EMERGING DISCOVERY PROCESS

Overview of the Biotech Industry
Srinivasan Seshadri, CEO, Strand Genomics

EMERGING DISCOVERY PROCESS
The drug discovery process is currently being transformed by emerging technologies EMERGING DISCOVERY PROCESS Biological validation Optimise leads Identify compounds to interact with target lead Identify disease mechanism – define targets Basic process Current impact of new technologies Fundamentally new approach Batter/faster Better/faster No major breakthrough Old world Molecular biology Physiology Biochemistry New world Genomics Combinatorial chemistry High throughput screening Still reliant on in vitro/in vivo models of disease Greater use of more sophisticated ‘genetic’ models, but currently complex and slow Old world Slow, largely manual chemical synthesis of leads Slow, manual screening on limited range of assays New world More sophisticated/automated (high throughput) screening has increased lead identification productivity by 30 times Rate of compound generation increased by factor >1,000 through combinatorial chemistry techniques (although not clear what % are useful)

GENOMICS: A BIOLOGICAL DEFINITION
Genomics is central to this evolving landscape. The goal of Genomics is to unravel the genetic basis of health and disease providing a huge array of potential drug targets. Given the complexity of the genome and the volumes of data being generated, significant challenges exist in accessing and leveraging this data effectively. New IT based arenas are emerging to do this GENOMICS: A BIOLOGICAL DEFINITION Genomics is the study of the genetic composition of an organism and provides information on the structure, role and genetic linkage of genes. Some gene function is implicated in disease and it is therefore believed that better, more specific information about the origins of a disease will lead to more effective treatments. AT TA GC CG CG GC AT TA The characteristics (phenotype) of each individual. . . . . . and their organs and tissues. . . . . . are determined by their genetic makeup (genotype). Every cell contains the full complement of individual’s genetic material – the genome The genome consists of a length of double standard DNA to which are attached 4 types of molecule of bases. There are 3 billion of these in total Some of these bases code for proteins [the cell manufactures protein using the DNA template). Others fill in the gaps and have no other function. A gene represents a section of DNA which codes for a protein or other functional piece of cellular machinery (e.g., tRNA, rRNA) “Genomics represents a paradigm shift in disease treatment from ‘underlying mechanism’ to ‘root cause” CSO, Genomic-co

BIOINFORMATICS: A DEFINITION
Bioinformatics describes one of two information driven arenas within pharmaceutical drug discovery. . . Focus on this document BIOINFORMATICS: A DEFINITION Description Key applications Relevance for pharmacos Bioinformatics Information technology designed/used to generate and access genetic data and derive information from it. Searching external genomic databases Constructing and managing proprietary databases Extracting information from data Gene expression in health and disease Gene function in health and disease Pharmacos need to be able to effectively access external sources Pharmacos need to create proprietary databases (derived from data from multiple sources) so that they can be tailored to the needs of internal discovery function Targets need to be identified and their role defined leveraging genetic data Informatics Cheminformatics Information technology used to design molecular libraries to interact with identified targets Molecular structure design Structure-activity relationships Molecular library management/manipulation IT solutions required to manage the increasing scale of molecule generation with discovery process Predominantly addressed within pharmacos although may require leveraging links with partners and across multiple geographies

INFORMATICS USED IN DRUG DISCOVERY
. . . and currently assists in leveraging genetic data. As the arena develops, the current boundaries with cheminformatics are likely to blur Analysing sequence data is just the starting point for bioinformatics – the key step will be relating that data back to protein structure J. Craig Venter, TIGR INFORMATICS USED IN DRUG DISCOVERY Define target Target role Identify lead compounds which interact with targets Optimise leads Function/ active site Protein sequence Gene sequence Bioinformatics Cheminformatics Activity Trawl genomic databases for genes of interest Define amino acid sequence derived from gene Determine structure of protein and how it folds into active molecule Define protein activity Define likely molecule structures to interact with target Trawl molecular databases for likely activity against target Refine search/ development of lead compound Links with combinational chemistry and high throughput screening

