Genomics Research InstituteGenomics Research InstituteUniversity of CincinnatiUniversity of Cincinnati

Compound LibraryCompound Library

Wm. L. Seibel

January 10, 2007

OverviewOverview

Library Overview

Compound Characteristics– Design Concepts– Drug-Like

Library Screening Options

Summary of Library Advantages

Compound Repository Compound Repository

Haystack Neat Compound Storage– Capacity = 200,000 bottles– Current = 207,000 bottles– Freezer storage when appropriate

Solar (Solution Archive) – DMSO solutions– Capacity = 1.8 million tubes, 10,000 deep well (96) plates, 13,600

shallow well (384) plates– Current = 325,000 unique compounds

Related Compound Handling and Dissolution instruments. Housed at P&G’s Mason Business Center in ca. 3000 sf lab

space

HaystackHaystack®® Neat Chemical Storage Neat Chemical Storage

HaystackHaystack®® Neat Chemical Storage Neat Chemical Storage

HaystackHaystack®® Neat Chemical Storage Neat Chemical Storage

SolarSolar® ® Solution StorageSolution Storage

SolarSolar® ® Solution StorageSolution Storage

SolarSolar® ® Solution StorageSolution Storage

Library Design PrinciplesLibrary Design Principles

TargetIdentification

TargetValidation

CompoundScreening

LEAD

CompoundOptimization

Drug Lead DiscoveryDrug Lead Discovery …greatly simplified…greatly simplified

Compound Selection can greatly enhance

Efficiency

H2L ActivityConfirmation

Library CompoundsLibrary Compounds

The UC/GRI Compound Library is comprised of compounds from four general categories:

– 1. Compounds purchased from numerous sources selected to provide a diverse representation across “drug-like” structural properties.

– 2. Compounds purchased that specifically target kinases and GPCRs

– 3. Compounds prepared in-house specifically for projects in kinases, GPCRs, phosphatases, ion channels and proteases donated from P&G Pharmaceuticals.

– 4. Combinatorial Chemistry contract syntheses (Lower Priority Cmpds).

This screening library is broadly diverse across drug-like space, with enhanced concentrations in areas of key biological relevance, including notably, kinases and GPCRs.

P&G Pharma Selected CompoundsP&G Pharma Selected Compounds

Chemically Diverse– Represented uniformly across drug-like space.– Want to ensure uniform, comprehensive and

diverse representation of compounds across the structural & property types that are typical of drugs and lead structures.

Compounds selected based on drug like properties (within “Drug-Like Space”)– Chemical and Property Filters– Lipinski, Veber etc. rules

Total P&G investment to assemble repository = $22 M (over past 10 years)

VendorDatabase

Remove duplicates

Remove reactives, Unusual groups, & toxicophores(80 substructures)

MW filterSolubility Filter

Lipinski Rule of Five•> 5 H-bond donors •MW < 500 •c log P < 5 N's + O's < 10

“Cleaned”database

26 databases

>4 million structures

Chemical Property FiltersChemical Property Filters

Diversity AnalysisDiversity Analysis

Describing Molecular StructureDescribing Molecular Structure

Convert molecular structure into numerical values bymaking computations of specific structural features

N

N N

CH3

OH

StructureComputations Numeric

Descriptors

Relevant to Binding Functions

Diversity Assessment MethodologyDiversity Assessment Methodology

Used BCUT descriptors *

– R.S. Pearlman, UT at Austin– DiverseSolutions (now available from Tripos)

Computed ~120 BCUTs

Selected a best subset of 6 BCUTs– 6D space – visualization is a challenge

* J. Chem. Inf. Comput. Sci. 1999, 39, 11-20.

