Companion Biomarkers in Drug Development
The term "companion biomarker" means that a particular diagnostic test is specifically linked to a therapeutic drug either in drug development or in the clinic. Biomarkers of disease have long played an important role in diagnostic medicine as evidenced by the intense use of specific clinical laboratory tests in the diagnosis of disease. Biomarkers can be used in five very distinct ways in drug development:
1) companion biomarkers can be correlated with biological events during drug development in order to validate drug targets or to predict drug response;
2) biomarkers can be used as companion diagnostics in drug development to characterize patient populations in order to better understand the extent to which new drugs reach intended therapeutic targets can alter proposed therapeutic pathways and achieve successful clinical outcomes;
3) biomarkers can be used to stratify patient populations for drug response in primary prevention or disease-modification studies, particularly in specific clinical areas such as neuron degeneration and cancer;
4) clinically useful biomarkers are becoming increasingly useful to make proper therapeutic decisions regarding candidate drugs; and
5) clinically useful biomarkers are becoming increasingly required by the FDA and other outside authorities to make proper regulatory decisions regarding candidate drugs.
This report describes new biomarker technology platforms developed for the analyses of drug targets that are connected to the effectiveness of therapeutic agents in a clinical setting. The emphasis is on those companies that are actively developing and marketing new companion diagnostic tests for performing biomarker tests during drug development, as opposed to the more routine and clinically accepted companion markers that are manufactured and marketed by large diagnostic companies for routine clinical use.
Table of Contents
- 1. Overview13
- 1.1 Statement of Report13
- 1.2 About This Report13
- 1.3 Scope of the Report13
- 1.4 Objectives13
- 1.5 Methodology 15
- 1.6 Executive Summary16
- 2. Introduction: Companion Diagnostics in Drug Development 19
- 2.1 Companion Diagnostics as Biomarkers20
- 2.1.1 Potential Benefits of Biomarkers as Companion Diagnostics22
- 2.2 Biomarkers in Different Phases of Drug Development22
- 2.2.1 Drug Discovery and Development Process22
- 2.2.2 Biomarkers in Drug Development24
- 2.3 Drug Targets 24
- 2.3.1 Target Discovery Using Functional Genomics26
- 2.3.2 Functional Genomics26
- 2.3.3 Target Validation28
- 2.3.3.1 Target Discovery28
- 2.3.3.2 Lead Identification 28
- 2.3.4 Target and Biomarker Discovery29
- 2.3.4.1 Biomarker Validation29
- 2.4 Biomarkers in Drug Discovery, Development and Clinical Diagnostics29
- 2.4.1 Role of Biomarkers in Drug Discovery, Preclinical, Clinical Development and Diagnostics29
- 2.4.2 The Pipeline Problem31
- 2.4.3 Biomarkers in the Drug Discovery Process32
- 2.4.4 Segmentation of Biomarker Usage32
- 2.4.5 Efficacy of Biomarkers as Surrogate Endpoints33
- 2.4.6 Biomarkers Used to Reduce the Cost of Drug Development34
- 2.4.7 Biomarkers: Challenges and Opportunities34
- 2.4.8 Biomarkers in Early Safety and Toxicity Assessment35
- 2.4.9 Biomarkers in Determining Validation Parameters35
- 2.4.10 Challenges in Development of Biomarkers36
- 2.4.11 Using Biomarkers in Early Clinical Development36
- 2.4.12 Translational Biomarkers36
