eugm15 - zoltán simon (printnet): drug profile matching - drug discovery by polypharmacology-based...
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Drug Profile MatchingDrug discovery by polypharmacology-based
interaction profiling
Binding pattern to the
members of the proteome
Bioactivity:
effects / side effects
Target
Effect
Drug
?
Similar binding patterns to
the proteome
Similar bioactivity:
effects / side effects
?
Scientific question and starting hypothesis
Interaction Profile (IP)
generation
Effect profile (EP)
generation
Interaction Profile MatrixAgainst random (non-target) proteins.
Patterns instead of individual interactions.
Effect Profile MatrixContains binary effects
Drug
Simon et al, J Chem Inf Model 2012
Target profile
(TP)
generation
Target
Profile Matrix
Checking similarity, round one:
Vector distance vs. angle
Protein 2
Protein 1
Drug 2Drug 1
Predictions based on angle distance:
predicted and real profile of ziprasidone
QT prolongation: 9 out of the 13 nearest neighbors
Predicted property Described property
Effect
(category)
Adrenergic agent Antipsychotic
Anaesthetic Dopamine antagonist
Anti-anxiety agent Serotonine antagonist
Antiemetic agent
Antihypertensive agent
Antipsychotic
Dopamine antagonist
Metabolizing enzyme
Cytochrome P450 3A4 (CYP3A4) Cytochrome P450 3A4 (CYP3A4)
CytochromeP450 2D6 (CYP2D6) Cytochrome P450 2D6 (CYP2D6)
Mechanism of action
Adrenergic Adrenergic
Dopamine antagonism Dopamine antagonism
Histamine antagonism Histamine antagonism
Serotonine antagonism Serotonine antagonism
Identification of PPARγ ligands by
One-Dimensional Drug Profile Matching
Kovacs et al
Drug Des Devel Ther 2013
Effect Profile
Matrix
Interaction
Profile
Matrix
Negative
Positive
Simon et al, J Chem Inf Model 2012
Effect Probability Matrix
Target Profile Matrix → Target Probability Matrix
Effect Profile Matrix Effect Probability Matrix
„False” positives
True positives
Simon et al, J Chem Inf Model 2012
ROC (Receiver Operating Characteristic) curves
TP
R
FPR
Sensitivity:
1-Specificity:
0.839±0.081
Comparison of the distributions of
the effect and target profile-based AUC values
%
%
129 effect categories
Average AUC = 0.791±0.147
77 targets
Average AUC = 0.839±0.081
Penicillin-binding proteins 0.969
Beta-2 adrenergic receptor 0.934
Beta-1 adrenergic receptor 0.925
Mu-type opioid receptor 0.915
Gamma-aminobutyric acid receptor 0.890
Histamine H1 receptor 0.886
5-hydroxytryptamine 1D receptor 0.859
Alpha-1A adrenergic receptor 0.858
D(2) dopamine receptor 0.855
Muscarinic acetylcholine receptor M1 0.842
Monoamine oxidase A 0.841
5-hydroxytryptamine 2A receptor 0.838
Cyclooxygenase 1 0.835
D(1) dopamine receptor 0.827
5-hydroxytryptamine 1A receptor 0.826
5-hydroxytryptamine 1B receptor 0.823
Peroxisome proliferator-activated receptor 0.811
D(3) dopamine receptor 0.794
Reverse transcriptase 0.791
Angiotensin-converting enzyme 0.754
In vitro tests: COX inhibition
Telmisartan Proline
Novobiocin Dasatinib
Captopril Captopril
COX-1 COX-2
Alpha-linolenic acid Nitroxoline
COX-1 COX-2
Out of the 43
high-probability compounds:
- 4 confirmed by the literature
- 2 falsified by the literature
-10 reached Ki<50 µM
Végner et al, J Med Chem 2013
In vitro tests: ACE inhibition
Drug name Ki (µM)
Telmisartan 6
Proline 86
Novobiocin 167
Adenine 246*
Creatine 254*
Lamotrigine 287*
Tipranavir# 285*
Rosiglitazone 356*
Chloramphenicol 419*
Cimetidine 439*
Nelfinavir 542*
Dasatinib 715
Pentoxifylline 1540*
Sulpiride 2840*
Telmisartan
Végner et al, J Med Chem 2013
In vitro tests: dopamine agonism/antagonism
Name
Predicted
probability
Dopamine D1 receptor Dopamine D2 receptor
Ki (µM) in
Agonist mode
Ki (µM) in
Antagonist
mode
Ki (µM) in
Agonist mode
Ki (µM) in
Antagonist
mode
Celecoxib 0.995 <1 <1
Doxazosin 0.991 1
Cyclobenzaprine 0.977 2 2.6
Mitoxantrone 0.976 52 245 12
Flavoxate 0.971
Promethazine 0.966 19 2.6
Imipramine 0.952
Desipramine 0.951
Desogestrel 0.936 26.6 6.8
Epinastine 0.916 4.2
Clomipramine 0.907 1 669 4
Olopatadine 0.881 400 46
Thioguanine 0.878 188
Rimantadine 0.864 72
Mefloquine 0.854 22 122 6
Etodolac 0.796 335
Raloxifene 0.796 7
Fosfomycin 0.761 122
Végner et al, J Med Chem 2013
10 out of
18
active
%
Initial screening of 600,000 druglike compoundsJChem Base filtering for dissimilar compounds
Ki (µM)
10 20 30 40
8 compounds
between
40-70 µM
Drug Profile Matching is able
- to predict effects/targets of druglike molecules
DRUG DISCOVERY
and
- to reveal hidden effects/targets of FDA-approved drugs
DRUG REPOSITIONING
DRUG SAFETY
Acknowledgement
• Ágnes Peragovics
• László Végner
• Balázs Jelinek
• Barna Peitl
• Zoltán Szilvássy
• Péter Hári
• István Bitter
• Pál Czobor
• András Málnási-Csizmadia