Pearlman’s Pearlman’s BCUTBCUT descriptors descriptors **

6D Chem-space (structure-space)– 2 atomic partial-charge descriptors– 2 atomic polarizability descriptors– a hydrogen-bond acceptor descriptor– a hydrogen-bond donor descriptor

* http://www.awod.com/netsci/Issues/Jun96/feature1.html

Concept of Concept of Chemistry SpaceChemistry Space

Desc-1

Des

c-2

Defining Drug SpaceDefining Drug Space

Based on structures of “drug-like” compounds from The World Drug Index (WDI) The Nation Cancer Institute Open Database

Desc-1

Des

c-2

Desc-1

Des

c-2

Diverse Subset SelectionDiverse Subset Selection

Avoiding “redundant” representations

DiversityAnalysis

Compound Supply Compound Supply

External Suppliers (20+ vendors)– Brokerage Houses

Individual Compounds (Diversity) Target Directed Libraries

– Combinatorial Chemistry Companies

Corporate Suppliers– P&G Pharmaceuticals

Focus Areas - Medicinal ChemistryKinase, GPCR, Phosphatase, Ion Channel, proteases

Lead ID – Combinatorial Chemistry

Vendor “Dependability”Vendor “Dependability”

vendor

# cmpds evaluated

by MS

# compounds that were

present in MS

# compounds that were not present in MS

Analytical confirmation

rate

ave conc (mM)

ave purity

(%)

# times a compound present in

MS confirmed in an HTS assay

160 143 17 89.4% 1.9 95.9 0

171 142 29 83.0% 1.9 94.5 7

182 145 37 79.7% 6.4 94.0 34

31 23 8 74.2% 9.5 98.3 3

649 470 179 72.4% 5.8 93.1 117

1137 788 349 69.3% 5.1 91.1 426

47 32 15 68.1% 6.2 79.0 15

20 13 7 65.0% 4.4 93.9 5

230 0 0 63.3% 4.6 91.0 0

16 10 6 62.5% 6.1 91.7 7

258 152 106 58.9% 4.9 76.4 227

1906 962 944 50.5% 8.3 77.4 1803

651 266 385 40.9% 2.5 61.2 115

54 18 36 33.3% 3.9 87.0 14

48 15 33 31.3% 4.4 90.2 19

Totals: 5560 3179 2151 2792Averages: 62.8% 5.1 87.7

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

On the Other Hand…On the Other Hand…

Even within Drug-like space, certain classes can be somewhat clustered.

This library therefore has added “focused libraries” from internal synthesis and external vendors emphasizing compounds relevant to:– GPCRs– Kinases

P&G Pharma Selected CompoundsP&G Pharma Selected Compounds

Defined by experienced medicinal chemists– Broad, uniform distribution across Drug Space with

concentrations of density in key areas from directed purchase and in house synthesis.

Compare to 5 vendor screening collections– 3,000 to 500,000 compounds– 27% - 56% of vendors’ compound collections do NOT

meet criteria for drug-like– UC Compound collection is 2X to 100X more

chemically diverse across Drug Space.

Vendor Libraries are inherently predisposed to clustered groupings.

We can pick the best, most relevant compounds from each.

Screening Library OptionsScreening Library Options

Screening Library Design OptionsScreening Library Design Options

Diverse broad collections– Comprehensive screening against all available compounds

(ca. 250,000 cmpds)– Screening against a representative subset of available

compounds (e.g. 5000 cmpds)

Class-associated compounds– Compounds with structural features often associated with a

particular target (e.g. kinases).

Structure-based compound selection– Virtual Screening of a crystal structure or high quality

homology model to identify the most likely inhibitors (ca. 2000 cmpds), followed by assay of these compounds.

– Virtual Screening as above based on pharmacophore models from known ligands of the target.

