Combining molecular modellingwith experiments:
Sulfonylureas and glinides as new PPARγ agonists
Marco Scarsi
Biozentrum-
Swiss Institute of Bioinformatics
Drug discovery and development
Preclinical research
Candidate drugs Clinical tests Drug
Registration and
marketing
5-6 years 5-6 years 1-2 years
12-15 years of work
1-2 years 1 year 3-4 years
Target identification
Molecular DB screening
Optimization toxicology
metabolism
Candidate drugs
Pre-clinical drug research:Real and virtual
Target identification
Molecular DB screening
Optimization toxicology
metabolism
Candidate drugs
Target identification
Molecular DB screening
Optimization toxicology
metabolism
Candidate drugs
Virtual screening
?
Does the molecule bind to the protein?
DB
Virtual screening• Principles of molecular mechanics:
– Atoms as basic units– Chemical bonds cannot be broken: They can be
stretched, bent, torqued.
– Good to simulate non-covalent binding
AutoDock
• The potential generated by the protein is calculated on a grid
• Each ligand is flexibly sampled on the grid (conformational search)
AutoDock: algorithm
• Lamarckian Genetic Algorithm
• The searcher modifies the phenotype, which is allowed to update the genotype
• Lamarck: “an adaptation of an individual to its environment can be inherited by its offsprings”
Our work…
Target selection: PPARγ
Virtual screening of known drugs
Experimental validation
Drug redirection
• Identify new PPARγ ligands among known drugs
• TheraSTrat was interested in side-effects of known drugs
Nuclear receptorsLigands:
Nuclear receptor
Response Element
PPARγ
• Pharmacological target for type-II diabetes (several drugs on the market)
• It controls lipid metabolism and glucose homeostasis
• Lots of experimental data available
PPARγ agonists
Thiazolidinedione (drug)
Fatty acid (endogenous)
Tyrosine-based agonist
(candidate drug)
X-ray: PPARγ bound to farglitazarHIS 323
TYR 473
HIS 449
SER 289
Farglitazar: potent synthetic agonist (nM)
Mutations is SER289, HIS323, TYR473, HIS449 strongly reduce activity
Agonists aligned (X-ray)
• Is there anything else binding to PPARγ ?
• Virtual screening
Compound libraries
• TheraSTrat AG database: ~8000 compounds– most marketed drugs– proprietary
• Chembank database: ~6000 compounds– bioactive compounds– freely available
Virtual screening on a grid
[BC]2 PC Desktop Grid(UD MP, Win32)
Basel
Vital-IT HPC cluster(LSF, Itanium 64)
Lausanne
[BC]2 HPC cluster(SGE, x86-32)
Basel
CSCS(PBS)Ticino
Docking a ligand to the receptor
Grid Manager
Gliquidone Repaglinide
Docking results
• Sulfonylureas and glinides bind to PPARγ
Farglitazar (x-ray)
Nateglinide
Mitiglinide
Glipizide
Glimepiride
• Why are sulfonylureas and glinides so
interesting as putative PPARγ agonists?
Type II Diabetes: drug therapies
Improve insulin secretion
Bind to sulfonylurea receptor
Sulfonylureas, Glinides
Reduce insulin resistance
Bind to PPARγ
PPARγagonists
?
Type II diabetes treatment
Experimental validation3 experiments:
• Binding to receptor– displacement of labeled ligand
• Activation of receptor– transactivation assays
• Activation of metabolicpathways– expression of PPARγ-regulated genes
biochemistry
cell biology
PPARγ Binding Assays
• PPARγ + fluorescent labeled high-affinity ligand
• Competitor assay
Gliquidone
-200
20406080
100120
0.001 0.1 10 1000
Compound (µM)
Ar(%
)
Nateglinide
-200
20406080
100120
0.001 0.1 10 1000
Compound (µM)
Ar(%
)
Sulfonylureas Glinides
...and
Repaglinide
Mitiglinide
...and
Glimepiride
Glipizide
Results of Binding Assays
IC50 = 8μM
IC50 = 125μM
IC50 ?
IC50 = 316μM
IC50 > 1.5mM
IC50 ?
Activation of receptor
PPAR
ExpressionVector
PPARE
RXR
Reportervector
LUC
PPAR
Ligand
light emitted by luciferin/luciferasereaction measuredby photometer
• Transactivationassay
• Measure ligandeffect on synthetictarget gene
Sulfonylureas and glinides activate PPARγ in the 10-100 mM range
Results of transactivation assays
Sulfonylureas
0
2
4
6
8
10
12
14
16
0.001 0.01 0.1 1 10 100 1000
Concentration (µM)m
Fold
act
ivat
ion
GliquidoneGlipizideGlimepiride
Glinides
0
1
2
3
4
0.001 0.01 0.1 1 10 100 1000
Fold
act
ivat
ion
RepaglinideNateglinideMitiglinide
Concentration (µM)
PPARγ-dependent gene activation
• Mouse pre-adipocyte cells
• Measure expression of selected genes induced by PPARγ signaling:– Adiponectin– aP2– GLUT4
0102030405060708090
100
glipizide 100microM
nateglinide 50microM
gliquidone 10microM
pioglitazone 10microM
% o
f ind
uctio
n ro
sigl
itazo
ne
adiponectinaP2GLUT4
Known PPARγactivator
Sulfonylureas and Glinides
PPARγ-dependent gene activation
• Sulfonylureas and glinides:
– bind to PPARγ
– activate PPARγ and enhance transcription
Experiments: Summarybiochemistry
cell biology
Clinically relevant?
• PPARγ activation observed at concentrations of 10-100 μM
• Do these drugs ever reach these plasmaconcentrations?
• Yes (gliquidone, glipizide, nateglinide)
Type II Diabetes: drug therapies
type II diabetes treatment
Improve insulin secretion
Bind to sulfonylurea receptor
Sulfonylureas, Glinides
Reduce insulin resistance
Activate PPARγ
Glitazones,TZDs
Conclusion: Chemical– carboxylic acids– thiazolidinediones
• Same acidity:– carboxylic acids pKa ~4.8– thiazolidinediones pKa ~6.5
• Same network of H-bonds
known PPARγ agonists
– sulfonylureas
– sulfonylureas pKa ~5.3
new
Conclusions: Pharmacological
• Sulfonylurea and glinide drugs can:– Enhance insulin secretion (SU receptor)– Reduce insulin resistance (PPARγ)
• Possible to design new drugs targeting PPARγ and the SU receptor
• A "favorable side effect"?
TheraSTrat
A broad sinergyMichael Podvinec
Adrian RothRenate Looser
Urs A. MeyerTorsten Schwede
Christoph RueckerHubert Hug
Hugo Albrecht
Sander Kersten
Patent?
Disease 1 Disease 1 Disease 2
Hard to patent: Patentable:
Pre-clinical research:real and virtual
1-2 years 1 year 2-3 years 1-2 years
Target identification
High-throughput screening
Leads Lead optimization
Toxicology, metabolism
Candidate drugs
Target identification
High-throughput screening
Leads Lead optimization
Toxicology, metabolism
Candidate drugs
Target identification
High-throughput screening
Leads Lead optimization
Toxicology, metabolism
Candidate drugs
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