Copyright © 2006 Turquoise Consulting. All Rights Reserved Pharmacophore Perception and Use in...

Copyright © 2006 Turquoise Consulting. All Rights Reserved

Pharmacophore Perception and Use in Computer-Aided Drug Design

Osman F. Güner, Ph.D.

Copyright © 2006 Turquoise Consulting. All Rights Reserved2

Outline

Pharmacophores: definition Examples: Cardiotonic drugs ACE Inhibitors

Function vs. Topological Query Concept (Angiotensin II blockers)Pharmacophore model and hit list analysisPeptidomimetic design strategy (HIV-2 inhibitors, endothelin)Training set selection (PDE IV inhibitors)Model refinement - Clustering and merging (5HT3 inhibitors)Predictive models – (Antimalarial agents, FPT inhibitors)Receptor-based pharmacophores - Dealing with multiple-binding modes (DHFR inhibitors, HIV-1 Protease inhibitors)Bibliography Reviews and published success stories


Pharmacophore, Definition


Pharmacophore – Value!

“Perceiving a pharmacophore is the most important first step towards understanding the interactions between a receptor and a ligand.” From the Preface of “Pharmacophore Perception, Development,

and Use in Drug Design,” Güner, O.F. ed., International University Line, 2000, La Jolla, p xv.

With a pharmacophore model, you can: Search databases to retrieve compounds that match the model Design and enhance compounds to better fit the models Align molecules that match a common pharmacophore framework Develop a sense of the significant receptor-ligand interactions Understand the different binding mechanisms Develop predictive (3D-QSAR) models


Example: Cardiotonic Drugs

Observe the following four drugs


Cardiotonics Pharmacophore Model

Two ring systems: aromatic (phenyl or pyridyl); 5-6 membered lactam with possible nitrogen and double bonds in the ring; with distance and planarity constraints

from: Güner, O. F. “Manual Pharmacophore Generation: Visual Pattern Recognition,” in Pharmacophore Perception and Development for Drug Design, 2000, 17-20


Cardiotonic Hit from MDDR-3D

One of the active compounds retrieved


Semi-Automatic ProcessesActive analog approach

Mayer, D.; Naylor, C. B.; Motoc, I.; Marshall, G. R. “A unique geometry of the active site of angiotensin-converting enzyme consistent with structure-activity studies,” J. Comput.-Aided Mol. Des. 1987, 1(1), 3-16.

Pharmacophore model from: Haraki, K. S.; Sheridan, R.P.; Venkataraghavan, R.; Dunn, D.A.; McCulloch, R. Tetrahedron Comp. Meth. 1990, 6C, 565-573


ACE Inhibitor Hit from Derwent’s World Drug Index

Captopril retrieved with a conformation that maps onto the query


Outline


Function vs. Topological Query Concept (Angiotensin II blockers)Pharmacophore model and hit list analysisPeptidomimetic design strategy (HIV-2 inhibitors , endothelin)Training set selection (PDE IV inhibitors)Model refinement - Clustering and merging (5HT3 inhibitors)Predictive models – (Antimalarial agents, FPT inhibitors)Receptor-based pharmacophores - Dealing with multiple-binding modes (DHFR inhibitors, HIV-1 Protease inhibitors)Bibliography Reviews and published success stories


Function vs. Topological Query

Chemical function-based queries represent the 3D arrangement of the biologically relevant functions. The search is more “exhaustive” in nature It attempts to minimize “false negatives” in the hit list,

compromising the selectivity Hit list contains compounds with high diversity

Structural topology-based queries represent 3D arrangement of the functional groups The search is more “suggestive” in nature It attempts to increase the selectivity in the hit list,

minimizing “false positives” Hit list contains compounds with high topological similarity


Structural Topology-based 3D Query

Pharmacophore model for angiotensin II blockers (developed by Erich Vorpagel via Apex-3D)


