Dispensing Processes Profoundly Impact Biological, Computational and Statistical Analyses

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.

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

Its 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 10000 Volume (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, 2009 Barlaam, B. C.; Ducray, R.; Kettle, J. G., US 7,718,653 B2, 2010

Compound #

5 0.002 0.553

4 0.003 0.146

7 0.003 0.778

W7b 0.004 0.152

8 0.004 0.445

W5 0.006 0.087

6 0.007 0.973

W3 0.012 0.049

W1 0.014 0.112

9 0.052 0.170

10 0.064 0.817

W12 0.158 0.250

W11 0.207 14.400

11 0.486 3.030

3.3

12.8

1.6

69.6

6.2

8.2

IC50 Acoustic (µM) IC50 Tips (µM) Ratio IC50Tip/IC50ADE

276.5

48.7

259.3

42.5

111.3

13.7

139.0

4.2

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

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5

log

IC5

0-t

ips

log IC50-acoustic

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

14 Structures

with Data

Acoustic

Model

Tip-based

Model

Generate

pharmacophore models

for EphB4 receptor

Acoustic

Model

Tip-based

Model

Test models

against new

data

Acoustic

Model

Tip-based

Model

Test models against

X-ray crystal structure

pharmacophores

Results

Results

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 Transfers Adapted from Spicer et al.,

Presentation at Drug Discovery

Technology, Boston, MA, August

2005

Adapted from Wingfield.

Presentation at ELRIG2012,

Manchester, UK

NOTE 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

60

80

100

-20

-40

20

40

0 10 20 30 40 50

0

10

20

30

40

50

Se

ria

l d

ilu

tio

n I

C50 μ

M

Acoustic IC50 μM

104

104

103

102

10

1

10-1

10-2

10-3

Se

ria

l d

ilu

tio

n I

C50 μ

M

Acoustic IC50 μM 103 102 10 1 10-1 10-2 10-3

Log IC50 acoustic

Lo

g I

C5

0 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

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.

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