Thierry

Parallel Pharmacophore-based Virtual Screening and SOSA: An Efficient Method for Target Fishing and Ligand Profiling Merging Medicinal Chemistry Tradition

With A Modern Computational Approach Prof. Thierry Langer Prestwick Chemical, Inc. Boulevard Gonthier d’Andernach 67400 Strasbourg-Illkirch, France

Contents •Introduction - Non HTS Hit Recognition •The SOSA Approach – Concept – Application Examples

•Pharmacophore-based Ligand Profiling – HT Model Generation – Software Solutions – Application Examples

•Conclusions Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Success Rate Is Too Low

1000 Mio $

Development phases of a new drug

market launch

phase III clinical study

preclinical studies

2 phase II clinical study

5 10

1

phase I clinical study

20

> 100.000 new compounds

0

2

4

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

6

Years

8

10

12

Efficiency Deficit ...

800 Mio $ 1

>> 100.000

150 Mio $ Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Efficiency Deficit ... Predicion of failure in early stage 800 Mio $ 1

>> 100.000

150 Mio $ Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Reasons For Attrition Phase III failures 1992-2002 … Cipemastat

Lazabemide Lotrafiban

4% pharmacokinetics 4% portfolio

35 % side

effects &

Sorivudine

toxicity

Tasosartan …

n=26

53% clinical efficacy

4% diverse

D. Schuster et al.: Curr. Pharm. Des. 11, 3545-3559 (2005) Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Reasons For Attrition Phase III failures 1992-2002 … Cipemastat

Lazabemide Lotrafiban

4% pharmacokinetics 4% portfolio

35 % side

effects &

Sorivudine

toxicity

Tasosartan …

n=26

53% clinical efficacy

4% diverse

D. Schuster et al.: Curr. Pharm. Des. 11, 3545-3559 (2005) Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Non-HTS Hit Recognition A retrospective analysis of the drug discovery routes other than HTS highlights four efficacious strategies giving access to hits and/or lead compounds: • Analogue design modification of existing active molecules to create an improved medicine (or new intellectual property) • Serendipitous observations of unexpected clinical or pharmacological activities (trinitrine, hypoglycemic sulfonamides, sildenafil, etc.) • Rational design of drugs resulting from the knowledge of the molecular mechanism and its role in disease (captopril, cimetidine) • Selective optimization of side activities of known drugs on new pharmacological targets (SOSA Approach) Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Chong & Sullivan, Nat. Drug Discov. 2007, 448, 645-646

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Chong & Sullivan, Nat. Drug Discov. 2007, 448, 645-646

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Sir James Black

Chong & Sullivan, Nat. Drug Discov. 2007, 448, 645-646

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

SOSA: New Leads from Old Drugs SOSA = Selective Optimization of Side Activities 1 – Start screening with a limited set of carefully chosen, structurally diverse, drug molecules (a smart library of about 1000 compounds). Already bioavailability and toxicity studies have been performed and as they have proven usefulness in human therapy, all hits that will be found are “drug-like”!

2 – Optimize hits (by means of traditional or parallel chemistry) in order to increase the affinity for the new target and decrease the affinity for the other targets. The objective is to prepare analogues of the hit molecule in order to transform the observed “side activity” into the main effect and to strongly reduce or abolish the initial pharmacological activity.

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Activity Profile Inversion of Minaprine Selective Optimization of a Side Activity yields a new lead

Minaprine (Cantor®)

+++ Serotoninergic: ++ Dopaminergic: Cholinergic:

1/2+

Wermuth, C. G. Il Farmaco 1993, 48, 253-274. Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Modified Analogue Dopaminergic: 0 Serotoninergic: 0 Cholinergic:

++++

Activity Profile Inversion of Minaprine Affinity for muscarinic M1 receptors

Wermuth, C. G. Il Farmaco 1993, 48, 253-274. Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Rationale of the «SOSA» Approach The rationale behind the SOSA approach lies in the fact that, in addition to their main activity, almost all drugs used in human therapy show one or several side effects. In other words, if they are able to exert a strong interaction with the main target, they exert also less strong interactions with some other biological targets. Most of these targets are unrelated to the primary therapeutic activity of the compound. The objective is then to proceed to a reversal of the affinities, the identified side effect is becoming the main effect and vice-versa.

