the power of open data linking transporter …...pharmacoinformatics research group department of...
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Pharmacoinformatics Research Group Department of Pharmaceutical Chemistry
The power of open data – linking transporter interaction profiles to
in vivo toxicity
Gerhard F. Ecker Dept. of Pharmaceutical Chemistry
pharminfo.univie.ac.at
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Transporters and ADMET
Nature Reviews Drug Discovery 9, 215-236 (March 2010)
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Drug Transporter
Aller et al. Science 2009
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QSAR Studies of Propafenones
N
OH
NH N
H
( )n
n = 0-2
calcd logP
log(1
/IC
50)
1 2 3 4 5 6 7
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
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Target-based design
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Target-based Design - Reality
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Docking into P-gp Model
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Experimental Data Guided Docking
Sarker et al., Mol Pharmacol 2010
Klepsch et al., PloS Comp Biol 2011
Richter et al., Nature Chem Biol 2012
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Validation via Pharmacophores
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Docking-based classification
1935 compounds
dock, score
take top scored
run distribution
Accuracy 0,75
Klepsch, J Chem Inf Mod 2013
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differences:
•localization
•expression rate
•kinetic parameters
•Cl--dependence
•(sequence)
•GAT-1:
highest expression in CNS
mainly neuronal localization
established drug target
•BGT-1:
peripheral osmolyte transport
distal CNS localization
regulates spillover, crosstalk
•GAT-2:
least studied subtype
mainly peripheral localization
•GAT-3:
mainly CNS, synaptic localization
higher expression in astrocytes
GABA transporters
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Ligand derived information
• structure-activity relationship observations
in literature:
• ideal linker length
• stereochemistry of GABA mimetic
moiety
• substitution of aromats
• polarity of the linker
• QSAR analysis of 161 consistently tested
compounds:
• importance of rigidity, polarity
distribution
Jurik, Mol Inform 2013
IC50: 1.59 µM
IC50: 0.06 µM
IC50: 1.92 µM
IC50: 0.07 µM
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Flexibility – ensemble docking
common binding mode
10 MD snapshots 100 poses / configuration
Σ=2000 poses / ligand
Placement ChemScore Rescoring GoldScore ChemPLP XScore London dG GBVI
consensus score: 10 top poses / ligand
visual analysis
snapshot 2 flexible candidate residues
non-polar linker compounds
pose evaluation literature cross-check
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Tiagabine
Liothyronine
Jurik, J Med Chem 2015
Validation
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BCRP Inhibitors
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inhibitor
inactive
Area under
ROC: 0.8
Manual compilation of data sets
Identification of literature sources
Redrawing structures
Manual addition of values
Analogous approach for
BSEP, OATP1B1, OATP1B3
Prioritising ligands in B1/G2
selectivity profiling
BCRP Inhibitors
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NO
O
OH
Optimal distance: 3.5 -CH2-
high partial logP
steric interactions
H-bond acceptor
H-bond acceptor
-interaction
-interaction
280 GPV compounds
It‘s all about data
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www.openphacts.org
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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?”
Approaching complex research questions needs integration of data sources
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Platform Explorer
Standards
Apps
API
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BCRP vs P-gp Profiling
Montanari et al., J Cheminform 2016
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Pred. SERT pIC50 = 6.089 (±0.217 ) + 5.030 (±0.374) p-mr
Bonano et.al, British Journal of Pharmacology (2015) 172 2433-2444 2433
Cathinones and MATs
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DAT
SERT
NET
Workflow Scheme
Rule-based Row Splitter
Binding Assay, IC50 Concatenate
Group by
ChEMBL ID
Row Filter
Missing Values
IC50 1015
at all 3 MATs
DAT
SERT
NET
SERT
DAT
NET
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Data Compilation - KNIME
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In vitro to in vivo
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Transporter Profiling
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Toxicity Profiling
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Interaction profiles and side effects
Michl, Neuropsychpharmacology 2014
5-HT6 and weigth gain
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Hyperbilirubinemia- Background
Liver Transporters & Cycle of Bilirubin
Sticova and Jirsa, 2013, World J Gastroenterol, 6398-6407
Hyperbilirubinemia
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Compile
literature
Datasets‘
curation
Descriptors‘
calculation
Generation of
Classification
Models
3 Sets of
Descriptors OATP1B1
6 equal classification
models (2 sets of classifiers
x 3 sets of descriptors)
Random Forest
+
MetaCost
SMO
+
MetaCost
OATP1B3
6 equal classification
models (2 sets of
classifiers x 3 sets of
descriptors)
References
1) De Bruyn et al., 2013, Mol Pharmacol, 1257-1267, Structure-Based
Identification of OATP1B1/3 Inhibitors
2) Karlgren et al., 2012, J Med Chem, 4740-4763, Classification of Hepatic
Organic Anion Transporting Polypeptides (OATPs): Influence of Protein
Expression on Drug-Drug Interactions
Workflow – OATP Models
Kotsampasakou, Mol Pharm 2015
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836 compounds for
Hyperbilirubinemia:
86 positives
749 negatives
ECFP6 + OATP Inhibition
MetaCost-
Attribute Selection-
SMO (RBF Kernel)
Model‘s Settings
Hyperbilirubinemia- Results
Valid.
Method
Accuracy Sensitivity
(TPR)
Specificity
(TNR)
MCC ROC
Area
10CV 0.675 0.651 0.678 0.209 0.687
5CV 0.690 0.581 0.702 0.184 0.679
OATP Inhib → important dscr
1st model for Hyperbilirubinemia
Suggestion for one more transporter for
bilirubin (Lin et al., 2015, Nature Reviews
Drug Discovery)
Hyperbilirubinemia
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Approaching new areas with increasing complexity
• BQ X1: Give me all pathways related to the regulation of P-
glycoprotein, and all compounds hitting targets in these pathways.
• BQ 17: for hyperbilirubinemia, give me all targets in the pathway and for these targets all the active compounds
• Give me all compounds annotated with liver toxicity and their interaction profiles with all transporters expressed in the liver
Limitations are no longer in your computer, they are in your mind!
Be careful on the quality of the data!
The near Future
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Pharmacoinformatics Research Group Department of Pharmaceutical Chemistry