Companion Biomarkers in Drug Development

Table of Contents

1. Overview 13

• 1.1 Statement of Report 13
• 1.2 About This Report 13
• 1.3 Scope of the Report 13
• 1.4 Objectives 13
• 1.5 Methodology 15
• 1.6 Executive Summary 16

2. Introduction: Companion Diagnostics in Drug Development 19

• 2.1 Companion Diagnostics as Biomarkers 20
  • 2.1.1 Potential Benefits of Biomarkers as Companion Diagnostics 22
• 2.2 Biomarkers in Different Phases of Drug Development 22
  • 2.2.1 Drug Discovery and Development Process 22
  • 2.2.2 Biomarkers in Drug Development 24
• 2.3 Drug Targets 24
  • 2.3.1 Target Discovery Using Functional Genomics 26
  • 2.3.2 Functional Genomics 26
  • 2.3.3 Target Validation 28
    • 2.3.3.1 Target Discovery 28
    • 2.3.3.2 Lead Identification 28
  • 2.3.4 Target and Biomarker Discovery 29
    • 2.3.4.1 Biomarker Validation 29
• 2.4 Biomarkers in Drug Discovery, Development and Clinical Diagnostics 29
  • 2.4.1 Role of Biomarkers in Drug Discovery, Preclinical, Clinical Development and Diagnostics 29
  • 2.4.2 The Pipeline Problem 31
  • 2.4.3 Biomarkers in the Drug Discovery Process 32
  • 2.4.4 Segmentation of Biomarker Usage 32
  • 2.4.5 Efficacy of Biomarkers as Surrogate Endpoints 33
  • 2.4.6 Biomarkers Used to Reduce the Cost of Drug Development 34
  • 2.4.7 Biomarkers: Challenges and Opportunities 34
  • 2.4.8 Biomarkers in Early Safety and Toxicity Assessment 35
  • 2.4.9 Biomarkers in Determining Validation Parameters 35
  • 2.4.10 Challenges in Development of Biomarkers 36
  • 2.4.11 Using Biomarkers in Early Clinical Development 36
  • 2.4.12 Translational Biomarkers 36
  • 2.4.13 Use of Biomarkers in "Go"/No-Go" Decisions 37
  • 2.4.14 Diagnostic Tests 37
  • 2.4.15 Biomarkers in Deal Making 37
  • 2.4.16 Payors Use Biomarkers in Decision-Making 37
• 2.5 World Pharmaceutical Markets 38
  • 2.5.1 World Market Summary 38
  • 2.5.2 Company Performance in this Segment 40
  • 2.5.3 Forces Affecting the Structure of the Pharmaceutical Industry 41
    • 2.5.3.1 Threats 41
    • 2.5.3.2 Competitive Forces 42
• 2.6.1 Industry Overview 42
  • 2.6.1.1 Pharmaceutical Industry Drug Pipeline 44
  • 2.6.1.2 Asia-Pacific to Replace United States and Europe as Pharmaceutical Industry Center 54
  • 2.6.1.3 The Changing Pharmaceutical Business Model 54
  • 2.6.2 Benefits for Companion Diagnostic Tests in Drug Development 55
  • 2.6.3 Strategies for the Creation of Partnerships - Predicting and Overcoming Challenges in Creating Drug Response Profiling Diagnostics 57
  • 2.6.4 Options and Applications 57
  • 2.6.4.1 Clinical Applications of Genomics: The Use of Evidence Based Frameworks by Decision-Makers 57
  • 2.6.5 Challenges, Drivers and Trends 58
  • 2.6.5.1 Macro Trends in Biomarkers 58
  • 2.6.5.2 Biomarkers: Industry SWOT Analysis 61
  • 2.6.6 Breakaway Technologies 62
  • 2.6.7 Collaboration for Companion Diagnostics 63
  • 2.6.8 Key Stake Holders in Companion Diagnostics 63
• 2.9 Future Developments 65