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

SOSA: Patentability & Interference Risk • The risk with the SOSA approach is to prepare a molecule already synthesized by the initial inventors and their early competitors. • In fact, in optimizing another therapeutic profile than that of the initial one, the medicinal chemist will rapidly prepare analogues with chemical structures very different from that of the original hit. • As an example, a medicinal chemist interested in phosphodiesterases and using diazepam as lead, will rapidly prepare compounds which are out of scope of the original patents, precisely because they exhibit dominantly PDE inhibiting properties and almost no more affinity for the benzodiazepine receptor. Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

SOSA: Safety & Bioavailability • During years of practicing SOSA approaches, we observed that starting with a drug molecule as lead substance in performing analogue synthesis, increased notably the probability of obtaining safe new chemical entities. • In addition most of them satisfy Lipinski’s1, Veber’s2, Bergström’s3, and Wenlock’s4 recommendations in terms of solubility, oral bioavailability, and drug-likeness. 1) Lipinski, C. A. et al. Adv. Drug. Delivery. Rev. 2001, 46, 3-26. 2) Veber, D. F.; et al. J. Med. Chem. 2002, 45, 2615-2623. 3) Bergström, C. A. et al. J. Med. Chem. 2003, 46, 558-570. 4) Wenlock, M. C. et al. J. Med. Chem. 2003, 46, 1250-1256.

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Where to begin ?

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

One Solution: In Silico Screening Descriptor/Fingerprint Filter

1D Filter

2D Filter

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

3D Fitting

3D Filter

Real 3D Fitting

One Solution: In Silico Screening Descriptor/Fingerprint Filter

1D Filter

2D Filter

1D Filter – properties – fingerprints e.g. MW 200-500 Ro5 / Lipinski

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

3D Fitting

3D Filter

Real 3D Fitting

One Solution: In Silico Screening Descriptor/Fingerprint Filter

1D Filter

1D Filter – properties – fingerprints e.g. MW 200-500

2D Filter

2D Filter – topology, mol. graphs

– (red. graphs, FTrees, …)

Ro5 / Lipinski

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

3D Fitting

3D Filter

Real 3D Fitting

One Solution: In Silico Screening Descriptor/Fingerprint Filter

1D Filter

1D Filter – properties – fingerprints e.g. MW 200-500

2D Filter

2D Filter

3D Fitting

3D Filter

3D Filter

– topology, mol.

– 3-point pharmacophores

– (red. graphs, FTrees, …)

– distance hashing

graphs

Ro5 / Lipinski

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Real 3D Fitting

One Solution: In Silico Screening Descriptor/Fingerprint Filter

1D Filter

1D Filter – properties – fingerprints e.g. MW 200-500

2D Filter

2D Filter

3D Fitting

3D Filter

3D Filter

– topology, mol.

– 3-point pharmacophores

– (red. graphs, FTrees, …)

– distance hashing

graphs

Ro5 / Lipinski

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Real 3D Fitting 3D Fitting - flexible - pre-computed conformers

One Solution: In Silico Screening Descriptor/Fingerprint Filter

1D Filter

1D Filter – properties – fingerprints e.g. MW 200-500

2D Filter

2D Filter

3D Fitting

3D Filter

3D Filter

– topology, mol.

