Post on 31-Jan-2018
Pharmacoinformatics Research Group Department of Pharmaceutical Chemistry
Topliss batchwise scheme reviewed in the era of Open Data
Lars Richter, Gerhard F. Ecker Dept. of Pharmaceutical Chemistry
gerhard.f.ecker@univie.ac.at
pharminfo.univie.ac.at
subst. π σ -σ π+σ Es
3,4-Cl2 1 1 5 1 2-5
4-Cl 2 2 4 2 2-5
4-CH3 3 4 2 3 2-5
4-OCH3 4-5 5 1 5 2-5
H 4-5 3 3 4 1
Topliss batchwise scheme
Topliss ranking schemes Topliss substituent
proposals
scheme new substituent selection1
π 3-CF3, 4-Cl; 3-CF3, 4-NO2; 4-
CF3; 2,4-C12; 4-c-C5H9; 4-c-
C6H11; 4-CH(CH3)2; 4-C(CH3)3;
3,4-(CH3)2; 4-O(CH3),CH3; 4-
OCH2Ph; 4-N(C2H5)
σ 3-CF3, 4-Cl; 3-CF3, 4-NO2; 4-
CF3; 2,4-C12; 4-c-C5H9; 4-c-
C6H11
-σ 4-N(C2H5)2; 4-N(CH3)2; 4-NH2;
4-NHC4H9; 4-OH; 4-
OCH(CH3)2; 3-CH3,4-OCH3
π+σ 3-CF3, 4-Cl; 3-CF3, 4-NO2; 4-
CF3; 2,4-C12; 4-c-C5H9; 4-c-
C6H11
1Topliss et al. J Med Chem 1977
Topliss batchwise scheme
substituion EC50 rank
3,4-Cl2 0.150 5
4-Cl 0.132 4
4-CH3 0.063 2
4-OCH3 0.045 1
H 0.079 3
Series of five phenyl-substituted propafenone
derivatives measured against P-Glycoprotein
Which compound should be synthesized next?
subst. π σ -σ π+σ Es
3,4-Cl2 1 1 5 1 2-5
4-Cl 2 2 4 2 2-5
4-CH3 3 4 2 3 2-5
4-OCH3 4-5 5 1 5 2-5
H 4-5 3 3 4 1
Topliss batchwise scheme
Topliss ranking schemes Topliss substituent
proposals
scheme new substituent selection
π 3-CF3, 4-Cl; 3-CF3, 4-NO2; 4-
CF3; 2,4-C12; 4-c-C5H9; 4-c-
C6H11; 4-CH(CH3)2; 4-C(CH3)3;
3,4-(CH3)2; 4-O(CH3),CH3; 4-
OCH2Ph; 4-N(C2H5)
σ 3-CF3, 4-Cl; 3-CF3, 4-NO2; 4-
CF3; 2,4-C12; 4-c-C5H9; 4-c-
C6H11
-σ 4-N(C2H5)2; 4-N(CH3)2; 4-NH2;
4-NHC4H9; 4-OH; 4-
OCH(CH3)2; 3-CH3,4-OCH3
π+σ 3-CF3, 4-Cl; 3-CF3, 4-NO2; 4-
CF3; 2,4-C12; 4-c-C5H9; 4-c-
C6H11
substituion EC50 rank
3,4-Cl2 0.150 5
4-Cl 0.132 4
4-CH3 0.063 2
4-OCH3 0.045 1
H 0.079 3
-σ
propafenone dataset
Topliss batchwise scheme
-σ
• 4-N(CH3)2 derivative was
synthesized and tested
• no affinity increase
substituion EC50 rank
3,4-Cl2 0.150 5
4-Cl 0.132 4
4-CH3 0.063 2
4-OCH3 0.045 1
H 0.079 3
4-N(CH3)2
How often do Topliss schemes
(π, σ, -σ, π+σ, Es) occur in large databases?
