Presentation given by Gerhard Ecker (University of Vienna) to Pharma IndoChina VIII, in Ho Chi Minh City, Vietnam

1. Experimental data guided docking allows
to elucidate the molecular basis of drug-
transporter interaction
Gerhard F. Ecker
Pharmacoinformatics Research Group
Department of Medicinal Chemistry, University of Vienna
Althanstrasse 14, A-1090 Wien, Austria
gerhard.f.ecker@univie.ac.at; http://pharminfo.univie.ac.at

2. Transporters involved in ADMET
Nature Reviews Drug Discovery 9, 215-236 (March 2010)

3. Drug Transporter
Aller et al. Science 2009

4. Major Challenge: Polyspecificity

5. QSAR Studies of Propafenones
N
OH
N
H N
H
( )n
n = 0-2
NR
O H
O
O
calcd logP
log(1/IC50)
1 2 3 4 5 6 7
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5

6. Ligand-based Design
NO
O
OH
Optimal distance: 3.5 -CH2-
high partial logP
steric interactions
H-bond acceptor
H-bond acceptor
π−π-interaction
π−π-interaction
1 2 3 4 5 6 7
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
calcd logP
log(1/IC50)
400 compounds
in house library
2D-QSAR
3D-QSAR Pharmacophormodeling
Ecker et al., J Med Chem 39:4767
Ecker et al., Mol Pharmacol 56:791
Chiba et al., J Med Chem 41:4001
Kaiser et al., J. Med Chem 50:1698
Cramer et al., ChemMedChem, 2007

7. Target-based Design
Aller et al., Science 2009, 323, (5922), 1718-22

8. SAR-driven docking/Scoring
Mouse P-gp Sav1866
Homology Models of P-gp

9. Docking into Protein Homology Models

10. Common Scaffold Clustering
N
N
N
O H
N
C H
3
O
NO
O
O N
O H
Klepsch et al., PloS Comp Biol 2011

11. Identification of new Ligands

12. Docking-based classification of
P-gp inhibitors
1935 compounds
Dock, score
take top scored
run distribution
Accuracy 0,75
Klepsch, JCIM 2013

13. So we know the molecular basis
of propafenone/P-gp interaction
• Poses consistent with QSAR
• Poses predictive for identifying new ligands
• Docking allows classification inhibitor/non-
inhibitor
BUT

14. Mutating Tyr307/310 to Phe

15. Multiple sites for the same ligand?
Keskin BMC Structural Biology 2007 7:31

16. Gln132/773Arg
Parveen et al, Mol Pharmacol 2011
O NR'
OH
R
O

17. Target-based Design - Reality

18. Docking into SERT
Imipramine and serotonin bind mutually exclusive
Affinity loss with Y95F (1,2 kcal/mol)
Carbamazepine and serotin bind simultaneously
Sarker et al, Mol Pharmacol 2010

19. Inclusion of Water

20. A novel platform for integrated data-
driven drug discovery
www.openphacts.org

21. What will users see?
A user-friendly, full featured
interface that allows scientists to
explore and interrogate integrated
biological and chemical data

22. What do we need?
ChEMBL DrugBank Gene
Ontology
Wikipathways
Uniprot
ChemSpider
UMLS
ConceptWiki
ChEBI
TrialTrove
GVKBio
GeneGo
TR Integrity
“Find me compounds
that inhibit targets in
NFkB pathway assayed
in only functional assays
with a potency <1 μM”
“Let me compare
MW, logP and PSA
for known
oxidoreductase
inhibitors”
“What is the
selectivity profile of
known p38 inhibitors?”
The Open PHACTS infrastructure can support many different domains & questions
The Need

23. Explorer: www.openphacts.org

24. Provenance

25. Search over Target Classes

26. The active Open PHACTS app ecosystem
Open PHACTS has engaged the
scientific community, and many
apps now consume the data within
the Open PHACTS API

27. Thank you!
Barbara Zdrazil
Ishrat Jabeen
Marta Pinto
Rita Schwaha
Lars Richter
Daniela Digles
Yogesh Aher
Freya Klepsch
Rene Weissensteiner
Andreas Jurik
Vasanathan Poongavanam
Daria Tsareva
Amir Seddik
Eric Haaksma
Peter Ettmayer
Oliver Krämer
Peter Chiba
Wilfried Gansterer
Johnny Gasteiger