- 2.4.13 Use of Biomarkers in "Go"/No-Go" Decisions37
- 2.4.14 Diagnostic Tests37
- 2.4.15 Biomarkers in Deal Making 37
- 2.4.16 Payors Use Biomarkers in Decision-Making37
- 2.5 World Pharmaceutical Markets38
- 2.5.1 World Market Summary38
- 2.5.2 Company Performance in this Segment 40
- 2.5.3 Forces Affecting the Structure of the Pharmaceutical Industry41
- 2.5.3.1 Threats41
- 2.5.3.2 Competitive Forces42
- 2.6.1 Industry Overview42
- 2.6.1.1 Pharmaceutical Industry Drug Pipeline44
- 2.6.1.2 Asia-Pacific to Replace United States and Europe as Pharmaceutical Industry Center54
- 2.6.1.3 The Changing Pharmaceutical Business Model54
- 2.6.2 Benefits for Companion Diagnostic Tests in Drug Development55
- 2.6.3 Strategies for the Creation of Partnerships - Predicting and Overcoming Challenges in Creating Drug Response Profiling Diagnostics57
- 2.6.4 Options and Applications57
- 2.6.4.1 Clinical Applications of Genomics: The Use of Evidence Based Frameworks by Decision-Makers57
- 2.6.5 Challenges, Drivers and Trends58
- 2.6.5.1 Macro Trends in Biomarkers58
- 2.6.5.2 Biomarkers: Industry SWOT Analysis61
- 2.6.6 Breakaway Technologies62
- 2.6.7 Collaboration for Companion Diagnostics63
- 2.6.8 Key Stake Holders in Companion Diagnostics63
- 2.9 Future Developments65
- 2.1 Companion Diagnostics as Biomarkers20
- 3. Biomarker Development Tools66
- 3.1 New Technologies in Functional Genomics66
- 3.1.1 Genomics-Derived Drug Pipeline66
- 3.1.2 Future of Genomics Technologies for Drug Target Identification66
- 3.2 Overview of Microarrays67
- 3.2.1 General Theory of Microarrays68
- 3.2.2 GeneChip Probe Array Technology69
- 3.2.3 DNA Microarrays69
- 3.2.3.1 DNA Microarray Market Size71
- 3.2.3.2 DNA Microarrays in SNP Analysis72
- 3.2.3.3 DNA Microarrays in Cancer72
- 3.2.4 Protein Microarrays 73
- 3.2.4.1 Reasons Why Researchers Use Protein Microarrays74
- 3.2.4.2 Factors for Adoption of Protein Microarrays Technology74
- 3.2.4.3 Future Innovations in Protein Microarray Technology74
- 3.2.5 New Technologies75
- 3.2.5.1 Antibody Microarrays75
- 3.2.5.2 Peptide Microarrays75
- 3.2.5.3 Peptide MHC Microarrays75
- 3.2.5.4 Tissue Microarrays 75
- 3.2.5.5 Key Points for Developing Microarray Based Applications76
- 3.2.5.6 Reasons Why Researchers use DNA Microarrays77
- 3.2.5.7 Factors for Difficulties Applying DNA Microarrays Technology 77
- 3.2.5.8 Emerging Microarray Trends78
- 3.2.5.9 Emerging Microarray Applications78
- 3.2.5.10 Key Findings on Use of Microarrays79
- 3.2.5.11 Advantages and Drivers of Microarrays79
- 3.2.5.12 Limitations and Barriers to Use of Microarrays81
- 3.2.5.13 qRT-PCR Use in Biomarker Identification and Drug Development83
- 3.2.5.14 Microarray Quality Control (MAQC) Project 84
- 3.3 Theranostics84
- 3.3.1 Theranostics in Drug Development84
- 3.3.2 Trends in Theranostics85
- 3.3.3 Timeline for Impact on Various Segments in Theranostics85
- 3.3.4 Challenges for Biomarker Based Therapeutics Development87
- 3.4 Pharmaceutical Development and Bioanalytical Services88
- 3.4.1 Wyeth Singulex's Erenna88
- 3.5 Metabolomics in Drug Discovery88
- 3.6 Bioinformatics90
- 3.6.1 Definition and Role of Bioinformatics90
- 3.6.2 Bioinformatics Sector Overview93
- 3.6.3 Future Status of Bioinformatics93
- 3.6.3.1 Future in Drug Discovery93
- 3.6.3.2 Mergers and Acquisitions Could Deter Bioinformatics Growth94
- 3.6.3.3 Barriers to Bioinformatics Growth94
- 3.6.3.4 Types of Data and Bioinformatics Applications94
