The introduction of new pharmaceutical drugs has slowed while money and effort expended by the industry has dramatically increased. We suggest that some of that effort may be inadvertently wasted in drug screening and quantitative structure-activity relationship studies where results can be strongly skewed by the method of liquid handling and the protocol used. Recent work has demonstrated that dispensing processes have a profound influence on estimates of IC50. What appear to be minor modifications in the design of concentration gradients coupled with long-standing liquid handling procedures have generated a 1.5 to 1,000-fold difference in IC50 showing no correlation or ranking between competing processes. Importantly when such data are used for computational modeling, the computed pharmacophores for each dataset are different and lead to the development of compounds with significantly different structures and chemico-physical properties. Dispensing processes are therefore an important source of error that impacts computational and statistical results. At the same time, commonly-used protocols can generate data can introduce errors independent of the dispensing technology. This paper is an overview of some of the experiences of the authors based on using online chemical compound databases, and publically available data.

1. Liquid Handling Processes Impact
Computational Modeling in Drug
Discovery
Joe Olechno1, Sean Ekins2, Antony Williams3, Rich Ellson1
Pittcon 2013
Session 2670
3:55 PM, March 21, 2013
1. Labcyte Inc.
2. Collaboration in Chemistry
3. Royal Society of Chemistry

2. Agenda
• What is Acoustic Liquid Handling?
• Serial Dilutions vs. Direct Dilutions
• Lead Optimization and Pharmacophores
• The Impact of Serial Dilutions on Drug Discovery
• Conclusions
2

3. Acoustic Droplet Ejection (ADE)
Comley J, Nanolitre Dispensing, Drug Discovery
World, Summer 2004, 43-54
3

4. Acoustic Droplet Ejection (ADE)
Acoustic energy expels droplets without physical contact
15.0
• Extremely precise 12.5
• Extremely accurate 10.0
• Rapid %CV 7.5
• Auto-calibrating 5.0
• Completely touchless 2.5
– No cross-contamination 0
0.1 1 10 100 1000 10000
– No leachates Volume (nL)
Comley J, Nanolitre Dispensing, Drug Discovery
– No binding World, Summer 2004, 43-54
4

5. Agenda
• What is Acoustic Liquid Handling?
• Serial Dilutions vs. Direct Dilutions
• Lead Optimization and Pharmacophores
• The Impact of Serial Dilutions on Drug Discovery
• Conclusions
5

6. Conventional Dose-Response Set-up by Serial Dilution
Source Plate
Assay Plate
Intermediate Buffer Dilution Plate

7. Serial Dilution vs. Direct Dilution
Serial with Tips Direct with Acoustics
• Equal volumes of changing • Changing volumes of equal
concentrations concentrations
• Compounds are sequentially • Maximum of one dilution step
diluted. Each new dilution is the
source for the next step.
• Many ―touches‖ with tips (or • Touchless—no carry-
significant potential for carry-over over, leachates or binding
or leachates) No solute lost
• Errors are compounded Serial Dilution
• Reduced error
• Low-volume assays with high • Low-volume
Direct Dilution assays with low
solvent concentration (or solvent concentration
compound loss)
7

8. Direct Dilution Process
Third Step
Transfer 75, 25,
7.5 and 2.5 nL
of each hit
to four consecutive 12-point
wells
curves
(30, 10, 3 and one
droplets, respectively)
Source Plate Assay Plate
Fourth Step
First Step Transfer 75, 25, 7.5 and
Transfer 252.5 2.5 nL of each diluted
and 2.5 nL to sample to four consecutive
two wells in an wells of the assay plate
intermediate plate (30, 10, 3 and one
droplets, respectively)
Second Step
Dilute intermediate
plate with 25 L
DMSO in each well
Intermediate Plate Intermediate Plate

9. Agenda
• What is Acoustic Liquid Handling?
• Serial Dilutions vs. Direct Dilutions
• Lead Optimization and Pharmacophores
• The Impact of Serial Dilutions on Drug Discovery
• Conclusions
9

10. Traditional Scaffold Modifications
Fibrinogen Receptor Inhibitor
Poor stability, poor
IC50 = 29 µM bioavailability, non-
patentable
Poor stability, poor
IC50 = 3 µM
bioavailability
IC50 = 0.15 µM Poor oral availability
Excellent oral
IC50 = 0.067 µM availability, good
stability
10

11. But what to do if the structures are dissimilar?
Both compounds bind
strongly to the GABAA
receptor.
Diazepam CGS-9896
These compounds are extremely different in structure but
both have the same effect. Is there a way to reconcile this
and generate information to make new drugs?

