• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Acs  dispensing processes profoundly impact biological assays, computational and statistical analyses
 

Acs dispensing processes profoundly impact biological assays, computational and statistical analyses

on

  • 484 views

ACS presentation April 8

ACS presentation April 8
-updated talk

Statistics

Views

Total Views
484
Views on SlideShare
414
Embed Views
70

Actions

Likes
0
Downloads
5
Comments
0

3 Embeds 70

http://lanyrd.com 54
http://eventifier.co 12
https://twitter.com 4

Accessibility

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Acs  dispensing processes profoundly impact biological assays, computational and statistical analyses Acs dispensing processes profoundly impact biological assays, computational and statistical analyses Presentation Transcript

    • Dispensing Processes Profoundly ImpactBiological, Computational and Statistical Analyses Sean Ekins1, Joe Olechno2 Antony J. Williams3 1 Collaborationsin 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
    • 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
    • ..drug structure quality isData 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
    • Structure Quality IssuesDatabase released and within days 100’s of errors found in structures Science Translational Medicine 2011NPC Browser http://tripod.nih.gov/npc/ DDT 17: 685-701 (2012)DDT, 16: 747-750 (2011)
    • Its not just structure quality we DDT editorial Dec 2011 need to worry aboutThis editorial led to the currentwork http://goo.gl/dIqhU
    • Finding structures of Pharma molecules is hardNCATS and MRCmade moleculeidentifiers frompharmas availablewith no structures Southan et al., DDT, 18: 58-70 (2013)
    • 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
    • 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 8Images courtesy of Labcyte Inc. http://goo.gl/K0Fjz
    • Using literature data from different dispensing methods to generate computational modelsFew molecule structures and corresponding datasets are publicUsing data from 2 AstraZeneca patents –Tyrosine kinase EphB4 pharmacophores (Accelrys DiscoveryStudio) were developed using data for 14 compoundsIC50 determined using different dispensing methodsAnalyzed correlation with simple descriptors (SAS JMP)Calculated LogP correlation with log IC50 data for acousticdispensing (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
    • 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
    • A graph of the log IC50 values for tip-based serial dilutionand dispensing versus acoustic dispensing with direct dilution shows a poor correlation between techniques (R2 = 0.246). 1.5 1 0.5 0 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 log IC50-tips -0.5 -1 -1.5 acoustic technique -2 always gave a more -2.5 potent IC50 -3 value log IC50-acoustic
    • Experimental Process Results Acoustic Acoustic Acoustic Model Model Model Generate Test models Test models against14 Structures pharmacophore models against new X-ray crystal structurewith 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
    • 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) rAcoustic mediated process 2 1 1 0.92Tip-based process 0 2 1 0.80 • Ekins et al., PLOSONE, In press
    • 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
    • Automated receptor-ligand pharmacophore generation method Pharmacophores for the tyrosine kinase EphB4 generated from crystalstructures 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.
    • 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.
    • Acoustic vs. Tip-based Transfers -40 -20 0 20 40 60 80 100 Adapted from Spicer et al., Presentation at Drug Discovery 50 Acoustic % InhibitionSerial dilution IC50 μM Technology, Boston, MA, August 200510 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 tipsSerial 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
    • 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
    • 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?
    • Simple Rules for licensing Could data ‘open accessibility’ “open” data equal ‘Disruption’As we see a future of increased 1: NIH and other internationaldatabase integration the scientific funding bodies shouldlicensing of the data may be a mandate …open accessibility forhurdle that hampers progress all data generated by publiclyand usability. funded research immediatelyWilliams, Wilbanks and Ekins. Ekins, Waller, Bradley, Clark andPLoS Comput Biol 8(9): Williams. DDT, 18:265-71, 2013e1002706, 2012
    • You can find me @... CDD Booth 205PAPER ID: 13433PAPER TITLE: “Dispensing processes profoundly impact biological assays and computational and statisticalanalyses”April 8th 8.35am Room 349PAPER ID: 14750PAPER TITLE: “Enhancing High Throughput Screening For Mycobacterium tuberculosis Drug DiscoveryUsing Bayesian Models”April 9th 1.30pm Room 353PAPER ID: 21524PAPER TITLE: “Navigating between patents, papers, abstracts and databases using public sources andtools”April 9th 3.50pm Room 350PAPER ID: 13358PAPER TITLE: “TB Mobile: Appifying Data on Anti-tuberculosis Molecule Targets”April 10th 8.30am Room 357PAPER ID: 13382PAPER TITLE: “Challenges and recommendations for obtaining chemical structures of industry-providedrepurposing candidates”April 10th 10.20am Room 350PAPER ID: 13438PAPER TITLE: “Dual-event machine learning models to accelerate drug discovery”April 10th 3.05 pm Room 350