How useful do Topliss schemes prove in
activity optimization?
propafenone dataset
www.openphacts.org
www.openphacts.org
1. Return 3,4-dichloro substituted compounds
in postgresql ChEMBL 20 using RDKit cartridge
2a. For each 3,4-Cl2 substituent check for availablity of
4-Cl, 4-OCH3, 4-CH3 and H substitutions
3. Check for each compound series for bioactivity data
(pChEMBL) measured in
- same target in same assay
- activity type = IC50 or Ki
- plus, if available, activity
for new subst. selection
3nM 5nM 8nM 9nM 10nM
1 2 3 4 5
540 x
9312 cpds
SQL query
200 series
How often do Topliss patterns occur?
new substitution
selection
1108 bioactivity data
for additional substituents
1108 bioactivity data
for additional substituents
new substitution
selection
Raw data output after mining ChEMBL
3nM 5nM 8nM 9nM 10nM
1 2 3 4 5
200 series
How often do Topliss patterns occur?
subst. π σ -σ π+σ Es
3,4-Cl2 1 1 5 1 2-5
4-Cl 2 2 4 2 2-5
4-CH3 3 4 2 3 2-5
4-OCH3 4-5 5 1 5 2-5
H 4-5 3 3 4 1
# of series 13 7 3 2 34
distribution of 200 series
π
σ
-σ
π+σ
Es
others
3nM 5nM 8nM 9nM 10nM
1 2 3 4 5
200 series
57 of 200 series (29%) extracted from ChEMBL 20
follow a Topliss pattern
Topliss
pattern
# of
series
substituent
selection [1]
more
active [2]
percent
age
π 13 29 9 31 %
σ 7 9 1 11 %
-σ 3 5 1 20 %
π+σ 2 2 1 50 %
[1] For each series, bioactivity for substituents, proposed
by Topliss new substituent selection were collected from
ChEMBL 20, if available.
[2] Check whether proposed substituents lead to more
active cpds
scheme new substituent selection
π 3-CF3, 4-Cl; 3-CF3, 4-NO2; 4-
CF3; 2,4-C12; 4-c-C5H9; 4-c-
C6H11; 4-CH(CH3)2; 4-
C(CH3)3; 3,4-(CH3)2; 4-
O(CH3),CH3; 4-OCH2Ph; 4-
N(C2H5)
σ 3-CF3, 4-Cl; 3-CF3, 4-NO2; 4-
CF3; 2,4-C12; 4-c-C5H9; 4-c-
C6H11
-σ 4-N(C2H5)2; 4-N(CH3)2; 4-NH2;
4-NHC4H9; 4-OH; 4-
OCH(CH3)2; 3-CH3,4-OCH3
π+σ 3-CF3, 4-Cl; 3-CF3, 4-NO2; 4-
CF3; 2,4-C12; 4-c-C5H9; 4-c-
C6H11
How useful do Topliss prove in activity optimization?
poor performance of -σ
is in agreement with
propafenone data
Topliss approach seems to have difficulties for
series following the σ scheme in activity
optimization for the series found in ChEMBL.
How useful do Topliss prove in activity optimization?
Topliss proposal for propafenone dataset,
4-N(CH3)2, did not show activity gain.
Are there -σ series in ChEMBL with
bioactivity data for 4-N(CH3)2
substitution?
target Type 4-OCH3 (nM) 4-N(CH3)2 (nM)
P-Glycoprotein EC50 45 82
Alpha-1a adrenergic receptor
(ChEMBL)
Ki 0.3 0.8
µ-opioid receptor (ChEMBL) Ki 0.50 63
substituion EC50 rank
3,4-Cl2 0.150 5
4-Cl 0.132 4
4-CH3 0.063 2
4-OCH3 0.045 1
H 0.079 3
-σ
propafenone dataset
Also in the two cases of ChEMBL the -σ proposal 4-N(CH3)2
failed to increase activity.
substituion EC50 rank
3,4-Cl2 0.522 5
4-Cl 0.190 4
4-CH3 0.063 1
4-OCH3 0.180 3
H 0.079 2
non Topliss
propafenone aryloxy
Topliss batchwise scheme
Ranking pattern 5 4 1 3 2 in
this dataset can‘t be assigned
to an existing Topliss scheme
How often does the pattern 5 4 1 3 2 occur
in ChEMBL?
In general, which other, non Topliss pattern
occur frequently in ChEMBL?