Hit Retrieved from Derwent WDI

Zolasartan, an angiotensin antagonist


Chemical Function-based 3D Query

Hypothesis for angiotensin II blockers by Peter Sprague (from Catalyst tutorial)


Hit Retrieved from Derwent WDI

Zolasartan, an angiotensin antagonist


In Derwent’s WDI, there are 448 compounds listed as “angiotensin-antagonists”

Function-based query retrieves higher percentage of known active compounds;Topology-based query retrieves hit lists with higher yield of known actives

Function- vs Topology-based Results

Query Type

No. of Hits

No. of Actives

% Yield

% Actives

GH-Score

Function Query

1,403 165 11.8 36.8 0.162

Topology Query

30 29 96.7 6.5 0.741


Outline




Database Domain

Schematic representation of a typical database and a hit list that contains some known active compounds

Database D

Actives AHa

Hits Ht


Analyzing Hit Lists

Different metrics can be used to evaluate the quality of a hit list Enrichment (E): indicates how many time more richer the hit list is

than the original database with respect to the yield of actives

where Ht is the total number of compounds and Ha is the number of know actives in the hit list, A is the active compounds in the database, and D is the number of compounds in the database.

AH

DH

DAH

H

Et

at

a


Analyzing Hit Lists

Selectivity (%Y) the percentage of known actives in the hit list

Coverage (%A) the percentage of known active compounds retrieved from the database

100% t

a

H

HY

100% A

HA a


GH-Score

2

21

A

Hw

H

Hw

GH

a

t

a

t

ta

AH

HwAwHGH

221


GH-Score

with w1 = 1.5 and w2 = 0.5, and correction for false positives

From: Güner, O. F. and Henry, D. R. “Metric for Analyzing Hit Lists and Pharmacophores,” in Pharmacophore Perception, Development, and

Use in Drug Design, IUL Biotechnology Series, 2000, La Jolla, 195-211.

2

21

A

Hw

H

Hw

GH

a

t

a

t

ta

AH

HwAwHGH

221

AD

HH

AH

HAHGH at

t

ta 14

3


Ht = D – AHa = 0

Testing the Functions Against the Best and the Worst Hit Lists

The “Best” Hit List

D D

A AA = Ha = Ht

The “Worst” Hit List


Functions Perform Evenly Against Typical Cases

where D = 50,000, A = 100Good hit list is 80 active compounds in a hit list of 200

Bad hit list is 100 active compounds in a list of 50,000

Case Enrichment %Y %A GH-ScoreBest 500 100 100 1Good 200 40 80 0.5Bad 25 5 50 0.2

Worst 0 0 0 0


Against Extreme Cases?

where D = 50,000, A = 100Good hit list is 80 active compounds in a hit list of 200

Bad hit list is 100 active compounds in a list of 50,000

ExtemeY is a single hit that is active

ExtremeA retrieves all actives together with the rest of the database

Case Enrichment %Y %A GH-ScoreBest 500 100 100 1

ExtremeY 500 100 1 0.75Good 200 40 80 0.5Bad 25 5 50 0.2

ExtremeA 1 0.2 100 0Worst 0 0 0 0


Published Successes with GH-Score Raymond, J.W. and Willett, P., “Effectiveness of graph-based and

fingerprint-based similarity measures for virtual screening of 2D chemical structure databases,” J. Comp.-Aided Molec. Des., 2002, 16(1), 59-71.

“They found that the cumulative recall and the “Goodness of Hit List” or Güner-Henry (GH) score were among the most successful of those tested for measuring the effectiveness of similarity retrieval.”

Klinger, S.; Austin, J., “Chemical Similarity Searching Using a Neural Graph Matcher,” ESANN’2005 proceedings – European Symposium on Artificial Neural Networks, Bruges (Belgium), 27-29 April 2005, 479-484.

“The position in the ranking corresponding to the maximum GH score is used as the cut-off point and subsequent structures in the list are removed from further consideration.”

