Asia’s Largest Global Software & Services Company Genomes to Drugs: A Bioinformatics Perspective Sharmila Mande Bioinformatics Division Advanced Technology

Asia’s Largest Global Software & Services Company

Genomes to Drugs: A Bioinformatics Perspective

Sharmila Mande

Bioinformatics Division

Advanced Technology Centre


Data types along the drug discover chain

Lead Lead

Optimisation Pre-clinical trials

Target

Validation

Target

Identification Identification

Genomics

SNP

EST/cDNA

Gene expression

Proteomics

Chemistry

HTS

ADMET

SNP


Traditional drug discovery process

• Development time per drug: 8 to 12 years.

• Development cost per drug: $500- $900 M

• High failure rates - especially in clinical stage


Post-genomics scenario

• Reduction of drug development time• Reduction of cost of developing drugs

– Develop high quality information early in drug discovery process

– Faster identification of new drug targets– Higher quality target and leads– Reduction of drug failure rates


Design principles

• Do experiments in silico first

Synthesis“Real World”

Bioassay

Selection Analysis

“in silico World”

Hypothesis


Whole genomes

• Complete genome sequences of – about 138 microbial organisms– Human– Parasites– Plant

• Possible to develop novel cures


Drug Target Identification

Three possible cases:

• Human protein is not functioning properly, or its activity needs to be modified.

Sickle cell anemia

• The potential target is a key protein from infectious organism, and has no counterpart in humans

Bacterial Cell wall

• The target is a protein from an infectious organism and a homologous protein exists in humans.

DHFR


How Many Drug Targets ?

• Present day therapy addresses only 500 molecular targets

• Cell membrane receptors constitute the largest class of current drug targets

• Human and other microbial genomes suggest that many more targets may be available


Target Identification: Comparative Genomics

• Target identification in the pre-genomics era was an extremely tedious and time consuming process.

• Genomics approaches - a major development in recent times in drug discovery

e.g. broad spectrum antibiotics

drugs against malaria parasite


Target Validation

• There is an opportunity to identify many more targets

• Bottleneck in validation of drug targets


Computational Approaches in Drug Design

without prior knowledge of receptor structure

Generation of a pharmacophore

3D arrangement of functionally important parts of molecules

Quantitative structure-activity relationships

with prior knowledge of receptor structure

Docking

Scan a database for optimal fitting of inhibitors

De-novo design

Generate a “custom” ligand for a given active site


Drug Design Cycle


Computational Approaches in Drug Design: No prior knowledge of receptor structure

Combinatorial Chemistry


Cyclic urea inhibitor of HIV protease from pharmacophore hypothesis.

Pharmacophore generation


Computational Approaches in Drug Design: Prior knowledge of receptor structure

• Captopril, the first therapeutic drug from structure based studies

• Antihypertensive

• Angiotensin converting enzyme modelled on carboxypeptidase

• 30,000 fold improvement in inhibitory activity obtained, from the first lead N-succinoyl-prolin to captopril


Examples of Structure Based Drug Design

HIV protease with amprenavir

Influenza with neuraminidase inhibitor


If HTS is available, why do docking at all?

Protein Tyrosine Phosphatase 1B inhibitors:

• 400,000 compounds screened by HTS vs. 365 compounds high scoring docking compounds.

• Hit-rate by computational approaches 1700 times higher. Computationally identified compounds more “drug-like”.

Dihydropicolinate reductase inhibitor:

• hit rate < 0.2% in HTS and > 6% in computer-based approach


Lead Optimization

Enzyme Enzyme Enzyme

active site

Ki~1mMKi~1-10 nM

Receptor/Enzyme

Receptor/Enzyme

Receptor/Enzyme


ADMET

Drug failures

• 39% Poor pharmacokinetics

• 11% Toxicity

ADMET prediction

• in-silico approaches


In silico ADMET

Identify• Non soluble compounds• Non permeable compounds• Non metabolically stable compounds• Toxicophores


Bioinformatics success stories

• Quick target identification (Merck)– potential drug targets for schizophrenia

• Expedited drug candidate identification (SKB)– drugs for bone tumor (target cathepsin K)

• Poor drug candidate elimination (Aventis vs GSK and SP)– anti IL-5 therapy against asthma


• Part of Advanced Technology Centre

• 40 persons (9 Ph.D’s)

• Undertaking R&D in selected areas of computational biology

TCS’ Bioinformatics Division

3D Modelling

Drug Design

Sequence Analysis

Structural Analysis

Structural Manipulations

Simulation

TCS

Bio-Suite

Genome Analysis

Comparative Genomics


Thank You

Documents

Asia’s Largest Global Software & Services Company Genomes to Drugs: A Bioinformatics Perspective Sharmila Mande Bioinformatics Division Advanced Technology