– 3-point pharmacophores

– (red. graphs, FTrees, …)

– distance hashing

graphs

Ro5 / Lipinski

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Real 3D Fitting 3D Fitting - flexible - pre-computed conformers

computationally expensive

Our Aim: Predict Activity Pattern ... • Modeling of all relevant targets – responsible for drug action and side effects – build feature-based pharmacophore models • Compile all models (+ relevant info) into a database – Activity profiling of leads / drug candidates – Determination of side effects / bio-hazards • Use this system for development of novel interesting lead molecules and drug candidates Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

The Usual Virtual Screening Protocol 10x molecules against one target

results in a hit list Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Why Not Do This ?

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Why Not Do This ? 10 molecules x

against

10 targets x

... needs a large number of models ! Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

What Is A Pharmacophore ? “A pharmacophore is the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological response.”

C.-G. Wermuth et al., Pure Appl. Chem. 1998, 70: 1129-1143

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Feature-based Pharmacophore Models

Totality of universal chemical features that represent a defined binding mode of a ligand to a bio-molecular target Features: Electrostatic interactions, H-bonding, aromatic interactions, hydrophobic regions, coordination to metal ions ...

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Feature-based Pharmacophore Models

Totality of universal chemical features that represent a defined binding mode of a ligand to a bio-molecular target Features: Electrostatic interactions, H-bonding, aromatic interactions, hydrophobic regions, coordination to metal ions ...

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Feature-based Pharmacophore Models

Totality of universal chemical features that represent a defined binding mode of a ligand to a bio-molecular target Features: Electrostatic interactions, H-bonding, aromatic interactions, hydrophobic regions, coordination to metal ions ...

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Feature-based Pharmacophore Models

Totality of universal chemical features that represent a defined binding mode of a ligand to a bio-molecular target Features: Electrostatic interactions, H-bonding, aromatic interactions, hydrophobic regions, coordination to metal ions ...

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Why Use Pharmacophore Models ? • Universal

- Pharmacophore models represent chemical functions, valid not only for the currently bound, but also unknown molecules

• Computationally Efficient

- Due to their simplicity, they are suitable for large scale virtual screening (>109 compounds, also in parallel settings)

• Comprehensive & Editable

- Selectivity-tuning by adding or omitting chemical feature constraints, information can be easily traced back

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

How To Build Pharmacophore Models ? • Starting from ligand information – Exploration of conformational space – Multiple superpositioning experiments – DISCO, Catalyst, Phase, MOE, Galahad ...

• Starting from 3D target information – GRID interaction fields: Convert regions of high interaction energy into pharmacophore point locations & constraints [S. Alcaro et al., Bioinformatics 22, 1456-1463, 2006]

– Start from target-ligand complex: Convert interaction pattern into pharmacophore point locations & constraints [G. Wolber et al., J. Chem. Inf. Model. 45, 160-169, 2005] Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

How To Build Pharmacophore Models ? • Starting from ligand information – Exploration of conformational space – Multiple superpositioning experiments – DISCO, Catalyst, Phase, MOE, Galahad ...

• Starting from 3D target information – GRID interaction fields: Convert regions of high interaction energy into pharmacophore point locations & constraints [S. Alcaro et al., Bioinformatics 22, 1456-1463, 2006]

– Start from target-ligand complex: Convert interaction pattern into pharmacophore point locations & constraints [G. Wolber et al., J. Chem. Inf. Model. 45, 160-169, 2005] Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Let’s have a look ...

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Implemented Procedure 1.

Detect ligand and clean-up the binding site in the protein (all amino acids within 7Å distance from the ligand)

2.

Interpret hybridization status and bond types in the ligand

3.

Perform chemical feature recognition for the ligand (H-bond donor, H-bond acceptor, positive ionizable, negative ionizable, hydrophobic, aromatic ring, metal ion coordination)

4.

Search for corresponding chemical features of the protein

5.

Add interaction features to the model only if a corresponding feature pair is found in the complex

6.

