ABCD

2nd European DOE User MeetingMarch 10-12, 2008Berlin, Germany

Statistical Analysis and Optimization of a Complex Enzymatic Assay for High-Throughput Screening using ‘DoE’ Techniques

Hans-Dieter SchubertBoehringer Ingelheim Pharma GmbH&Co KGDept. Lead Discovery - High-Throughput ScreeningBiberach - Germany

ABCDStatistical Analysis and Optimization of a Complex Enzymatic Assay for High-Throughput Screening using ‘DoE’ Techniques

AbstractHigh-throughput screening (HTS) is one of several steps in the drug discovery process in the pharmaceutical industry. During an HTS campaign, large collections of several hundred thousands of different substances are tested to determine their activity on a target molecule which is suspected to be involved in diseases. Crucial for this is the use of robust assays with high sensitivity to identify the few active substances in the vast majority of the inactive substances in the screening compound collection. On the other side, these assays must also show high specificity that is necessary to reliably detect inactive substances and not to flood the set of active substances with false positive results. The analysis and optimization of an enzyme assay will be shown. In this assay, the activity of this target enzyme is coupled to further enzymes and substrates and in the end measured as change in light absorbance of the sample. This analysis and optimization was done in two experiments. The first experiment was a 2-level full factorial design with six factors and two response variables. The second experiment was performed to confirm the impact of the factors and to predict the final assay setup applying a 3-level factorial response surface design. The experiments were performed on a Biomek® FX liquid handling workstation in conjunction with the AAO Software for automated assay optimization, both components developed by Beckman Coulter Inc. The AAO software is the key link between the DoE experiment and the liquid handling robot by automatically generating all necessary liquid handling scripts for the robot and the deconvolution of the data back to the DoE software for further analysis.

ABCDStatistical Analysis and Optimization of a Complex Enzymatic Assay for High-Throughput Screening (HTS) using DoE Techniques

Overview• Introduction

– Boehringer Ingelheim– Pharma R&D business model

• ‘DoE’ utilization in HTS– Planning an experiment– Implementation and workflow for assay development

• Example of enzymatic assay for the quantification of the activity of acetyl-CoA carboxylase– Assay principle– Results– ‘DoE’ analysis

• Summary and acknowledgements

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Boehringer Ingelheim is one of the top 20 leadingresearch-based pharmaceutical companies in the world.

Boehringer Ingelheim

Founded: in Ingelheim am Rhein in 1885by Albert Boehringer (1861 — 1939), with 28 employees

Employees worldwide: 37,139of these:• 15 percent in research and development• 30 percent in production• 37 percent in marketing and sales

• Production sites in 20 countries• Net sales: EUR 10.574 billion • Research and development: EUR 1.574 billion • Affiliated companies worldwide: 144• Capital investments: EUR 427 million

(As at: business year 2006)

ABCD

4 research sites fully dedicated to specific indication areasVienna, Austria oncologyLaval, Canada virologyRidgefield, USA immunology, inflammatory diseases, cardiovascular diseasesBiberach, Germany metabolic disorders, airway diseases, CNS diseases

Research Centers of BI

VIEBC

LAV

RDG

ABCDPharma R&D Business Model

1

2 0.4

2.7

1.6

7Approximate time (years)

Total time: 14.7 years

Biology Chemistry Development

Target ID Validation Screening Optimisation Preclinical Clinical

Source: Boston Consulting Group (November 2001)

ABCD‘Filter Process’ in Early Drug Discovery

PrimaryScreening

"Hit-to-Lead“phase

Lead structureoptimisation

NS

N

NN

Cl

N

NN

N

O

O

O

O

Cl

NNH

O N

N

F F

Compound libraries2k - 2M+ samples

Hits~ 2 000

Leads~ 5

NS

N

NN

Cl

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Robotic system forcellular assays

Robotic system formultipletechnologies

uHTS systemfor opticaldetection

HCS system

Automateddevices(pipettor,reader)

HTSBioLabDB

IT system forData evaluation

IT system Lab database

‘Filter Process’ Infrastructure at BI’s Research Center in Germany

Screening Support

Dispensary

ABCD

Internat.DB

ABCDStatistical Analysis and Optimization of a Complex Enzymatic Assay for High-Throughput Screening (HTS) using DoE Techniques

Overview• Introduction

– Boehringer Ingelheim– Pharma R&D business model

• ‘DoE’ utilization in HTS– Planning an experiment– Implementation and workflow for assay development

• Example of enzymatic assay for the quantification of the activity of acetyl-CoA carboxylase– Assay principle– Results– ‘DoE’ analysis

• Summary and acknowledgements

ABCDUtilization of ‘DoE’ in High-Throughput Screening

Biologicaltargets

Biologicaltargets

Compoundlibrary

Compoundlibrary

Assaydevelopment

Assaydevelopment

ScreeningadaptationScreeningadaptation HTS

HTS Dataanalysis

Dataanalysis

• Find optimum buffer composition• Find optimum assay component conc.• Find optimum cofactor concentrations • Find robust assay conditions• .....

