SLAS Screen Design and Assay Technology SIG: SLAS2013 Presentation

The challenges associated with conducting HTS/HCS campaigns in the current

academic funding environment

Paul A. Johnston

Research Associate ProfessorUniversity of Pittsburgh

Department of Pharmaceutical Sciences, School of Pharmacy

University of Pittsburgh Drug Discovery Institute• Established 2005

– School of Medicine• John S. Lazo – Department of Pharmacology & Chemical Biology

– School of Pharmacy• Barry I. Gold – Department of Pharmaceutical Sciences

– School of Arts and Sciences• Peter Wipf – Chemistry Department

• Pittsburgh Molecular Library Screening Center, 2005– Member of the NIH pilot phase MLSCN, U54MH074411 (Lazo, PI)

• Pittsburgh Specialized Application Center, 2010– Member of the NCI Chemical Biology Consortium– Lazo & Johnston (Co‐PI’s)

• University of Pittsburgh Cancer Center, 2010– Chemical Biology Facility (ChBF)– Cancer Center Support Grant (Davidson, PI)

2UPCI Chemical Biology

Facility (ChBF)

HTS Facility Functions

• HTS/HCS assay development collaboration/consultation– Development & optimization primary, secondary & tertiary assays

• HTS/HCS Validation– Automated process– Z’‐factor, S:B ratio, DMSO validation, LOPAC & NIH Clinical Collection library screening

• HTS/HCS campaign– HTS/HCS data processing & quality control review– Active identification & confirmation

• Data Generation & Reporting– HTS/HCS data analysis – Compound classification, clustering & similarity searches; Cross target queries – biological promiscuity

• Hit Characterization– Counter screens, secondary & tertiary assays

• Lead Optimization– Iterative bioassay support of the SAR effort

• Grant Submissions & Contracts ‐ collaboration/consultation – Preliminary data– HTS/HCS specific aims & statements of work

• Publications & Teaching

3

AssayDevelopment

AssayValidation

HTS/HCSCampaign

ActiveConfirmation

HitCharacterization

Lead Optimization

HTS/HCS: Testing the Hypothesis• 21 primary HTS/HCS campaign collaborations 2006‐2012

– 11 screens funded by the Molecular Library Screening Center Network (MLSCN) (Lazo, PI)

4

21 Primary HTS/HCS Campaign Collaborations• Johnston research group 2006‐2012• 4.62 million data points collected & 3.85 million compounds screened

– Assay development & HTS/HCS implementation • Caleb Foster*, Jennifer Phillips*, Sunita Shinde*, Salony Maniar*, John Skoko*, Yun Hua , Daniel

Camarco, David Close, Stephanie Leimgruber*, Seia Comsa*, & Richard DeBiasio*– HTS/HCS informatics & Chem‐informatics

• Tong Ying Shun & Harold Takyi– 21 publications (2006‐2012) & several manuscripts in preparation

5

Establishing an Academic HTS/HCS FacilityInitial funds from Institution & Grants

• Capital investment HTS/HCS hardware $$$• Capital investment informatics hardware $$$• Capital investment informatics software $$$• Purchase a compound &/or siRNA library $$$• Equipment service contracts $$$• Software licensing fees $$$• Suitable institution space available – rent? $$$ • Salaries, reagents & supplies – Grant $$$

6

Funding and Maintaining an Academic Screening Center

• Institutional investment– Space, equipment, IT hardware & software– Compound & siRNA libraries

• Core facility or independent institute model?– Core facility – institutional support

• Grants, contracts, foundations & donations– Personnel salaries – Equipment service contracts (multi‐year)– Software licensing fees (multi‐users)– Reagents & supplies

• Grants ‐ current funding level ≤ 7‐8 %, 3‐5 yrs support– RO1 grant modular budget $250K/yr, 3‐5 yrs– RO1 grant budget > $250K/yr ‐ need to justify– R21 grant modular budget $125K/yr, 1‐2 yrs

• Large equipment grants – multi‐user consortium

7

Sustaining a Funding Stream:It’s all about the collaborations!