Chang C.; Bahadduri, P.M.; Polli, J.E.; Swaan, P.W.; Ekins, C., “Rapid Identification of P-glycoprotein Substrates and Inhibitors,” Drug Met. and Disp. 2006, 34(12), pp 1976-1984

“Specifically, the implementation of the GH score here can be used as a determinant of the effectiveness of the model in retrieving true and false-positive …”

Cai, W. , Xu., J.; Shao, X.; Leroux, V.; Beautrait, A.; Maigret, B., “SHEF: a vHTS geometrical filter using coefficients of spherical harmonic molecular surfaces,” J. Mol. Model. 2008, 14(5), 393-401.

“The important measurement to indicate the effectiveness of the filtering is the Güner-Henry score (GH), which suggests that the performance of SHEF is …”


Outline




Vector-based 3D Searching

Objective is to replace a flexible backbone of a peptide with a rigid frame; then synthetically attach the functional groups

Lauri, G.; Bartlett, P.A. J. Comput.-Aided Molec. Des. 1994, 8, 51-66.

Example: a peptide in its bound conformation to HIV-2 protease (from Brookhaven PDB 2phv)


Development of vector query

1 Identify the C bonds that you would like to exchange with exocyclic bonds

2 Connect the C atoms with distance constraints

3 Delete the rest of the molecule

4 Connect the C atoms with distance constraints C = can be part of an

aromatic or aliphatic ring C = any atom not on a ring


A Hit from ACD

8-Bromoadenosine (CAS No. 2946-39-6), a commercially available chemical

The final step is to synthetically modify the compound to attach the desired functional groups to the identified exocyclic bonds


Search for Peptidomimetic Endothelin AntagonistsCyclic peptide endothelin antagonist, and a hit

from ACD retrieved by a vector-based search

Güner, O. F.; Hempel, J. C.; Lie G. C. in The Collection of Theses, China Int. Symposium on Biotechnology and Pharmaceutical Industry, 1996, 545-547.


Synthetic Process

A synthetic transformation is needed to attach the desired amino acid functional groups to the vector tips from the original query


From Peptide to Peptidomimetic

Note the excellent overlay of the important functional groups at the surface


Outline




Significance of Training Set Selection

Diverse set of patented phosphodiesterase IV inhibitors selected based on cluster analysis of topological descriptors

Similar set is selected based on the compounds most similar to rolipram


Nine Most Diverse PDE IV Inhibitors


Nine Most Similar PDE IV Inhibitors


Top Scoring Hypotheses

On the left is the top hypothesis obtained from the diverse training set; on the right is the one from the similar set


Rolipram Mapped to the Hypotheses


Diverse vs Similar Training Set Search Results

Comparison of the results obtained from searching with the top scoring hypotheses from diverse and similar training sets

from: Güner, O. F.; Waldman, M.; Hoffmann, R.; Kim, J.-H. “Strategies for Database Mining and Pharmacophore Development,” in Pharmacophore Perception and Development for Drug Design, 2000, 213-231

Query # Actives(Ha)

# Hits(Ht)

%Y %A Enrichment(E)

GH score

Database 207 10,318 2.01 100.0 1.0 0

Diverse 73 1,589 4.59 35.3 2.3 0.105Similar 51 986 5.17 24.6 2.6 0.091


Outline




Use of Query Clustering and Merging

Two strategies can be applied for query merging to improve the ratio of known active compounds in the hit list

- increase selectivity to maximize the number of active compounds in the hit list -

increase coverage

Use query clustering to identify similar and diverse models

Scenario: Hypotheses generated using a topologically diverse set of

23 5-HT3 with an activity range of 0.2 to 1,400 nm. The top hypothesis has an r correlation of 0.8275 with respect to predicted vs actual activities