• Use optimal liquid handling settings• Use optimal reader settings • Use optimal process parameter• .....

ABCD‘DoE’ – Statistically Designed Experiments

How does it help us?• Multi-variable optimization problems

– Assay development and optimization– Instrument optimization

• Optimize to achieve desirable properties– Assay signal (high; low; S/B ratio; Z’ value)– Variability

• Develop robust assays for HTS– High specificity (assign a negative result to an inactive compound)– High sensitivity (assign a positive result to an active compound)

• Reduction in time for assay development• Reduction in cost for consumables

ABCD‘DoE’ – Statistically Designed ExperimentsPlanning an experiment

• ‘DoE’ is no substitute for scientific or engineering knowledge• ‘DoE’ helps to plan experiments and allows valid and conclusive

analysis of the data• Systematic approach to experimentation that is in contrast to the

conventional procedure in assay development (‘OFAT’)• Based on statistical theory

– Analysis of variance (ANOVA) to identify and measure the various sources of variation within a data set

– Level of confidence for conclusions is determined precisely– Clear analysis of the data– Predictive and precise mathematical models

• Graphs for the visualization of data and results– Interaction plots– Contour plots

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Scientific/engineering expertise

Statistic software

AAO Software and Biomek FX

Statistic software

Scientific/engineering expertise

Recognition of the problem and develop the objective(s)Assess the experimental environmentChoose factors, levels, and ranges (independent variables)Choose the response variable(s)Choice of experimental designDefine the run orderReview the operabilityWatch the experimentAnalyze the resultsDraw conclusions and recommendations

– for the next run– final

‘DoE’ Guidelines for Planning Experiments

ABCDImplementation and Workflow in a Laboratory for Assay Development

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Data Data analysisanalysis

Parse dataParse dataRun Run

experimentexperiment

Create Create robotic robotic

methodsmethods

Create Create random random

plate mapsplate maps

Configure Configure labwarelabware and and techniquestechniques

Role of ‘AAO’ in Statistically Planned Experiments (1)

• AAO = Automated Assay Optimization Software– Software product from Beckman-Coulter developed together with

SmithKlineBeecham and Stat-Ease – Smart robotics– Automated transformation of experiments into liquid handling methods

Design of Design of ExperimentsExperiments

Statistic softwareDesignExpert

Beckman Coulter’s FX liquid handling robots

Beckman Coulter’s AAO

ABCDRole of ‘AAO’ in Statistically Planned Experiments (2)

ABCDStatistical Analysis and Optimization of a Complex Enzymatic Assay for High-Throughput Screening (HTS) using DoE Techniques

Overview• Introduction

– Boehringer Ingelheim– Pharma R&D business model

• ‘DoE’ utilization in HTS– Planning an experiment– Implementation and workflow for assay development

• Example of enzymatic assay for the quantification of the activity of acetyl-CoA carboxylase– Assay principle– Results– ‘DoE’ analysis

• Summary and acknowledgements

ABCDEnzymatic Assay for the Quantification of the Activity of Acetyl-CoA Carboxylase (ACC)

Acetyl-CoA

Malonyl-CoA

HCO3- + ATP

Pi + ADP

ACC

PEP

Pyruvate

PK

Decrease in absorption at 340 nm

LDHLactate + NAD++ NADH

AbbreviationsATP Adenosine-tri-phosphateADP Adenosine-di-phosphateACC Acetyl CoA carboxylaseCoA Coenzyme AHCO3

- Hydrogen carbonateLDH Lactate dehydrogenase

NAD+ Nicotineamide adenine dinucleotide ox.NADH Nicotineamide adenine dinucleotide red.Pi Inorganic phosphatePEP Phosphoenol pyruvatePK Pyruvate kinase