• NIH pilot phase MLSCN, U54MH074411 (Lazo, PI)– 11 HTS campaigns funded

• NCI Chemical Biology Consortium (Lazo & Johnston Co‐PI’s)– NeXT STAT3 pathway inhibitor project (Grandis, PI)– NeXT cMyc inhibitor project (Prochownik, PI)

• NCI contract– Drug combination screening in the NCI 60 cell line panel (Eiseman, PI)

• HTS/HCS Collaborations –co‐investigators– STAT3‐GFP nuclear localization assay development (Reich, PI)– AR‐GFP nuclear localization assay development & HTS (Zhou, PI)– TLR 3 signaling assay development and HTS (Sarkar, PI)– MCAD assay development (Moshen, PI)– ATZ assay development and HTS (Silverman, PI)

• HTS/HCS assay development and screening ‐ PI– AR‐TIF2 protein‐protein interaction biosensor NINDS R21, (Johnston, PI)– AR‐TIF2 protein‐protein interaction biosensor NCI RO1, (Johnston, PI)

8

Screen Design and Assay Technologies Special Interest Group: Screening in this economy….What makes ‘centsDouglas Auld, Ph.D.Novartis Institutes for Biomedical ResearchCambridge, Mass., USA

Leveraging Focus Libraries and Quantitative HTS in Assay Pilot Testing

Testing multiple hypothesis earlyBetter starting points for drug discovery

Focus libraries can be used to characterize assaysand help choose the right set of assays for the project

LMW libraries• Probes, drugs, tools• Natural products• Previously found program compounds (small molecules of unknown

targets –”SMUTS”)

RNAi libraries• Down-regulation of target only• Time-scale of response very different than LMW treatments• Specificity

Biochemicals• Peptides• Metabolites• Nucleic acid mimics

What to screen?Types of libraries

LMW Chemical librariesDrugs, Probes, and Tools

LMW Chemical librariesFocused library sets

Diversity-based/Random sets

Assay interference

Chemical biology questionsanticipate secondary assays

Anticipate counter-screens/orthogonal assays

OH

Focus libraries in understanding phenotypic assaysTwo opportunities

| Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only6

1. Build a hypothesis prior to HTS

2. Generate a hypothesis post HTS

?

• Old view: Assay is used to characterize the compound library• New view: Compound libraries are used to characterize

assays –choose the best assay(s) and compound subset for the project

Focus librariesSize and types of libraries at NIBR


1,400 – one 1536w plate (Challenge) –test for frequent hitters (solubility dataRead-Out artifacts (e.g. fluorescence)

2,733 – two 1536w plates. Drugs, clinical candidates, tool compounds (MoA)

4~10K – eight 1536w plates Random set hit rate estimate

250K - Focus screen (~180 1536w plates)

-plate based diversity set -Biodiverse set –target class annotation.

MOA

Epi

Challenge BioDiv.

Sources of chemical biology informationAnnotation of compounds, pathways, mechanisms

Bioinformatics Resources• EntrezGene• InterPRO• GeneGo• SCOP• UniProt• Clinicaltrials.gov• Broad Connectivity Map

• PDB• Pubchem BioAssay• Binding DB

Compound Databases• ChEMBL• ChEBI• DrugBank• Thomson Reuters Integrity• World Drug Index• PubChem structures• eMolecules (8M compounds)

Semantic Standardization enables interoperability•Merges chemical and biological data•Internal, historical data + external data•Maps assay metadata to results data•Provides chemical structure (InChIKeys) and target normalization (Gene ID)

NDFI: Novartis Data Federation InitiativeGoal: Allow rapid mining of data to generate knowledge


Forming a searchable chemical biology database

Assay registrationAssay data

Data Warehouse

Assay Registration

Results Publication

Results Capture

Assay DataAssay Metadata

Sample registration

Assay Request

Assay Configuration

Reporting

Sample Information

Assay RequesteLN

Biologist

Chemist

Data AnalysisData Analysis

LiteratureDatabases

eLN

Focused screen with qHTS formatAllows for both plate-based performance and validation data – 1 experiment


First four concentrations represent typical single concentration screening scenarios Last four provide robust curve fitting. One experiment yields:

Plate-based single concentration data for assay performance stats.Validation data obtained – dose response dataA “truth matrix” is the output: True positives (TP), false positives (FP), false negatives (FN), true negatives (TN), confirmation rate (CR) and Hit rate per concentration can be considered in choosing the final screening concentration.

quantitative HTS“qHTS”

Two types of data from qHTSScatterplots and concentration-response curves


Annotate activity based on hit threshold and CRC informationTP, FP, FN, TN

CRCsRetrospective analysis

11

22

qHTS Retrospective analysisAnalysis with Pipeline Pilot


• Top tier – get curve fit information from database file and tag data as “CRC” if active criteria are met

• Bottom tier – get hits from database file based on a cut-off threshold (e.g. <-30%), tag data as ”Hits”

• Annotate all data as either TP, FP, FN, or TN depending if the “Hits” are found as “CRC” actives.

Truth matricesSingle-concentration data retrospective analysis

(TP_N = # of true positives, FN_N = # false negatives, ect)

Assay 1 Assay 2

Challenge Library ConstructionFocus on biochemical assay interferences

Challenge Library

Solubility / Aggregation/ “PAINS”• Solubility data • Read-Out artifacts (e.g. fluorescence) • Hits in counter-screen

Covalent Protein Modifiers• LC/MS assay for covalent modification data

During construction, target unselective ligands (e.g. non-specific kinase inhibitors) were not taken as “frequent hitters”, left out of Challenge library

∑ 1,408 compounds, fits in one 1536-well plate

Luciferase inhibitors are available as a separate subset (for reporter-gene assay characterization)

Challenge Library ConstructionFocus on biochemical assay interferences

Frequent hitter analysis Many interfere with fluorescence and AlphaScreen

Freq. hitter = screened in at least 10 assays and hit >50% of these(compare - same analysis with1,400 randomly picked compounds yields only 1 freq hitter)

Firefly luciferase (FLuc) is a popular choice for RGAsFLuc inhibitors can confound the interpretation of RGA results


Observations:• FLuc inhibitors compose a ~4% of typical screening libraries (determined in an biochemical FLuc enzyme

assay)• FLuc inhibitors are highly enriched (40-98%) in hit list derived from FLuc-based RGAs• Can show apparent activation in cell-based FLuc-RGAs due to inhibitor-based enzyme stabilizationTool set:• Known luciferase inhibitors available to characterize primary and counter-screen assays • Mechanistic understanding of luciferase inhibitors can be used to develop robust orthogonal assays

PubChem examples:See Thorne et al. (2012) Chem Biol. 2012 Aug 24;19(8):1060-72.

Frequent Hitter analysis of confirmed FLuc inhibitors

Biochemical assay against the Challenge LibraryComparison of two buffer systems biochemical assay

Biochemical assay for an essential enzyme in bacterial cell wall synthesis

Fluorescent read-out in 1536w plates

Use Challenge library to identify a buffer system that reduces interference with the assay• Lower hit rate against Challenge library

Biochemical against Challenge LibraryComparison of two buffer systems biochemical assay

• “Modified buffer” showed a 1% lower hit rate at any of the concentrations while FPR was similar. FNR shows weakening of interference so this was chosen for the screen

• Additional definitions: • Diagnostic FNR reports on the fraction false negatives relative to the

total true positives• Absolute is relative to total samples• Relative rates is to total # of hits

Role of MoA library in assay pilot testingA comprehensive set for understanding the MoAs underlying an assay


Understand targets and pathways influencing a screen in the pilot stage• Most useful for understanding mechanism underlying phenotypic responses

Enable decisions about wanted or unwanted molecular mechanisms to facilitate design of counter-screens or secondary assays for compound triage and prioritization for follow-up work.