Clustering the Hypotheses

Cluster analysis results for the top ten 5-HT3 hypotheses

Hierarchical “average linkage” clustering

Number of Clusters 2 3 4 5 6 7 8 9

5HT3.1 1 1 1 1 1 1 1 15HT3.2 2 2 2 2 2 2 2 25HT3.6 2 2 2 2 2 2 2 35HT3.9 2 2 2 2 2 2 2 35HT3.5 2 2 3 3 3 3 3 45HT3.3 2 3 4 4 4 4 4 55HT3.7 2 3 4 4 4 5 5 65HT3.10 2 3 4 4 4 5 6 75HT3.4 2 3 4 5 5 6 7 85HT3.8 2 3 4 5 6 7 8 9


Hypotheses Considered for Merging

5HT3.1 on the left and 5HT3.5 on the right


New Merged Hypothesis

5HT3.1 and 5HT3.5 were merged by using 1.2 Å tolerance


Merging Similar Hypotheses

HT3.6 and HT3.9 aligned before merger on the left, and following merger on the right


Search Results with Merged Queries

Comparison of the results with 5HT3.1 and the two merged hypotheses


Query # Actives(Ha)

# Hits(Ht)

%Y %A Enrichment(E)

GH score

Database 225 10,318 2.18 100.0 1.0 0

HT3.1BEST

64 1,889 3.39 28.4 1.6 0.079

Merged(1&5)

53 1,667 3.18 23.6 1.5 0.070

Merged(6&9)

174 3,772 4.61 77.3 2.1 0.147


Merged Queries Conclusions 5HT3.1 represents the activities

of the training set well (r2=0.8275 vs for the merged[6&9] query, r2=0.6088), but not necessarily accommodate the diverse sets of active compounds in the entire database

The merged[6&9] query has a much better coverage but also surprisingly retrieved a list with improved selectivity as well

On the right, a known 5-HT3 antagonists patented by Eli Lilly is displayed. Note how well the features of the compounds maps on the the merged[6&9] query.


Outline




Use of Variable Weights in Predictive Models

Taken from the literature (M. Grigorov, et al., J. Chem. Inf. Comput. Sci., 1997, 37, 124).

Data (synthetic 1,2,4-trioxanes) artemisinin yingzhaosu

IC90 values against the Plasmodium falciparum in vitro. Ranging from 0.4 - 1184. (16 training set, 4 prediction set)

Pharmacophores developed to estimate Antimalarial Activity and to mine for new leads.


Pharmacophore (Constant Weights)

Antimalerial agents

Three feature pharmacophore: Hydrogen Bond Acceptor 2 Hydrophobic groups

Correlation: 0.88

Each Weight=2.49


Pharmacophore (Variable Weights)

Weight 3.15

Weight 3.15

Weight 3.15

Weight 2.36

Antimalerial agents

Four feature pharmacophore: 2 Hydrogen Bond

Acceptors 2 Hydrophobic groups.

Correlation: 0.95


Predicting the Test Set

RActualActivity

PredictedActivity

(Cons. Wts)

PredictedActivity(Var. Wts)

H inactive 290 1200

Me 1135 260 1300

Ph 568 260 110

Bu 30 200 37


Predictive Pharmacophore Model Development Example: Farnesyl Protein Transferase

Primary therapeutic target in cancer research

This enzyme farnesylates cysteine-186 on RAS-encoded proteins renders protein lipophilic enough to associate with cell

membranes critical step for the expression of cell transforming activity

responsible for unregulated cell grown found in several carcinomas

Two binding domains, one for farnesyl group and so-called CaaX box


Available SAR Data

Tetrapeptide structures (2D) with associated activity data

“What compounds should I make next?”

