Download Test Set - Focus Synthesis LLC

TIDEA
Target (and Lead)
Independent
Drug
Enhancement
Algorithm
Receptor affinity versus Diversity: A classic problem
Drug discovery methods too often require trading
potency for diversity
Our goal to develop a single metric for predicting
potency and enhancing hit rates with the following
properties:
• Independent of overall ligand shape and size
• Independent of macromolecular target site shape.
• Effective for a wide variety of ligand scaffolds,
• Effective for multiple targets and target classes
• Effective for multiple disease indications, and
• No knowledge of target structure or SAR required.
2
What is TIDEA?
• An algorithm for predicting small
molecule potency
• Independent of target/ligand
complementarity and ligand shape
• Identifies highly diverse, potent
ligands
3
Development of TIDEA-Learning Set + Test Set
Combined Learning Set + Test Set
200 Small (FW<700) non peptide ligands
Recently published*, drug-like
IC50 or Ki between 10 pM and 10 mM
>60 targets, 60+ligand scaffolds
Learning Set
120 Ligands
>40 ligand scaffold types
>40 targets
Targets distinct from Test Set
Test Set
80 Ligands
20 ligand scaffold types
20 targets
Targets distinct from Learning Set
4
Development of TIDEA-Continued
Learning Set
120 Ligands
1. Complex parameters each
designed to increase %
subnanomolar ligands for several
different target classes/ligand
shapes. Required years of work.
2. Combine >50 of these complex
parameters to create and algorithm
that calculates a single number
with predictive value for affinity in
>50 targets and target classes.
TIDEA
algorithm
Test Set
80 Ligands
Test Set
80 TIDEA scores
5
Affinity (PLogIC50) is significantly higher in a diverse Test Set of 80 ligands
Test Set: Average potency
increases significantly
with TIDEA Score
Average Potency (PLogIC50)
nM 9
8
7
T ID E A score
ra nge
P LogIC50
Score < 10
7.112248648
Score ≥ 10
7.888672126
TTEST:
p<0.01
2-taile d,
heteroscedastic
mM 6
1-4
5-7
8-12
TIDEA Score Range
13-17
6
Test Set potency does not increase significantly with FW.
Average Potency (PLogIC50)
Test Set: Average potency
does not Increase significantly
with Molecular Weight
nM 9
8
7
mM 6
300350
350400
400450
450500
Molecular Weight Range
7
Test Set potency does not increase significantly with ClogP
Average Potency (PLogIC50)
Average Potency Does Not
Increase Significantly with
ClogP
nM 9
8
7
mM 6
<2
2-3
3-4
>4
ClogP Range
8
Test Set: More Potent Ligands (<1nM)
are Enriched at Higher TIDEA Scores
% of Potent (IC50 < 1 nM) Ligands
60%
50%
40%
Potent (<1nM)
Weaker(1nM)
Score < 10
1
52
Score10
6
21
30%
Yates Chi Square: 6.9 Yates p-value = 0.009
20%
10%
0%
1-4
5-7
8-12
13-17
TIDEA Score Range
9
The 8 highest TIDEA values show more diversity (8÷5 =1.6 molecules per target)
than the whole Test Set (80÷20=4 molecules per Target)
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Is TIDEA selective does it mostly find promiscuous inhibitors?
Set Description
Average
TIDEA
score
95%
Confidence
limits
28 Promiscuous
Inhibitors
4.1
0.6
69 Clinical Drugs
7
1.1
80 Drug Candidates (
90 pM to 20 mM)
7.9
0.6
The difference in average TIDEA score between promiscuous inhibitors
and drugs, or between promiscuous and drug candidates, is statistically
significant by the T test (P<0.0001)
11
How does TIDEA compare to ligand-based approaches?