KEY TECHNOLOGICAL AND SCIENTIFIC HURDLES/CHALLENGES
Many significant hurdles remain – before the value of bioinformatics can be fully exploited High Medium Low KEY TECHNOLOGICAL AND SCIENTIFIC HURDLES/CHALLENGES Key challenges Why important Size of challenge Technology/ IT-based Developing tools which improve human-computer interface Allowing disparate systems to interface with genomic databases Developing industry standard low cost infrastructure to access databases (internal and external) Increasing the efficiency and effectiveness of database mining Lower the barriers for effective use of computers by multiple disciplines to access databases and translate data into user-friendly format IT architectural differences constrain access to databases Developing proprietary infrastructure is often too costly/time consuming Current database data mining generates vast quantities of irrelevant to search criteria Key challenges for bioinformatics Science-based Predicting tertiary protein structure currently carried out using laborious methodologies of moderate efficacy e.g., X-ray crystallography Predicting protein function currently expensive and time consuming Understanding how genes and proteins are expressed/modified in vivo currently unknown Most drug targets are proteins Need to have clear understanding of role of protein to drug design Experimental methodologies being developed e.g. gene knockouts, although time consuming and ill-developed Gene structure predicted from genetic sequences may not reflect the gene expressed in vivo, and proteins can be modified into alternative structures with differing function to those predicted Source: interviews, press search

BIOINFORMATICS HIERARCHY OF POSSIBILITIES
The current activity is only a small part of what bioinformatics (integrating with the other emerging technologies) could contribute to the way we understand and treat disease BIOINFORMATICS HIERARCHY OF POSSIBILITIES Examples Status Replacing animal-based ‘wet biology’ with computer-based predictive models Very preliminary Insilica research Replacing crystallography to determine protein structure with predictive models Generating better information about disease and patient populations allowing better targeting of clinical trials In early development In medium stage development Increasing the productivity of discovery Mining genetic databases from normal and diseased populations to elucidate gene function Increasing the effectiveness/efficiency of generating information from genomic data Ongoing Increasing the effectiveness and efficiency of genomic database mining Source: interviews, articles

BIOINFORMATICS INDUSTRY EVOLUTION: A DESCRIPTION
To-date, bioinformatics has developed symbiotically with Genomics. It is now emerging as a field in its own right BIOINFORMATICS INDUSTRY EVOLUTION: A DESCRIPTION Academia-driven Genomics-driven Gene function-driven Multiple academic groups leveraging existing IT competencies to develop insights into identification and role of genes As genomic data becomes easier to generate, genomic companies (positional cloners and sequencers) develop IT systems to facilitate access to genome sequences Key differentiating factor is scope and scale of gene databases to which clients have access Focus of effort becomes role and function of genes, and in particular, gene products. Organisations develop IT skills to push the boundaries of knowledge (e.g., predicting protein structure-activity relationships). Key differentiating factor is becoming a company’s ability to provide bioinformatic solutions to extract ‘information’ from genes Source: Team interviews

BIOINFORMATICS INDUSTRY EVOLUTION: KEY MILESTONES
1981 First 579 human genes mapped 1983 Method for automated DNA sequencing (Carruthers & Hood) Key Scientific Milestones 1983 Huntingdon’s disease gene demonstrated to be on chromosome 4 (Gusella) 1991 Expressed sequenced tags (ESTs) created (Venter) 1992 First genetic linkage map of entire human genome published, and first whole human chromosome physical maps (Y and 21) 1977 Chemical method for sequencing DNA devised (Gilbert & Maxam) 1972 first DNA cloning (Boyer & Cohen GENOMICS-DRIVEN ACADEMIA-DRIVEN GENE FUNCTION-DRIVEN 1996/7 Genomic industry broadening value proposition 1990 Human Genome Project launched Genomics/ Bioinformatics Industry Activity 1977 First genetic engineering company, Genentech, founded 1982 Genbank established 1993 Incyte goes public, the first of many U.S. genomic companies to do so 1997 Emergence of bioinformatic players with no genomic heritage e.g., - Pangea - MAG - NetGenics 1988 Human Genome Organisation (HUGO) founded

SUMMARY TECHNOLOGIES Three broad enabling technologies are driving progress in drug R&D : Genomics leads to better disease understanding and target identification Combinatorial chemistry generates more lead compounds High throughput screening tests more leads on a greater number of targets BIOINFORMATICS The explosion of data and the increasing demand for sophisticated analytical tools has given rise to a rapidly growing bioinformatics market with three major service areas : Database providers who generate and organize genome and discovery data Discovery software providers who provide cutting-edge IT solutions to elements of the discovery process Research enterprise ASPs who integrate multiple databases and analysis tools into a single platform

THREE BROAD TECHNOLOGIES ARE DRIVING DRUG DISCOVERY
Study of both structural and functional aspects of the genome, including both genes and proteins, leading to a greater understanding of cellular processes and disease GENOMICS Supported by BIOINFORMATICS HIGH THROUGHPUT SCREENING CATALYTIC/ COMBINATORIAL CHEMISTRY Rapid and systematic generation of a variety of molecular entities, or building blocks, in many different or unique combinations Use of robotic automation to allow for massive parallel experimentation and testing of many compounds or targets

