Computational Techniques in Support of Drug Discovery

October 2, 2002

Jeffrey Wolbach, Ph. D.

Who Is Tripos?

Core Science & Technology Software

Consulting Services

Chemistry Products& Services

Discovery Research& Process Implementation

Discovery Software& Methods Research

Sequential Drug Discovery

Many cycles of synthesis/testing to identify and optimize lead Role of molecular modeling

o unrealistic to jump from validated target to optimized leado useful to reduce the number of synthesis/testing cycleso enables “first to file”o enlarge number of targets

ChooseDisease

LeadIdentification

LeadValidation

LeadOptimization

ADMECandidateto Clinic

TargetIdentification

TargetValidation

Choose a Disease

Lead Identification

Lead Validation

Lead Optimization

ADME

Candidate to Clinic

Target Identification

Target Validation

Drug Discovery in Parallel

• Knowledge-sharing environment: genomics, HTS, chemistry, ADME,

toxicology

• Collect more data, on more compounds, more quickly

• Apply predictive models of “developability” early

• Enhanced understanding & predictive model building

• Increase share of patented time on market

Ligand-Based Design

Ligand Structures w/ActivitiesLigand Structures w/ActivitiesNo Target StructureNo Target Structure

PharmacophorePharmacophoreAnalysisAnalysis QSARQSAR

Database SearchingDatabase Searching

New Candidate Structures for Synthesis/Testing

Discern Similarities and Differences in Active Structures

• Assume active molecules share a binding modeo Search for common chemical features of active molecules

• Don’t know binding mode, so active molecules are considered flexible

o Search set of pre-determined conformerso Allow molecules to flex during search

Pharmacophore Analysis

• Typical features:o H-Bond Donorso H-bond acceptorso Hydrophobic groups

Pharmacophore Models• Chemical features in 3-D space

• Distance constraints between chemical features

QSAR• Relates bioactivity differences to molecular structure

differences

• Structure represented by numerical descriptorso Traditional (2D) QSARo 3D QSAR - CoMFA

• Statistical techniques relate

descriptors to activity

Activity

Descriptor

+

++

+

++

+

+

+

+

++

Activity = D0 + 0.5 D1 + 0.17 D2 + ...

QSAR - Traditional (2D)• Descriptors are molecular properties

o logP, dipole moment, connectivity indices ...

Structures + Activity Descriptors

logP = 1.9 = 2.8Estate = 7.2

logP = 2.1 = 3.5Estate = 5.5

logP = 1.7 = 2.3Estate = 6.7

Predictive Model (QSAR Equation)pKi=5.3

pKi=3.7

pKi=2.9

pKi=A + B(logP) + C() + D(Estate) + ...

PLSMLR

.

.

QSAR - 3D QSAR - CoMFA

• Comparative Molecular Field Analysis

• Descriptors are field strengths around molecules - electrostatic, steric, H-bond ..

• Fields can have easy physical interpretation

pKi=A + B(D1) + C(D2) + ...

QSAR/CoMFA - Interpretation• High Coefficient (important) lattice points can be

plotted around molecular structures

N

S

OHO

O

N

O N

OO

O

010110010010101

2D Database Searching

• Searches often performed on bit-stringso “Fingerprints” (many types)o Fingerprints display neighborhood behavior

• Also includes substructure searching

• Can search for similarity or dissimilarity

• Query is a collection of features in 3-D spaceo Pharmacophoreo Lead compound / specific atomic groups

3D Database Searching

• Search a database of flexible, 3-D molecules

o Molecules can’t be stored in every possible conformation

o Allow molecules to flex to fit the query

Example of Structure-Based Design

• Not restricted to ligand-based design

• Information about target can be included in the query

o Can define steric hindrances

o Additional interaction sites

o Serves to filter hits from the search

3D Database Searching

Identification of Novel Matrix Metalloproteinase (MMP) Inhibitors

A fibroblast collagenase-1 complexed with a diphenyl-ether sulphone-based hydroxamic acid

MMPs•Zinc-dependent proteases•Involved in the degradation and remodeling of the extracellular matrix

They are important therapeutic targets with indications in:•Cancer•Arthritis•Autoimmunity•Cardiovascular disease

Objectives

Design high affinity MMP inhibitors based on the diketopiperazine scaffold by:

•Creating a virtual combinatorial library of candidate inhibitors

•Using virtual screening tools to identify candidates with the highest predicted affinity

•Perform R-group and binding mode analysis to guide library design

Synthesis ofDKP-MMP inhibitors

DKP-I DKP-II

1.) Esterification of the solid support (HO-) with an amino acid2.) Reductive alkylation of the amino acid and acylation of the resulting secondary amine 3.) Deprotection of the N-alkylated dimer followed by cyclic cleavage from the resin yielding diketopiperazine (DKP)

Finding & Filtering Reagents

UNITY 2D structure search of the ACD

Filtered out:•Metals•MW > 400•RB > 15

Filtered out:•Metals•MW > 250•RB > 8

73 Boc protected amino acids1154 aldehydes

Selecting Reagents & Building the Virtual Library

Selector™ Diverse selection of amino acids (R1) and aldehydes (R2) using:

•2D Finger Prints•Atom Pairs•Hierarchical Clustering

Legion™ Model the reaction and create virtual combinatorial library

55 amino acids (R1)x 95 aldehydes (R2)x 14 amino acids (R3)= 73,150 compounds (~75k, 8.5 MB)

Randomly selected 14 amino acids for R3

The CombiFlexX Protocol•Select a diverse subset of compounds using OptiSim

•Dock and score the compounds in the diverse subset using FlexX

•Select unique core placements using OptiSim

•Hold each core placement fixed in the binding site as each R-group is independently attached, docked, and scored.

•Sum the scores of the "R-cores" and subtract the score of the common core

Computation times scale as the sum of the number of R-groups rather than as the product of the number of R-groups

Virtual Screeningof DKP-MMP Inhibitors

• ~75k compound library

• MMP target structure collagenase-1 (966c.pdb)

• 150 diverse compounds selected and docked

• 39 non-redundant core placements based on RMSD > 1.5 Å.

Virtual Screening Results

•Docked 91% of the library

•36 compounds/minute

•331 compounds predicted to be more active than those published

Consensus Scoring Results

•Extracted the top 1000 library compounds based on Flex-X score

•Ranked the top library compounds and published “highly actives” using CScore

•10 compounds predicted by all scoring functions to be more active than “highly actives”

Frequency of R-group use among 331 active virtual compounds

R1 (55 reagents)

R2 (95 reagents)

R3 (14 reagents)

R-Group Analysis in HiVol

R1

R-Group Analysis in HiVol, con’t.Frequency of R-group use among 331 active virtual compounds

SummaryUsed CombiFlexX and HiVol to:

• Identify highly promising candidates• Perform R-group analysis & Binding mode analysis to guide further computational design of libraries

Further Work

• Diversity/similarity analysis of the published and virtual libraries• Use docking results for library design in Diverse Solutions• SAR development

Binding Mode Analysis

Frequency of core placement use

Frequency of R-group use among 331 active virtual compounds

R1 (55 reagents)

R2 (95 reagents)

R3 (14 reagents)

R-Group Analysis in HiVol