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