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SLAS Screen Design and Assay Technology SIG: SLAS2013 Presentation

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This file includes the SLAS2013 presentations of Paul A. Johnston of University of Pittsburgh; Douglas Auld of Novartis Institutes for Biomedical Research; and Lisa Minor of In Vitro Strategies, LLC.

This file includes the SLAS2013 presentations of Paul A. Johnston of University of Pittsburgh; Douglas Auld of Novartis Institutes for Biomedical Research; and Lisa Minor of In Vitro Strategies, LLC.


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  • 1. The challenges associated with conducting HTS/HCS campaigns in the current  academic funding environment Paul A. Johnston Research Associate Professor University of Pittsburgh Department of Pharmaceutical Sciences,  School of Pharmacy
  • 2. 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) UPCI Chemical Biology  Facility (ChBF) 2
  • 3. HTS Facility Functions Assay Assay HTS/HCS Active Hit Lead Development Validation Campaign Confirmation Characterization Optimization • 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
  • 4. 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
  • 5. 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
  • 6. Establishing an Academic HTS/HCS Facility Initial 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
  • 7. 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
  • 8. 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
  • 9. Leveraging Focus Libraries and Quantitative HTS in Assay Pilot TestingScreen Design and Assay Technologies Special Interest Group:Screening in this economy….What makes ‘centsDouglas Auld, Ph.D.Novartis Institutes for Biomedical ResearchCambridge, Mass., USA
  • 10. Testing multiple hypothesis earlyBetter starting points for drug discovery Focus libraries can be used to characterize assays and help choose the right set of assays for the project
  • 11. What to screen?Types of libraries 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
  • 12. LMW Chemical librariesDrugs, Probes, and Tools
  • 13. LMW Chemical librariesFocused library sets Chemical biology questions anticipate secondary assays Diversity-based/Random sets OH Assay interference Anticipate counter-screens /orthogonal assays
  • 14. Focus libraries in understanding phenotypic assays Two opportunities • 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 1. Build a 2. Generate a hypothesis prior hypothesis post HTS ? to HTS6 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 15. Focus librariesSize and types of libraries at NIBR 1,400 – one 1536w plate (Challenge) –test for frequent hitters (solubility data Read-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. Epi Challenge BioDiv. MOA7 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 16. Sources of chemical biology information Annotation of compounds, pathways, mechanismsCompound Databases Bioinformatics Resources• ChEMBL • EntrezGene• ChEBI • InterPRO• DrugBank • GeneGo• Thomson Reuters Integrity • SCOP• World Drug Index • UniProt• PubChem structures • Clinicaltrials.gov• eMolecules (8M compounds) • Broad Connectivity  Map • PDB • Pubchem BioAssay • Binding DB 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)
  • 17. NDFI: Novartis Data Federation InitiativeGoal: Allow rapid mining of data to generate knowledge Forming a searchable chemical biology database Reporting Literature Databases Data Analysis Results Publication Data Data Analysis Warehouse Assay Data Assay Metadata Assay Assay data registration Sample Information Results Capture Assay Registration Chemist Sample Biologist registration Assay Request Assay Configuration eLN eLN Assay Request9 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 18. Focused screen with qHTS formatAllows for both plate-based performance and validation data – 1 experiment quantitative HTS “qHTS” 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 data A “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.10 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 19. Two types of data from qHTSScatterplots and concentration-response curves 1 2 CRCs Retrospective analysis Annotate activity based on hit threshold and CRC information TP, FP, FN, TN11 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 20. 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.12 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 21. Truth matricesSingle-concentration data retrospective analysis (TP_N = # of true positives, FN_N = # false negatives, ect) Assay 1 Assay 2
  • 22. 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)
  • 23. Challenge Library Construction Focus on biochemical assay interferencesFrequent 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)
  • 24. Firefly luciferase (FLuc) is a popular choice for RGAs FLuc inhibitors can confound the interpretation of RGA resultsObservations:• 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: Frequent Hitter analysis of confirmed See Thorne et al. (2012) Chem Biol. 2012 Aug 24;19(8):1060-72. FLuc inhibitors 16 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 25. 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
  • 26. Biochemical against Challenge Library Comparison 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
  • 27. Role of MoA library in assay pilot testingA comprehensive set for understanding the MoAs underlying an assayA comprehensive chemical probe set for understanding the MoAs underlyingan 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 biology19 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 28. 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 targets20 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 29. Use of MoA library analysisAssay flow chart development Enriched target classes Primary √ HDAC Counterscreen binning Orthogonal assayy Hit prioritizaiton Secondary21 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 30. Chemical vs. biodiversity Chemical diversity is necessary but not sufficient for biodiversityP.M. Petrone, A. M. Wassermann, E. Lounkine, P.Kutchukian, B. Simms, J. Jenkins, P. Selzer and M.Glick
  • 31. Plate-based biodiversity selectionDiverse Gene Selection (DiGS)Novartis screening deck, annotated with  Sort plates according to number of targets per  biological activity plate ... Targets that have been covered on plates higher in  the list, are not  counted on plates lower in the list Eliminate redundant scaffold‐target  occurrences 50-80% coverage Select the top n plates of bioactive e.g. 710 plates (384) = 250k  compounds compounds covered.P.M. Petrone, A. M. Wassermann, E. Lounkine, P.Kutchukian, B. Simms, J. Jenkins, P. Selzer and M.Glick23 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 32. 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 components (protein surfaces, binding pockets) and therefore should be useful to widely probe biology.
  • 33. 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 Chemistry & Biology 18, October 28, 2011, 1205.
  • 34. 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 analysis26 D.S.Auld | Pilot Libraries and qHTS| Sept. 21, 2012| 8th Compound Management & Integrity| Business Use Only
  • 35. AcknowledgementsSLAS workshop Focus libraries (NIBR): Meir Glick qHTS (NIBR): Jeremy Jenkins Ji Hu Zhang Ansgar Schuffenhauer Hanspeter Gubler Jutta Blank Ophelia Ardayfio Peter Fekkes Zhao Kang Marjo Goette Adam Hill Martin Klumpp Shin Numao SMG (NIBR): Johannes Ottl Scott Bowes Günther Schee Manori Turmel Caroline Engeloch Greg Wendel Ben Cornett Florian Nigsch Christian Parker27 | Reporters in cell-based assays: Understanding fact from fiction| Doug Auld| 1-14-2013 | SLAS 2013t | Business Use Only
  • 36. Implementation of qHTS paradigm Pilot 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.
  • 37. Assay and Screening Strategies to Survive an Ever Changing World Lisa Minor In Vitro Strategies, LLC
  • 38. Outline• The changing world of HTS• Assay challenges• Assay survival• The changing world of the Drug Discovery Industry• Personal survival
  • 39. 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
  • 40. 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
  • 41. 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
  • 42. 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?
  • 43. 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
  • 44. 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
  • 45. 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
  • 46. 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