• They differ too much for direct comparison. TIDEA is not a replacement for ligandbased approaches, and vice-versa. Hit rates and average potency increase
significantly with increasing TIDEA score even when every molecule binds to a
different target:
% Potent inhibitors (1nM or less)
Average Potency (PLogIC50 )
nM 9
Ultradiverse Set
65 molecules, 65 targets
Ultradiverse Set
65 molecules, 65 targets
8
7
mM 6
1-4
5-7
8-12 13-17
TIDEA Score Range
50%
40%
30%
20%
10%
0%
1-4
5-7
8-12
13-17
TIDEA Score Range
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Independent, Prospective Trial of TIDEA
Dr. Matthew Soellner, at the College of Pharmacy, Univ.
Michigan, has carried out out a prospective study of
the ability of TIDEA to identify active kinase inhibitors
in collaboration with Focus Synthesis. He designed
and screened a diverse, 181-molecule subset of a
3186-molecule kinase-targeted library using Src, Abl,
and Hck kinases and identified 27 hits (>20% inhibition
for 1 or more kinases). The TIDEA scores in the 181
molecule subset ranged from 0 to 19. The results
show that high TIDEA values predict high activity.
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Prospective Trial by Matt Soellner: The hit rate (% of molecules that
inhibit Src, Abl, or Hck by 20%+) increases with increasing TIDEA score
for 181 nitrogen heterocycles
% Actives (src, abl, and/or hck)
Prospective study of TIDEA using hit rates for 3
types of kinase inhibitors (src, abl, hck)
25%
20%
15%
10%
5%
0%
0-2
3-5
6-8
>8
TIDEA Score Range
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Prospective Trial: The increase in hit rate at higher TIDEA values
is statistically significant (Chi square)
TIDEA: Hit Rate Improvement, Cost Savings, and
maintenance of Hit Number for 3 kinase inhibitor types
TIDEA score
cutoff
Hit rate (low
TIDEA below
cutoff)
Hit rate (high
TIDEA above
cutoff)
6.5
7.5
8.5
9.5
2%
4%
7%
8%
20%
21%
20%
21%
Increase in hit
Statistical
rate above
significance (Chi
cutoff
Square p value)
10.8x
4.7x
2.7x
2.8x
0.0033
0.005
0.0395
0.0224
Cost savings using
TIDEA score cutoff
% Hits
maintained
above cutoff
39%
52%
64%
76%
96%
89%
81%
78%
A high TIDEA score subset (score>6.5, 128 molecules) contained 26 hits out of 128
while a low TIDEA score subset (score<6.5) contained only 1 hit out of 53:
a 10.7-fold increase in hit rate above 6.5. 96% of the hits (26/27) had TIDEA scores >
6.5. 39% of the entire 3186-molecule library had a TIDEA score below 6.5.
In conclusion, TIDEA can cut purchase and screening costs ~39%
while retaining ~96% of the hits for 3 kinases.
15
Comparison of TIDEA and Ligand-Based methods
TIDEA
Ligand-Based Methods
Designed to be independent
of overall ligand shape and
size
Defines molecular shape and
size as part of methodology
Determines adhesiveness
potential independent of
ligand/target shape
complementarity
Determines degree of
ligand/target shape
complementarity
A single model identifies
potent, selective inhibitors
independent of target and
target class
Mutliple, distinct models built
for each target each identify
potent, selective inhibitors of
a single target type or a
narrow range of targets.
No need to for knowledge of
SAR or even target identity.
Requires SAR information for
each target.
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Where does TIDEA fit in as a drug discovery tool?
• Earliest stage screening of large, diverse libraries
prior to application of target-specific methods.
• For new targets. When the target is unknown or
knowledge is limited (no 3D structure and little
or no SAR data).
• For unkown targets. Prior to cell-based
screening, phenotypic screening, chemical
genetics.
• In combination with target-specific methods.
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Benefits of TIDEA
• Maintains diversity
• Enhances discovery rates
• Does not require knowledge of target or
SAR
• Saves money and time
• Identifies “drug-like” molecules
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Additional slides
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TIDEA also identifies drug-like small molecules
• TIDEA is an excellent drug-like nature metric. For examples, Lipitor
has a score of 13, much higher than the average (2.5) score for nondrug organic molecules.
