Rational Drug Design by Bernd Wendt-EMBL

8/14/2019 Rational Drug Design by Bernd Wendt-EMBL

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Rational Drug Design

Bernd Wendt

EMBL, Heidelberg



• Small molecule• 3D model

• Ligand• “Key”• Aspirin

• Large molecule• X-ray structure• Protein• Target

• “Lock”• COX-1

• surface representation

This is not

the keyhole!

Large and small molecules



Large and small molecules

• Look inside the protein• Display of the protein fold• Active site in green dots• Aspirin

• Heme-group

• ribbon representation

This is the

keyhole!



From serendipity to rational drug design: Aspirin

• 400 BC: Hippocrates: extracts from willow tree (containing salicylic acid) for painrelieve

• 1890’s: synthesis of acetylsalicylic acid (Aspirin)– improvement on side effects(irritation of stomach)

• 1970’s: Aspirin target identified as cyclo-oxygenase (COX)• 1980’s: Two forms of COX identified:

• COX-1, constitutive

• COX-2, induced at sites of inflammation

• Mid 1990’s: X-ray structures of COX-1 and COX-2 available• Rational design of selective COX-2 inhibitors

• 1998/99: Celebrex and Vioxx approved by FDA for osteoarthritis and rheumatoidarthritis

• Mid 2000’s: Randomized placebo-controlled trial showed an increased risk of heartattack and stroke

• Vioxx withdrawn from market

• Celebrex with warning label



A detailed look at Aspirin and its target

COX-1 with natural substrate (arachidonic acid) COX-1 inhibited by Aspirin

COX-1 with non-selective inhibitor (fluorbiprofen) COX-2 with selective inhibitor



Drug Discovery Process

• Duration: 12-15 years

• Costs: 500-800 Million $

TargetIdentification

TargetValidation

LeadIdentification

LeadOptimization

ClinicalPhases I-III

MarketLaunch

Biology Chemistry Develop-ment



Rational Approaches: Biological Universe

• Biological Space (large molecules)

• Number of predicted genes:

• ~30.000

• Number of proteins in proteome:

• 21,688

• Number of estimated druggabletargets:

• 3,051

• Number of targets of currentdrugs:

• 400-500

42%

16%

16%

10%

7%5%2%2% other

Proteases

Protein kinase

GPCR

Ion channelsTransporters

Cytochrom P450

NHR

The druggable genome



Rational Approaches: Chemical Universe

• Chemical Space (small molecules)

• Estimated number of possible drug-like molecules:

• 1060

• Number of synthesized molecules:• 107

• Typical size of corporate compound collection:

• 105

to 107

• Parallel/Combinatorial Chemistry

• Linking of chemical components A*B*C

• Library sizes of 102 to 104

• Virtual Chemistry

• Size of AllChem database: 1020



Rational approaches: Filtering on properties

• Prescreening of chemicals for biological relevance and‘druglikeness’• Use computer-calculated properties for in silico screening

• Lipinski’s rule of ‘5’:• Poor absorption or permeation more likely when:

• There are more than 5 H-bond donors

• The MWT is over 500

• MLOGP is over 4.15 ( or CLOGP is over 5)

• The sum of N‘s and O‘s is over 10

• Further properties to consider• Not more than 10 rotatable bonds• Limited polar surface area



Rational Approaches: Docking

• Use binding pocket from X-ray structure

• Define required interactions:

• H-bonds

• Hydrophobic interactions

• Metal- and water atoms

• Find complementary poses oftest compounds

• Score those poses

• Rank all test compoundsaccording to score

hydrophilic

hydrophobic

X-ray structure of human carboanhydrase



Rational Approaches: Docking

• Carboanhydrase: X-ray-structure as template

• Commercially availablecompound database as pool

• 13 compounds selected forpurchase and screening

• 3 subnanomolar, 1 nanomolarand 7 micromolar inhibitorsfound

~180.000 compounds

Filtering

Shape-

Matching

Docking

Proof-of-

Concept:X-ray structureof bound ligand

13 compounds



Rational Approaches: Fragment-based discovery

• Chemical Microarrays(Graffinity, Heidelberg)• ~100,000 fragments linked onto

chip• parallel detection of weak,

specific interactions

• Confirmation of hits by functional

testing and X-ray• Thrombin-example

• Novel fragment identified thatbinds to S1-pocket (usually

occupied by benzamidine-group)• Confirmed by X-ray

S1



Rational Approaches: Comparative Molecular FieldAnalysis (CoMFA)

QSARequation

PLS

ContourMaps

QSAR Table = SYBYL MSS

Bio



Rational Approaches: Comparative Molecular FieldAnalysis (CoMFA)• Example Steroids:

• Dataset of 31 ligands

• Measured activity data(CBG-Affinity)

• CoMFA-steps• Build molecules in 3D

• Align all molecules on top ofeach other

• Calculate fields• Run statistical analysis

• Display results in from ofcontour maps

• Use regression equation forprediction of untestedcompounds



Rational Drug Design at EMBL: Aurora A

• Cell-cycle and cancer:

• Family of 3 related kinases with central role in cell cycle

• Aurora A (localized and activated by TPX2)

• Aurora B

• Aurora C

• Aurora A has elevated expression in> 50% colorectal , ovarian, gastric cancers

> 95% invasive adenocarcinomas (breast cancer)

Amplification of locus correlates strongly with poor clinical prognosis



Brief History of Aurora

• 1997: Aurora A gene overexpressed in breast tumors

• 1998: Aurora A established as an oncogene

• 2002: first X-ray structure of inactive Aurora A resolved

• 2003: X-ray structure of activated Aurora A resolved byElena Conti et al. (EMBL)

• 2004: Aurora-kinase inhibitor suppresses tumor growth invivo

• To-date: most major pharmaceutical companies with

discovery programs on Aurora-kinases targeting ATP site• Novel approach targeting the allosteric site



Aurora A: Binding Sites

Aurora-A overview of binding sites Aurora-A: TPX2-site

Aurora-A: ATP binding site with bound ATP Aurora-A: ATP-binding site blocked by inhibitor



18

Aims for Aurora A Project

• Identify compounds active against Aurora using coupleATP consumption assay

• Use leads to dissect the function of Aurora in the cellcycle

• Optimize for efficacy and potency

• Proof-of concept in in-vivo tumor model



19

Aurora A: Characterisation of Compounds

• 91 (from ~53000) in total had an IC50 < 50 µM

• 89 compounds fall predominantly into the ATP NON-Competitive class

• Structurally diverse

• IC50s from 200nM to high micromolar

• Potential allosteric inhibitors

• Two novel ATP competitive inhibitors found as unique

compounds



20

Aurora A:Comparison of ATP versus allosteric inhibitor

103444

Compound Concentration [µM]1 10 100

Inhibition (%)

-10

20

50

80

110

ATP [µM] IC50 [µM] r² Hill-Slope

20 1.7 0.996 1.537

200 1.9 0.993 1.759

1000 0.84 0.978 2.165

ATP [µM] IC50 [µM] r² Hill-Slope

20 6.68 0.997 1.163

200 49.8 0.997 1.06

1000 >100 0.975 0.42

123671

Compound Concentration [µM]1 10 100

Inhibition (%)

0

20

40

60

80

ATP competitive non-competitive

The majority of compounds tested fall into non-ATP competitive category



21

Allosteric inhibitors of Aurora A:TPX2 competition

103421:02IC50 = 4.8 µM; Slope = 1.33; r2 = 0.995

w/o TPX2Concentration (µM)

1 10 100

I n h i b i t i o n ( % )

0

30

60

90

103421:02IC50 = 78.8 µM; Slope = 5.9; r2 = 0.988

with TPX2Concentration (µM)

1 10 100

I n h i b i t i o n

( % )

-10

20

50

80

110

IC50 Aurora A

IC50 Aurora A + TPX2

Of ~100 compounds tested

61 showed a strong shift to less potentIC50s- potentially allosteric inhibitorsat the TPX2 site

4 showed no significant shift of IC50i.e. not competed by TPX2



Aurora A Project: Summary

• Rational design of allostericinhibitors

• Examine H-bond interactions

•

Hydrophobic interactions• Water molecules

• Fit active compounds intobinding pockets

•

Improve hypothesis• Docking and scoring of

untested compounds

• Help guiding synthesis and

medicinal chemistry efforts One of the TPX2-binding sites



Rational Drug Design:Unsolved problems and limitations• Experimental data in early stages is inaccurate

• X-ray structures give static picture

• Conformational changes upon binding

•

Role of water molecules and protonation states of functionalgroups in binding pocket often unknown

• Limited experimental data in later stages of development• Complexity of biological systems

• Too many targets (whole proteome)• Proteome variability

• Short-term versus long-term effects

• Computers need data to build models

S



Summary

• Rational drug design• Replaced traditional approaches

• Numerous successful examples

• (still) holds great promises (rightly so!)

• Limitations

• challenging

A k l d



Acknowledgements

• Elena Conti

• Friedrich Reinhard (Chemical Biology Core Facility)

• Joe Lewis (Chemical Biology Core Facility)

Rational Drug Design by Bernd Wendt-EMBL

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