CompoundActivity (µM)CVFM 0.01CLIM 0.08CVIM 0.15CVLM 0.2

N-AcCVIM 0.25CCVQ 0.35CKIM 0.7CGIM 3CVIA 4CVIP 5

CompoundActivity (µM)CVAM 6CPIM 7CVIL 10.5

CVGM 20CVIE 40

CVEM 70CAIL 100CVIG 100

S-AcmCVIM 1000CVKM 1000


Initial Pharmacophore Model from Automated Process

CVFM maps all the features of the lowest cost hypothesis (activity = 10 nM, estimated activity = 32 nM)

Hydrophobic

Hydrophobic

NegativeIonizable

Acceptor

Acceptor


Which Groups are Not Needed?

L-731,735 (IC50 = 18 nM) is as active in vitro as CVFM

Hydrophobic

Hydrophobic

NegativeIonizable

Acceptor

Acceptor


Do We Need to Accommodate Additional Sites?

3-S diastereomer predicted more active, but distal phenyls are not described by the hypothesis model


Or Adjust Constraints?

Doubling the tolerance permits 3AMBA-M to map all features, aligning estimated activity with experimental value


FTP Inhibitors Research(from James Kaminski, Schering-Plough)

In vivo pharmacologic profile of “tricyclic” farnesyl transferase (FPT) inhibitors optimized via a systematic SAR study directed by chemical synthesis

N

ClBr

BrH

N

O N

O

NH2

Sch 66336 R - (+) - enantiomer in vitro FPT IC50 = 0.002 µM (2 nM)

Clinical candidate - Phase III

N

Cl

N

O

N

Sch 44342in vitro FPT IC50 = 0.25 µM (250 nM)


Top Scoring FTP Hypothesis

RMS = 0.84


Hypothesis Explains Observations Related to Stereochemistry

in vitro FPT IC50 (µM)

Experimental R - (+) : 0.49

Estimated (R - isomer) : 0.65N

NNO

ClN

R

N

NNO

ClN

S


Experimental S - (-) : 0.14

Estimated (S - isomer) : 0.014


New Classes of Active Compounds Identified

Class of “azole” antifungal agents identified that exhibits in vitro FPT inhibition, IC50 5 µM

S

O

NN

Cl

N

O

OCl

S

O(CH3)3C

NN

NN

S NNCOCH3

in vitro FPT IC50 = 4.8 µM

FPT Ras / TCA IC50 = 5.3 µM

GGPT IC50 > 39 µM

Selectivity > 8

in vitro FPT IC50 = 2.5 µM

FPT Ras / TCA IC50 = 3.1 µM

GGPT IC50 > 1.4 µM

Selectivity > 0.5


Azole Lead Mapped onto the Hypothesis

cis isomer


Experimental : 4.8(cis isomer)Estimated : 3.8(cis isomer)

S

CH3

CH3CH3

ON NN

N

SNN

O

CH3


Selective Cis-trans Isomerism

trans isomer


Experimental : 4.8(cis isomer)

Estimated : 0.18(trans isomer)

S

CH3

CH3CH3

ON NN

N

SNN

O

CH3


Most Active Analog


Experimental : 0.2Estimated : 0.9

S

O

N

N

ClS


Non Selectivity in Cis-trans Isomerism

cis - isomer (±) - enantiomersin vitro FPT IC50 = 0.45 µM

S

O

NN

CH3

ON

Cl

(+) - enantiomer , []D = +30.3 ° in vitro FPT IC50 = 0.5 µM

S

O

NN

CH3

ON

Cl

(-) - enantiomer , []D = -30.1 ° in vitro FPT IC50 = 0.4 µM

S

O

NN

CH3

ON

Cl


Explained Well with the Hypothesis

in vitro FPT IC50 = 0.5 µM in vitro FPT IC50 = 0.4 µM

S

O

N

N

CH3ON

Cl

R

R

S

O NN

CH3ON

Cl

S S


This Work Described in Publications

Kaminski, J. J.; Rane, D. F.; Snow, M. E.; Weber, L. Rothovsky, M. L.; Anderson, S.; Lin, S.L., “Identification of novel farnesyl protein transferase inhibitors using three-dimensional database searching methods,” J. Med. Chem. 1997, 40, 4103-4112.