Objective of the assay optimizationChange in ACC activity must directly translate into change in absorption without lag phase

ABCDEnzymatic Assay for the Quantification of the Activity of Acetyl-CoA Carboxylase (ACC)

• Plates384-well MTPs (Greiner μClear; black); 50 μl assay volume

• BufferTris 100 mM; sodium citrate 10 mM; BSA (fatty acid free) 1 mg/ml; glutathione 3.75 mM; KHCO325 mM; MgCl2 10 mM; pH 7.5

• Substrates and coupled enzymesLDH 1.5 U/ml; PEP 0.5 mM; PK 1.5 U/ml; NADH 500 μM; acetyl-CoA 200 μM; ATP 500 μM

• ReaderZeiss plate::vision OD@340/10 nm with 1 min interval

Full enzyme reactionV(slope) = -0.280 ± 0.054 ΔOD/hrw/o acetyl CoAV(slope) = -0.021 ± 0.023 ΔOD/hr

S/B 14.5Z’ 0.23

0 5 10 15 20 250.00

0.25

0.50

0.75

Time [min]

OD

@ 3

40nm

ABCDStatistical Analysis and Optimization of the ACC Assay for HTS (1)

• Experiments run on the Beckman FX pipetting platform• Objective of ‘DoE’ run 1

– Identify the influence of the different assay components– Design: 2-level full factorial

(2 replicates; 3 center points; 2∗131 experiments → 262 wells → 1 MTP)– Model: 6-factor linear interaction

• Objective of ‘DoE’ run 2– Confirm the influence of the different assay components and predict the

final assay setup– Design: Response surface 3-level factorial

(2∗87 experiments → 174 wells → 1 MTP)– Model: Nonlinear quadratic

ABCDStatistical Analysis and Optimization of the ACC Assay for HTS (2)

• Experimental environment– Buffer: Tris 100 mM; sodium citrate 10 mM; BSA (fatty acid free) 1 mg/ml;

glutathione 3.75 mM; KHCO3 25 mM; MgCl2 10 mM; pH 7.5– Enzyme: ACC– Plates: 384-well MTPs (Greiner μClear; black); 50 μl assay volume– Reader:

Zeiss plate::vision 340/10 nm; kinetic reading of absorbance @ RT with 1 min interval over 25 min

• Factors (regarded as important for the study)– ATP; acetyl-CoA; PEP; PK; NADH; LDH

• Response variables– Slope of full ACC assay (ΔOD/hr)– Slope of background reaction without acetyl-CoA (ΔOD/hr)– Signal-to-background ration (S/B)

ABCDACC Assay – Results of ‘DoE’ Run 1

Not used

Background reaction w/o acetyl CoA

Full reaction with acetyl CoA

ABCDACC Assay – Results of ‘DoE’ Run 2

Not used

Background reaction w/o acetyl CoA

Full reaction with acetyl CoA

ABCDACC Assay – Analysis of ‘DoE’ Run 1

Half normal probability plot of data

Full ACC reaction Background reaction S/B

ABCDACC Assay – Analysis of ‘DoE’ Run 1

Factor plots of S/B response

ATP NADHAcetyl-CoA

DESIGN-EXPERT Plot

S/B

X = A: ATP

Design Points

Actual FactorsB: AcCoA = 300.00C: PEP = 1250.00D: PK = 3.25E: NADH = 350.00F: LDH = 3.25

300 475 650 825 1000

1.0

1.5

2.1

2.6

3.1

A: ATP

S/B

One Factor PlotWarning! Factor involved in an interaction.

DE

S/

X =

SIGN-EXPERT Plot

B

B: AcCoA

Design Points

tual Factors ATP = 650 PEP = 1250.00 PK = 3.25 NADH = 350.00 LDH = 3.25

AcA:C:D:E:F:

100 200 300 400 500

1.0

1.5

2.1

2.6

3.1

B: A cCoA

S/B

One Factor PlotWarning! Factor involved in an interaction.

DE

S/

X =

SIGN-EXPERT Plot

B

E: NADH

Design Points

tual Factors ATP = 650 AcCoA = 300 PEP = 1250.00 PK = 3.25 LDH = 3.25

AcA:B:C:D:F:

200 275 350 425 500

1.0

1.5

2.1

2.6

3.1

E: NADH

S/B

One Factor PlotWarning! Factor involved in an interaction.

