Mats KihlénHead of Research Informatics

Biovitrum AB

Lecture for Molekylär bioinformatik X3 Feb 24 2004

Computational Chemistryin Drug Discovery

Overview

» The role of computational chemistry» Basic concepts

– The pharmacophore concept - conformational analysis– Database searching– Virtual combinatorial chemistry– Molecular Dynamics– Structure based drug design– Predicting ADME properties– Protein modeling

» Trends and future directions

The Drug Development Process

Years0 5 10

Clinical

Phase I - III

New Drug Application

Pre-clinical

InvestigationalNew DrugApplication

Medicinal chemistry

Pharmacology

Drug Design

Mol biology

Tox

ADME

Market

The role ofcomputational chemistry

» Aid chemists in the design of compounds– Improve affinity– Find SAR (Structure Activity Relationship)– Select building blocks for combinatorial libraries– Predict permeability & solubility

» Provide protein models to biologists– Target identification– Genetic constructs– Specificity guidance

» Analyse biological data– Make sure data is captured and stored– Coordinate data flow in projects

The pharmacophore concept

Use the features as search pattern

Amine

Hydroxyl

Aromatic ring

Lipophilic

D2

D3D1

Superimpose featuresfrom several compounds

O

O

NH

OH

NH

O

Conformational analysis

» Identify the bioactive conformation» Vary all torsion angles and calculate lowest

internal energy» Typically done with MacroModel

Water

O

O

NH

OH

NH O

FreeLigand

Protein

“Water”

εr = 4

εr = 80

... but the free binding energy depends on:» Solvent» Protein ligand interactions

Structure Searching

» Pharmacophores– Finding novel scaffolds– Refining substituents– Typically ACD (Available Chemicals Directory), in-house

databases or virtual libraries (100k - 1M compounds)– Rigid or flexible search - fast

» High troughput docking– Protein structure required !– Time consuming, despite crude model

Design Synthesis

Activity measurement

Structure determi-nation of complex

“The structure based drugdesign cycle”

A project screening funnel

Design

Synthesis

Protein Xassay Caco-2

Cell basedassay

Co-crystallisation with Protein X

ADME

Selectivity

Mouse model

Papp > 1*10-6 cm/sKi < 5 µM

Virtual PPARγ Libraries

R1: 13 acid chloridesR2: 128 alcohols 1 664

R1: 95 acid chloridesR2: 647 alcohols

61 465

Full expansio

n

R1: 1 acid chlorideR2: 647 alcohols

R1: 95 acid chloridesR2: 1 alcohol 712

Iterativ

e desig

n

All reagents selected from ACD:Purity: > 95%Price: < $50MW: < 250Quantity: > 1g

OH

NH2

+

R2OH

R1 O

Cl O

NH

R2

R1

O

Building libraries in AfferentBuilding libraries in Afferent

Binding energy prediction

Water

LigandWaterLigand

Molecular Dynamics Simulations

- 10-15 CPU hours per compound- Full flexibility and solvent within simulation sphere

“As close to reality as we can get today”∆Gbinding = β∆Vel + α∆Vvdw

The Åqvist & Medina equation:

Protein

High Throughput Docking

Applications:

» Selection of compounds for screening– Smaller number of compounds to test– Possible to cover compounds not in the compound collection

» Selection of reagents for focussed libraries– Make large virtual libraries, but synthesise only the most

promising compounds» Virtual ”SAR by NMR”

– Identification of small binding fragments which could be joinedto create potent compounds

Several weak binders can beturned into one strong

» Linking two weak binders may result in a ligand with theproduct of their binding energies.

» Case study: Combinatorial linking of two weak c-Src tyrosinekinase ligands gave a 64 nM binder.

Each fragment showedappr 70% inhibition at500µMMaly D, Choong I, Ellman J, “Combinatorialtarget-guided ligand assembly: Identification ofpotent subtype-selective c-Src inhibitors”, PNAS97 (2000)

Selection for screening

» Dock public compound databases as starting pointfor compound acquisition or screening:– Example: ACDscreen - 1.2M compounds

» Pre-filtering necessary– Reduce computational needs– Remove junk, e.g. SLN-based filters– Require known features

» Good for small compound collections

Pre-filtering

Implemented as Sybyl substructure filters

O

R R X

XX

OR

OR

H R O R

O O

O

O

R

» Versatile syntax, high capacity filters

# Sul pho ny l ha l i de sS( =O) ( =O) Ha l

# Ac y l ha l i de sC( =O) Ha l

# Pe r ha l o ke t o ne sCC( =O) C( Ha l ) ( Ha l ) Ha l

# Sul pho na t e s t e r sO=S( =O) OC

# Pho s pho na t e s t e r sO=P( =O) OC

# Al pha ha l o c a r bo ny l c o mpo undsO=CCAny [ i s =Cl , Br , I ]

# He t - he t s i ng l e bo nd but no t N- 5 r i ng - he t r o c y c l e s o r s ul pho na mi de sAny - S[ no t =S=O] - He t - AnyAny [ i s =N, O, P; no t =N* [ 1 ] ~Any ~Any ~Any ~Any ~@1 ] -Any [ i s =S, N, O, P; no t =S* =O] - Any

» Definition of unwanted groups as SLNs

Docking method used at Biovitrum

» Fixed protein, fully flexible ligands– Fails if induced fit– MC generation of conformers and positions– No intial bias (positions, restraints etc)

» Docks e.g. PTP1B and PPARg binders close to crystalstructures, but...