3. Biomarker Development Tools 66

• 3.1 New Technologies in Functional Genomics 66
  • 3.1.1 Genomics-Derived Drug Pipeline 66
  • 3.1.2 Future of Genomics Technologies for Drug Target Identification 66
• 3.2 Overview of Microarrays 67
  • 3.2.1 General Theory of Microarrays 68
  • 3.2.2 GeneChip Probe Array Technology 69
  • 3.2.3 DNA Microarrays 69
    • 3.2.3.1 DNA Microarray Market Size 71
    • 3.2.3.2 DNA Microarrays in SNP Analysis 72
    • 3.2.3.3 DNA Microarrays in Cancer 72
  • 3.2.4 Protein Microarrays 73
    • 3.2.4.1 Reasons Why Researchers Use Protein Microarrays 74
    • 3.2.4.2 Factors for Adoption of Protein Microarrays Technology 74
    • 3.2.4.3 Future Innovations in Protein Microarray Technology 74
  • 3.2.5 New Technologies 75
    • 3.2.5.1 Antibody Microarrays 75
    • 3.2.5.2 Peptide Microarrays 75
    • 3.2.5.3 Peptide MHC Microarrays 75
    • 3.2.5.4 Tissue Microarrays 75
    • 3.2.5.5 Key Points for Developing Microarray Based Applications 76
    • 3.2.5.6 Reasons Why Researchers use DNA Microarrays 77
    • 3.2.5.7 Factors for Difficulties Applying DNA Microarrays Technology 77
    • 3.2.5.8 Emerging Microarray Trends 78
    • 3.2.5.9 Emerging Microarray Applications 78
    • 3.2.5.10 Key Findings on Use of Microarrays 79
    • 3.2.5.11 Advantages and Drivers of Microarrays 79
    • 3.2.5.12 Limitations and Barriers to Use of Microarrays 81
    • 3.2.5.13 qRT-PCR Use in Biomarker Identification and Drug Development 83
    • 3.2.5.14 Microarray Quality Control (MAQC) Project 84
• 3.3 Theranostics 84
  • 3.3.1 Theranostics in Drug Development 84
  • 3.3.2 Trends in Theranostics 85
  • 3.3.3 Timeline for Impact on Various Segments in Theranostics 85
  • 3.3.4 Challenges for Biomarker Based Therapeutics Development 87
• 3.4 Pharmaceutical Development and Bioanalytical Services 88
  • 3.4.1 Wyeth Singulex' s Erenna 88
• 3.5 Metabolomics in Drug Discovery 88
• 3.6 Bioinformatics 90
  • 3.6.1 Definition and Role of Bioinformatics 90
  • 3.6.2 Bioinformatics Sector Overview 93
  • 3.6.3 Future Status of Bioinformatics 93
    • 3.6.3.1 Future in Drug Discovery 93
    • 3.6.3.2 Mergers and Acquisitions Could Deter Bioinformatics Growth 94
    • 3.6.3.3 Barriers to Bioinformatics Growth 94
    • 3.6.3.4 Types of Data and Bioinformatics Applications 94
    • 3.6.3.5 Validated Core Modeling Technology 95
    • 3.6.3.6 Applicability of Bioinformatics for Biomarker Discovery 95
    • 3.6.3.7 Biomarker Data Management Compliant with Industry Standards 96
    • 3.6.3.8 Data Management for Biomarkers 96
      • 3.6.3.8.1 Data Transformation for Biomarker Development 96
      • 3.6.3.8.2 Biomarker Data Collaboration 96
      • 3.6.3.8.3 Interface for Online Data Sources for Genomic Structures 96
      • 3.6.3.8.4 Target Markets for Informatics Software 96
      • 3.6.3.8.5 Bioinformatics Drivers and Challenges in the Pharmaceutical Industry 97
      • 3.6.3.8.6 Products of Bioinformatics 100
      • 3.6.3.8.7 Informatics Tools and Functionalities 101
      • 3.6.3.8.8 Bioinformatics in Lead Identification and Optimization 101
      • 3.6.3.8.9 Bioinformatics in Drug Development and Formulation 102
      • 3.6.3.8.10 Role of Bioinformatics in the Drug Discovery Value Chain 102
      • 3.6.3.8.11 Bioinformatics Software for Drug Discovery and Biomarker Development 102
      • 3.6.3.8.12 Bioinformatics Services 104
• 3.7 Biomarkers and Proteomics 105
  • 3.7.1 Scientific Background 105
  • 3.7.2 Applying Proteomics to Biomarker Discovery 106
    • 3.7.2.1 Challenges Facing Biomarker Developers 106
  • 3.7.3 Limitations of Proteomic Approaches to Biomarker Discovery 108
  • 3.7.4 Validation of Biomarkers Using LC-MS/MS Systems 109
  • 3.7.5 Use of Mass Spectrometry in Biomarker Discovery 109
    • 3.7.5.1 Multiple Reaction Monitoring Assays (MRMs) 110
    • 3.7.5.2 Gel-based Approaches 110
    • 3.7.5.3 Non-Gel-based Approaches 111
    • 3.7.5.4 SELDI-TOF MS 111
    • 3.7.5.5 SELDI and Prognosis 112
    • 3.7.5.6 SELDI and Treatment Monitoring 112
    • 3.7.5.7 Limitations of Mass Spectroscopy 112
  • 3.7.6 Partnerships for Developing Proteomic Biomarkers 114
  • 3.7.7 Proteomics in Developing a New Cancer Marker 114
    • 3.7.7.1 Translating Proteomic Oncology Discoveries to the Clinic: Development of Analytical Reference Materials, Reagents, Data, and Technology Assessment and Validation 115
    • 3.7.7.2 Challenges of Discovering and Validating Clinical Protein Biomarkers 115
    • 3.7.7.3 Importance of Proteomics in Biomarker Discovery 115
• 3.8 Toxicogenomics 115
  • 3.8.1 Toxicogenomics Concerns in Drug Safety Data 116
  • 3.8.2 Toxicogenomics and Prioritization of Drug Candidates 116
  • 3.8.3 Genomic Biomarkers for Drug-Induced Nephrotoxicity 117
  • 3.8.4 Use of Biomarkers of Drug-Induced Cardiotoxicity 117
  • 3.8.5 Use of Biomarkers of Drug-induced Hepatotoxicity 117
  • 3.8.6 Transgenic Biomarkers for Adverse Drug-Drug Interactions 117
  • 3.8.7 Challenges to Toxicogenomics 118
  • 3.8.8 The Future Use of Toxicogenomics in Drug Discovery 118