ABCDACC Assay – Analysis of ‘DoE’ Run 2

3D Analysis of S/B response

ABCDNumerical Optimization of the ACC Assay

• Statistical procedure that searchesfor a combination of factors

• Desirability function– Zero outside the limits– One at the goal

• Constraints– Factors within experimental concentration range

• Goal– Max. or min. for optimal assay performance

Sol. ATP AcCoA NADH LDH Full slope Bkg. slope S/B Desirability # μM μM μM U/mL1 133 1000 295 0.5 -0.527 -0.084 8.3 0.6352 158 1000 294 0.5 -0.530 -0.085 8.2 0.6353 144 1000 289 0.5 -0.527 -0.084 8.2 0.6354 131 1000 252 0.5 -0.508 -0.081 8.0 0.6275 338 1000 289 1.2 -0.520 -0.091 6.1 0.5556 296 1000 277 1.5 -0.518 -0.090 6.1 0.5537 292 1000 277 1.5 -0.517 -0.090 6.0 0.5538 292 993 279 1.5 -0.517 -0.090 6.0 0.5539 403 1000 273 1.5 -0.528 -0.093 6.1 0.551

10 308 1000 279 1.4 -0.517 -0.091 6.0 0.551

List of solutions

ABCDStatistical Analysis and Optimization of a Complex Enzymatic Assay for High-Throughput Screening (HTS) using DoE Techniques

Overview• Introduction

– Boehringer Ingelheim– Pharma R&D business model

• ‘DoE’ utilization in HTS– Planning an experiment– Implementation and workflow for assay development

• Example of enzymatic assay for the quantification of the activity of acetyl-CoA carboxylase– Assay principle– Results– ‘DoE’ analysis

• Summary and acknowledgements

ABCD‘DoE’ Summary of the Analysis and Optimization of the ACC Assay for HTS

• After 2 ‘DoE’ runs (= 2 days of experimentation)

– Detailed understanding of a complex assay– Predict final assay conditions for screening – ↑ Acetyl-CoA; ↓ ATP; →↑ NADP

compared to starting conditions• Down-scaling of the assay into 1536-well/8 μL format

– S/B of approx. 8– Z’ between 0.13 and 0.68

ABCDAcknowledgements

ABCD

Lutz ErhardJürgen HungerDr. Christoph KrüllTorsten Ropeter

Dr. Heike NeubauerUrsula Schmid

2nd European DOE User Meeting�March 10-12, 2008�Berlin, GermanyStatistical Analysis and Optimization of a Complex Enzymatic Assay for High-Throughput Screening using ‘DoE’ TechniquesStatistical Analysis and Optimization of a Complex Enzymatic Assay for High-Throughput Screening (HTS) using DoE TechniquesResearch Centers of BIPharma R&D Business Model‘Filter Process’ in Early Drug Discovery‘Filter Process’ Infrastructure at BI’s Research Center in GermanyStatistical Analysis and Optimization of a Complex Enzymatic Assay for High-Throughput Screening (HTS) using DoE TechniquesUtilization of ‘DoE’ in High-Throughput Screening‘DoE’ – Statistically Designed Experiments‘DoE’ – Statistically Designed Experiments�Planning an experiment‘DoE’ Guidelines for Planning ExperimentsImplementation and Workflow in a Laboratory for Assay DevelopmentRole of ‘AAO’ in Statistically Planned Experiments (1)Role of ‘AAO’ in Statistically Planned Experiments (2)Statistical Analysis and Optimization of a Complex Enzymatic Assay for High-Throughput Screening (HTS) using DoE TechniquesEnzymatic Assay for the Quantification of the Activity of Acetyl-CoA Carboxylase (ACC)Enzymatic Assay for the Quantification of the Activity of Acetyl-CoA Carboxylase (ACC)Statistical Analysis and Optimization �of the ACC Assay for HTS (1)Statistical Analysis and Optimization �of the ACC Assay for HTS (2)ACC Assay – Results of ‘DoE’ Run 1ACC Assay – Results of ‘DoE’ Run 2ACC Assay – Analysis of ‘DoE’ Run 1ACC Assay – Analysis of ‘DoE’ Run 1ACC Assay – Analysis of ‘DoE’ Run 2Numerical Optimization of the ACC AssayStatistical Analysis and Optimization of a Complex Enzymatic Assay for High-Throughput Screening (HTS) using DoE Techniques‘DoE’ Summary of the Analysis and Optimization of the ACC Assay for HTSAcknowledgements