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Dispensing Processes Profoundly Impact Biological Assays and Computational and Statistical Analyses

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Dispensing Processes Profoundly Impact
Biological, Computational and Statistical Analyses


   Sean Ekins1, Joe Olechno2 a...

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Dispensing Processes Profoundly Impact Biological Assays and Computational and Statistical Analyses

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Dispensing processes profoundly influence estimates of biological activity of compounds. In this study using published inhibitor data for the tyrosine kinase EphB4, we show that IC50 values obtained via disposable tip-based serial dilution and dispensing versus acoustic dispensing differ by orders of magnitude with no correlation or ranking of datasets. Importantly, the computed EphB4 pharmacophores derived from this data differ for each dataset. Acoustic dispensing correctly highlights multiple hydrophobic features in the pharmacophore and correlates with calculated LogP values. Significantly, the acoustic dispensing-derived pharmacophore correctly identified active compounds in a test set. The subsequent analysis of crystal structures for other published EphB4 inhibitors and automated development of pharmacophores, indicated they were comparable to those developed with acoustic dispensing data. In short, dispensing processes are another important source of error in high-throughput screening that impacts computational and statistical analyses. These findings have far-reaching implications in biological research and in drug discovery.

Dispensing processes profoundly influence estimates of biological activity of compounds. In this study using published inhibitor data for the tyrosine kinase EphB4, we show that IC50 values obtained via disposable tip-based serial dilution and dispensing versus acoustic dispensing differ by orders of magnitude with no correlation or ranking of datasets. Importantly, the computed EphB4 pharmacophores derived from this data differ for each dataset. Acoustic dispensing correctly highlights multiple hydrophobic features in the pharmacophore and correlates with calculated LogP values. Significantly, the acoustic dispensing-derived pharmacophore correctly identified active compounds in a test set. The subsequent analysis of crystal structures for other published EphB4 inhibitors and automated development of pharmacophores, indicated they were comparable to those developed with acoustic dispensing data. In short, dispensing processes are another important source of error in high-throughput screening that impacts computational and statistical analyses. These findings have far-reaching implications in biological research and in drug discovery.

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Dispensing Processes Profoundly Impact Biological Assays and Computational and Statistical Analyses