4. Market for Biomarkers in Drug Development 119

• 4.1 C-KIT (CD117) Expression 122
• 4.2 CCR5 -Chemokine C-C Motif Receptor 122
• 4.3 CYP2C19 Variants 123
• 4.4 CYP2C9 Variants 123
• 4.5 CYP2D6 Variants 124
• 4.6 CYP2D6 Variants with Alternate Context 124
• 4.7 Clinical Biomarkers 124
• 4.8 Targeting Kidney Toxicity 125
  • 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-1 126
• 4.9 Targeting Hepatotoxicity 127
  • 4.9.1 Breast Cancer 128
  • 4.9.2 Colorectal Cancer 128
  • 4.9.3 Prostate Cancer 128
  • 4.9.4 Cystic Fibrosis 128
• 4.10 Biomarker Application in Oncology Clinical Development 128
  • 4.10.1 Specific Example of Companion Biomarkers in Clinical Oncology 135
  • 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 Drugs 135
    • 4.10.2.2 Celera to Work on Companion Diagnostics for Merck Cancer Drugs 136
    • 4.10.2.3 BioMerieux to Develop Companion Test for Ipsen' s New Breast Cancer Drug 136
    • 4.10.2.4 Perlegen and Roche' s 454 Develop Companion Tests 136
    • 4.10.2.5 Ventana Medical Systems and the Critical Path Institute 136
    • 4.10.2.6 Biomarkers in Recentin/AZD 2171 Development 136
    • 4.10.2.7 Biomarkers in Development of Iressa 136
    • 4.10.2.8 Epigenomics' Methylation Biomarker Septin 136
• 4.11 Targeting Diabetes Related Heart Disease 137
• 4.12 Key Challenges and Opportunities in Developing Targeted Therapeutics 137

