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.

1

Outline

• Assay development: current practices

• Liquid handler optimization: current practices

• Process optimization – What? Why? How?

• Example: model assay & findings

• Conclusions

2

OVERVIEW:

ASSAY DEVELOPMENT

3

Foundations Of Assay Development

4

Select MethodDefine Assay

Purpose

Sample MatrixIdentify

Process Steps

Assay

Components

Risk

AssessmentCharacterize Validate

TrainSet

SpecificationsDocument

High Throughput Screening Assay Validation

5

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

6

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

8

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

9

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

10

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

12

Sources Of Assay Variability

Biology

• Diffusion

• Binding equilibrium

• Steric hindrance

• Cell population diversity

• Protein activity

Environment

• Temperature

• Light

• Humidity

• People

13

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.

14

How Is Process Optimization Accomplished?

15

• 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

OC

ES

S O

PT

IMIZ

AT

ION

OV

ER

AL

L P

RO

CE

SS

OP

TIM

IZA

TIO

N

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.)

16

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

19

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

21

Sources Of Error: Assay Buffer

22

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

23

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

24

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

25

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

26

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.

27

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.

28

Assay optimization or LH qualification alone isn’t enough.

Putting It Together…

29

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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