Compugen Ltd. · NASDAQ:CGEN
Compugen — RDLeaders Forum 2004
30 slides from Compugen's conference presentation, 2004
Slide 1: Increasing the Probability of Success in Discovering Validated Targets, Therapeutic Proteins, and Diagnostic Markers
Increasing the Probability of Success in Discovering Validated Targets, Therapeutic Proteins, and Diagnostic Markers Mor Amitai, Ph.D. President & CEO, Compugen Ltd.
Slide 2: Agenda
Agenda • Introduction • Genomic Infrastructure • Discovery Engines: Therapeutic Proteins • Discovery Engines: Biomarkers • Lead Discovery • Summary
Slide 3: Compugen
Compugen • Compugen is a drug and diagnostic discovery company that focuses on increasing the probability of success of drug and diagnostic development. • Founded 1993, public since 2000 • 150 employees: Israel, US
Slide 4: R&D Process
R&D Process Analysis, mathematical modeling, predictions Lab studies, Computational validation, Manual proof
Slide 5: Unique Structure
Unique Structure • Small teams comprised of biologists/chemists/physicians, mathematicians, and software developers work together side-by-side • Both biologists and mathematicians program and run experiments • No “bioinformatics services” group • Very fast development cycle
Slide 6: Agenda
Agenda • Introduction • Genomic Infrastructure • Discovery Engines: Therapeutic Proteins • Discovery Engines: Biomarkers • Lead Discovery • Summary
Slide 7: The LEADS Process
The LEADS Process • Computational biology platform • Accurate modeling of transcriptome • In-silico process, wet validation • Used by, among others: • Unique modeling of biological phenomena
Slide 8: The LEADS Process
LEADS The LEADS Process Create an annotated transcriptome and proteome Cleaning Mapping Clustering Assembly Post-Assm GO SNPs Tissue Homology Proteins Annotated Transcriptome
Slide 9: Some Underlying Components
Some Underlying Components • Novel clustering algorithms • Advanced graph-analysis methods • Advanced statistical analysis • Improved multiple-alignment algorithms
Slide 10: Alternative Splicing
Alternative Splicing Genome Res. July 2002 Science. May 2003 Genome Res. July 2003 Trends in Genetics. Feb. 2004
Slide 11: Antisense
Antisense Sco
Slide 12: Agenda
Agenda • Introduction • Genomic Infrastructure • Discovery Engines: Therapeutic Proteins • Discovery Engines: Biomarkers • Lead Discovery • Summary
Slide 13: Therapeutic Proteins Engine
Therapeutic Proteins Engine Biological phenomena Engine monitored Engine and improved LEADS Models and Experts “Wet” queries Additional data Mining Novel proteins with desired features
Slide 14: Initial Pipeline
Initial Pipeline • Internal focus: therapeutic proteins and biomarkers • VEGF-114 • CGEN-40 • PSA-LM and K-LM • CGEN-G11 • CGEN-B2 • CGEN-R1
Slide 15: VEGF-114
VEGF-114 • Shortest known variant of VEGF • Patent granted – Even though no new amino acids • Licensed to MultiGene Vascular Systems (MGVS) for gene and cell therapy in cardiovascular disease • Further validated by MGVS – Shown to have significant effect on endothelial cell proliferation and apoptos
Slide 16: VEGF-114 Proliferation Assay
VEGF-114 18000 16000 cell number (*10) 14000 12000 10000 8000 6000 4000 2000 0 1 7 10 days GFP V114G V165G control Proliferation assay with recombinant retro VEGF114 transduced EC EC were transduced with recombinant retro VEGF114–GFP, VEGF165– GFP or GFP viruses. As additional control group served n
Slide 17: CGEN-40 Candidates
CGEN-40 Candidates • 5 novel secreted forms – Both agonists and antagonists • Existence verified in the lab • Natural molecules – probably: – Fewer side effects – Less immunogenic • Potential indications: autoimmunity, transplantation, inflammation, cancer
Slide 18: Binding Characteristics of CGEN-40 by BIACore Analysis: Dose-Response
Binding Characteristics of CGEN-40 by BIACore Analysis: Dose-Response RU 800 700 600 500 Resp. Diff. 400 300 Control/WT Protein 200 100 0 -100 0 100 200 300 400 500 600 700 800 900 1000 RU Time s 800 700 600 500 Resp. Diff. 400 300 Variant 1 200 100 0 -100 0 100 200 300 400 500 600 700 800 900 1000
Slide 19: Bioactivity Characteristics of CGEN-40 in In-vitro Cell-Based Assay
Bioactivity Characteristics of CGEN-40 in In-vitro Cell-Based Assay: Dose-Response 500 RANTES (pg/ml) 400 300 WT Control Variant 1 200 100 0 0 2 10 50 150 300 CGEN-40 (nM) ¾ CGEN-40 inhibits stimulated production of RANTES in-vitro
Slide 20: Agenda
Agenda • Introduction • Genomic Infrastructure • Discovery Engines: Therapeutic Proteins • Discovery Engines: Biomarkers • Lead Discovery • Summary
Slide 21: Cancer Biomarker Engine
Cancer Biomarker Engine Dry analysis Wet analysis Dry Oligo Identify Microarray Analysis design candidates over- expressed in cancer Quantitative IP RT-PCR Initially validated novel markers
Slide 22: Biomarkers – Initial Results
Biomarkers – Initial Results
Slide 23: PSA-LM and K-LM
Scientific Leadership PSA-LM and K-LM Licensed to DPC J Biol Chem. 2002 May 17;277(20):18084-90
Slide 24: Engines Summary
Engines Summary • Engines feed our internal pipeline • Rely on integration of mathematical modeling and wet biology • Unique IP stems from accurate modeling • Current engines focus on TP and Dx, and validate capability for additional discovery engines • Promising candidates quickly identified; initi
Slide 25: Agenda
Agenda • Introduction • Genomic Infrastructure • Discovery Engines: Therapeutic Proteins • Discovery Engines: Biomarkers • Lead Discovery • Summary
Slide 26: Lead Discovery
Lead Discovery • Novel approach to drug lead identification • Given: A target protein, reliable assay • Provide: A variety of pharmacologically desirable strong inhibitors • Intermediate step: Experimental reconstruction of active site
Slide 27: Approach in Brief
Approach in Brief • Design and build a library containing a complete set of carefully designed small molecules that theoretically complement any/all active sites – <100,000 novel compounds – Not drug-like molecules – One-time effort • Screen target with library • Analyze results, reconstruct active
Slide 28: Advantages
Advantages • Potentially, drug leads for any target • No structure information required • Relatively small yet exhaustive screening library • Great flexibility in design of leads: – Address toxicity issues – Specificity among similar targets – IP: next-generation candidates
Slide 29: Agenda
Agenda • Introduction • Genomic Infrastructure • Discovery Engines: Therapeutic Proteins • Discovery Engines: Biomarkers • Lead Discovery • Summary
Slide 30: Summary
Summary • Increasing POS of drug discovery through: – Mathematical models and algorithmic approach – Processes that integrate “dry” and “wet” – A better understanding of biological phenomena – Iterative improvements to processes • Quickly established attractive pipeline of therapeutic proteins and b