1. Toxophore Results
Recommended safety targets with ChEMBL binding measurements:
MedChemica
Pharmacophore Extraction from
Matched Molecular Pair Analysis (MMPA)
Al G. Dossetter, Ed J. Griffen, Andrew G. Leach, Lauren Reid, Jess Stacey (MedChemica)
References
1Griffen, E. et al. Matched Molecular Pairs as a Medicinal Chemistry Tool. J. Med. Chem. 2011, 54(22), pp.7739-7750.
2Leach, A.G. et. al. Matched Molecular Pairs as a Guide in the Optimization of Pharmaceutical Properties; a Study of Aqueous Solubility, Plasma Protein Binding and Oral Exposure. J. Med. Chem. 2006, 49(23), pp.6672-6682.
3Papadatos, G. et al. Lead Optimization Using Matched Molecular Pairs: Inclusion of Contextual Information for Enhanced of hERG Inhibition, Solubility, and Lipophilicity. J. Chem. Inf. Model. 2010, 50(10), pp.1872-1886.
4J.-F. Truchon and C. I. Bayly, “Evaluating Virtual Screening Methods: Good and Bad Metrics for the ‘Early Recognition’ Problem,” J. Chem. Inf. Model., vol. 47, no. 2, pp. 488–508, Mar. 2007.
5Minato Nakazawa (2015). fmsb: Functions for Medical Statistics Book with some Demographic Data. R package version 0.5.2. http://CRAN.R-project.org/package=fmsb
6Jacob Cohen (1988). Statistical Power Analysis for the Behavioral Sciences (second ed.). Lawrence Erlbaum Associates.
7Bold et al. A Novel Potent Oral Series of VEGFR2 Inhibitors Abrogate Tumor Growth by Inhibiting Angiogensis. J. Med. Chem. 2016, 59, pp 132-146.
8Mainolfi et al. Evolution of a New Class of VEGFR-2 Inhibitors from Morphing and Redesign. ACS Med. Chem. Lett. 2016
Problem
Is it possible to extract pharmacophores from
matched molecular pair analysis (MMPA)?
Solution
Post MMPA, pharmacophore dyads can be generated and predictions can be made
from these through a PLS model
MMPA - a method of determining structure activity relationships (SAR’s) within sets of compounds. Matched molecular pairs (MMP’s)
are identified and differences in their measured data are used to link properties to structure.1 Pharmacophore process explained in 4 steps:
Pharmacophore Extraction
• For a pharmacophore to be considered it must be sensitive and selective
• The pair of biophores and the shortest path between them constitutes the pharmacophore dyad
• Sensitivity is then found by finding the Cohen’s d coefficient of the pharmacophore dyad
• A cross validated partial least squares (PLS) model is then run – potency predictions can be made from this model
• Selectivity is found by finding the odds ratio of the pharmacophore dyad by comparison of ChEMBL18 database, a
ROC and BEDROC curve is produced
Case Study - Several different toxicity assays were then analysed to find toxophores, below are some examples
Assay
No. of
compounds
ROC score
(against ChEMBL
18)
BEDROC score
(against ChEMBL
18)
Geometric
mean odds
ratio R2
y-scrambled
R2
Acetylcholinesterase human 383 0.80 0.35 3.77 0.43 -0.03
Beta-1 adrenergic receptor 505 0.96 0.85 832.89 0.64 0.00
Dopamine D2 receptor human 3873 0.70 0.02 110.34 0.42 0.00
Dopamine D2 receptor rat 1807 0.78 0.41 125.08 0.29 0.00
Dopamine transporter rat 1470 0.88 0.34 141.25 0.58 0.00
GABA-A receptor; anion channel rat 848 0.97 0.72 560.31 0.70 -0.01
hERG human 4189 0.92 0.49 55.46 0.61 -0.01
Monoamine oxidase A human 264 0.48 0.04 180.53 0.12 -0.02
Vascular Endothelial Growth Factor
receptor 2 human 4466 0.95 0.76 79.44 0.64
0.00
contact@medchemica.com
Critical Fragment Extraction
Statistical analysis of data sets of SMIRKS to extract chemical
fragments that are predominantly found in more or less
potent compounds:
3)
Identify and group Frag A SMARTS
Calculate parametric paramaters
If n ≥ 8, perform a one-tailed binomial test to
determine the significance of the ‘decrease’
or ‘increase’ occurrences
Perform the Holm-Bonferroni adjustment on
the p value
If FragA >> FragB passes the 95% cut off,
after the Holm-Bonferroni adjustment has
been applied, Frag A is classified as a
‘biophore’
Search each significant biophore back in
the original assay data set