  1. 1. Dispensing Processes Profoundly Impact Biological, Computational and Statistical Analyses Sean Ekins1, Joe Olechno2 and Antony J. Williams3 1 Collaborations in Chemistry, Fuquay Varina, NC. 2 Labcyte Inc, Sunnyvale, CA. 3 Royal Society of Chemistry, Wake Forest, NC. Disclaimer: SE and AJW have no affiliation with Labcyte and have not been engaged as consultants
  2. 2. Where do scientists get chemistry/ biology data?  Databases  Patents  Papers  Your own lab  Collaborators “If I have seen further  Some or all of the than others, it is by above? standing upon the  What is common to shoulders of giants.” all? – quality issues Isaac Newton
  3. 3. ..drug structure quality is Data can be found – but … important  More groups doing in silico repositioning  Target-based or ligand-based  Network and systems biology  Integrating or using sets of FDA drugs..if the structures are incorrect predictions will be too..  Need a definitive set of FDA approved drugs with correct structures  Also linkage between in vitro data & clinical data
  4. 4. Structure Quality Issues Database released and within days 100’s of errors found in structures Science Translational Medicine 2011 NPC Browser http://tripod.nih.gov/npc/ DDT 17: 685-701 (2012) DDT, 16: 747-750 (2011)
  5. 5. It’s not just structure quality we DDT editorial Dec 2011 need to worry about This editorial led to the current work http://goo.gl/dIqhU
  6. 6. Finding structures of Pharma molecules is hard NCATS and MRC made molecule identifiers from pharmas available with no structures Southan et al., DDT, 18: 58-70 (2013)
  7. 7. How do you move Plastic leaching a liquid? McDonald et al., Science 2008, 322, 917. Belaiche et al., Clin Chem 2009, Images courtesy of Bing, Tecan 55, 1883-1884
  8. 8. Moving Liquids with sound: Acoustic Droplet Ejection (ADE) Acoustic energy expels droplets without physical contact  Extremely precise 15.0 12.5  Extremely accurate 10.0  Rapid %CV 7.5  Auto-calibrating 5.0  Completely 2.5 touchless 0 0.1 1 10 100 1000 10000 Volume (nL)  No cross- Comley J, Nanolitre Dispensing, Drug Discovery World, Summer 2004, 43-54 contamination  No leachates  No binding 8 Images courtesy of Labcyte Inc. http://goo.gl/K0Fjz
  9. 9. Using literature data from different dispensing methods to generate computational models Few molecule structures and corresponding datasets are public Using data from 2 AstraZeneca patents: Tyrosine kinase EphB4 pharmacophores (Accelrys Discovery Studio) were developed using data for 14 compounds IC50 determined using different dispensing methods Analyzed correlation with simple descriptors (SAS JMP) Calculated LogP correlation with log IC50 data for acoustic dispensing (r2 = 0.34, p < 0.05, N = 14) Barlaam, B. C.; Ducray, R., WO 2009/010794 A1, 2009 Barlaam, B. C.; Ducray, R.; Kettle, J. G., US 7,718,653 B2, 2010
  10. 10. 14 compounds with structures and IC50 data. Compound # IC50 Acoustic (µM) IC50 Tips (µM) Ratio IC50Tip/IC50ADE 5 0.002 0.553 276.5 4 0.003 0.146 48.7 7 0.003 0.778 259.3 W7b 0.004 0.152 42.5 8 0.004 0.445 111.3 W5 0.006 0.087 13.7 6 0.007 0.973 139.0 W3 0.012 0.049 4.2 W1 0.014 0.112 8.2 9 0.052 0.170 3.3 10 0.064 0.817 12.8 W12 0.158 0.250 1.6 W11 0.207 14.400 69.6 11 0.486 3.030 6.2 Barlaam, B. C.; Ducray, R., WO 2009/010794 A1, 2009 Barlaam, B. C.; Ducray, R.; Kettle, J. G., US 7,718,653 B2, 2010
  11. 11. A graph of the log IC50 values for tip-based serial dilution and dispensing versus acoustic dispensing with direct dilution shows a poor correlation between techniques (R2 = 0.246). acoustic technique always gave more potent IC50 value
  12. 12. Experimental Process Results Acoustic Acoustic Acoustic Model Model Model Generate Test models Test models against 14 Structures 14 Structures pharmacophore models against new X-ray crystal structure with Data with Data for EphB4 receptor data pharmacophores Tip-based Tip-based Tip-based Model Model Model Results Initial data set of 14 Independent data set of 12 Independent crystallography data WO2009/010794, US 7,718,653 WO2008/132505 Bioorg Med Chem Lett 18:2776; 12 18:5717; 20:6242; 21:2207
  13. 13. Tyrosine kinase EphB4 Pharmacophores Generated with Discovery Studio (Accelrys) Cyan = hydrophobic Green = hydrogen bond acceptor Purple = hydrogen bond donor Each model shows most potent molecule mapping Acoustic Tip based   Hydrophobic  Hydrogen  Hydrogen  Observed vs.  features (HPF) bond acceptor  bond donor  predicted IC50  (HBA) (HBD) r Acoustic mediated process 2 1 1 0.92 Tip-based process 0 2 1 0.80 • Ekins et al., PLOSONE, In press