5. Imaging Biomarkers in Drug Discovery 138

• 5.1 Introduction 138
  • 5.1.1 Validation of Imaging Biomarkers 138
  • 5.1.2 Types of Imaging Used in Drug Development 138
  • 5.1.3 Development of Imaging Technologies 139
• 5.2 Molecular Imaging 139
  • 5.2.1 Use in Drug Discovery 139
  • 5.2.2 Use in Clinical Applications 139
  • 5.2.3 Use in Clinical Trials 139
  • 5.2.4 Cell-based Screening Technologies in Drug Development 139
  • 5.2.5 Optical Biomarkers 140
• 5.3 Magnetic Resonance Imaging 140
• 5.4 Positron Emission Tomography 140
• 5.5 FDG-PET Patient Phase I Studies 141
• 5.6 Imaging Biomarkers as Study Endpoints 142
  • 5.6.1 Oncology 142
  • 5.6.2 Parkinson' s Disease 142
  • 5.6.3 Cardiac Disease 142
• 5.7 IT Solutions for Imaging Biomarkers in Biopharmaceutical Research and Development 144

6. Clinical Biomarkers Improving Trial Design 145

• 6.1 Strategies to Improve the Measurement of Biomarkers for Drug Trials 145
• 6.2 Key Opportunities in Biomarker Discovery, Development and Commercialization 145
• 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 Endpoints 148

• 7.1 What is a Surrogate Endpoint? 148
• 7.2 Benefits and Drawbacks of Surrogate Endpoints 148
  • 7.2.1 Benefits 148
  • 7.2.2 Drawbacks 148
• 7.3 Improving the Efficacy of Clinical Surrogate End Points Using Biomarkers 148
• 7.4 Surrogate Endpoint Validation 149
• 7.5 Effective Use of Surrogates 149
  • 7.5.1 FDG-PET as a Surrogate Endpoint in Oncology Studies 149
• 7.6 Conclusions 149

8. Market Size, Collaborations and Future Directions for Companion Diagnostics in Drug Development 150

• 8.1 Strategies to Improve the Measurement of Biomarkers for Drug Trials 150
  • 8.1.1 Key Opportunities in Biomarker Discovery, Development and Commercialization 150
  • 8.1.2 The Rationale Behind Biomarker Strategy 150
  • 8.1.3 New Development Strategies and Their Implications for Deal Making 151
  • 8.1.4 How Biomarkers Are Being Used To Reduce Attrition in Development 151
  • 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 Company 152
• 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

9. Regulatory Issues for Biomarkers in Drug Development 155

• 9.1 Introduction 155
  • 9.1.1 Role of Regulatory Agencies in Development of Biomarkers 156
• 9.2 FDA Perspective of Biomarkers in Clinical Trials 156
  • 9.2.1 FDA as a Gatekeeper of Companion Biomarkers 156
  • 9.2.2 FDA Criteria for a Valid Biomarker 157
  • 9.2.3 FDA Product Submission and Review Process 158
  • 9.2.4 FDA Pipeline for Biomarker Tests 158
  • 9.2.5 Adaptive Clinical Trial Design 159
  • 9.2.6 Orphan Drug Act and Biomarkers: Options and Opportunities 159
• 9.3 Role of StaRT-PCR"! in Increasing Value of Pharmacogenomic Data 160
• 9.4 Supporting IND, NDA, and BLA Submissions 161
• 9.5 Performance Characteristics of Biomarker Tools 163
• 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 Validity 166
  • 9.7.3 Biomarker Qualification Process Map 166
  • 9.7.4 Biomarker Qualification Pilot Process 166
  • 9.7.5 The Pipeline Problem 168
  • 9.7.6 FDA Critical Path 169
    • 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 Consortium 171
  • 9.7.7 Negotiating the Critical Path 171
  • 9.7.8 Technical Dimensions along the Critical Path 172
  • 9.7.9 Product Development Toolkit 173
  • 9.7.10 Tools for Assessing Safety 174
  • 9.7.11 Tools for Demonstrating Medical Utility 176
  • 9.7.12 Tools for Manufacturing 179
  • 9.7.13 Orphan Products Grant Program 179
  • 9.7.14 Slowdown in New Medical Products 180
  • 9.7.15 Factors Contributing to the Decline in New Product Applications 182
  • 9.7.16 Factors that Cause Unnecessary Delays in New Product Approvals 184
  • 9.7.17 Reducing Avoidable Delays in Time to Approval 186
  • 9.7.18 Reducing Delays in Medical Device Reviews 187
  • 9.7.19 Reducing Delays in Animal Drug Reviews 187
  • 9.7.20 Quality Systems Approach to Medical Product Review 187
    • 9.7.20.1 Instituting Quality Systems in Review of New Drugs and Biologics 188
    • 9.7.20.2 Implementing of the Common Technical Document (CTD) and the electronic CTD 189
    • 9.7.20.3 Implementing Medical Device Quality Initiatives 189
  • 9.7.21 Case Study: Nephrotoxicity Biomarkers 189
  • 9.7.22 Role of the FDA 189
• 9.8 CMS Regulatory Responsibilities 190
• 9.9 Role of National Institute of Standards and Technology in Validation of Biomarkers 191
• 9.10 Biomarkers and FDA' s Voluntary Genomic Data Submission 191
• 9.11 Federal Health Oncology Biomarker Qualification Initiative 193
• 9.12 Orphan Drug Act and Pharmacogenomics: Options and Opportunities 194
• 9.13 Post-market Covigilance Programs 195
• 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 Trials 198