» Docking and scoring are different things !

» Fully automated procedure using ICM or GLIDE» Capacity ~40k compounds per day using 30 CPUs

ICM docking of troglitazone, rosiglitazone, pioglitazone andPNU91325 into PPARγ ligand binding domain.

Docking test - PTP1B inhibitors

Prediction of actives vs inactives

0%

20%

40%

60%

80%

100%

Active Inactive

Conservative score threashold (-40), n = 107

FalseTrue

Activity threshold: <50uM

55 random drugs:100% predicted inactive

Relatively close analogues. Hard to explain from structure why some are inactive.

Correct prediction of actives: 82% Correct prediction of inactives: 26%

Green compound from crystal complex vs white docked analogue

Crucial interaction with Asp48

“Phosphate mimetic”

“Greasy C-term patch”

NovoNordisk Xtal vs PNU Xtal“Structure-Based Design of aLow Molecular Weight,Nonphosphorus, Nonpeptide, andHighly Selective Inhibitor ofProtein-tyrosine Phosphatase 1B”Iversen et al, J Biol Chem. 2000PDB code: 1ECV

SNH

O OH

ONH

OHO

NH

O OH

O

OOH

I

Docked “Novo #5” vs PNU

Asp48

Novo #5 vs 1ECV

SNH

O OH

ONH

OHO

NH

O OH

O

OOH

I

Created virtual libraryfrom 250 aldehydes toexplore nearby pocket

Virtual library hits

Virtual library hits

Virtual library hits

Virtual library hits

Virtual library hits

Virtual library hits

Reaching 2nd pTyr site

Structure Based Focussing

– Align with a dockedpharmacophore

– Score against the surface

LigandProtein

Combine the pharmacophore conceptwith high troughput docking:

Virtual screening summary

» ICM & GLIDE are fast and robust enough to beused as a standard docking tools

» Can clearly enrich screening sets of diversecompounds

» Not reliable enough to predict small differenciesin binding affinity

» More work should be done to improve scoring

Predicting ADME properties

Typical aspects:» Cell permeability» Aqueous solubility» Liver enzymes: inhibitiors or substrates?» Protein binding

» Physical properties vs. biological interactions

[Cneutral][Ccharged]

pKa, pH

∆G0 Size

“Water”

“Water”

“Lipids”[C]

[C] = 0

A model of passive diffusion

Predicting absorption

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

FA%

FApr

ed%

ln ln%

100100 −

���

���

�

��

�

�� = − + +

FAPSA ASAα β γ

20 diverse compoundswith known absorptionin humans (from Palm et al 1997)

Predicting aqueous solubility

-12

-10

-8

-6

-4

-2

0

2

4

-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Obs

erve

d

Predicted

924_sort.M3 (PLS), Untitled, PS-924_sortlogSol, Comp 4 (Cum)

RMSEP=0.865623Simca-P 8.0 by Umetrics AB 2000-10-15 18:57Npc = 4 Ntraining = 91 Ntest = 833 RMSEP = 0.87

Predicted logS

Expe

rimen

tal l

ogS

Experimental vs predicted solubility for 833 mixed compounds

PLS model based on 3Dmolecular descriptorscalculated by Cerius2

PLS model based on 3Dmolecular descriptorscalculated by Cerius2

Blood Brain Barrier model

» In-house data from 75compounds

» High level descriptorsfrom Ab initiocalculations

» PLS statistics

-2

-1

0

1

2

-2 -1 0 1 2

Y

Predicted

all_TR.M4 (PLS), train_50_X_21, Work setlog(B/P), Comp 2(Cum)

RMSEE=0.510595

1

2

4

6

9

12

14

171821

222526

2729 31

32

33

34

35

36

37

3839

40

41

42

43

444647

48

49

5153

54

55

58

59

62

63

65

6669

7071

72

74

75

77

Simca-P 8.0 by Umetrics AB 2000-11-01 16:23

Predicted vs. observed log(B/P)

Npc = 2n = 50R2 = 0.73Q2 = 0.61

Pharmacophore model for 2D6 inhibitors

» 3D QSAR model built in Catalyst» 36 compounds from Lily paper» Correlation fitted vs. observed Km: 0.93» Correctly predicted 82% of P&U compounds < 1

log» Activities 0.0046–1000 µM

Protein Modeling

» Models usually too poor for SBDD» Sufficient for selectivity guidance» Increasing demand due to Bioinformatics revolution

– Auto-building and classification of structural domains– If family identified: select initial compound set for testing

» Major tool: ICM– Multiple sequence alignment– Structure optimisation with Monte Carlo

ZACRP7 QGDPGLPGVCRCGSIVLKSAFSVGITTSYPEER--LPIZACRP2 KGEPGLPGPCSCGSGHTKSAFSVAVTKSYPRER--LPI1c28a_a ---------------MYRSAFSVGLETRVTVPN--VPIhuzsig39 RSESRVP----------------------PPSD--APL

» More parallel synthesis and combi.chem.– Larger data volumes for theoretical evaluation

» Earlier ADME studies– Prediction of physical properties– Metabolism models

» Faster project turnover» Need for efficient data management

» Novel targets» Specialisation on target classes vs. therapeutic areas» Virtual screening as primary source for hits

» Small companies without large compound collections

Trends & Guesses

New tasks forcomputationalchemistry !