HIGH THROUGHPUT SCREENING COMBINATORIAL CHEMISTRY
MANY SPECIFIC EMERGING TECHNOLOGIES HAVE LED TO THE ADVANCES IN GENOMICS, COMBINATORIAL CHEMISTRY AND HIGH THROUGHPUT SCREENING Antisense Transgenics Gene therapy Pathway mapping Surrogate markers Animal-free disease models Genetic networks GENOMICS HIGH THROUGHPUT SCREENING CATALYTIC/ COMBINATORIAL CHEMISTRY HT DNA sequencing HT proteomics Biochip microarrays Pharmacogenomics Biosensors Synthetic biopolymers Biochemical drug delivery and encapsulation systems Lab Automation Micromachines/miniaturization Intelligent chemical systems CC library arrays Chemical chips Advanced biophysical assays Note : HT = High Throughput; CC = Combinatorial Chemistry

TECHNOLOGIES Three broad enabling technologies are driving progress in drug R&D : Genomics leads to better disease understanding and target identification Combinatorial chemistry generates more lead compounds High throughput screening tests more leads on a greater number of targets BIOINFORMATICS The explosion of data and the increasing demand for sophisticated analytical tools has given rise to a rapidly growing bioinformatics market with three major service areas : Database providers who generate and organize genome and discovery data Discovery software providers who provide cutting-edge IT solutions to elements of the discovery process Research enterprise ASPs who integrate multiple databases and analysis tools into a single platform

BIOINFORMATICS IS THE “BRAINS OF BIOTECHNOLOGY”
In order for Genomics, HTS, and combinatorial chemistry to have impact, they must increasingly rely on bioinformatic capabilities. BIOINFORMATICS “BROAD SCIENCE THAT INVOLVES BOTH CONCEPTUAL AND PRACTICAL TOOLS FOR THE UNDERSTANDING, GENERATION, PROCESSING, AND PROPAGATION OF BIOLOGICAL INFORMATION”1 GENOMICS HIGH THROUGHPUT SCREENING CATALYTIC/ COMBINATORIAL CHEMISTRY Supported by BIOINFORMATICS 1 Science, “Bioinformatics in the Information Age” April 2000; 287; 1221 Source : “Brains of Biotechnology” is from Karl Thiel, Biospace.com

HIGH THROUGHPUT SCREENING COMBINATORIAL CHEMISTRY
NEW TECHNOLOGIES ARE DRIVING THE NEED FOR BIOINFORMATICS DATA AND ANALYSIS CAPABILITIES EXPLOSION OF DATA ANALYSIS TOOLS GENOMICS HIGH THROUGHPUT SCREENING CATALYTIC/ COMBINATORIAL CHEMISTRY Gene (DNA) sequences Protein sequences SNP mapping and disease mapping Gene expression profiles by tissue, species, and drug influence Protein expression profiles Protein:protein interaction profiles Protein structure information CC libraries Screening activity data (SAR) Toxicology databases Sequence alignment searches (BLAST) Relational alignment programs (phylogeny) Virtual lab processes software (PCR, elongation) Protein folding algorithms Structure-based target design using virtual SAR modeling Virtual CC generation and screening ADME and toxicology profiling software NEW TECHNOLOGIES Demand for different types of databases Demand for discovery software

Growth in Global Bioinformatics
The global market for bioinformatics is expected to show significant growth over the next five years. However the state of infancy of the industry poses credibility issues on the estimates from research houses Growth in Global Bioinformatics $10-20Bln $5Bln Numbers likely to include Software solutions Automations tools “Hardware” such as microarrays $300m 1998 2003

Growth in Indian Biotechnology
The current biotechnology market in India is focused on the AgBio, Industrial and Vaccine sectors, but will see emerging opportunities in Bioinformatics and vaccines Growth in Indian Biotechnology 100%=?? Bioinformatics Genome Technologies Vaccines 100%=$500m The future will witness additional opportunities in Informatics and related genome based technologies Industrial 22% Ag Products 25% Health Products 47% 1998 2003 Source: Biosupportinida

Pharmaceutical R&D Budgets
Projected growth in Pharma and Biotech R&D spending will enable the industry to attain its projected targets Pharmaceutical R&D Budgets 100%=100Bln $46B $20B $13B $13B $7B Typical IT budgets will be 10-20% of total R&D Discovery PreClinical Clinical CMC Clinical Trials Production/ manufacturing Source: PhRMa