Compare the mean of the compounds
containing the biophore with the mean of
the remaining compounds for significance
(Welch’s t test and effect size Cohen’s d)
This yields a set of significant fragments
4)
These
data
sets
were
chosen
as
they
had
more
than
2000
compounds
in
and
that
there
were
from
a
wide
range
of
targets
to
show
that
this
does
not
just
work
for
one
target
type
Actual < 7 Actual >= 7
Predicted < 7 384 98
Predicted >= 7 129 682
Actual: 8.4 7
Predicted: 7.5
Actual: 7.6 7
Predicted: 7.5
Actual: 7.7 8
Predicted: 7.1
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4
6
8
10
5 7 9
pIC50_pred
pIC50
Left: graph of the
predicted pIC50 against
the actual pIC50 for the
VEGF set
Right: ROC curve and
BEDROC score4 to
indicate how selective
the pharmacophore
dyads are
0 20 40 60 80 100
0
20
40
60
80
100
top % of ranked database
%foundActivities(yield)
area under the curve: 0.9537
BEDROC score: 0.7581
From the VEGFR model predictions of Novartis VEGF compounds could be made:
Potency Predictions- Example recent Novartis compounds
Confusion matrix for VEGF set
Assay: Dopamine
D2 receptor human
Actual: 9.5
Predicted: 9.1
Mean with: 8.0
Mean without: 6.6
Odds Ratio: 339
Assay: Dopamine
D2 receptor rat
Actual: 9.4
Predicted: 9.2
Mean with: 7.6
Mean without: 6.5
Odds Ratio: 13
Assay: GABA-A
Actual: 9.0
Predicted: 8.7
Mean with: 8.0
Mean without: 6.8
Odds Ratio: 1506
Assay: β1 Adrenergic
receptor
Actual: 7.8
Predicted: 7.7
Mean with: 6.5
Mean without: 5.7
Odds Ratio: 1500
Assay: Dopamine
Transporter
Actual: 9.1
Predicted: 9.1
Mean with: 8.1
Mean without: 6.7
Odds Ratio: 26.5
Cohen’s d
pKi/
pIC50
Compounds
containing
pharmacophore
dyad
Remaining
Compounds
Effect
size =
Cohen’s
d test
• Measurement of distance between two means
• Cohen’s d equals 6
• Where
• This pharmacophore dyad has a Cohen’s d
coefficient of 2.50
1
σ = A
2
σ + B
2
σ
2
d = A
µ −
B
µ
1
σ
Effect sizes:
Large >= 0.8
Medium 0.5 – 0.8
Small 0.2 – 0.5
Trivial 0.1 – 0.2
No effect < 0.1
• What are the odds of the pharmacophore dyad hitting a molecule in the potency set
against ChEMBL? Odds ratio and it's confidence limits calculated using the R fmsb package 5
• Odds of finding in potency set:
• Odds of finding in ChEMBL:
• Odds ratio = selectivity:
• Odds ratio = 257 (95% confidence limits 135 - 492) therefore odds of hitting a potent compound
are 135 to 492 times greater than a random compound in ChEMBL
Odds Ratio
17
4466
20
1348205
17 / 4466
20 /1348205
n(pharmacophoredyad hitsin potencyset)
n(in potencyset)
Oddsof findingin potencyset
Oddsof findinginChEMBL(not potencyset)
n(pharmacophoredyad hitsinChEMBLnotin potencyset)
n(inChEMBL)
Pharmacophore dyad example
Fragment1 – yellow
Fragment2 – purple
Path – mixture or orange
Advanced MMP’s
• Two pair finding techniques are available
• Not all pairs are found by a single method, both methods
are needed to maximize the MMP output
Molecules that differ only by a particular, well-
defined, structural transformation2
A MMP found by both methods:
1)
CHEMBL318733 (VEGF inhibitor)CHEMBL101461 (VEGF inhibitor)
FI method (MMP defining cut shown by red line)
MCSS method (MCSS shown in red)
Environment Capture
• Chemical transformations are encoded as SMIRKS and recorded
along with their delta property value(s)
• The SMIRKS contain the structural change along with the chemical
environment spanning up to 4 atoms out
Essential for understanding the context of the transformation3
[c:6]1[c:4]([H])[c:2]([H])[c:1]([c:3]([H])[c:5]1[c:
7])([H])>>[c:6]1[c:4]([H])[c:2]([H])[c:1]([c:3]
([H])[c:5]1[c:7])[F]
2)
[c:4][c:2]([H])[c:1]([c:3]([H])[c:5])([H])
>>[c:4][c:2]([H])[c:1]([c:3]([H])[c:5])[F]
[c:2][c:1]([c:3])([H])>>[c:2][c:1]([c:3])[F] [c:1]([H])>>[c:1][F]
The MMP as a transformation:
4 atom environment: 3 atom environment:
2 atom environment: 1 atom environment:
Δ data A to B
Δ data A to BΔ data A to B
Δ data A to B
Fragment1 – yellow
Fragment 2 – purple
Path –mixture or orange
FragA >> FragB
O
O
O
N
N
N
N
N
O
N
N