12. Pharmacophores
• Describes the optimal binding of a protein to a
ligand.
• Shows how different structures bind to same site.
• Designed from screening data.
12

13. GABAA Receptor Pharmacophore
Hydrogen bond acceptor
Hydrogen bond donor
Hydrophobic pocket

14. GABAA Receptor Pharmacophore
Hydrogen bond acceptor
Hydrogen bond donor
Hydrophobic pocket

15. Agenda
• What is Acoustic Liquid Handling?
• Serial Dilutions vs. Direct Dilutions
• Lead Optimization and Pharmacophores
• The Impact of Serial Dilutions on Drug Discovery
• Conclusions
15

16. Real World Data – EphB4 Receptor
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
14 compounds with structures and IC50 data.
Barlaam et al., WO2009/010794
Barlaam et al., US 7,718,653

17. Real World Data – EphB4 Receptor
2
The acoustic
1 technique
always
provided a
Log IC50-tips
more potent
0 IC50 value.
-3 -2 -1 0 1 2
The greater
-1 the distance
from the red
line, the
greater the
-2 difference in
IC50 values.
-3
Log IC50-acoustic
17

18. Experimental Process Flow
Acoustic
Model
Generate
14 Structures
pharmacophore models
with Data
for EphB4 receptor
Tip-based
Model
Initial data set of 14
WO2009/010794, US 7,718,653
18

19. AZ Pharmacophores
Pharmacophore Hydrophobic Hydrogen Hydrogen Observed vs
features bond bond donors predicted
acceptors IC50
Tip-based 0 2 1 0.80
Acoustic based 2 1 1 0.92
Tip-based pharmacophore Acoustic-based pharmacophore

20. Experimental Process Flow
Results
Acoustic Acoustic
Model Model
Generate Test models
14 Structures
pharmacophore models against new
with Data
for EphB4 receptor data
Tip-based Tip-based
Model Model
Results
Initial data set of 14 Independent data set of 12
WO2009/010794, US 7,718,653 WO2008/132505 20

21. Compounds Tested with Tip-based Pharmacophore
Tip-based IC50 Tip-based IC50
Name
Prediction (mM) Actual (mM)
W084.1 0.3488 0.297
W084.2 0.3806 0.456
W084.4 0.6994 0.374
W082.2 0.8392 0.808
W082.4 1.4989 6.270
W083 2.8229 0.198
W084.3 2.9119 0.473
W082.1 3.3829 1.120
WO81 NOT RETRIEVED 38.300
WO82.3 NOT RETRIEVED 1.780
Barlaam wo2008/132505

22. Tip-Based Pharmacophore – Predicted vs. Measured
10.000 8
7
R² = 0.000
Measured Tip-based IC50
Measured Rank Order
R² = 0.183
6
5
1.000
0.1 1 10 4
3
2
0.100 1
1 2 3 4 5 6 7 8
Predicted Tip-based IC50 Predicted Rank Order
The pharmacophore developed from tip-based data is an
extremely poor predictor of measured activity.
23

23. Results of Testing Pharmacophores
Acoustic Pharmacophore Tip-based Pharmacophore
Poor correlation (R2<0.0002)
between predicted and measured
The model was inadequate to
predict activity of 20% of
Correctly predicted rank of the compounds
most potent compounds Compound with highest measured
activity was predicted to have poor
binding
Compound predicted to be most
active actually had poor activity

24. Experimental Process Flow
Results
Acoustic Acoustic Acoustic
Model Model Model
Generate Test models Test models against
14 Structures
pharmacophore models against new X-ray crystal structure
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;
25
18:5717; 20:6242; 21:2207

25. Final Nail in the Coffin – X-Ray Crystallography
• All pharmacophores created from X-ray structures
had both hydrophobic and hydrogen bonding
features.
• The EphB4-ligand crystal pharmacophore most
closely reflects the acoustic pharmacophore.
Pharmacophore Hydrophobic Hydrogen bond Hydrogen
features acceptors bond donors
Tip-based 0 2 1
Acoustic based 2 1 1
Crystal based
2 1 1
(consensus)

26. Agenda
• What is Acoustic Liquid Handling?
• Serial Dilutions vs. Direct Dilutions
• Lead Optimization and Pharmacophores
• The Impact of Serial Dilutions on Drug Discovery
• Conclusions
27

27. Reasons to Worry
• This case strongly suggests that
aqueous, serial dilutions transferred with tip-
based techniques lead researchers away from
the most potent drugs.
• How universal is this phenomenon?

28. Acoustic vs. Tip-based Transfers
-40 -20 0 20 40 60 80 100
Adapted from Spicer et
al., Presentation at Drug
10 20 30 40 50
Acoustic % Inhibition
Serial dilution IC50 μM
Discovery
Technology, Boston, MA, August
2005
Adapted from Wingfield.
Presentation at ELRIG2012,
Manchester, UK
0
0 10 20 30 40 50 -40 -20 0 20 40 60 80 100
Acoustic IC50 μM Aqueous % Inhibition
104
Adapted from Wingfield et al.,
103
Amer. Drug Disco. 2007,
Serial dilution IC50 μM
3(3):24
Log IC50 tips
102
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

29. Reasons to Worry
• This case strongly suggests that
aqueous, serial dilutions transferred with tip-
based techniques lead researchers away from
the most potent drugs.
• How universal is this phenomenon?
• Sticky surfaces
– Many solutes stick to walls and tips at low concentrations
– Dose-response experiments require precision solute-handling over
many logs.

30. Conclusions
• Tip-based aqueous serial dilutions
• Databases, public and private, should annotate
this meta-data along with biological data.
• We encourage researchers to make their data
available to expand this study.