The Lehman Report consisted of interviews with decision makers in Pharma and Biotech and highlighted some interesting findings Summary of Findings New Biology will significantly increase R&D costs- a large chunk of which are technology driven Companies will see substantial pressure on earnings Attempts to use “today's relatively immature technology” will result in higher failure rates amongst “novel” targets. These failures will likely also stretch out the time period for the arrival of new drugs that Genomics promises High risk of “novel target failure” Less understood (only 8 publications per novel target vs. 100 for those generated by conventional methods Companies pushing these less understood targets through the drug pipeline Traditional chemical technologies will n to be sufficient to identify novel chemical entities that can interact with a target- could have adverse outcomes during the clinical trial process Source: & Company

Assuming no increase in technology
Genomics influenced increase in R&D Costs Assuming no increase in technology More than doubles From current 2010 3.6 3.6 2 2005 3.2 1 1.6 2 2000 1.6 0.8 1995 Total Annual R&D Budget NCEs Annual R&D Budget/NCE output

Assuming moderate increase in technology
Genomics influenced increase in R&D Costs Assuming moderate increase in technology Promise of productivity expansion 2010 2.7 4.4 2 2005 2.6 0.6 1.3 2 2000 1.6 0.8 1995 Total Annual R&D Budget NCEs Annual R&D Budget/NCE output

Most technologies are likely to make an impact only 5-10 years from today
5-10 FROM IMPACT Value Mapout biological Pathways Delineate disease mechanisms Seq Human Genome Map out human proteome Map out human genome We are still years away from the real impact of Genomics technology. Most of them have just got started -Biotech Executive *Integrated technologies include both experimental and informatics approach

Identify Differential
Most technologies are likely to make an impact only 5-10 years from today 5-10 FROM IMPACT Value Identify Differential Expression Profile complex diseases Identify some Cellular proteins Identify key Post-translational modifications It will be a few years before we have a protein chip that is cheap, fast and accurate -Biotech Executive Proteomics will be a big help with target validation. H however, we still need to increase speed and improve Productivity Pharma R&D executive *Integrated technologies include both experimental and informatics approach

Most technologies are likely to make an impact only 5-10 years from today
5-10 FROM IMPACT Assign single Function based On functional Genomics data Value Correlate expression data And protein interaction data Basic protein Structure Homology queries Correlate gene/protein Expression date with function Most of the data mining algorithms are pretty primitive and straightforward today -Biotech Executive We are facing more explosive data produced by Genomics technologies. Unfortunately, the informatics tools are still not there to allow us to explore them fully -Pharma R&D executive *Integrated technologies include both experimental and informatics approach

THRESHOLD LEVEL OF INVESTMENT NECESSARY
Large investments are necessary to reap the benefits of technology THRESHOLD LEVEL OF INVESTMENT NECESSARY iNFORMATICS TARGET VALIDATION LEAD OPTIMIZATION EXPLORATORY DEVELOPMENT Threshold annual Expenditures Key means/technol ogies to achieve impact at bottleneck $20-40m $20-40m $ $20-30m Process improvements Pharmacogenomics Computer aided trial design Bioinformatics Chemoinformatics Clinical Informatics Functional Genomics tools Database subscriptions Closed loop chemistry ADME HTS

BIOINFORMATICS PRODUCT/SERVICE MODELS
There are three broad organizational models emerging BIOINFORMATICS PRODUCT/SERVICE MODELS Provide user friendly access to proprietary and public gene databases compatible with customer IT architecture Requires bioinformatic and genomic competencies/assets Assumes customer does not need to develop significant in-house capabilities Gene Database Designer Discovery Services Provider Conduct discrete stages of discovery process Requires broad informatic and drug discovery capabilities Value proposition built on superior informatic capabilities IT Architects Provide off-the-shelf/bespoke informatic solutions Requires leading edge bioinformatic capabilities Assumes customer has in-house skills and competencies to be able to leverage and manipulate genetic data “The trouble is that bioinformatics is so new, and the market so ill-defined, that companies are having difficulty settling on the business model they will follow” In Vivo Source: ; press search