- 3.6.3.5 Validated Core Modeling Technology95
- 3.6.3.6 Applicability of Bioinformatics for Biomarker Discovery95
- 3.6.3.7 Biomarker Data Management Compliant with Industry Standards96
- 3.6.3.8 Data Management for Biomarkers96
- 3.6.3.8.1 Data Transformation for Biomarker Development96
- 3.6.3.8.2 Biomarker Data Collaboration96
- 3.6.3.8.3 Interface for Online Data Sources for Genomic Structures96
- 3.6.3.8.4 Target Markets for Informatics Software96
- 3.6.3.8.5 Bioinformatics Drivers and Challenges in the Pharmaceutical Industry97
- 3.6.3.8.6 Products of Bioinformatics100
- 3.6.3.8.7 Informatics Tools and Functionalities101
- 3.6.3.8.8 Bioinformatics in Lead Identification and Optimization101
- 3.6.3.8.9 Bioinformatics in Drug Development and Formulation102
- 3.6.3.8.10 Role of Bioinformatics in the Drug Discovery Value Chain102
- 3.6.3.8.11 Bioinformatics Software for Drug Discovery and Biomarker Development102
- 3.6.3.8.12 Bioinformatics Services104
- 3.7 Biomarkers and Proteomics105
- 3.7.1 Scientific Background105
- 3.7.2 Applying Proteomics to Biomarker Discovery106
- 3.7.2.1 Challenges Facing Biomarker Developers106
- 3.7.3 Limitations of Proteomic Approaches to Biomarker Discovery108
- 3.7.4 Validation of Biomarkers Using LC-MS/MS Systems109
- 3.7.5 Use of Mass Spectrometry in Biomarker Discovery109
- 3.7.5.1 Multiple Reaction Monitoring Assays (MRMs)110
- 3.7.5.2 Gel-based Approaches110
- 3.7.5.3 Non-Gel-based Approaches 111
- 3.7.5.4 SELDI-TOF MS111
- 3.7.5.5 SELDI and Prognosis112
- 3.7.5.6 SELDI and Treatment Monitoring112
- 3.7.5.7 Limitations of Mass Spectroscopy112
- 3.7.6 Partnerships for Developing Proteomic Biomarkers114
- 3.7.7 Proteomics in Developing a New Cancer Marker114
- 3.7.7.1 Translating Proteomic Oncology Discoveries to the Clinic: Development of Analytical Reference Materials, Reagents, Data, and Technology Assessment and Validation115
- 3.7.7.2 Challenges of Discovering and Validating Clinical Protein Biomarkers115
- 3.7.7.3 Importance of Proteomics in Biomarker Discovery115
- 3.8 Toxicogenomics115
- 3.8.1 Toxicogenomics Concerns in Drug Safety Data 116
- 3.8.2 Toxicogenomics and Prioritization of Drug Candidates116
- 3.8.3 Genomic Biomarkers for Drug-Induced Nephrotoxicity117
- 3.8.4 Use of Biomarkers of Drug-Induced Cardiotoxicity117
- 3.8.5 Use of Biomarkers of Drug-induced Hepatotoxicity117
- 3.8.6 Transgenic Biomarkers for Adverse Drug-Drug Interactions117
- 3.8.7 Challenges to Toxicogenomics118
- 3.8.8 The Future Use of Toxicogenomics in Drug Discovery118
- 3.1 New Technologies in Functional Genomics66
- 4. Market for Biomarkers in Drug Development119
- 4.1 C-KIT (CD117) Expression122
- 4.2 CCR5 -Chemokine C-C Motif Receptor122
- 4.3 CYP2C19 Variants123
- 4.4 CYP2C9 Variants123
- 4.5 CYP2D6 Variants124
- 4.6 CYP2D6 Variants with Alternate Context124
- 4.7 Clinical Biomarkers124
- 4.8 Targeting Kidney Toxicity125
- 4.8.1 Proximal and Distal Tubular Injury (alpha-GST & Pi-GST)125
- 4.8.2 Collecting Duct and Loop of Henle Injury (RPA-1 and RPA-2)126
- 4.8.3 Glomerular Injury (Collagen IV)126
- 4.8.4 KIM-1126
- 4.9 Targeting Hepatotoxicity127
- 4.9.1 Breast Cancer128
- 4.9.2 Colorectal Cancer128
- 4.9.3 Prostate Cancer128
- 4.9.4 Cystic Fibrosis128
- 4.10 Biomarker Application in Oncology Clinical Development128
- 4.10.1 Specific Example of Companion Biomarkers in Clinical Oncology135
- 4.10.2 Integration of a Companion Diagnostic Strategy into Oncology Drug Development 135