10. Business Decisions Using Companion Biomarkers in Drug Development 199

• 10.1 Advantages of a Pharmacogenomic Assessment of Biomarkers to Determine Clinical Dose 199
• 10.2 Key Opportunities in Biomarker Discovery, Development and Commercialization 199
• 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 Strategies 203
• 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 Diagnostics 204
• 10.23 Companion Biomarkers Can Increase Value in an Associated Drug 205

11. Company Profiles 206

• 11.1 Abbott Laboratories 206
• 11.2 Accelrys 207
• 11.3 Affymetrix 208
• 11.4 Agilent Technologies 211
• 11.5 Amgen 213
• 11.6 Ananomouse 214
• 11.7 Applied Maths 215
• 11.8 Ariadne Genomics 215
• 11.9 ArrayIt (Integrated Media Holdings) 215
• 11.10 AstraZeneca 216
• 11.11 AutoGenomics 217
• 11.12 Axontologic 217
• 11.13 Beckman Coulter 218
• 11.14 BD 224
• 11.15 Bender MedSystems 225
• 11.16 Bioalma 225
• 11.17 BioAnalytics Group 226
• 11.18 BioCat GmbH 226
• 11.19 Biocept 226
• 11.20 BioChain 226
• 11.21 BioData 227
• 11.22 BioDiscovery 227
• 11.23 BioForce Nanosciences 227
• 11.24 BioGenex 228
• 11.25 Bioinformatics Solutions 228
• 11.26 Biomax Informatics 228
• 11.27 BioMerieux 229
• 11.28 Biomind 229
• 11.29 Bio-Rad Laboratories 229
• 11.30 Biosite 230
• 11.31 BioSystems International 230
• 11.32 Biotrin 230
• 11.33 BioWisdom 230
• 11.34 Bristol-Myers Squibb Company 231
• 11.35 Caliper Life Sciences 232
• 11.36 Caprion Proteomics 235
• 11.37 Carestream Health 237
• 11.38 Celera 237
• 11.39 Cepheid 239
• 11.40 Chang Bioscience 241
• 11.41 Clontech Laboratories 241
• 11.42 CombiMatrix 241
• 11.43 Compugen 243
• 11.44 Corimbia 244
• 11.45 Covance 244
• 11.46 Cybrdi 244
• 11.47 CyVera 244
• 11.48 Dako A/S 244
• 11.49 Decodon 245
• 11.50 Definiens 245
• 11.51 DiagnoSwiss 246
• 11.52 Discerna 246
• 11.53 DNAStar 246
• 11.54 DNATools 246
• 11.55 Eidogen-Sertanty 247
• 11.56 Electric Genetics 247
• 11.57 Eli Lilly and Company 247
• 11.58 Entelos 248
• 11.59 ePitope Informatics 248
• 11.60 Eurogentec 248
• 11.61 Exiqon A/S 249
• 11.62 Forensic Bioinformatics 249
• 11.63 Fujitsu 249
• 11.64 Future Diagnostics 250
• 11.65 Genaissance Pharmaceuticals 250
• 11.66 Gene Codes 250
• 11.67 Genedata 250
• 11.68 GeneGo 250
• 11.69 Gene Network Sciences 251
• 11.70 Geneva Bioinformatics 251
• 11.71 Genomatica 251
• 11.72 Genomic Solutions 251
• 11.73 Genomining 252
• 11.74 Gen-Probe 252
• 11.75 GE Healthcare 256
• 11.76 GeneStudio 256
• 11.77 Genomatix Software 256
• 11.78 GenomeQuest 257
• 11.79 Genus BioSystems 257
• 11.80 Genzyme 257
• 11.81 Geospiza 258
• 11.82 GlaxoSmithKline 259
• 11.83 Golden Helix 259
• 11.84 Grace Bio-Labs 260
• 11.85 Gyros AB 260
• 11.86 HealthCare IT 260
• 11.87 High Throughput Genomics 260
• 11.88 Human Genome Sciences 261
• 11.89 Illumina 261
• 11.90 Imgenex 264
• 11.91 Imaxia 264
• 11.92 INCOGEN 264
• 11.93 Incyte 265
• 11.94 InforSense 265
• 11.95 Ingenuity Systems 265
• 11.96 InPharmix 266
• 11.97 Insightful Corporation 266