TIDEA scores for best-selling small molecule
Generic Name
TIDEA Score
Aripiprazole
9
Atorvastatin (Lipitor)
13
Clopidogrel
5
Esomeprazole
7
Montelukast
13
Pioglitazone
6
Quetiapine
7
Rosuvastatin
12
TIDEA scores for sets of
Average TIDEA
druglike and non-druglike
score
molecules.
average for 8 recent bestselling
9.0
drugs (2010)
average for 70 clinical drugs
average for 911 organic
molecules from ACD
average for 21 bioactive
molecules (IC50 or Ki>1 mM)
95%
CONFIDENCE
LIMITS
2.3
8.8
1.2
2.5
0.2
6.4
1.4
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Learning Set and Test Set targets
56 Learning Set Targets
20 Test Set Targets
5-HT1A inhibitor IC50
human gamma-PPAR binding
human H3 receptor antagonist Ki
5-H T 2A a ffinity Ki
5-H T 4 inhibition Ki
H uma n ka ppa Opiod R e ce ptor H k IC50
Human Papillomavirus E1 Helicase
5-HT7 Ki
IC50 for huAchE inhibition
Adenosine A1 Receptor Antagonist Ki
inhibition of fibrinogen/GPIIb-IIIa interaction
adenosine A2a receptor antagonist Ki
Kd for dissociation of LFA-1/CAM-1 interaction
Kd for SIAP-BIR3 Domain of XIAP
adenosine A2alpha receptor Ki
Adenosine deaminase inhibitor Ki
KD R IC50
AT2 receptor agonist
Ki for 5-HT7 receptor binding
Ki for binding to human brain A1 receptor
AT2 receptor antagonist Ki
Ki for D5 dopamine receptor
AD O Kina se
Ki for GABA-A alpha 5
BChE inhibition IC50
Bcl-xL
c_S rc kina se
CDK1/Cyclin B
Ki for Inhibition of AVP binding to human platelet V1
Ki for inhibition of dopamine uptake
Ki for inhibition of epibatidine binding to nicotinate receptor
Ki for inhibition of epibatidine binding to nicotinate receptor
CDK2/Cyclin A inhibition IC50
COX-2 Inhibitor IC50
CRF1 receptor antagonist IC50
delta Opioid Receptor
Ki for inhibition of neurokinin A binding to human Tachykinin NK-2 Receptor
Ki for inhibition of norepinephrine uptake
Ki for MMP-2 inhibition
Ki for NOP receptor binding
delta-opioid receptor Ki
D opa mine D 3 re ce ptor a nta gonist Ki
MMP -3 Inhibition IC50
Dopamine D4 ligand Ki
mu-Opioid receptor binding Ki
DPP II inhibition (IC50)
NK1 receptor agonist
Electric Eel Acetylcholinesterase
NK1 receptor antagonist IC50
Endothelin A antagonist
Endothelin A IC50
NMDA antagonist (hNR1a/NR2B receptor)
p38-alpha inhibition IC50
endothelin ETA receptor
p56lck tyrosine kina se inhibitor IC50
e ndothe lin E T B re ce ptor Ki
P D E 5 inhibition Ki
estrogen receptor/NFkappaB-luc IC50
P D GFR Inhibition IC50
Fa ctor Xa inhibitor IC50
PfDHFR inhibitor Ki
GABA-A alpha3
poly (ADP-Ribose) polymerase
glycine binding site of N MD A re ce ptor Ki
hBK1 receptor antagonist binding affinity
Poly(ADP-ribose) polymerase 1 inhibitor IC50
R e nin inhibitor IC50 in buffe r
HCV NS3 Protease
HGRH antagonist Ki
Histone Deacetylase type 1
HIV Protease Ki
human AChE inhibition IC50
S S T 2 S oma tosta tin R e ce pte r Binding IC50
thrombin inhibitors
V a nnilloid R e ce ptor 1 Anta gonist IC50 (CAP a ssa y)
Y 5 re ce ptor a nta gonist IC50
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