Add to knowledge base - linking known compounds to new biology

A comprehensive chemical probe set for understanding the MoAs underlying an assay

MoA Library compositionReflective of current pharmacopeia


Anti infective

Antiinflammatory

Apoptosis

Enzyme(other)

Epigenetics

Ion Channels

Lipid kinase/metabolism

Metabolism/antioxidants

Nuclear receptors

P450s

Phosphodiesterases/cyclases

PPI

Proteases

Protein Kinases

Receptors

Stress

Transcription/Translation

Transportors

~3K compound, 1,700 targets

Use of MoA library analysisAssay flow chart development


Primary

Orthogonal

Counterscreen

Hit prioritizaiton

y

HDAC binningassay

Secondary

√

Enriched target classes

Chemical vs. biodiversityChemical diversity is necessary but not sufficient for biodiversity

P.M. Petrone, A. M. Wassermann, E. Lounkine, P.Kutchukian, B. Simms, J. Jenkins, P. Selzer and M.Glick

Plate-based biodiversity selectionDiverse Gene Selection (DiGS)


50-80% coverage of bioactive compoundscovered.

Novartis screening deck, annotated with biological activity

Sort plates according to number of targets per plate

...Targets that have been covered on plates higher in the list, are not counted on plates lower in the list

Select the top n platese.g. 710 plates (384) = 250k

compounds

Eliminate redundant scaffold‐target occurrences

P.M. Petrone, A. M. Wassermann, E. Lounkine, P.Kutchukian, B. Simms, J. Jenkins, P. Selzer and M.Glick

Annotations only tells us what we already know. Compounds will target what we have already found and will not get us into new areas of biology.

Annotations reflect “bioactive” (“privileged”) structures which are capable of interacting with biological components (protein surfaces, binding pockets) and therefore should be useful to widely probe biology.

Two viewsAnnotated libraries

Annotations only tells us what we already know. Compounds will target what we have already found and will not get us into new areas of biology.

Annotations reflect “bioactive” (“privileged”) structures which are capable of interacting with biological targets

Two viewsAnnotated libraries

Chemistry & Biology 18, October 28, 2011, 1205.

Summary Focus library design and testing is an increasing practice

during assay optimization• Generate target hypothesis pre and post-HTS

Three pilot libraries are available in qHTS format at NIBR• Challenge (artifacts sensitivity)- anticipate counter-screens and orthogonal assays

• MoA (pathway analysis) – anticipate secondary assays• Random set – hit rate, screening concentration estimation

Focus library testing benefits from full titration-based analysis

26 D.S.Auld | Pilot Libraries and qHTS| Sept. 21, 2012| 8th Compound Management & Integrity| Business Use Only

AcknowledgementsSLAS workshop


Focus libraries (NIBR):Meir GlickJeremy JenkinsAnsgar SchuffenhauerJutta BlankPeter FekkesMarjo GoetteMartin KlumppShin NumaoJohannes OttlGünther ScheeCaroline EngelochBen CornettFlorian NigschChristian Parker

qHTS (NIBR):Ji Hu ZhangHanspeter GublerOphelia ArdayfioZhao KangAdam Hill

SMG (NIBR):Scott BowesManori TurmelGreg Wendel

Implementation of qHTS paradigmPilot testing of focus libraries at NIBR

Traditional pilot testing: Run assay against Pilot151 library at one concentration in duplicate.• Examine hit rate, order hits, dilute compounds, validate by determining

CRCs, and calculate confirmation rate • Oftentimes if the hit rate/confirmation rate is unacceptable - repeat the

process at a different concentration

qHTS approach• Develop a titration-based archive for specific pilot libraries

- MoA, Challenge, Random sets

• Two data sets are obtained from experiment:- Scatterplots at each concentration - primary hit rate - Concentration-response curve – pharmacological information on every

compound- Retrospective analysis of the data can be use to calculate FNR, FPR, CR,

and hit rate as a function of concentration to determine optimal screening concentration.

Assay and Screening Strategies to Survive an Ever Changing World

Lisa Minor

In Vitro Strategies, LLC

Outline

• The changing world of HTS

• Assay challenges

• Assay survival

• The changing world of the Drug Discovery Industry

• Personal survival

The Changing World of HTS • Long long ago- “the great new world of HTS”

– HTS was the place to be • Screen more compounds/faster/cheaper • Screen in simple uni-dimensional platforms • The assay balance was weighted toward biochemical assays and fewer cellular assays • Was a need for data analysis and archiving databases • Need for new robotic platforms/new dispensing platforms/higher density formats….