- 4.10.2.1 Lilly to Co-Develop Companion IVDs for Cancer Drugs135
- 4.10.2.2 Celera to Work on Companion Diagnostics for Merck Cancer Drugs136
- 4.10.2.3 BioMérieux to Develop Companion Test for Ipsen's New Breast Cancer Drug136
- 4.10.2.4 Perlegen and Roche's 454 Develop Companion Tests 136
- 4.10.2.5 Ventana Medical Systems and the Critical Path Institute136
- 4.10.2.6 Biomarkers in Recentin/AZD 2171 Development136
- 4.10.2.7 Biomarkers in Development of Iressa136
- 4.10.2.8 Epigenomics' Methylation Biomarker Septin 136
- 4.11 Targeting Diabetes Related Heart Disease137
- 4.12 Key Challenges and Opportunities in Developing Targeted Therapeutics137
- 5. Imaging Biomarkers in Drug Discovery138
- 5.1 Introduction138
- 5.1.1 Validation of Imaging Biomarkers138
- 5.1.2 Types of Imaging Used in Drug Development138
- 5.1.3 Development of Imaging Technologies139
- 5.2 Molecular Imaging139
- 5.2.1 Use in Drug Discovery139
- 5.2.2 Use in Clinical Applications139
- 5.2.3 Use in Clinical Trials139
- 5.2.4 Cell-based Screening Technologies in Drug Development139
- 5.2.5 Optical Biomarkers140
- 5.3 Magnetic Resonance Imaging140
- 5.4 Positron Emission Tomography 140
- 5.5 FDG-PET Patient Phase I Studies141
- 5.6 Imaging Biomarkers as Study Endpoints142
- 5.6.1 Oncology142
- 5.6.2 Parkinson's Disease142
- 5.6.3 Cardiac Disease142
- 5.7 IT Solutions for Imaging Biomarkers in Biopharmaceutical Research and Development144
- 5.1 Introduction138
- 6. Clinical Biomarkers Improving Trial Design145
- 6.1 Strategies to Improve the Measurement of Biomarkers for Drug Trials145
- 6.2 Key Opportunities in Biomarker Discovery, Development and Commercialization145
- 6.2.1 Contract Research Companies 145
- 6.3 What Strategies Help Translate Biomarkers from Preclinical to Clinical Development?147
- 6.4 How Should Biomarker Data Be Compared to "Traditional" Safety and Efficacy Data?147
- 7. Biomarkers as Surrogate Endpoints148
- 7.1 What is a Surrogate Endpoint?148
- 7.2 Benefits and Drawbacks of Surrogate Endpoints148
- 7.2.1 Benefits148
- 7.2.2 Drawbacks 148
- 7.3 Improving the Efficacy of Clinical Surrogate End Points Using Biomarkers148
- 7.4 Surrogate Endpoint Validation149
- 7.5 Effective Use of Surrogates149
- 7.5.1 FDG-PET as a Surrogate Endpoint in Oncology Studies149
- 7.6 Conclusions149
- 8. Market Size, Collaborations and Future Directions for Companion Diagnostics in Drug Development150
- 8.1 Strategies to Improve the Measurement of Biomarkers for Drug Trials150
- 8.1.1 Key Opportunities in Biomarker Discovery, Development and Commercialization150
- 8.1.2 The Rationale Behind Biomarker Strategy150
- 8.1.3 New Development Strategies and Their Implications for Deal Making151
- 8.1.4 How Biomarkers Are Being Used To Reduce Attrition in Development151
- 8.1.5 Combined Therapeutics and Diagnostics Biomarker Business Makes Sense 152
- 8.1.6 Use of Biomarkers In House or Partner with a Diagnostics Company152
- 8.2 What is the Best Balance of Resources to Have the Most Efficient Pathway to Develop Biomarkers?152
- 8.3 Current and Future Trends in Drug Development 152
- 8.4 Future Role of Biomarkers in Healthcare 153
- 8.5 What are the Current Organizational Obstacles in Biomarker Implementation?154
- 8.1 Strategies to Improve the Measurement of Biomarkers for Drug Trials150
- 9. Regulatory Issues for Biomarkers in Drug Development155
- 9.1 Introduction155
- 9.1.1 Role of Regulatory Agencies in Development of Biomarkers156