• 11.98 Integromics, S.L 266
• 11.99 IBM 266
• 11.100 IO Informatics 267
• 11.101 Ipsen 268
• 11.102 Jerini AG 268
• 11.103 Johnson & Johnson 268
• 11.104 Koada Technology 269
• 11.105 KOOPrime 269
• 11.106 Life Technologies Corporation 269
• 11.107 LINCO Research 270
• 11.108 Luminex 270
• 11.109 Marligen Biosciences 271
• 11.110 Matrix Science 271
• 11.111 MDS 272
• 11.112 Merck & Company 272
• 11.113 Merck KGaA 273
• 11.114 Meso Scale Discovery 273
• 11.115 Metabolon 274
• 11.116 Microbionix 274
• 11.117 MicroDiscovery 274
• 11.118 Millennium Pharmaceuticals 275
• 11.119 Millipore 275
• 11.120 MiraiBio 276
• 11.121 Molecular Connections 276
• 11.122 MolMine AS 276
• 11.123 Molsoft 277
• 11.124 Monogram Biosciences 277
• 11.125 MTR Scientific 278
• 11.126 Multimetrix 278
• 11.127 Nanogen 278
• 11.128 Nanosphere 280
• 11.129 NetGenics 280
• 11.130 NextGen Sciences 280
• 11.131 NimbleGen Systems 281
• 11.132 Nonlinear Dynamics 281
• 11.133 Novartis 281
• 11.134 Nuvera Biosciences 282
• 11.135 Ocimum Biosolutions 282
• 11.136 OmniViz 282
• 11.137 One Lambda 282
• 11.138 Oracle 283
• 11.139 Ore Pharmaceuticals 284
• 11.140 Orla Protein Technologies 285
• 11.141 Osmetech 285
• 11.142 Oxonica 285
• 11.143 PamGene BV 286
• 11.144 Panomics 286
• 11.145 Partek 286
• 11.146 Pepscan 287
• 11.147 Perbio Science 287
• 11.148 Perlegen Sciences 287
• 11.149 Pfizer 287
• 11.150 PharmaSeq 288
• 11.151 Pierce Biotechnology 288
• 11.152 Platypus Technologies 288
• 11.153 Predictive Patterns Software 288
• 11.154 Proceryon 288
• 11.155 Protagen AG 289
• 11.156 ProteinOne 289
• 11.157 Proteome Sciences 289
• 11.158 PubGene 289
• 11.159 Qiagen 290
• 11.160 Radix BioSolutions 293
• 11.161 Randox Laboratories 294
• 11.162 RayBiotech 294
• 11.163 Redasoft 294
• 11.164 RedStorm Scientific 294
• 11.165 Reel Two 294
• 11.166 Rescentris 295
• 11.167 Roche 295
• 11.168 Rosetta Biosoftware 296
• 11.169 Rules-Based Medicine 296
• 11.170 SAS 296
• 11.171 Schleicher & Schuell BioScience 297
• 11.172 SciTegic 297
• 11.173 Semantx Life Sciences 297
• 11.174 Sequenom 297
• 11.175 Sigma-Aldrich 298
• 11.176 Silicon Genetics 299
• 11.177 Singulex 299
• 11.178 Softberry 299
• 11.179 SoftGenetics 299
• 11.180 SomaLogic 299
• 11.181 Spotfire 300
• 11.182 SPSS 300
• 11.183 Strand Life Sciences 301
• 11.184 Stratagene 301
• 11.185 SuperBioChips Laboratories 301
• 11.186 SurroMed 301
• 11.187 Sun Microsystems 301
• 11.188 Sygnis Pharma AG 302
• 11.189 Techne Corporation 302
• 11.190 Tepnel Life Sciences 303
• 11.191 Teranode 303
• 11.192 Textco BioSoftware 303
• 11.193 TG Services 304
• 11.194 Thermo Fisher Scientific 304
• 11.195 Third Wave Technologies 305
• 11.196 TIBCO Software 305
• 11.197 TimeLogic 305
• 11.198 TriStar Technology Group 305
• 11.199 Tyrian Diagnostics (formerly Proteome Systems) 306
• 11.200 VBC-Genomics Bioscience Research GmbH 306
• 11.201 Ventana Medical Systems 306
• 11.202 ViaLogy 307
• 11.203 Wyeth 307
• 11.204 Zeptosens 307
• 11.205 Zeus Scientific 308
• 11.206 Zyagen 308

