assay variability through the study
of the entire process rather than individual components. Goal is to
optimize the LH system based on assay design specifications,
coupled with the assay itself.
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How Is Process Optimization Accomplished?
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• Step 1 – break down proposed assay into operational
units
• Step 2 – convert assay into MVS readable format;
Replace proposed assay components with Artel MVS
reagents
• Step 3 – read MVS plates to determine performance of
liquid handler, order of addition, incubation and mixing
efficiencies
• Step 4 – insert assay reagents back into process based
on new information
LH
PR
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S O
PT
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OV
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L P
RO
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SS
OP
TIM
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TIO
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N. Hentz and E. Avis, SLAS2017, Improving Assay Performance Using a Process Optimization Approach
The Value of Process Optimization
• Minimize overall assay variability
• Maximize data quality and reproducibility
• Improve efficiency by reducing assay transfer problems
• Process can be optimized to its fullest extent before the assay is
transferred
– e.g. mixing, incubation time, liquid handler and order of reagent
addition
• Only requirement is knowledge of basic assay configuration (e.g.,
volumes, incubation times, incubation temperatures, etc.)
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Optimizing the process as a whole (assay and ALH together)
allows for better control to be gained over the process,
thus reducing variability and minimizing assay transfer time.
CASE STUDY
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Assessing Effects Of Liquid Handling On
The Assay
• Phase I: Using a well-characterized model assay,
develop and optimize the assay platform at bench scale.
• Phase II: Perform method transfer to ALH: examine
effects of automated liquid handling parameters on the
same assay.
• Phase III: “Deconstruct” the assay: decide which
parameters, when altered, significantly vary assay
outcome.
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Streptavidin: Biotin-Fl Assay Principle
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Streptavidin (SA) is a tetravalent biotin-binding protein that is isolated
from Streptomyces avidinii and has a mass of 60.0 kDa. SA has a very
high affinity for biotin (Kd = 10-14 to -15 M).
1. Waner, MJ; Mascotti DP. Journal of Biochemical and Biophysical Methods 70(6), 2008, 873-877. A simple spectrophotometric
streptavidin–biotin binding assay utilizing biotin-4-fluorescein.
2. Ebner, A; Marek, M; Kaiser, K; Kada, G; Hahn, CD; Lackner, B; Gruber, HJ. Methods in Molecular Biology, 418, 2008, 73-88.
Application of biotin-4-fuorescein in homogeneous fluorescence assays for avidin, streptavidin, and biotin or biotin derivatives.
Biotin-Fluorescein (B-Fl)Quenched
fluorescence
SAFl
FlFl
Fl
SA
Enhanced fluorescence
Streptavidin Assay Details
• Assay Components
– Phosphate buffered saline, pH 7.4, containing 0.1% BSA
– Black, non-binding 96-well plates
– Streptavidin (SA)
– Biotin-fluorescein (labeled ligand)
– Inhibitors, various concentrations
• Experimental Conditions
– Add 25 µL of Biotin-FL
– Add 25 µL of inhibitor
– Add 25 µL of SA
– Incubate for 30 min at room temperature
– Read fluorescence: Ex = 485 nm and Em = 515 nm
• Compare inhibitor potency (IC50)20
Assay Development Parameters Evaluated
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Sources Of Error: Assay Buffer
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Effect of buffer on assay outcome (potency) varies between the two
liquid handling methods.
0 1 2 3 4 50
1000
2000
3000
4000
Log Inhibitor Concentration (nM)
Flu
ore
scen
ce S
ign
al (R
FU
)
PBS + BSA
PBS
PBS + Glycerol
0 1 2 3 4 50
1000
2000
3000
4000
Log Inhibitor Concentration (nM)
Flu
ore
scen
ce S
ign
al (R
FU
)
PBS + BSAPBSPBS + Glycerol
Manual ALH
Sources Of Error: Mixing
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For this assay, mixing affects potency, but the discrepancy is more
pronounced on the ALH.
-1 0 1 2 3 4 5
1000
2000
3000
4000
Log Inhibitor Concentration (nM)
Flu
ore
scen
ce S
ign
al (R
FU
)
No MixingMix 3X
-1 0 1 2 3 4 5
1000
2000
3000
4000
5000
Log Inhibitor Concentration (nM)
Flu
ore
scen
ce S
ign
al (R
FU
)
No Mixing
Mix 3X
Manual pipette ALH
Sources Of Error: Reagent Stability
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Reagent stability must be determined on the ALH as
well as during bench scale assay development.
-1 0 1 2 3 4 5
1000
2000
3000
Log Inhibitor Concentration (nM)
Flu
ore
scen
ce S
ign
al
(RF
U)
< 30 min on ALH
6 h on ALH
Sources of Error: Assay Plate Type
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Consumables are often considered interchangeable – not so!
Uncoated Coated (non-binding
surface)
0
200
400
600P
ote
ncy (
IC50, n
M)
41%
-0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
500
1000
1500
2000
2500
Log Inhibitor Concentration (nM)
Flu
ore
scen
ce S
ign
al
(RF
U)
Dispense Rate: 1
Dispense Rate: 3
Dispense Rate: 5
ALH Dispense Rate
For this assay:
• Aspiration rate: no effect
• Air gaps: no effect
• Dispense rate: critical
Sources of Error: ALH Parameters
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Study Conclusions
• Four assay parameters were identified which required
optimization both during development at bench-scale and
on the ALH.
– Assay buffer selection
– Mixing
– Reagent stability
– Plate type
• One LH parameter was determined to be critical to assay
outcome: dispense rate.
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Overall Conclusions
• Performing assay optimization AND liquid handler
optimization together as a whole process reduces
potential for error introduction and prolonged/difficult
method transfer.
• Evaluate critical liquid handling parameters and potential
sources of variability at bench scale and on the ALH for
each new assay.
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Assay optimization or LH qualification alone isn’t enough.
Putting It Together…
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Assay results are impacted
by liquid handling variability
Assays
depend on
reagent
concentrations
Reagent
concentrations
are volume-
dependent
THEREFORE:
Assay integrity
is dependent
on accurate
volume
delivery
THANK YOU FOR YOUR ATTENTION!
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