Kaminski, J. J.; Rane, D. F.; Rothovsky, M. L., “Database mining using pharmacophore models to discover novel structural prototypes,” in Pharmacophore Perception, Development, and Use in Drug Design, Güner, O. F. Ed., IUL Biotechnology Series, 2000, La Jolla, pp 251-267.

4-5 Years Later !!!


Crystal Structure of FTP Became Available

FPT - and - Subunits Interact to Form a Large Active Site Cavity


How Does the Tri-cyclic Lead Dock?


N

Cl

N

O

N


Bound vs Predicted Conformation

X - Ray Conformation

Catalyst Conformation


Matching the Conformations to Pharmacophore

X - Ray Conformation

Catalyst Conformation


Functional Water Interference



Functional Water Interaction is Critical!


in vitro FPT IC50 = 16 µM (16,000 nM)

N

Cl

N

O

N

N

Cl

NN

O

Phe 360


How Does the Azole Lead Dock?

SO

N

N

CH3

O

N

Cl

R

R

(+) - enantiomerin vitro FPT IC50 = 0.5 µM


Different Binding Modes!!!

N

Cl

NO

N S

O

N

N

CH3ON

Cl

R

R


Inside the Active Site

N

Cl

N

O

N


(+) - enantiomerin vitro FPT IC50 = 0.5 µM

SO

N

N

CH3

O

N

Cl

R

R


Outline




Receptor-Based Pharmacophores

The active site of DHFR-methotrexate complex (PDB 4dfr)


Ligand-based Query

Pharmacophore based on the bound conformation of methotrexate and the features involved in binding


Merged Shape/Pharmacophore Query


Shape vs Pharmacophore vs Merged Query Results

Comparison of results with shape, Pharmacophore, and merged query


Query # Actives(Ha)

# Hits(Ht)

%Y %A Enrichment (E) GH score

Database 80 10,318 0.78 100.0 1.0 0

Shape 13 2,244 0.58 16.3 0.8 0.035Pharmacophore 23 1,144 2.01 28.8 2.6 0.077Merged 4 20 20.00 5.0 25.8 0.163


Hits and Leads

On the left, folate retrieved as a false positive since it was not listed as folate antagonists

On the right is a commercially available chemical retrieved from ACD


TGF- Inhibitor from Biogen-Idec

Using the X-ray structure of a weak inhibitor of TRI, they developed a combined shape and pharmacophore model. They then built a multi-conformational database of commercially available compounds and screened the database with the pharmacophore+shape model. They retrieved a highly potent inhibitor from this screening

200,000 compounds

87 compounds

Drug Target (eg. TRI)

N

NHN

N

IC50 27nM


Same Compound Discovered by Eli Lilly via Experimental HTS Methods

Eli Lilly Work published at J. Med. Chem. (December 2003)

Biogen-Idec work published at Bioorg. Med. Chem. Lett. (December 2003)

The story of two simultaneous discoveries created additional press coverage Nature Rev. Drug. Disc. (December 2003) Bio-It World (February 2004)

Finally Biogen and Lilly scientists jointly publish the story Singh et el., Curr. Opin. Drug Disc. Dev. 2004, 7(4), 437-445


Outline




Pharmacophore Review Articles

Gund, P. “Three-dimensional Pharmacophoric Pattern Searching,” in Progress in Molecular and Subcellular Biology, vol. 5, Hahn, F. E. Ed.; Springer-Verlag, 1979, Berlin, pp. 117-143.

Gund, P. “Pharmacophoric Pattern Searching and Receptor Mapping,” Ann. Reports Med. Chem. 1979, 14, 299-308.

Humblet, C. and Marshall, G. R. “Pharmacophore Identification of Receptor Mapping,” Ann. Reports Med. Chem. 1980, 15, 267-276.