Appendix 1: FDA Guidance for Industry: Pharmacogenomic Data Submission 309

• A 1.1 Introduction 309
• A 1.2 Background 309
• A 1.3 Submission Policy 310
• A 1.3.1 General Principles 310
• A 1.3.2 Specific Uses of Pharmacogenomic Data in Drug Development and Labeling 311
• A 1.3.3 Benefits of Voluntary Submissions to Sponsors and FDA 312
• A 1.4 Submission of Pharmacogenomic Data 313
• A 1.4.1 Submission of Pharmacogenomic Data during the IND Phase 313
• A 1.4.2 Submission of Pharmacogenomic Data to a New NDA, BLA, or Supplement 314
• A 1.4.3 Submission to a Previously Approved NDA or BLA 315
• A 1.4.4 Compliance with 21 CFR Part 58 315
• A 1.4.5 Submission of Voluntary Genomic Data from Application-Independent Research 316
• A 1.5 Format and Content of a VGDS 316
• A 1.6 Process for Submitting Pharmacogenomic Data 317
• A 1.7 Agency Review of VGDSs 317

Glossary 319

INDEX OF FIGURES

• Figure 2.1: Drug Discovery and Development Paradigm 24
• Figure 2.2: Paradigm of Drug Discovery and Development Illustrating the Central and Essential Role of Biomarkers in Screening 25
• Figure 2.3: Functional Genomic Process for Drug Development 26
• Figure 2.4: Reimbursement for Diagnostics in Healthcare Decision Making 30
• Figure 2.5: Market Growth and Evolution of Companion Biomarkers 31
• Figure 2.6: Medical Product Development Models 32
• Figure 2.7: Segmentation of the Biomarker Development Market 33
• Figure 2.8: Medical Research in the U.S. Outpaces the Rest of the World 45
• Figure 2.9: Worldwide Pharmaceutical Products Markets 48
• Figure 2.10: Biomarkers Market Drivers 58
• Figure 2.11: Challenges in the Biomarkers Space 59
• Figure 2.12: FDA Co-Developed Products 64
• Figure 3.1: Informatics Applications Along the Drug Discovery Value Chain 91
• Figure 3.2: Bioinformatics Software Flow Chart 91
• Figure 3.3: Growth of GenBank, 1982 - 2008 92
• Figure 3.4: Role of Bioinformatics in the Drug Discovery Value Chain 102
• Figure 3.5: Challenges in the Study or Utilization of Proteomic Biomarkers 107
• Figure 3.6: Challenges in the Study or Utilization of Companion Diagnostic Biomarkers 107
• Figure 3.7: Top Unmet Needs in Products in the Biomarkers Space 108
• Figure 4.1: Growth and Evolution of the Biomarker Space 120
• Figure 4.2: Revenue Forecast Projections for Global Biomarker Markets by Segments, 2005 - 2012 121
• Figure 4.3: Biomarker Discovery by Therapeutic Area 122
• Figure 4.4: Kidney Biomarker Paradigm 125
• Figure 4.5: Hepatic Biomarker Paradigm 127
• Figure 9.1: IPRG Biomarker Qualification Process 167
• Figure 9.2: Critical Path for Drug Development 180
• Figure 9.3: Path for R&D Product Development 181
• Figure 9.4: Dimensions of the Critical Path 181
• Figure 9.5: FDA Interactions During Drug Development 182
• Figure 9.6: Problem Resolution During the FDA Review Process 182
• Figure 9.7: VGDS Process Flow 193
• Figure 10.1: Discovery, Validation and Use of Biomarkers 201