Add excluded volume spheres for opposite hydrophobic features

G. Wolber, T. Langer: J. Chem. Inf. Model. 45 , 160-169 (2005) Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

LigandScout Graphical User Interface

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

LigandScout Graphical User Interface

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Binding Mode Specificity

One pharmacophore model accounts for one binding mode …

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Binding Mode Specificity

One pharmacophore model accounts for one binding mode …

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Binding Mode Specificity

One pharmacophore model accounts for one binding mode …

How to analyze and align these objects ? Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Alignment By Pharmacophore Points

Methotrexate

Dihydrofolate

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Alignment By Pharmacophore Points

Methotrexate

Dihydrofolate

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Alignment By Pharmacophore Points

Methotrexate

Dihydrofolate

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Alignment By Pharmacophore Points

Methotrexate

Dihydrofolate

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Alignment By Pharmacophore Points

Methotrexate

Dihydrofolate

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Alignment By Pharmacophore Points

Methotrexate

Dihydrofolate

Wrong

Correct Böhm, Klebe, Kubinyi:

Wirkstoffdesign (1999) p. 320f Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Alignment By Pharmacophore Points

1RX2

1RB3 Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Alignment By Pharmacophore Points

1RX2

1RB3 Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Alignment By Pharmacophore Points

1RX2

1RB3 Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Pharmacophoric Alignment molecule

pharmacophore

best

pairing

3D rotation (Kabsch)

Super-

position

Is pairing valid? If not, remove invalid pairs and retry

Wolber G. et al., J. Comput.-Aided Mol. Des. 20: 773-388 (2006) Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

How To Find The Best Pairs ...

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

How To Find The Best Pairs ...

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

How To Find The Best Pairs ...

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

How To Find The Best Pairs ...

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

How To Find The Best Pairs ...

Hungarian Matcher (Marrying Problem)

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

How To Find The Best Pairs ...

Hungarian Matcher (Marrying Problem) [Edmods 1965] [Kuhn 1955] [Richmond 2004]

Matching and a Polyhedron with 0-1 Vertices. J. Res. NBS 69B (1965), 125-30 [nonbipartite application] The Hungarian method for the Assignment Problem. Noval Research Quarterly, 2 (1955) [bipartite variant] Application to chemistry: N. Richmond et al. Alignment of 3D molecules using an image recognition algorithm. J. Mol. Graph. Model. 23 (2004) 199-209

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Hungarian Matching • How to define the pharmacophore feature matching cost (similarity)? – Use only few feature types – Create selectivity by defining geometric relations

=> Solution: Encode geometry in each feature!

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Typed Distance Shells

Acceptor

Donor Lipophilic Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Typed Distance Shells

Acceptor

Donor Lipophilic Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

0|0|1

Typed Distance Shells

Acceptor

Donor Lipophilic Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

0|0|1 0|1|1

Typed Distance Shells

Acceptor

Donor Lipophilic Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

0|0|1 0|1|1 0|1|1

Typed Distance Shells

Acceptor

Donor Lipophilic Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

0|0|1 0|1|1 0|1|1

Typed Distance Shells

Acceptor

Donor Lipophilic Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

0|0|1 0|1|1 0|1|1

Distance Characteristics Result: Best matching pairs for each feature

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Distance Characteristics Result: Best matching pairs for each feature

1

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

1

Distance Characteristics Result: Best matching pairs for each feature

2 2 1

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

1

Distance Characteristics Result: Best matching pairs for each feature 4

3

2

5

5

2 3 1

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

4

1

Distance Characteristics Result: Best matching pairs for each feature 4

3

2

5

5

2 3 1

4

1

Final step: 3D rotation using Kabsch algorithm Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Flexible Alignment • Generation of conformer ensemble (OMEGA 2.0) • Alignment experiment on bio-active conformation

1ke8

1rb3

1xp0

Wolber G. et al., J. Comput.-Aided Mol. Des. 20: 773-388 (2006) Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Understand Common Features ... Example: RET Kinase Inhibitors