ANALYTICAL CAPABILITIES
THESE DEMANDS FOR BIOINFORMATICS ARE ADDRESSED BY THREE MAJOR SERVICE MODELS... RESEARCH ENTERPRISE ASPs INTEGRATED Provide user friendly interface that can access both off-the-shelf bioinformatics software and more sophisticated IT solutions Require extensive IT capabilities DATABASE FOCUS DATABASE PROVIDERS DISCOVERY SOFTWARE PROVIDERS Provide access to proprietary and public databases, e.g., gene and protein sequences Require data acquisition assets (e.g., Genomics heritage) along with solid bioinformatics capabilities Provides cutting-edge computational solutions to discrete components of the discovery process Requires extensive expertise in drug discovery and bioinformatics capabilities NARROW SIMPLE COMPLEX ANALYTICAL CAPABILITIES Source: analysis

...AND MANY PLAYERS HAVE ADOPTED EACH SERVICE MODEL
INTEGRATED RESEARCH ENTERPRISE ASPs eBioinformatics DoubleTwist NetGenics Viaken Base4 Bioreason DATABASE FOCUS Strand Celera Genomics Structural GenomiX Incyte Compugen Hyseq Tripos Molecular Simulations Spotfire NARROW DATABASE PROVIDERS DISCOVERY SOFTWARE SIMPLE COMPLEX ANALYTICAL CAPABILITIES Source: analysis; company websites

CORE BELIEFS AND CHALLENGES FOR EACH BUSINESS MODEL
It is not yet clear which if any of the current approaches will prove sustainable CORE BELIEFS AND CHALLENGES FOR EACH BUSINESS MODEL Service model Gene Database Designer IT Architects Discovery Services Provider Core beliefs Databases sufficiently fragmented thus rendering inefficient for pharmacos to ‘go it alone’ Ability to remain ahead of pharmacos vis-a-vis technological innovation Genomic heritage a prerequisite for success IT skills are the defining basis of competition not knowledge of Genomics IT solution will not emerge from existing pharmaco IT suppliers Ability to remain ahead of other entities vis-a-vis technological innovation Pharmacos will increasingly seek discovery-oriented solutions requiring broader skill set (increasing proportion of research investments are external) Value creating in longer term as provides a base for full integration Issues Multiple public databases challenging role of proprietary databases Pharmacos are developing skills to create bespoke databases in-house Real risk that skill could become a commodity (e.g., cost of sequencing a bacterial genome fell from $12m to $0.5m in 1997) Unclear who are the natural owners/developers (“several pharmacos have thought about this longer than we have we need to stay on the cutting edge” VP S&M Molecular Applications Group) Clear potential for non pharma IT players to enter market Potential commoditisation of services Not clear under which conditions pharmacos will outsource discovery functions Issues of skills, critical mass and focus present real challenges to companies developing from a Genomics/IT heritage Source: Team interviews; articles

CATEGORISING TODAY’S BIOINFORMATICS COMPANIES
The traditional genomic companies are polarising into two categories; those that design databases, and those are broadening their value proposition to encompass ‘discovery’ offerings. The new breed of bioinformatic companies are establishing themselves in a third category – IT architects CATEGORISING TODAY’S BIOINFORMATICS COMPANIES Product services providers Gene database designers Building and distributing annotated gene databases and services from public and private Alphagene Digital Gene Technologies Inc. Genome Therapeutics Corp. human Genome Sciences Inc. Hyseq Incyte Pharmaceuticals Myriad Genetics Sequana Therapeutics IT architects Building IT systems to enable the sequencing, synthesis and access of genomic data Base 4 bioinformatics Genecodes GeneTrace Systems Genomica Corp Informax Inc. MDL Information Systems Inc. Molecular Applications Group Molecular informatics Inc. Netgenics Oncormed Oxford Molecular Pangea Systems Inc. PE Applied Biosystems Discovery Services Provider Conducting discrete stages of the drug discovery process using proprietary systems and knowledge Acacia Biosciences Affymetrix Ariad Pharmaceuticals Chiroscience (acq. Darwin Molecular) Exelixis Pharmaceuticals Inc. Genelogic Genetech Millennium Mitokor Ontogency Pharmagene Progenitor Structural Bioformatics Inc. Xenometrix Source: Annual reports; text lines; interviews; team analysis

MOST OF THE LATEST R&D TECHNOLOGIES WERE DEVELOPED OUTSIDE BIG PHARMA
Genomics Chem-informatics Bio- informatics Transgenic animals High throughput screening Pharmaco- Genomics Combinatorial Chemistry Proteomics Molecular modelling Antisense

