Dispensing Processes Profoundly Impact Biological Assays and Computational and Statistical Analyses
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Transcript of Dispensing Processes Profoundly Impact Biological Assays and Computational and Statistical Analyses
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
“If I have seen further than others, it is by standing upon the
shoulders of giants.”
Isaac Newton
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
Data can be found – but …..drug structure quality is
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 Issues
NPC Browser http://tripod.nih.gov/npc/
Database released and within days 100’s of errors found in structures
DDT, 16: 747-750 (2011)
Science Translational Medicine 2011
DDT 17: 685-701 (2012)
DDT editorial Dec 2011
http://goo.gl/dIqhU
This editorial led to the current work
It’s not just structure quality we need to worry about
Southan et al., DDT, 18: 58-70 (2013)
Finding structures of Pharma molecules is hard
NCATS and MRC made molecule identifiers from pharmas available with no structures
How do you move a liquid?
Images courtesy of Bing, Tecan
McDonald et al., Science 2008, 322, 917.Belaiche et al., Clin Chem 2009, 55, 1883-1884
Plastic leaching
Extremely precise Extremely accurate Rapid Auto-calibrating Completely touchless
No cross-contamination
No leachates
No binding
Moving Liquids with sound: Acoustic Droplet Ejection (ADE)
Acoustic energy expels droplets without physical contact
8
0
2.5
5.0
7.5
10.0
12.5
15.0
0.1 1 10 100 1000 10000Volume (nL)
%CV
Comley J, Nanolitre Dispensing, Drug Discovery World, Summer 2004, 43-54
Images courtesy of Labcyte Inc. http://goo.gl/K0Fjz
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, 2009Barlaam, B. C.; Ducray, R.; Kettle, J. G., US 7,718,653 B2, 2010
Compound #
5 0.002 0.5534 0.003 0.1467 0.003 0.778
W7b 0.004 0.1528 0.004 0.445
W5 0.006 0.0876 0.007 0.973
W3 0.012 0.049W1 0.014 0.1129 0.052 0.17010 0.064 0.817
W12 0.158 0.250W11 0.207 14.40011 0.486 3.030
3.312.8
1.669.6
6.2
8.2
IC50 Acoustic (µM) IC50 Tips (µM) Ratio IC50Tip/IC50ADE
276.548.7
259.342.5
111.313.7
139.04.2
14 compounds with structures and IC50 data.
Barlaam, B. C.; Ducray, R., WO 2009/010794 A1, 2009Barlaam, B. C.; Ducray, R.; Kettle, J. G., US 7,718,653 B2, 2010
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
14 Structures with Data14 Structures with Data
Acoustic Model
Acoustic Model
Tip-based Model
Tip-based Model
Generate pharmacophore models
for EphB4 receptor
Acoustic Model
Acoustic Model
Tip-based Model
Tip-based Model
Test models against new
data
Acoustic Model
Acoustic Model
Tip-based Model
Tip-based Model
Test models against X-ray crystal structure
pharmacophores
ResultsResults
ResultsResults
Independent crystallography data Bioorg Med Chem Lett 18:2776; 18:5717; 20:6242; 21:2207
Independent data set of 12 WO2008/132505
Initial data set of 14 WO2009/010794, US 7,718,653
Experimental Process
Hydrophobic
features (HPF)
Hydrogen
bond acceptor
(HBA)
Hydrogen
bond donor
(HBD)
Observed vs.
predicted IC50
r
Acoustic mediated process 2 1 1 0.92
Tip-based process 0 2 1 0.80
• Ekins et al., PLOSONE, In press
Acoustic Tip based
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
• 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
Test set evaluation of pharmacophores
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.
•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.
Summary
Acoustic vs. Tip-based TransfersAdapted from Spicer et al., Presentation at Drug Discovery Technology, Boston, MA, August 2005
Adapted from Wingfield. Presentation at ELRIG2012, Manchester, UKNOTE DIFFERENT ORIENTATION
Adapted from Wingfield et al., Amer. Drug Disco. 2007, 3(3):24
Aqueous % Inhibition
Ac
ou
sti
c %
In
hib
itio
n
0 20 40
0
-20
-40
60 80
100
6080
100
-20
-40
2040
0 10 20 30 40 50
010
2030
4050
Se
ria
l d
ilu
tio
n I
C50
μM
Acoustic IC50 μM
104
104
103
102
10
1
10-1
10-2
10-3
Se
ria
l d
ilu
tio
n I
C50
μM
Acoustic IC50 μM10310210110-110-210-3
Log IC50 acousticLog
IC
50
tip
s
Data in this presentation
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? 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?
Simple Rules for licensing “open” data
Williams, Wilbanks and Ekins. PLoS Comput Biol 8(9): e1002706, 2012
1: NIH and other international scientific funding bodies should mandate …open accessibility for all data generated by publicly funded research immediately
Could data ‘open accessibility’ equal ‘Disruption’
Ekins, Waller, Bradley, Clark and Williams. DDT, 18:265-71, 2013
As we see a future of increased database integration the licensing of the data may be a hurdle that hampers progress and usability.
You can find me @... CDD Booth 205
PAPER ID: 13433PAPER TITLE: “Dispensing processes profoundly impact biological assays and computational and statistical analyses”April 8th 8.35am Room 349
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PAPER TITLE: “Navigating between patents, papers, abstracts and databases using public sources and tools”April 9th 3.50pm Room 350PAPER 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