- 9.2 FDA Perspective of Biomarkers in Clinical Trials156
- 9.2.1 FDA as a Gatekeeper of Companion Biomarkers156
- 9.2.2 FDA Criteria for a Valid Biomarker157
- 9.2.3 FDA Product Submission and Review Process158
- 9.2.4 FDA Pipeline for Biomarker Tests158
- 9.2.5 Adaptive Clinical Trial Design159
- 9.2.6 Orphan Drug Act and Biomarkers: Options and Opportunities159
- 9.3 Role of StaRT-PCR™ in Increasing Value of Pharmacogenomic Data160
- 9.4 Supporting IND, NDA, and BLA Submissions161
- 9.5 Performance Characteristics of Biomarker Tools163
- 9.6 Biomarker Initiative and VGDs 164
- 9.7 Biomarker Qualification Pilot Process at the FDA 165
- 9.7.1 Introduction 165
- 9.7.2 Biomarker is Validity166
- 9.7.3 Biomarker Qualification Process Map166
- 9.7.4 Biomarker Qualification Pilot Process166
- 9.7.5 The Pipeline Problem168
- 9.7.6 FDA Critical Path169
- 9.7.6.1 Challenge and Opportunity on the Critical Path to New Medical Products 170
- 9.7.6.2 The NIH Roadmap 171
- 9.7.6.3 Predictive Safety Testing Consortium171
- 9.7.7 Negotiating the Critical Path171
- 9.7.8 Technical Dimensions along the Critical Path172
- 9.7.9 Product Development Toolkit 173
- 9.7.10 Tools for Assessing Safety174
- 9.7.11 Tools for Demonstrating Medical Utility176
- 9.7.12 Tools for Manufacturing179
- 9.7.13 Orphan Products Grant Program179
- 9.7.14 Slowdown in New Medical Products180
- 9.7.15 Factors Contributing to the Decline in New Product Applications182
- 9.7.16 Factors that Cause Unnecessary Delays in New Product Approvals184
- 9.7.17 Reducing Avoidable Delays in Time to Approval186
- 9.7.18 Reducing Delays in Medical Device Reviews187
- 9.7.19 Reducing Delays in Animal Drug Reviews187
- 9.7.20 Quality Systems Approach to Medical Product Review187
- 9.7.20.1 Instituting Quality Systems in Review of New Drugs and Biologics188
- 9.7.20.2 Implementing of the Common Technical Document (CTD) and the electronic CTD189
- 9.7.20.3 Implementing Medical Device Quality Initiatives189
- 9.7.21 Case Study: Nephrotoxicity Biomarkers189
- 9.7.22 Role of the FDA189
- 9.8 CMS Regulatory Responsibilities190
- 9.9 Role of National Institute of Standards and Technology in Validation of Biomarkers191
- 9.10 Biomarkers and FDA's Voluntary Genomic Data Submission191
- 9.11 Federal Health Oncology Biomarker Qualification Initiative193
- 9.12 Orphan Drug Act and Pharmacogenomics: Options and Opportunities194
- 9.13 Post-market Covigilance Programs195
- 9.14 Technology Options, Potential Diagnostic Partners and Regulatory Hurdles 196
- 9.15 What Regulatory Guidance Is Needed for Companion Biomarkers? 197
- 9.16 U.S. Patent and Trademark Office (USPTO)198
- 9.17 IRB Approval in Clinical Trials198
- 9.1 Introduction155
- 10. Business Decisions Using Companion Biomarkers in Drug Development199
- 10.1 Advantages of a Pharmacogenomic Assessment of Biomarkers to Determine Clinical Dose199
- 10.2 Key Opportunities in Biomarker Discovery, Development and Commercialization199
- 10.3 What Are the Current Obstacles in Biomarker Implementation?199
- 10.4 How Do Business Strategies, Such as Those Relating to Acquisition, Drive Biomarker Strategies?200
- 10.5 What is the Right Balance Between Using External Partnerships and Developing Internal Infrastructure? 200
- 10.6 How Might Novel Biomarker Development Lead to Acquisition Strategies and Their Implications For Deal Making? 200
- 10.7 Which Types of Biomarkers Should Be Developed at Various Stages in the Drug Pipeline?200