Diverse Subset SelectionDiverse Subset Selection

Same Concept as Previously

DiversityAnalysis

5,000 Cmpd Abstract250,000 Cmpd Library

Diverse Subset SelectionDiverse Subset Selection

Execute Assay on subset of compounds

MTSAssay

Identify Hits in Assay5,000 Cmpd Abstract

Diverse Subset SelectionDiverse Subset Selection

Pull Similar Compounds from original 250K Set

SimilaritySearch

300 Cmpd Similarity Library

250,000 Cmpd Library

Diverse Subset SelectionDiverse Subset Selection

Pull Similar Compounds from original 250K Set

MTSAssay

Identify Hits in Assay

300 Cmpd Similarity Library

This Cycle can be repeated several times until no new actives are found

Selection of Nearest

Neighbors of Hits

Biological hit

Near neighbor

Iterative CyclingIterative Cycling

Assay

5000 CmpdRepresentative

Library

~1000 CmpdNN Library

~20 CmpdHit List

NN Search of UC/GRI Library

NN Search of Commercial Compounds

~50 CmpdHit List

Assay

2-3 Iterations

2-3 Iterations

Final Set

Final Hit List

~1000 CmpdNN Library

Diverse Subset SelectionDiverse Subset Selection

Pull Similar Compounds from Commercial 4.8M Set

SimilaritySearch

4.8 M Commercial Library 300 Cmpd Similarity Library

Assay for actives, and cycle hits back through similarity search loop.

Class-Associated CompoundsClass-Associated Compounds

Select compounds similar to compounds known to intereact with target class

SimilarityAnalysis

250,000 Cmpd Library 15,000 Cmpd Library

Target Active

Virtual ScreeningVirtual Screening

Screen GRI/UC library

Screen Commercial Cmpds

Iterative CyclingIterative Cycling

Assay

5000 CmpdRepresentative

Library

~1000 CmpdNN Library

~20 CmpdHit List

NN Search of UC/GRI Library

NN Search of Commercial Compounds

~50 CmpdHit List

Assay

2-3 Iterations

2-3 Iterations

Final Set

Final Hit List

~1000 CmpdNN Library

Hits of any origin can enter the cycle at this point.

Hit to Lead Follow-up (H2L)Hit to Lead Follow-up (H2L)

Hit to Lead Follow-up (H2L)Hit to Lead Follow-up (H2L)

How to determine optimal hits for follow-up– Confirm ID and activity of hits– Cluster into groups of related compounds– Develop preliminary SAR info on each cluster

ID Key features for binding & selectivity– Assess Each Cluster for optmization

“Which compounds have fewest problems?” Synthetic Ease Proprietary Assessment Selectivity Issues Physical Properties Metabolic Handles Cellular Activity

SummarySummary

UC/P&GP Library AdvantagesUC/P&GP Library Advantages

Quality Advantages– Library carefully constructed to span drug-like space.– Compounds restricted to those with properties consistent with clinical materials. – Proven to produce viable hits for follow-up programs.– Comparisons have uniformly been favorable relative to commercial vendor sets.– Includes targeted subsets of compounds for key areas: GPCRs, Kinases,

Phosphatases, Ion channels.

Practical Advantages– SD file of structures and ID tags furnished for unrestricted use.– Many compounds from commercial sources, so resupply likely to be easy.– Materials supplied in microtiter plates (96 or 384) as requested.– Solution Stores made from local dry stores, so follow-up assays will be rapid.

Technical Advantages– Act as Liaison with screening group (internal or external).– Participate in advisory committee for compound acquisition decisions.

Library Use and Data Interpretation Library Use and Data Interpretation

Library Design Assistance – Computational assistance in selecting diverse subsets or

directed subsets.– Computational assistance in selecting compounds similar to

known leads (Nearest Neighbor).– Computational assistance in virtual screening by

pharmacophore or protein docking.

Library Use and Data InterpretationLibrary Use and Data Interpretation

Follow-up Assistance– Resupply assistance, synthesis info, supplier info– Assistance in obtaining related available compounds

(Similarity, substructure, Unity, Pharmacophore).– Provide preliminary lit search info (known info, IP, etc) on

prominent hits.– Clustering of hits into chemical/pharmacophore classes

included.– Provide help identifying chemistry groups with related

interests for collaborations– Provide assistance in connecting with contract chemistry

services (consult).

QuestionsQuestions

Thank youThank you

AcknowledgementsAcknowledgements

Operations– Stacey Frazier– Kathy Gibboney

Computational– Matt Wortman – David Stanton– Prakash Madhav

Management– Ruben Papoian– Sandra Nelson– Joseph Gardner– Kenny Morand