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Dispensing Processes Profoundly Impact Biological, Computational and Statistical Analyses

Sean Ekins 1 , Joe Olechno 2 Antony J. Williams 3 1 Collaborations in Chemistry, Fuquay Varina, NC. 2 Labcyte Inc, Sunnyvale, CA. 3 Royal Society of Chemistry, Wake Forest, NC. Dispensing Processes Profoundly Impact Biological, Computational and Statistical Analyses.

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Dispensing Processes Profoundly Impact Biological, Computational and Statistical Analyses

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  1. Sean Ekins1, Joe Olechno2 Antony J. Williams3 1 Collaborations in Chemistry, Fuquay Varina, NC. 2 Labcyte Inc, Sunnyvale, CA. 3 Royal Society of Chemistry, Wake Forest, NC. Dispensing Processes Profoundly Impact Biological, Computational and Statistical Analyses Disclaimer: SE and AJW have no affiliation with Labcyte and have not been engaged as consultants

  2. Where do scientists get chemistry/ biology data? • Databases • Patents • Papers • Your own lab • Collaborators • Some or all of the above? • What is common to all? – quality issues “If I have seen further than others, it is by standing upon the shoulders of giants.” Isaac Newton

  3. ..drug structure quality is important Data can be found – but … • 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. 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. Its not just structure quality we need to worry about DDT editorial Dec 2011 This editorial led to the current work http://goo.gl/dIqhU

  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. Plastic leaching How do you move a liquid? McDonald et al., Science2008, 322, 917. Belaiche et al., ClinChem2009, 55, 1883-1884 Images courtesy of Bing, Tecan

  8. Moving Liquids with sound: Acoustic Droplet Ejection (ADE) Acoustic energy expels droplets without physical contact • Extremely precise • Extremely accurate • Rapid • Auto-calibrating • Completely touchless • No cross-contamination • No leachates • No binding Images courtesy of Labcyte Inc. http://goo.gl/K0Fjz

  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. 14 compounds with structures and IC50 data. 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. 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 a more potent IC50 value

  12. Experimental Process Results Acoustic Model Acoustic Model Acoustic Model Generate pharmacophore models for EphB4 receptor Test models against new data Test models against X-ray crystal structure pharmacophores 14 Structures with Data Tip-based Model Tip-based Model Tip-based Model Results Initial data set of 14 WO2009/010794, US 7,718,653 Independent data set of 12 WO2008/132505 Independent crystallography data Bioorg Med Chem Lett 18:2776; 18:5717; 20:6242; 21:2207 12

  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 • Ekins et al., PLOSONE, In press

  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. 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 ChemLett2010, 20, 6242-6245. Bioorg Med ChemLett2008, 18, 5717-5721. Bioorg Med ChemLett2008, 18, 2776-2780. Bioorg Med ChemLett2011, 21, 2207-2211.

  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. Acoustic vs. Tip-based Transfers Adapted from Spicer et al., Presentation at Drug Discovery Technology, Boston, MA, August 2005 100 50 80 60 40 40 Serial dilution IC50μM 30 Acoustic % Inhibition 20 20 0 Adapted from Wingfield. Presentation at ELRIG2012, Manchester, UK NOTE DIFFERENT ORIENTATION -20 10 -40 0 0 10 20 30 40 50 -40 -20 0 20 40 60 80 100 Acoustic IC50μM Aqueous % Inhibition 104 Adapted from Wingfield et al., Amer. Drug Disco. 2007, 3(3):24 103 102 10 Log IC50 tips Serial dilution IC50μM 1 Data in this presentation 10-1 10-2 10-3 10-3 10-2 10-1 1 10 102 103 104 Log IC50 acoustic Acoustic IC50μM No Previous Analysis of molecule properties

  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. The stuff of nightmares? • How much of the data in databases is generated by tip based serial dilution methods • 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. Simple Rules for licensing “open” data Could data ‘open accessibility’ equal ‘Disruption’ As we see a future of increased database integration the licensing of the data may be a hurdle that hampers progress and usability. 1: NIH and other international scientific funding bodies should mandate …open accessibility for all data generated by publicly funded research immediately Williams, Wilbanks and Ekins. PLoSComputBiol 8(9): e1002706, 2012 Ekins, Waller, Bradley, Clark and Williams. DDT, 18:265-71, 2013

  21. CDD Booth 205 You can find me @... PAPER ID: 13433PAPER TITLE: “Dispensing processes profoundly impact biological assays and computational and statistical analyses”April 8th 8.35am Room 349 PAPER ID: 14750PAPER TITLE: “Enhancing High Throughput Screening For Mycobacterium tuberculosis Drug Discovery Using Bayesian Models” April 9th 1.30pm Room 353PAPER 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: 13382PAPER TITLE: “Challenges and recommendations for obtaining chemical structures of industry-provided repurposing candidates” April 10th 10.20am Room 350 PAPER ID: 13438PAPER TITLE: “Dual-event machine learning models to accelerate drug discovery” April 10th 3.05 pm Room 350

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