- 10.8 What Strategies Help Translate Biomarkers From Preclinical to Clinical Development?200
- 10.9 In What Class of Drugs Is the Value of Using Biomarkers in Decision Making the Highest?201
- 10.10 Increased Clinical Trial Costs in Targeted Phase I Trials 202
- 10.11 How Can Big Pharma Co-develop Biomarkers in a Cost-sharing Model for Regulatory Acceptance?202
- 10.12 How Are Biomarkers Being Used to Reduce the Attrition Rate in Drug Development?202
- 10.13 How Is ROI Measured Using Biomarkers in Drug Development? 202
- 10.14 How Might Organizational Structures Limit the Use of Biomarkers in Drug Development and How Should R&D Organizations Address This Problem? 202
- 10.15 How to Maximize Business Development through Biomarker Strategies203
- 10.16 What Is the Best Type of Business Model for Developing Biomarkers?203
- 10.17 What Are Organizational Impediments Limiting the Use of Biomarkers in Drug Development?203
- 10.18 What Are Internal Capabilities for Novel Biomarker Development and Application? 203
- 10.19 How Can Key Biomarker Technical Expertise Be Applied Across a Complex and Highly-Stratified R&D Value Chain?204
- 10.20 At What Stage of Drug Development Have Biomarkers Provided the Most Benefit? 204
- 10.21 What Companies Are the most Innovative in Development of Biomarkers?204
- 10.22 Best Values for Biomarkers in Drug Development and in Diagnostics204
- 10.23 Companion Biomarkers Can Increase Value in an Associated Drug205
- 11. Company Profiles206
- 11.1 Abbott Laboratories206
- 11.2 Accelrys207
- 11.3 Affymetrix208
- 11.4 Agilent Technologies 211
- 11.5 Amgen213
- 11.6 Ananomouse214
- 11.7 Applied Maths215
- 11.8 Ariadne Genomics215
- 11.9 ArrayIt (Integrated Media Holdings)215
- 11.10 AstraZeneca216
- 11.11 AutoGenomics217
- 11.12 Axontologic217
- 11.13 Beckman Coulter218
- 11.14 BD224
- 11.15 Bender MedSystems225
- 11.16 Bioalma225
- 11.17 BioAnalytics Group 226
- 11.18 BioCat GmbH226
- 11.19 Biocept226
- 11.20 BioChain226
- 11.21 BioData227
- 11.22 BioDiscovery227
- 11.23 BioForce Nanosciences227
- 11.24 BioGenex 228
- 11.25 Bioinformatics Solutions228
- 11.26 Biomax Informatics 228
- 11.27 BioMérieux229
- 11.28 Biomind229
- 11.29 Bio-Rad Laboratories229
- 11.30 Biosite230
- 11.31 BioSystems International230
- 11.32 Biotrin230
- 11.33 BioWisdom230
- 11.34 Bristol-Myers Squibb Company231
- 11.35 Caliper Life Sciences232
- 11.36 Caprion Proteomics 235
- 11.37 Carestream Health237
- 11.38 Celera237
- 11.39 Cepheid239
- 11.40 Chang Bioscience241
- 11.41 Clontech Laboratories241
- 11.42 CombiMatrix241
- 11.43 Compugen 243
- 11.44 Corimbia244
- 11.45 Covance244
- 11.46 Cybrdi244
- 11.47 CyVera244
- 11.48 Dako A/S244
- 11.49 Decodon245
- 11.50 Definiens245
- 11.51 DiagnoSwiss246
- 11.52 Discerna246
- 11.53 DNAStar246
- 11.54 DNATools 246
- 11.55 Eidogen-Sertanty247
- 11.56 Electric Genetics247
- 11.57 Eli Lilly and Company247
- 11.58 Entelos248
- 11.59 ePitope Informatics248
- 11.60 Eurogentec 248
- 11.61 Exiqon A/S 249
- 11.62 Forensic Bioinformatics249
- 11.63 Fujitsu249
- 11.64 Future Diagnostics250
- 11.65 Genaissance Pharmaceuticals 250
- 11.66 Gene Codes250
- 11.67 Genedata250
- 11.68 GeneGo250
- 11.69 Gene Network Sciences251
- 11.70 Geneva Bioinformatics251
- 11.71 Genomatica251
- 11.72 Genomic Solutions251
- 11.73 Genomining252
- 11.74 Gen-Probe 252
- 11.75 GE Healthcare256
- 11.76 GeneStudio256
- 11.77 Genomatix Software256