HT DNA Sequencing Technology basics Competitive landscape
Status and current issues Typically, a sample of DNA is amplified using PCR* with specific fluorescent probes for AGTC; separated by electrophoresis through automated technology and DNA sequence is analyzed. For sequencing of both genomic DNA and expressed genes (cDNA) Supplements DNA mapping and positional or functional cloning Many players are involved in sequencing the genome, contributing to both proprietary and public databases : Public : Human Genome Project Human Genome Sciences Incyte Genomics Celera Genomics Entire human genome will be sequenced by end of 2001 (Celera appears to be leading the way) All 3 billion nucleotides, on 23 pairs of chromosomes, composing about ~100,000 genes! Sequencing does not provide any insights about gene function, merely a blueprint for proteins Viability of business model for companies only sequencing DNA is questionable. Most recognize need to move towards functional Genomics and protein studies Patents on genes or gene fragments (expressed sequence tags, or ESTs), without annotated function data, are not likely to be approved DNA SEQUENCING TECHNOLOGY Nucleotides/day Old method : 1,000,000s** 1000s 1990 2000 * PCR refers to Polymerase Chain Reaction, a technique for amplifying specific sequences of DNA ** Celera’s shotgun approach and powerful computers can sequence 11,000,000 nucleotides per day

HT Proteomics Technology basics Competitive landscape
Status and current issues Analysis of proteins and protein expression in diseased and normal states Proteomics deals with two areas: protein sequence, expression, and modification analysis using techniques of protein separation, including 2-dimensional electrophoresis (2-DE) and protein chips, and identification, typically involving mass spectrometry 3D structure analysis by X-ray crystallography and nuclear magnetic resonance (NMR), as well as complex computer modeling. These structures are useful for structure-based drug design. Fewer companies are engaged in HT proteomics work than HT DNA sequencing Key players in HT proteomics : Oxford GlycoSciences Large Scale Proteomics Corp. Proteome Inc. Ciphergen Biosystems Players in 3D protein folding (mostly software) include : Structural GenomiX, Inc Structural Bioinformatics Bio-IT Ltd. Protein function depends on 3-D structure and at present, even the best computer software is not good at modeling protein structure Understanding how proteins are modified after expression, especially in the presence of drugs and/or disease, will dramatically aid drug development PROTEIN ANALYSIS TECHNOLOGY Proteins analyzed/day 100,000s 1000s <1 1990 2000 Prototypes* * Prototypes, which should be commercial within 2 years, involve high throughput separation techniques (HPLC) and advanced mass spectroscopy (MALDI-TOF) Source: Science journals, popular press, public biotechnology reports

Biochip Microarrays Technology basics Competitive landscape
Status and current issues Current market for biochips is about ~$175 Million, and is dominated by Affymetrix; however, many new players are entering the market with alternative chip technologies : Nanogen (electroactive chips) Illumina (fiber optic bead-based) Sequenom (“industrial Genomics” with mass spectroscopy) Ciphergen (protein chips) Affymetrix business model : It nearly “gives away” a detection machine ($175,000) and then hopes to make money from the sale of its disposable GeneChips (Razor blade approach) Biochip microarrays are ordered sets of known molecules (DNA, proteins, etc…) attached to a solid support (silica, fibers, etc…) that allow for a vast number of parallel experiments in miniature. DNA chips are made by either “building” short sequences of DNA on chips or by attached pre-made oligonucleotides (short pieces of DNA) to the chip Expressed cDNA prepared from samples is then allowed to interact with the DNA on the chips and these interactions are detected. This same principle can be applied with proteins and small molecules As of today, chips with ~250,000 probes are commercially available; in near future, probes representing entire genomes should be available “The use of DNA arrays to interrogate biological information represents a paradigm change that will profoundly alter biology and medicine” Dr. Leroy Hood University of Washington Uses for biochip microarrays are exploding : gene sequencing polymorphism identification genetic testing gene expression profiling toxicology analysis forensics immunoassays proteomics drug screening Source: Literature, BioInsight

GENE CHIP MICROARRAYS ARE SMALL GRIDS CONTAINING PIECES OF DNA
Technology Basics Gene (or DNA) chips are grids Each square (feature) on the grid contains the same known repeating DNA sequence Different squares contain different sequences T A T C T G T T A T C A T G A T A T G A T G C G T G T T C A C T T C G C T A T A T C A T G A G A C A C A C G A C T A G A G C G A A G T A A C A G A T G A T G C T G T G T G A G C G G T G A G C G G T G C A C G C G G C T C A T C T C G T C A G C A C C G C T Because the DNA sequence is known at each location on the DNA chip, unknown probe sequences can be determined by monitoring where on the DNA chip these probes stick C G A G C G C C G T A C A C C A G C A T Add mixture of unknown flourescently labeled probes to DNA chip T A . Probes stick (hybridize) to squares that have a similar sequence to the probe A laser reads out which squares the probes stick to Software makes the information intelligible

