“Bio Space” Chemical Libraries:

Perspectives on Rapidly Designing and

Identifying Drug Molecules

Suhaib M. Siddiqi

What is PharmaInformatics?

Integration of Data from:ChemistryBiologyGenomics & ProteomicsComputational Chemistry (QSAR, QSPR, Structure Based Drug Design, Flexible DB searching, and ComiChem etc)

For rapidly designing and optimizing “Drug-Candidates.”

Orphan Receptors, Enzymes, and Proteins as Disease Targets…Validation Issues…

One of the major challenges facing the pharmaceutical industry is the validation of the orphan Receptors and Enzymes etc., discovered through Human Genome and Proteomics Projects as drug targets and the identification of selective ligands as the blockbuster pharmaceuticals of the future.

Current Drug Discovery Trends…

More…Cheaper…

Faster…

“Better"…

How do we accomplish the goal of “More… Cheaper… Faster… and Better Drug Candidates”?

Efficient utilization of Computational Chemistry technology by integrating R&D data from various departments (e.g. Chem., Biology, Pre-clinical, Genomics and Proteomics etc) A unique combination of Combinatorial Chemistry, Biology and Structure Based Drug Design to design “Bio Space” Combinatorial libraries with “Drug-Like” features.

High Valued New Chemical Entities

Approx. 40% drugs in clinical trials are discarded because they do not show the correct adsorption, distribution, metabolism, and excretion properties (ADME) [Drug Discovery Today 1997, 2, 436].

Results? Loss of hundreds of Millions of dollars.

Scaffolds with correct ADME…

Design scaffold from existing Drugs “building-blocks” with correct adsorption, distribution, metabolism, and excretion properties (ADME).

Results? Lesser chances of failure in clinical trials due to incorrect ADME.

Design of Libraries with “Drug-like” features…

Performs a retro-synthetic analysis of the small molecules in the “MDDR” and “CMC” Drug databases (~150K drug candidates) and determine which fragments occur repeatedly in drug candidates. Build a database with building blocks include many of these fragments that were not previously commercially available.

Use ADME calculation to eliminate non-drug-like building blocks from library design.Optimize three-dimensional coordinates for library scaffolds.optimal virtual library - eliminate overlap in design and space of compounds based on different core structures

Library Comparison

“Bio-Space Chemical Library”Filling “Void” with “Bio-Space” library will lead to more potential hits

Technical Advantage…

“Medicinal Chemistry-wise and Pharmacokinetics-wise” to yield meaningful “Hits”…Hits found through “Bio-Space” libraries will be more “inherently” meaningful because all the “Hits” will be “Drug-Like.”

Computational Chemistry Methodologies

Rapid Dual Filtration of “Bio-Space” Chemical Libraries!

MaestroUnified Interface

GlideDocking

LiaisonBinding Affinity

QSiteQM/MM

pKa

QikPropADME

Structure Based Filter

LigandBased Filter

The FirstDiscovery Suite

Genomics and Proteomics The Integration of Genomics or

Proteomics into a drug discovery program enhances the target selection process. Early access to this data provides a competitive advantage. The informatics system should track data, annotations, and decisions made at this early stage to enable future analysis of the selection process. Links to sequence, structure (if available) and other data should be provided. The storage of images related to this data may also be desirable.

Integration of Data and Images

Pharmaceutical/Biotech R&D involves the effective integration of a wide variety of data. In addition to more traditional chemical and biological data (both “HTS” and “secondary”), genomics sequences and annotations, target protein structures, images derived from proteomics and pre-clinical analysis must be readily available for review to support timely decision making, both by management and by the scientists working directly with the data.

Conventional SAR Approach…

Conventional SAR approaches establish relationships between the structure of a compound and the activity. Links to proteomics and tissue information is missing in conventional SAR.

Image Informatics SAR Approach—ISAR

Image informatics provides a new source of information a researcher can readily utilize to gain insight into experimental results. Retrieval of database images that are associated with compounds or assay results of interest.Associating images from experiments with structures and activity data will allow researchers to understand better the biological effects of those structures.

Image-Enhanced SAR Tables

View Tissue Samples Search for features in tissues

Effects of Structure on Expression

Images fromSciMagix

Explore commonality and differences in protein expression…

Extract, analyze and mine protein image-data from 2D electrophoresis gels.

The proteomics scientists can query entire collection of gel experiments to find similar "protein signatures."

2/2/2002

Target Selction

2/2/2002

Library Design

2/2/2002

Diversity & ADMEassessment

2/2/2002

Robotic Synthesis

2/2/2002

HTS

2/2/2002

Library Focusing

2/2/2002

Quality Control (QC)

2/2/2002

ADME

LEAD

A Typical High-Throughput

Drug DiscoveryProcess

Library Design Strategies

Modify “known” organic molecules. (Too resource intensive)Use of Chemical “data mining” (e.g. docking) strategies to identify potential “lead” compounds from “available” or “virtual” libraries. (Not Preferable – Expensive and time intensive)Use of known protein or antibody structure to design scaffolds and target compounds.

Library Design and Chemo-informatics Integration Issues

Target Selection (Genomics/Proteomics Analysis)Creation/Acquisition of Diverse and Focused Libraries with Efficient Reagent Utilization and Reaction Optimization. “Scientist-Friendly” Integration of RoboticsSynthesis/Acquisition of “Drug-Like” MoleculesAnalytical and Chemical Data Storage, Retrieval, & AnalysisBiological Data AnalysisAssociation of Genomics, Chemical, Biological & Modeling Data

Diversity Estimation

Several applications of Diversity Estimation…

Selection of a diverse or focused subset of compounds from a “real” or “virtual” libraryComparison of a proposed library to the corporate library or to commercially available compoundsSelection of “nearest neighbors” to an identified “hit” or lead compoundSelection of reagents using a reactant-biased, product-biased strategy (Pearlman & Smith UT Austin)

Disease Targets through “Bio-Space” Libraries

Diseases for which well known targets exists (i.e.)

CancerCNSDiabetes

Rapidly Identify New Drug Candidates for Orphan Receptors from Genomics and Proteomics projects.

Data Mining / HTS Screening

Summary…

Leads

Comp. Chem.

Comp. Chem.

Che

m.

Che

m.

F. Lib.F. Lib.

HTS

HTS

Opt. DrugOpt. Drug

Com

p. C

hem

.

Com

p. C

hem

. ClinicalClinical

Pre

-Cli

nic

.P

re-C

lin

ic.

BiologyBiology

Chem

.

Chem

.

HitsHits

Comp. Chem.

Comp. Chem.

Lib

rary

Lib

rary

HTSHTS

ConclusionsSuccessful Drug Discovery…Robust InformaticsEffective Utilization of Genomics and Proteomics Data Consideration of Diversity and “Drug-like” Quality of Scaffolds Effective Application of HT Synthesis & HTSHT-QSAR, QSPR, Predictive ADMET, and Bioavailability calculationsDB miningTeamwork, Cooperation, and Information Sharing