Liquid Handling Processes Impact Computational Modeling in Drug Discovery
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Transcript of Liquid Handling Processes Impact Computational Modeling in Drug Discovery
Joe Olechno1, Sean Ekins2, Antony Williams3, Rich Ellson1
Pittcon 2013Session 26703:55 PM, March 21, 2013
Liquid Handling Processes Impact Computational Modeling in Drug Discovery
1. Labcyte Inc.2. Collaboration in Chemistry3. Royal Society of Chemistry
•What is Acoustic Liquid Handling?
•Serial Dilutions vs. Direct Dilutions
•Lead Optimization and Pharmacophores
•The Impact of Serial Dilutions on Drug Discovery
•Conclusions
Agenda
2
3
Acoustic Droplet Ejection (ADE)
Comley J, Nanolitre Dispensing, Drug Discovery World, Summer 2004, 43-54
4
• Extremely precise• Extremely accurate• Rapid• Auto-calibrating• Completely touchless
– No cross-contamination– No leachates– No binding
Acoustic Droplet Ejection (ADE)
Acoustic energy expels droplets without physical contact
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
•What is Acoustic Liquid Handling?
•Serial Dilutions vs. Direct Dilutions
•Lead Optimization and Pharmacophores
•The Impact of Serial Dilutions on Drug Discovery
•Conclusions
Agenda
5
Conventional Dose-Response Set-up by Serial Dilution
Assay Plate
Intermediate Buffer Dilution Plate
Source Plate
7
Serial Dilution vs. Direct DilutionSerial with Tips Direct with Acoustics
• Equal volumes of changing concentrations
• Errors are compounded
• Compounds are sequentially diluted. Each new dilution is the source for the next step.
• Many “touches” with tips (or significant potential for carry-over or leachates)
• Touchless—no carry-over, leachates or binding
No solute lost
• Low-volume assays with low solvent concentration
• Maximum of one dilution step
• Changing volumes of equal concentrations
• Reduced errorSerial Dilution
Direct Dilution• Low-volume assays with high solvent concentration (or compound loss)
Direct Dilution Process
Third StepTransfer 75, 25,
7.5 and 2.5 nL of each hit to four consecutivewells
Source Plate Assay Plate
Intermediate Plate
First StepTransfer 252.5and 2.5 nL to two wells in an intermediate plate
Second StepDilute intermediateplate with 25 mL DMSO in each well
Fourth StepTransfer 75, 25, 7.5 and 2.5 nL of each dilutedsample to four consecutive wells of the assay plate(30, 10, 3 and one droplets, respectively)
(30, 10, 3 and one droplets, respectively)
Intermediate Plate
12-point curves
•What is Acoustic Liquid Handling?
•Serial Dilutions vs. Direct Dilutions
•Lead Optimization and Pharmacophores
•The Impact of Serial Dilutions on Drug Discovery
•Conclusions
Agenda
9
Traditional Scaffold Modifications
Fibrinogen Receptor Inhibitor
10
HNH2N
NH
HN
NH
HN
NH2 O
OO
O
OH
OHO
HNH2N
NH
NH
HN
O
OH
OHO
O
O
H2N
NH
NH
N
O
O
O CH3
H2N
NH
HN
NH
O
O
O
O CH3
CH
IC50 = 29 µM
IC50 = 3 µM
IC50 = 0.15 µM
IC50 = 0.067 µM
Poor stability, poor bioavailability, non-patentable
Poor oral availability
Excellent oral availability, good stability
Poor stability, poor bioavailability
But what to do if the structures are dissimilar?
N
H3CO
NCl
N
N
NH
Cl
OBoth compounds bind strongly to the GABAA receptor.
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?
Diazepam CGS-9896
12
•Describes the optimal binding of a protein to a ligand.
•Shows how different structures bind to same site.
•Designed from screening data.
Pharmacophores
Hydrophobic pocket
GABAA Receptor Pharmacophore
Hydrogen bond acceptor
Hydrogen bond donor
Hydrophobic pocket
GABAA Receptor Pharmacophore
Hydrogen bond acceptor
Hydrogen bond donor
•What is Acoustic Liquid Handling?
•Serial Dilutions vs. Direct Dilutions
•Lead Optimization and Pharmacophores
•The Impact of Serial Dilutions on Drug Discovery
•Conclusions
Agenda
15
Real World Data – EphB4 Receptor
16
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
Barlaam et al., WO2009/010794Barlaam et al., US 7,718,653
14 compounds with structures and IC50 data.
Real World Data – EphB4 Receptor
17
-3 -2 -1 0 1 2
-3
-2
-1
0
1
2
Log IC50-acoustic
Lo
g IC
50-t
ips
The acoustic technique always provided a more potent IC50 value.
The greater the distance from the red line, the greater the difference in IC50 values.
18
Experimental Process Flow
14 Structures with Data
Acoustic Model
Tip-based Model
Generate pharmacophore models
for EphB4 receptor
Initial data set of 14 WO2009/010794, US 7,718,653
AZ Pharmacophores
Pharmacophore Hydrophobic features
Hydrogen bond
acceptors
Hydrogen bond donors
Observed vs predicted
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
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
Results
Results
Independent data set of 12 WO2008/132505
Initial data set of 14 WO2009/010794, US 7,718,653
Compounds Tested with Tip-based Pharmacophore
22
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
NameTip-based IC50
Prediction (mM)Tip-based IC50
Actual (mM)
Barlaam wo2008/132505
Tip-Based Pharmacophore – Predicted vs. Measured
23
0.1 1 100.100
1.000
10.000
R² = 0.000181499868866175
Predicted Tip-based IC50
Me
as
ure
d T
ip-b
as
ed
IC5
0
1 2 3 4 5 6 7 81
2
3
4
5
6
7
8
R² = 0.183673469387755
Predicted Rank OrderM
ea
su
red
Ra
nk
Ord
er
The pharmacophore developed from tip-based data is an extremely poor predictor of measured activity.
Results of Testing Pharmacophores
Acoustic Pharmacophore Tip-based Pharmacophore
Correctly predicted rank of the most potent compounds
Poor correlation (R2<0.0002) between predicted and measured
The model was inadequate to predict activity of 20% of compounds
Compound with highest measured activity was predicted to have poor binding
Compound predicted to be most active actually had poor activity
24
25
Experimental Process Flow
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
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 features
Hydrogen bond acceptors
Hydrogen bond donors
Tip-based 0 2 1
Acoustic based 2 1 1
Crystal based (consensus)
2 1 1
•What is Acoustic Liquid Handling?
•Serial Dilutions vs. Direct Dilutions
•Lead Optimization and Pharmacophores
•The Impact of Serial Dilutions on Drug Discovery
•Conclusions
Agenda
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 TransfersAdapted from Spicer et al., Presentation at Drug Discovery Technology, Boston, MA, August 2005
Adapted from Wingfield. Presentation at ELRIG2012, Manchester, UK
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
10
06
08
0
100
-20
-40
20
40
0 10 20 30 40 50
01
02
03
04
05
0S
eri
al
dil
uti
on
IC
50 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
μM
Acoustic IC50 μM10310210110-110-210-3
Log IC50 acousticL
og
IC
50 ti
ps
Data in this presentation
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.