BIOCHIPS CAN BE USED IN GENE EXPRESSION MONITORING AS A POWERFUL TOOL FOR IDENTIFYING KEY GENES INVOLVED IN OR AFFECTED BY DISEASE PROCESSES Technology basics Approach Compare readouts from chips exposed to healthy and diseased samples (probes) Differences (dashed boxes) indicate genes that may be involved in the disease process Gene products (proteins) from these genes may serve as good disease targets, therapeutics, or markers DNA RNA Healthy Tissue Cell Probes DNA chip DNA RNA Diseased Tissue Cell Probes DNA chip Find healthy and diseased individuals Isolate healthy and diseased tissues Isolate RNA from each sample (RNA tells us which genes are turned on) Make fluorescently labeled probes from RNA (probes are pieces of DNA that represent genes which are turned on) Expose DNA chips (which have thou-sands of known genes on them) to probes – probes will only stick to DNA chips in certain loca-tions (see next page)

THE COMPETITIVE LANDSCAPE FOR BIOCHIP ARRAYS IS HEATING UP AS THE TECHNOLOGY RAPIDLY EVOLVES
Five example companies and their technologies Uses of biochip microarrays continues to expolode : Gene sequencing Polymorphism identification Genetic testing (genotyping) Gene expression profiling Toxicology analysis Forensics Immunoassays Proteomics Drug screening Many others Affymetrix Disposable GeneChip array has oligos* attached to it by photolithography Early leader in biochip development Oligos are bound by fluorescent probes Nanogen Pre-made oligos are bound to reuseable semiconductor chip Electroactive spots on chip direct and move attached oligos, which interact with fluorescent probes Illumina Oligos (or drugs, proteins) are attached to micro-beads, which self-assemble onto the tips of fibers in an optical fiber bundled microarray Analyzed by fluorescence with fiber optics Sequenom MassArray chips have oligos attached to them Analysis by laser-ionization and mass spectroscopy Called “industrial Genomics” Over 75 public and private Biotech firms make biochip technology CipherGen ProteinChip array has defined proteins (like antibodies) bound to it which interact with ligands in the sample Analyzed by laser-ionization and mass spectroscopy * Oligos are oligonucleotides, or short (25 bases) sequences of DNA Sources : Press reviews, scientific journals, company reports

WHILE AFFYMETRIX HAS DOMINATED THE BIOCHIP MARKETPLACE, STRONG COMPETITION FROM NEW BIOCHIP TECHNOLOGIES WILL LIKELY FRAGMENT THE SECTOR FURTHER Competitive Landscape Market Share percent, 1999 Affymetrix Incyte Phase -1 Caliper ACLARA Ciphergen Homemade Other 1999 Market ~$176 Million Trends in competitive landscape Biochip market expected to grow to ~$1 Billion by 2005 New biochip technology players will cut into Affymetrix’s marketshare Use of homemade chips will likely decrease as complexity and versatility of commercial chips increases The market for hardware and bioinformatic software for chip detection and data collection/ analysis will also explode Source: lLiterature; BioInsights

Pharmacogenomics Technology basics Competitive landscape
Status and current issues Every individual has a distinct set of “polymorphism” or gene variants. These variants could lead to enhanced or diminished responses to therapy. It applies genetic testing techniques to identify these variants that are predictive of a patient’s response to a therapeutic agent Pharmacogenomics can be used to: increase the likelihood of a drug’s success in the clinic by identifying patients who are more likely to have responses to drugs rescue previous drugs who failed or were taken off the market for safety concerns by identifying safe patient populations Key players include : Genset is working on a map of SNPs for clinical testing (with Abbott Labs) Others companies include: Affymetrix Celera GeneLogic Incyte LJL Biosystems Lynx Therapeutics Millennium Predictive Medicine Pharma community is in agreement that pharmaco-Genomics is important - but its effects are uncertain: “The FDA has asked us (senior pharma people) to come in and discuss pharmacogenomic testing with them” B. Michael Silber Director of Clinical Diagnostics Pfizer “Rescuing drugs has the potential to absolutely take off, or it might not” Greg Miller, Head of Molecular Profiling, Genzyme