  14. 14. Test set evaluation of pharmacophores • An additional 12 compounds from AstraZeneca Barlaam, B. C.; Ducray, R., WO 2008/132505 A1, 2008 • 10 of these compounds had data for tip based dispensing and 2 for acoustic dispensing • Calculated LogP and logD showed low but statistically significant correlations with tip based dispensing (r2= 0.39 p < 0.05 and 0.24 p < 0.05, N = 36) • Used as a test set for pharmacophores • The two compounds analyzed with acoustic liquid handling were predicted in the top 3 using the ‘acoustic’ pharmacophore • The ‘Tip-based’ pharmacophore failed to rank the retrieved compounds correctly
  15. 15. Automated receptor-ligand pharmacophore generation method Pharmacophores for the tyrosine kinase EphB4 generated from crystal structures in the protein data bank PDB using Discovery Studio version 3.5.5 Cyan = hydrophobic Green = hydrogen bond acceptor Purple = hydrogen bond donor Grey = excluded volumes Each model shows most potent molecule mapping Bioorg Med Chem Lett 2010, 20, 6242-6245. Bioorg Med Chem Lett 2008, 18, 5717-5721. Bioorg Med Chem Lett 2008, 18, 2776-2780. Bioorg Med Chem Lett 2011, 21, 2207-2211.
  16. 16. Summary •In the absence of structural data, pharmacophores and other computational and statistical models are used to guide medicinal chemistry in early drug discovery. •Our findings suggest acoustic dispensing methods could improve HTS results and avoid the development of misleading computational models and statistical relationships. •Automated pharmacophores are closer to pharmacophore generated with acoustic data – all have hydrophobic features – missing from Tip- based pharmacophore model •Importance of hydrophobicity seen with logP correlation and  crystal structure interactions •Public databases should annotate this meta-data alongside biological data points, to create larger datasets for comparing different computational methods.
  17. 17. Acoustic vs. Tip-based Transfers Adapted from Spicer et al., -40 -20 0 20 40 60 80 100 Presentation at Drug Discovery 50 Acoustic % Inhibition Serial dilution IC50 μM Technology, Boston, MA, August 2005 10 20 30 40 Adapted from Wingfield. Presentation at ELRIG2012, Manchester, UK NOTE DIFFERENT 0 0 10 20 30 40 50 ORIENTATION -40 -20 0 20 40 60 80 100 Acoustic IC50 μM Aqueous % Inhibition 104 Adapted from Wingfield et al., 103 Amer. Drug Disco. 2007, Log IC50 tips Serial dilution IC50 μM 102 3(3):24 10 1 Data in this presentation 10 -1 10-2 10-3 10-3 10-2 10-1 1 10 102 103 104 Acoustic IC50 μM Log IC50 acoustic No Previous Analysis of molecule properties
  18. 18. Strengths and Weaknesses • Small dataset size – focused on one compound series • No previous publication describing how data quality can be impacted by dispensing and how this in turn affects computational models and downstream decision making. • No comparison of pharmacophores generated from acoustic dispensing and tip-based dispensing. • No previous comparison of pharmacophores generated from in vitro data with pharmacophores automatically generated from X-ray crystal conformations of inhibitors. • Severely limited by number of structures in public domain with data in both systems • Reluctance of many to accept that this could be an issue • Ekins et al., PLOSONE, In press
  19. 19. The stuff of nightmares?  How much of the data in databases is generated by tip-based serial dilution methods? We don’t know…the meta data doesn’t tell us!  How much is erroneous?  Do we have to start again?  How does it affect all subsequent science – data mining etc?  Does it impact Pharmas productivity?
  20. 20. Simple Rules for licensing Could data ‘open accessibility’ “open” data equal ‘Disruption’ As we see a future of increased 1: NIH and other international database integration the scientific funding bodies should licensing of the data may be a mandate …open accessibility for hurdle that hampers progress all data generated by publicly and usability. funded research immediately Williams, Wilbanks and Ekins. Ekins, Waller, Bradley, Clark and PLoS Comput Biol 8(9): Williams. DDT, 18:265-71, 2013 e1002706, 2012
  21. 21. You can find me @... CDD Booth 205 PAPER ID: 13433 PAPER TITLE: “Dispensing processes profoundly impact biological assays and computational and statistical analyses” April 8th 8.35am Room 349 PAPER ID: 14750 PAPER TITLE: “Enhancing High Throughput Screening For Mycobacterium tuberculosis Drug Discovery Using Bayesian Models” April 9th 1.30pm Room 353 PAPER ID: 21524 PAPER TITLE: “Navigating between patents, papers, abstracts and databases using public sources and tools” April 9th 3.50pm Room 350 PAPER ID: 13358 PAPER TITLE: “TB Mobile: Appifying Data on Anti-tuberculosis Molecule Targets” April 10th 8.30am Room 357 PAPER ID: 13382 PAPER TITLE: “Challenges and recommendations for obtaining chemical structures of industry-provided repurposing candidates” April 10th 10.20am Room 350 PAPER ID: 13438 PAPER TITLE: “Dual-event machine learning models to accelerate drug discovery” April 10th 3.05 pm Room 350

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