Which non Topliss pattern occur in
ChEMBL?
subst. new1 new2 new3 aryloxy
3,4-Cl2 1 5 5 5
4-Cl 2 2 3 4
4-CH3 4 4 1 1
4-OCH3 3 1 4 3
H 5 3 2 2
# series 6 4 4 0
distribution of 200 series
π
σ
-σ
π+σ
Es
new1
new2
new3
The pattern found in aryloxy
dataset, does not occur in ChEMBL
However: High similarity to new3
Do we find an underlying physicochemical
driving force in the new3 pattern?
Can we extrapolate to aryloxy dataset?
Correlation analysis within new3
series
target name pattern # of cpds in series [1] r (π) r (σ ) r (vdw_area)
Prostanoid EP 1 rec 5 3 1 4 2 5 + 8 -0.81**
Adenosine A3 rec 5 3 1 4 2 5 + 8 -0.54*
Adenosine A3 rec 5 3 1 4 2 5 + 8 -0.67**
Chymase 5 3 1 4 2 5 + 13 -0.49**
P-Glycoprotein 5 4 1 3 2 5
[1] Next to the 5 datapoints from 3,4-Cl2, 4-Cl, 4-OCH3, 4-CH3 and
H, bioactivity data from other substituents listed in Topliss et al
1977 were selected for correlation analysis.
Correlation analyses were undertaken to calculate the Pearson
correlation coefficient (r) between physicochemical features π,
σ , vdw_area and the respective bioactivity data.
** p < 0.05 , * p < 0.10
Statistically significant negative vdw_area correlations
indicate that new3 pattern & aryloxy bind to a tight pocket
There are 120 (5!) ranking possibilites (patterns)
(1,2,3,4,5), (2,1,3,4,5), (1,3,2,4,5), … (5,4,3,2,1)
Calculation of Spearman’s rank correlation
distance matrix for 120 possibilities
(R function corDist)
Spherical MDS to represent the distance matrix
on the surface of a sphere (R function
smacofSphere), Kruksal-Stress = 0.15
Each point represents a pattern (e.g. 1,2,3,4,5)
similar patterns are in vincinity to each other
Discover the ranking globe
How to look at the ranking space globally?
Frequency contour map
Color coding based on
frequency of patterns.
Red = high frequency
Blue = low frequency
Map analysis
*Es
Van der Waals contour map
Color coding based on
vdw_area correlations with
bioactivity.
Only series with activity data
for five additional derivatives
(e.g. 4-CF3, 4-OH ...) are used
in correlation analysis
(n>=10). Resulting
correlations with p > 0.1 were
omitted.
The remaining coefficients
were used for color coding.
Red ... positive correlation
Blue ... negative correlation
π and σ continent steric island
steric island π and σ continent
- σ
aryloxy
Frequency contour map
Color coding based on
frequency of patterns.
Red = high frequency
Blue = low frequency
Only Topliss patterns (π, σ, π+σ, Es ) and rankings patterns with
four or more series (new1, new2, new3) are schown.
trench
• surrounded by Es
pattern
• lies in area with
negative vdw_area
correlation
• Only three –σ
pattern in ChEMBL
• In the investigated
cases, poor
predictability of –σ
scheme
Summary & Outlook
• Open medicinal chemistry data such as those in ChEMBL allow
analysis of complex SAR patterns
• Connecting these data with data from pathways and diseases like
implemented in the Open PHACTS Discovery Platform will
open up completely new possibilities for linking chemical SAR
patterns to biological endpoints
• Quality of data is key for the analysis (assays)
Next steps
• Look for X-ray structures of complexes
• Analyse with respect to target classes
Pharmacoinformatics Research Group Department of Pharmaceutical Chemistry
Pharmacoinformatics Research Group Department of Pharmaceutical Chemistry
ChEMBL 20 postgreSQL > 13 000 000 activities
RDKit Chemoinformatics
toolkit 2014.03
RDKit cartridge
SQL query: get all 3,4-Cl2 compounds
SMILES
Data processing in python
200 series
Pharmacoinformatics Research Group Department of Pharmaceutical Chemistry
-> 120 ranking possibilies are created -> Spearman ranking distance matrix calculated -> Spherical MDS is undertaken -> X,Y,Z coordinates are exported as CSV file
Spherical MDS in R software
Coordinates.csv Python data preprossesing
2D - EquidistantCylindrical
Projections
3D - Orthographic
Basemap toolkit
• provides list of globe projections • create contour maps
Pharmacoinformatics Research Group Department of Pharmaceutical Chemistry
For each series bioactivity data for 3,4-Cl2, 4-Cl, 4-CH3, 4-OCH3 and 4-H is available
• For the majority of the series (91%) there are bioactivity data for more substituents e.g. 4-CF3, 4-OH, 4-F, ... available. (Substituents taken from „new substituent selection“) • More than 57% of the series have activity data for five or more additional substituents.