Kurogi, Y. and Güner, O. F. “Pharmacophore Modeling and Three-dimensional Database Searching for Drug Design Using Catalyst,” Curr. Med. Chem. 2001, 8, 1035-1055.

Güner, O. F. “History and Evolution of the Pharmacophore Concept in Computer-Aided Drug Design,” Curr. Top. Med. Chem., 2002, 2, 1321-1332.


Published Successes with Pharmacophore-based 3D Searching

"… this hitlist identified five compounds representing three structurally novel classes, that exhibited in vitro FTP inhibitory activity (IC50 5 M)."

J. J. Kaminski, et al. J. Med. Chem., 1997, 40, 4103

"… the excluded volumes reduced the size of the hit list… The single remaining compound was subsequently shown to bind to THR-a with an IC50 of 69 M."

P. A. Greenidge, et al. J. Med. Chem. 1998, 41, 2503.

Pharmacophores selectively separating sub-type antagonism Bremner, J.B. et al., Bioorg. Med. Chem. 2000, 8, 201-214

Predictive metabolism with pharmacophores Ekins S. et al., J. Pharm. Exp. Therap., 1999, 290(1), 429-438

Predictive model from diverse structures Tronchet, J.M.J. et al., Eur. J. Med. Chem., 1997, 32, 279-299

Common pharmacophore for two different classes Liao, N. et al., Chin. Chem. Lett., 1999, 10(0), 755-758

Dynamic receptor-based pharmacophores Carlson, H.A. et al., J. Med. Chem. 2000, 43, 2100-2114


Hypoglycemic Agents

Glu, Ins and TG in Dex-induced rat at 100 mg/kg poY.Kurogi, J.Synth.Org.Chem., 2000, 676.Y.Kurogi, O.Güner, Curr. Med. Chem., 2001, 1035-1055.

One Hit (1/6)Novel CompoundLow MW (274)

OT-5226OT-5226Glu: 47 %Ins : 37 %TG : 34 %

RF05274RF05274Glu: 41 %Ins : 11 %TG : 41 %

CatalystTM

N

N

Cl

O

P

O

OO

NOF3C

NH

O O


MC Proliferation Inhibitors (MCPI)

High hit ratio (100 %)Structural diversityNovel MCPIs

OP

NH

N O

Cl

O

O O

N

O

HN

HN

OO

O

O

Cl

Cl

Cl

CatalystTM

MCPI = 69 %a MCPI = 90 %a

a% inhibition at 100 nMMCPI:Mesangial Cell Proliferation InhibitionNCPI:Normal Cell Proliferation Inhibition

Increases Safety

NCPI = 30 %a NCPI = 0 %a

Y.Kurogi, et. al., J. Med. Chem., 2001: 44, 2304-2307


Novel Fragrance Development through Olfactophores

Looking for “odoriferous compounds,” sense of smell via olfactory lobes olfactophores Scientists at Givaudan Roure have used Catalyst to design

fragrances

O

O

OR

R'Ambrox … to … New “ambery” odorant

Baigrowitz et al., Enantiomer, 2000, 5, 225-

234

Kraft, P. et al., Angew. Chem. Int. Ed. Engl.

2000, 39, 2980-3010


Pharmacophores in Patents

WO 98/04913 Filed in 24 July 1997 by Juswinger Singh and co-workers,

this was an application by Biogen for pharmacophore model for VLA-4 inhibitors

WO 98/46630 Filed in 16 April 1998 by Terance Hart and co-workers, this

was an application by Peptide Therapeutics Limited for pharmacophore model for Hepatitis C NS3 Protease Inhibitors.

US 2002/0013372 Filed on March 12, 2001 by Sean Ekins, this is an

application by Pfizer Inc. for identification of CYP2D6 inhibitors.


Acknowledgments

James Kaminski – Schering-Plough

Luke Fisher - Accelrys

Copyright © 2006 Turquoise Consulting. All Rights Reserved Pharmacophore Perception and Use in...

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