2ivv Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

2ivu

Shared Feature Pharmacophore RET-Kinase inhibitor ZD62015, bound conformation (pdb entry 2ivu)

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Shared Feature Pharmacophore RET-Kinase inhibitor ZD62015, bound conformation (pdb entry 2ivu)

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Shared Feature Pharmacophore RET-Kinase inhibitor PP12014, bound conformation, pdb entry 2ivv

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Shared Feature Pharmacophore RET-Kinase inhibitor PP12014, bound conformation, pdb entry 2ivv

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Shared Feature Pharmacophore

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Shared Feature Pharmacophore

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Shared Feature Pharmacophore

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Merged Feature Pharmacophore

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Merged Feature Pharmacophore

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Merged Feature Pharmacophore

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Pharmacophore-based Alignment

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Ligand Profiling Case Study - 5 viral targets - 50 pharmacophore models - 100 antiviral compounds

Will their activity profiles be predicted correctly ?

Ligand Profiling: Targets Target

Disease

Function

Mechanism

HIV protease

HIV infection,

Cleavage of gag and gag-pol

Inhibition at active site

AIDS

precursor polyproteins into functional viral proteins

HIV reverse transcriptase HIV infection,

Synthesis of a virion DNA,

(RT)

integration into host DNA and

AIDS

Inhibition at allosteric site

transcription Influenza virus

Influenza

neuraminidase (NA)

Viral envelope glycoprotein,

Inhibition at active site

cleave sialic acid residues for viral release

Human rhinovirus (HRV)

Common cold

coat protein Hepatitis C virus (HCV)

Hepatitis C

RNA polymerase

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Attachment to host cell receptor,

Binding in hydrophobic pocket

viral entry, and uncoating

(capsid stabilization)

Viral replication, transcription of

Inhibition at various allosteric

genomic RNA

sites

Results Matrix

T. Steindl et al., J. Chem. Inf. Model., 46, 2146-2157 (2006) Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Ligand-directed Analysis Ratio ≥ 1 90% of the compounds correctly predicted

Ratio < 1 8% more often predicted for one specific false target than for correct one

for 2% of the compounds no activity prediction possible Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Pharmacophore-directed Analysis HIV protease

HIV RT

HIV protease

HIV RT

Influenza NA

HRV coat protein

HCV polymerase 123

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Influenza NA

HRV coat protein

HCV polymerase 1 2 3

Pharmacophore-directed Analysis HIV protease

HIV RT

HIV protease

HIV RT

Influenza NA

HRV coat protein

HCV polymerase 123

Model with lowest selectivity: 70% of actives (HIV RT), but 75% from one specific false target (HRV coat protein) 40% active and 60% inactive compounds in hit list Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Influenza NA

HRV coat protein

HCV polymerase 1 2 3

Pharmacophore-directed Analysis HIV protease

HIV RT

Influenza NA

HRV coat protein

HCV polymerase 1 2 3

HIV protease

HIV RT

Influenza NA

HRV coat protein

HCV polymerase 123

Model with lowest selectivity: 70% of actives (HIV RT), but 75% from one specific false target (HRV coat protein) 40% active and 60% inactive compounds in hit list Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Model with highest selectivity: 100% of actives (HCV polymerase 1), 100% active and 0% inactive compounds in hit list

Pharmacophore-directed Analysis HIV protease

HIV RT

Influenza NA

HRV coat protein

HCV polymerase 1 2 3

HIV protease

HIV RT

Influenza NA

HRV coat protein

HCV polymerase 123

Model with lowest selectivity: 70% of actives (HIV RT), but 75% from one specific false target (HRV coat protein) 40% active and 60% inactive compounds in hit list Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Model with 85% hit rate

Model with highest selectivity: 100% of actives (HCV polymerase 1), 100% active and 0% inactive compounds in hit list

ADME: Performance Analysis Retrieve many known substrates / inhibitors from training and test set Focus: are highly active inhibitors found?