• Long ago- – HTS still the place to be but no longer a great new world

• Screen more compounds with smaller volume • More complicated assay platforms • Robots were common

– Attempts for large robotic platforms – Attempts for large cell culture platforms

• Cellular assays with very directed output were emerging • Data archiving and analysis databases are emerging • Now:

– Screen target directed compound libraries – Workstation robotic platforms – Screen smaller diversified compound libraries – Screen in small volumes – Increase in cellular assays so cellular assays outnumber biochemical assays – Increase in phenotypic cellular assays – Finally, an increase in high content assays

Drug Discovery Process

• First: Target Identification and Validation – Can be molecular target or phenotypic result

• Identify a screen to interrogate the target • Identify parallel assay to test the hits so don’t have assay bias

(example: luciferase inhibitors paradoxically cause luciferase increases in cell based assays)

• Identify lead series and begin chemistry • Identify secondary testing strategy or testing funnel

– Test activity for similar receptors/enzymes/species activity overlap etc. – Test toxicity profile – Test for solubility – Identify biomarkers for compound activity and ideally for target

engagement – Test for in vivo activity

Assay Challenge/Assay type

• Biochemical target/assay – Good: great SAR potential/best for target engagement/may be easier

to develop the assay/assay rules are in place/you know all of the players

– Poor: there may be fewer low hanging fruit here as targets

• Defined cellular single target: – Good: good SAR potential/OK for target engagement – Poor: many targets may require more than one readout so single

target may not cut it

• Multiple readout for a single cellular target; – GPCRs (multiple signaling path); good in that final compound may be

more specific, have better toxicity profile but need to develop multiple compounds with each profile to ID the right profile

• Phenotypic target readout – Good: may have more physiological relevance – Poor: more difficult to lead SAR/no real target engagement so

development of the compounds may be more difficult

Assay Challenges: Cell type • Cell line

– Easy to run

– Can transfect with target if target is known

– Good SAR development

• Physiological relevant cell – primary or primary like

– Good: may yield results that are more realistic

– SAR may be challenging if target is not known

– Can run fewer compounds

– Expensive

– Stem cells?

• 2D vs. 3D

– Potential for 3D being more relevant but still new area

• how is drug delivered completely to the 3D structure

• How are 3D structures organized, self organized?

• How based in reality is the 3D structure?

• Does your assay used in 2D exactly transfer to 3D?

– Not likely

– Needs complete validation ex. Does lysis reagent completely lyse the cells or are you getting artifacts?

What is the Best Assay?

• Criteria – Target known if possible

• Find a way to deal with multiple signaling pathways up front

• Strategy is key

– Good reliability/low variability

– Assay format to suit your company’s screening paradigm

– Adequate throughput

– Adequate cost

– Strategy required to follow through on hits both to validate target and to develop druggable leads

– Toxicity strategy

– In vivo follow-up strategy

What can you do to be successful? • Do your best to vet the target

– Ask questions of your target validation team

– Work with them to devise the best strategy

• Make the best assay possible

• Don’t run an assay if assay is not reliable: data is only as good as the assay

• Have a secondary assay in place with appropriate throughput – assay should be with different but parallel platform to eliminate assay bias

• Run neat compound to validate hits

• Run resynthesized compound to validate hits

• Keep some of the initial compound in solution to test composition if necessary

• Identify must haves assays vs nice to have as your time is valuable

Changing World of Industry

• Big pharma Consolidations

• Big pharma outsourcing projects/chemical synthesis, screening

• Big pharma reducing jobs

• Big pharma partnering more with academics/biotech in early drug discovery

• More academic drug discovery research

• All makes for uncertainty in the employee

What can you do in this changing world? • Do your best job/make the best assays/make the best

decisions/be reliable

• Present your work internally/externally

• Become invaluable internally

• Be confident

• Keep your CV current

• Network – Inside your company

– Outside of your company

– In social networks such as Linkedin.

• Be willing to move from pharma to biotech or academia and to move locations

• Be an entrepreneur and start your own business or work as a temp if necessary

• Keep a positive attitude (glass half full)

• Have fun

Date post:	08-Jun-2015
Category:	Documents
Upload:	society-for-laboratory-automation-and-screening-slas
View:	555 times
Download:	0 times

SLAS Screen Design and Assay Technology SIG: SLAS2013 Presentation

Documents