- 11.78 GenomeQuest257
- 11.79 Genus BioSystems257
- 11.80 Genzyme257
- 11.81 Geospiza258
- 11.82 GlaxoSmithKline259
- 11.83 Golden Helix259
- 11.84 Grace Bio-Labs260
- 11.85 Gyros AB 260
- 11.86 HealthCare IT260
- 11.87 High Throughput Genomics260
- 11.88 Human Genome Sciences261
- 11.89 Illumina261
- 11.90 Imgenex264
- 11.91 Imaxia264
- 11.92 INCOGEN 264
- 11.93 Incyte265
- 11.94 InforSense 265
- 11.95 Ingenuity Systems265
- 11.96 InPharmix266
- 11.97 Insightful Corporation266
- 11.98 Integromics, S.L266
- 11.99 IBM266
- 11.100 IO Informatics267
- 11.101 Ipsen268
- 11.102 Jerini AG268
- 11.103 Johnson & Johnson268
- 11.104 Koada Technology 269
- 11.105 KOOPrime269
- 11.106 Life Technologies Corporation269
- 11.107 LINCO Research270
- 11.108 Luminex270
- 11.109 Marligen Biosciences271
- 11.110 Matrix Science271
- 11.111 MDS272
- 11.112 Merck & Company 272
- 11.113 Merck KGaA273
- 11.114 Meso Scale Discovery273
- 11.115 Metabolon274
- 11.116 Microbionix274
- 11.117 MicroDiscovery274
- 11.118 Millennium Pharmaceuticals275
- 11.119 Millipore275
- 11.120 MiraiBio276
- 11.121 Molecular Connections276
- 11.122 MolMine AS276
- 11.123 Molsoft277
- 11.124 Monogram Biosciences277
- 11.125 MTR Scientific278
- 11.126 Multimetrix278
- 11.127 Nanogen 278
- 11.128 Nanosphere280
- 11.129 NetGenics280
- 11.130 NextGen Sciences280
- 11.131 NimbleGen Systems281
- 11.132 Nonlinear Dynamics281
- 11.133 Novartis 281
- 11.134 Nuvera Biosciences282
- 11.135 Ocimum Biosolutions282
- 11.136 OmniViz 282
- 11.137 One Lambda282
- 11.138 Oracle283
- 11.139 Ore Pharmaceuticals284
- 11.140 Orla Protein Technologies285
- 11.141 Osmetech 285
- 11.142 Oxonica285
- 11.143 PamGene BV286
- 11.144 Panomics286
- 11.145 Partek286
- 11.146 Pepscan287
- 11.147 Perbio Science287
- 11.148 Perlegen Sciences287
- 11.149 Pfizer287
- 11.150 PharmaSeq288
- 11.151 Pierce Biotechnology288
- 11.152 Platypus Technologies288
- 11.153 Predictive Patterns Software 288
- 11.154 Proceryon 288
- 11.155 Protagen AG289
- 11.156 ProteinOne289
- 11.157 Proteome Sciences 289
- 11.158 PubGene289
- 11.159 Qiagen290
- 11.160 Radix BioSolutions293
- 11.161 Randox Laboratories294
- 11.162 RayBiotech294
- 11.163 Redasoft 294
- 11.164 RedStorm Scientific294
- 11.165 Reel Two 294
- 11.166 Rescentris 295
- 11.167 Roche295
- 11.168 Rosetta Biosoftware296
- 11.169 Rules-Based Medicine296
- 11.170 SAS296
- 11.171 Schleicher & Schuell BioScience297
- 11.172 SciTegic297
- 11.173 Semantx Life Sciences297
- 11.174 Sequenom297
- 11.175 Sigma-Aldrich298
- 11.176 Silicon Genetics299
- 11.177 Singulex299
- 11.178 Softberry 299
- 11.179 SoftGenetics299
- 11.180 SomaLogic299
- 11.181 Spotfire300
- 11.182 SPSS300
- 11.183 Strand Life Sciences301
- 11.184 Stratagene301
- 11.185 SuperBioChips Laboratories301
- 11.186 SurroMed 301
- 11.187 Sun Microsystems 301
- 11.188 Sygnis Pharma AG 302
- 11.189 Techne Corporation302
- 11.190 Tepnel Life Sciences303
- 11.191 Teranode303
- 11.192 Textco BioSoftware303
- 11.193 TG Services304
- 11.194 Thermo Fisher Scientific304
- 11.195 Third Wave Technologies305
- 11.196 TIBCO Software 305
- 11.197 TimeLogic305
- 11.198 TriStar Technology Group305
- 11.199 Tyrian Diagnostics (formerly Proteome Systems)306
- 11.200 VBC-Genomics Bioscience Research GmbH306
- 11.201 Ventana Medical Systems306
- 11.202 ViaLogy307
- 11.203 Wyeth307
- 11.204 Zeptosens 307
- 11.205 Zeus Scientific308
- 11.206 Zyagen308
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