IN ANTI-TARGET SCREENING (ADME/TOX): It is most important to find the „trouble-makers“! „false positive“ hits are expected „false negatives“ should be avoided

Cyp P450 Model Performance P450

Testset Substrates

Testset Inhibitors

WDI Substrates / Inhibitors

1A2

67%

100%

35% / 40%

2C9

71%

90%

35% / 39%

2C19

77%

86%

54% / 14%

2D6

65%

76%

25% / 41%

3A4

86%

80%

44% / 37%

Cyp P450 Performance Analysis Prediction Color Codes

correct no data or not applicable

false negative false positive

Results Matrix Compound

1A2-S

1A2-L

2C9-S

2C9-L

2C19-S

2C19-L

2D6-S

2D6-L

3A4-S

3A4-L

resc.

clozapine

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

+/+

naproxen

+/+

n.d.

+/+

n.d.

-/+

n.d.

-/+

n.d.

-/-

n.d.

-/-

miconazole

n.d.

+/+

n.d.

+/+

n.d.

+/+

n.d.

+/+

n.d.

+/+

-/-

citalopram

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

+/+

mirtazapine

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

+/+

delavirdine

-/-

-/+

-/+

+/+

-/+

+/+

+/+

-/+

+/+

+/+

-/-

aprepitant

-/+

+/-

-/-

-/+

+/+

+/+

-/+

-/+

+/+

+/+

-/-

efavirenz

+/+

+/+

-/-

+/+

-/+

+/-

-/-

+/+

+/-

+/-

-/-

estazolam

-/+

n.d.

-/-

n.d.

-/+

n.d.

-/-

n.d.

+/+

n.d.

-/-

fluconazole

n.d.

-/+

n.d.

+/+

n.d.

+/-

n.d.

-/-

n.d.

+/-

-/-

itraconazole

n.d.

-/-

n.d.

-/-

n.d.

-/-

n.d.

-/-

n.d.

+/+

-/-

nevirapine

-/+

-/+

-/-

-/+

-/+

-/-

+/+

-/+

+/-

-/-

-/-

oxatomide

-/-

-/+

-/-

-/-

-/+

-/-

+/+

+/+

+/-

+/-

-/-

SCH-351125

-/-

n.d.

+/+

n.d.

-/-

n.d.

-/+

n.d.

+/+

n.d.

-/-

TR-14035

-/-

n.d.

+/+

n.d.

-/+

n.d.

-/-

n.d.

-/+

n.d.

-/-

voriconazole

-/+

-/+

+/+

+/+

+/+

+/+

-/-

-/+

+/+

+/-

-/-

ziprasidone

-/+

-/+

-/-

-/+

-/+

-/+

-/+

+/+

+/+

+/+

-/-

Schuster D. et al., Curr. Drug Discov. Technol. 2006; 3: 1-48

Cyp P450 Performance Analysis 122 predictions

-/-

+/+ -/+

67%

+/-

27% 8%

Cyp P450 Performance Analysis 122 predictions

-/-

+/+ -/+

67%

+/-

27% 8%

6% P450 3A4

Some Special Considerations •e.g. ketoconazole / Cyp P450 3A4 (pdb 2v0m)

Some Special Considerations •e.g. ketoconazole / Cyp P450 3A4 (pdb 2v0m)

Development of special Fe-binding pharmacophore feature in LigandScout 2.0

Analysis of PDB •Distance Fe - Ligand

Iron-Ligand distance plot (0 to 4.3 Å in 0,1 Å steps)

Analysis of PDB •Distance Fe - Ligand

Iron-Ligand distance plot (0 to 4.3 Å in 0,1 Å steps)