Series_8 3,4-Cl2 4-Cl 4-CH3 4-OCH3 4-H
pIC50
6.3 7.0 7.4 7.6 8
vdw_area
134 117 116 131 99
4-CF3 4-F 4-OH 3,4-(CH3)2 4-C(CH3)3
6.9 7.7 6.6 7 6.1
129 103 109 134 152
For series with 5 or more additional substituents (n>=10) correlation analysis were run:
In this example: R = -0.70, p = 0.03 Series 8 with pattern 5 4 3 2 1, has R(vdw) = -0.7
Pharmacoinformatics Research Group Department of Pharmaceutical Chemistry
First 2D MDS bad Kruksal-Stress-1 > 0.2 Second 3D MDS good Kruksal-Stress-1 = 0.11 but visualization not helpful Third Spherical MDS moderate Kruksal-Stress-1 = 0.15, good visualization
get120Possibilities() ... creates a vector with 120 rankings [(1,2,3,4,5), (2,1,3,4,5) ...] corDist () ... calculates Spearman‘s rank correlation distance smacofSphere() ... runs spherical MDS, type=„ordinal“ because we have rankings, algorithm=„primal“ ... handling of ties xyz.120 ... x,y,z – coordinates of the MDS run Coordinates (xyz.120) are exported to CSV file and are the input for Basemap
✔
Details to Multidimensional Scaling with
Pharmacoinformatics Research Group Department of Pharmaceutical Chemistry
• Potentielle Fragen:
• -> Wie lange dauert so eine Suche wenn der Workflow steht ~ 1 Tag (4 Prozessoren Rechner, 8GB RAM)
• -> Wie werden Salze behandelt? Skript ist so geschrieben dass diese nicht berücksichtigt werden. Soll heißen es wäre potentiell möglich dass die diChloro verbindung ein Natriumsalz ist und das Methylderivat ein Kaliumsalz. Wie auch immer in den 200 Serien war dies nie zu finden und spielt somit keine Rolle.
• -> Wie steht es um Chiralität. Ich habe die Chiralität nicht berücksichtigt in der Query. Dies wäre möglich gewesen aber da die Codierung von Chiralitäten in ChEMBL nicht umfassend ist habe ich es nicht berücksichtigt.
• -> wie groß muss den Unterschied sein zwischen den Bioaktivitäten damit es als Serie anerkannt wurde? Im Topliss paper findet man rankings mit log >0.1 zwischen den Verbindungen. Wir haben darauf keine Rücksicht genommen und alle Daten verwendet (so wie es übrigens auch die Gruppe die 2014 eine ähnliche Analyse auch gemacht haben)
• Die Datenanalyse zeigt von den 200 serien: Haben 43 eine Differenz von mindestens „>0.1 log“ zwischen den rankings. 77 series haben 1 verstoß dieser regel, d.h. die differnz zwischen 2 rankings ist ein mal kleiner 0.1. 80 haben dann 2 oder mehr verstöße.
• Warum habt ihr die anderen pattern 2pi-pi^2, pi-sigma usw. nicht berücksichtigt? Die Komplexität wäre deutlich höher gewesen ohne dass es einen nennenswerten Informationsgewinn gegeben hätte. Zur Abgrenzung, die neuen pattern „new 1, new 2, new 3) fallen in keines der von Topliss postulierten pattern auch nicht in die erweiterte Auswahl (2pi- pi^2, pi-3sigma, usw.)