Lab Automation Technology basics Competitive landscape
Status and current issues Key players include : Robotics : LJL Biosystems, Robocon, Zymark Microplate : Perkin Elmer, Molecular Devices, Dynex Liquid : Beckman Coulter, Gilson Software : Oxford Molecular Group, Tripos, MSI, MDL Information systems With the explosion of compounds from combinatorial chemistry and the accelerated identification of gene targets from Genomics, the ability to analyze and screen compounds becomes critical rate-limiting step. So highly automated lab technologies have developed in four major areas : Microplate readers and equipment Liquid handling, manipulating, and dispensing devices Robotics Software to control the process Likely to see high growth in the next few years as lab automation increases Miniaturization will lead to lower reagent costs; likely value shift to equipment and software Huge need for quality bioinformatics software that is capable of data acquisition/ collection as well as data analysis and storage. Liquid Handling/Manipulating/ Dispensing dispensers workstations organic synthesizers solid-phase extraction devices Robotics and Software Microplate-related equipment Market breakdown by sector 1998, Total market $1.1 Billion 1993 1998 2003E $, Millions Lab Automation market (WW) 13% CAGR Source: Literature, Genetic Engineering News

Database suppliers/designers
Technology basics Competitive landscape Status and current issues Provides remote access to their proprietary database, as well as public ones; typically using an internet or intranet platform Data acquisition skills (e.g., DNA sequencing heritage) is a prerequisite for success in this segment Generally, three main revenue models : Subscription-based access Royalties-based and shared risk Fee-for-service Key players include : Celera Genomics (subscriptions to gene database, ESTs) Incyte Genomics (online “Incyte 2.0” : LifeSeq and LifeExpress databases) Human Genome Sciences (exclusive databases for Human Gene Consortium) GeneLogic (Expression databases) AlphaGene (DNA) Hyseq (GeneSolutions.com provides access to proprietary data) Myriad Genetics (ProNet, a protein:protein interaction database) Sequana Genset (SNPs database) Orchid Biosciences (SNPs) Oxford GlycoSciences (LifeExpress with Incyte) Multiple public databases, like GenBank, are challenging the role and importance of proprietary databases in many areas (especially Genomics). Many pharmacos/biotechs are developing their own bioinformatics skills to handle databases in-house Large risk that gene data acquisition skills could be commodity (and therefore limit value of proprietary databases), e.g., cost of sequencing a bacterial genome fell from $12m to $0.5m by 1997 Belief that current databases are fragmented and inefficient - leading many pharmaco/biotech firms to outsource database management Source: Literature; press releases

Discovery Software Providers
Technology basics Competitive landscape Status and current issues Provides cutting-edge informatics solutions to discrete components of the discovery process, e.g., protein folding or CC library selection and screening Drug discovery process is increasingly seeking more sophisticated IT solutions/software that require a specialized skill set Requires deep expertise in drug discovery as well as leading edge bioinformatics/IT capabilities Simply put, these are drug discovery tool kit companies Key players include : Structural Bioinformatics, Inc. (structure-based target id using sophisticated protein structure modeling and database) Tripos (offers several discovery tools, including FlexX, a virtual CC library software) Molecular Simulations, Inc. (Pharmacopeia subsidiary, software simulates molecular interactions of drugs, proteins) Compugen’s LabOnWeb.com (aimed at early gene sequence PCR work) Bioreason (chemical entity analysis programs) Spotfire (decision analytic software aimed at researcher productivity) Molecular Mining Corp. Not clear which activities will be outsourced and which will be developed in-house Critical mass, skills, and focus are important issues for firms developing from a data acquisition heritage. Value proposition must include superior IT tools. Source: Literature; press releases

Research Enterprise ASPs
Technology basics Competitive landscape Status and current issues Offer ASP platforms that integrate broad databases and sophisticated IT applications, coupled with “research portal” functionality. Provides user-friendly interface that offers a suite of off-the-shelf bioinformatics solutions enabling users to access broad range of applications for data and analysis Requires leading edge IT capabilities, but does not rely on any specific drug discovery or data acquisition knowledge. Key players include : DoubleTwist (leader in the research enterprise ASP space; formerly Pangea) eBioinformatics Base4 (collaborative knowledge and project management platform with database handling applications) NetGenics (subscription ASP distributing computing platform with broad discovery applications) Genomica (Discovery Manager software suite) Viaken (a premier life science ASP for database hosting and analytic software) Clear potential entry point for non-life science IT players Potential threat of commoditization of services Unclear who are the natural owners of this space Source: Literature; press releases

EMERGING DISCOVERY PROCESS

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EMERGING DISCOVERY PROCESS

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Presentation on theme: "EMERGING DISCOVERY PROCESS"— Presentation transcript:

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About project

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