All ligands between 1,4 and 3,5 Å -> 1359 PDB-entries

New Metal-binding Features, e.g. Fe

heme-ligands

non-heme-ligands

Hannes Wallnöfer, Diploma Thesis, University of Innsbruck, 2007

New Feature Boosts Enrichment ... Hypothesis

Nonsteroidal with restrictive Fe-Feature

Nonsteroidal with HBAFeature

Training set (5)

Test set (41)

WDI

Maybridge

NCI

Specs

Virtual

(67050)

(59652)

(123219)

(216823)

DB

3

23

19

40

48

100

25

(60%)

(56%)

(0,028%)

(0,067%)

(0,039%)

(0,046%)

(0,2%)

3

22

216

442

983

770

213

(60%)

(54%)

(0,32%)

(0,74%)

(0,8%)

(0,36%)

(1,67%)

Enrichment

149,78

29,26

New Feature Boosts Enrichment ... Hypothesis

Steroidal with new Fe-Feature

Steroidal with HBA-Feature

Training

Test

Set

Set

(4)

(70)

4

WDI

Maybridge

NCI

Specs

Virtual

(67050)

(59652)

(123219)

(216823)

DB

37

15

0

2

1

0

(100%)

(53%)

(0,02%)

(0%)

(0,002%)

(0,0005%)

(0%)

4

14

261

4

156

25

0

(100%)

(20%)

(0,38%)

(0,007%)

(0,13%)

(0,012%)

(0%)

Underlying Screening Platform

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Underlying Screening Platform PipelinePilot Script & CatalystTM DB Search

K. Chuang J. Benedict N. Triballeau-Hugounencq Rémy D. Hoffmann Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Web Based Parallel Screening

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Web Based Parallel Screening

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

How Can This Information Be Used ? • Pharmacophores only give geometric fit values • Don’t forget about other parameters: – solvation / entropy – kinetic parameters – conformational strain energy …

• Pharmacophores are excellent filter tools for rapid pre-screening of large compound libraries Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Summary ... First published examples of applications of extensive parallel screening approach based on pharmacophores • Multitude of pharmacophore models (up to several thousand …) • Large set of molecules (up to several million …)

Results indicate • Correct assignment of selectivity in most cases • Independent of search algorithms used

Fast, scalable in silico activity profiling is now possible !

Summary ... First published examples of applications of extensive parallel screening approach based on pharmacophores • Multitude of pharmacophore models (up to several thousand …) • Large set of molecules (up to several million …)

Results indicate • Correct assignment of selectivity in most cases • Independent of search algorithms used

Fast, scalable in silico activity profiling is now possible !

Inte:Ligand’s Pharmacophore Database ~ 300 unique targets ready to use* • Represented in ~ 200 ligand-based pharmacophore models ~ 2300 structure-based pharmacophore models • Covering a selection of all major therapeutic classes • Contains anti-target models for finding adverse effects • Categorized according to the pharmacological target * out of ~650 categorized by June 2008 Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Conclusions • SOSA together with parallel pharmacophore-based virtual screening is a straightforward and rapid method for the generation of new lead compounds • Combined with informatics-based molecular building tools, optimized design of novel and promising compounds will become feasible • Assessment of risks in later development stages becomes possible on a rational & transparent basis

Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Pharmacophores can also be a passion ...

Pharmacophores can also be a passion ...

Acknowledgements Markus Böhler Oliver Funk

Johannes Kirchmair Eva Maria Krovat

Daniela Ladstätter

Christian Laggner*

Rémy D. Hoffmann*

Claudia Schieferer

Nicolas Triballeau-Hugounencq*

Theodora Steindl*

Fabian Bendix

Patrick Markt

Konstantin Poptodorov

Daniela Schuster

Kareen Chuang Martin Biely

Alois Dornhofer Robert Kosara

Thua Huynh Buu Thi Hoang Thomas Seidel

Gerhard Wolber* Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008

Thank you for your attention ...

www.prestwickchemical.com www.inteligand.com Summer School on Drug Design and Molecular Modeling Istanbul, September 10-14, 2008