INDEX OF TABLES

• Table 2.1: Utility of Biomarkers as Companion Diagnostics to Drug Development 20
• Table 2.2: Biomarker End Points in Drug Development 22
• Table 2.3: Value of Biomarkers in Phase II Clinical Trials 24
• Table 2.4: Comparative Genome Sizes of Humans and Other Organisms 27
• Table 2.5: Global Pharmaceutical Drug Sales, 2004 - 2012 38
• Table 2.6: Worldwide Generic Pharmaceutical Drug Market, 2003 - 2012 39
• Table 2.7: Worldwide OTC Pharmaceutical Drug Market, 2003 - 2012 39
• Table 2.8: Worldwide Biopharmaceutical Drug Market, 2003 - 2012 40
• Table 2.9: Top Ten Pharmaceutical Companies by Worldwide Sales, 2008 40
• Table 2.10: Pharmaceutical Companies' Drug Sales as Percent of the Worldwide Market, 2008 41
• Table 2.11: Threats to Pharmaceutical Industry Productivity 42
• Table 2.12: Competitive Forces Governing the Pharmaceutical Industry 42
• Table 2.13: Time Line for Development of Companion Diagnostics 43
• Table 2.14: Leading Therapy Classes for R&D, 2008 44
• Table 2.15: Global Pharmaceutical Industry R&D Spending, 1995 - 2008 46
• Table 2.16: Pharmaceutical R&D Expenditures by World Region, 1990 - 2006 46
• Table 2.17: U.S. Government NIH Research Budget, 1995 - 2008 47
• Table 2.18: Pharmaceutical Companies Ranked by Total R&D Expenditures, 2006 47
• Table 2.19: Global Pharmaceutical Sales by Region, 2007 48
• Table 2.20: World' s Top-Selling Drugs, 2007 49
• Table 2.21: Top Pharmaceutical Companies by Healthcare Revenue, 2008 50
• Table 2.22: Leading Therapy Classes by Global Pharmaceutical Sales, 2007 50
• Table 2.23: Leading Ten Therapeutic Classes by U.S. Sales, 2003, 2006 and 2007 50
• Table 2.24: Top Ten Therapeutic Classes by U.S. Dispensed Prescriptions, 2006 and 2007 51
• Table 2.25: Top Ten Brand Drugs by Retail Dollars, 2007 51
• Table 2.26: Pharmaceuticals Industry Challenges 54
• Table 2.27: Reasons for Developing Phase I Biomarkers 55
• Table 2.28: Percentage of Non-Responders in Various Drug Classes 56
• Table 2.31: High Profile Drug Withdrawals from the Marketplace 56
• Table 2.30: Market Opportunities in Biomarkers 59
• Table 2.31: Challenges for Market Adoption of the Various Biomarkers Tests 60
• Table 2.32: Biomarkers Industry SWOT 62
• Table 3.1: Worldwide Microarray Market Size, 2004 - 2012 71
• Table 3.2: List of DNA Array Manufacturers 78
• Table 3.3: U.S. qRT-PCR Market, 2007 - 2013 84
• Table 3.4: Theranostics Technology Platforms-Timeline of Impact 85
• Table 3.5: Impact of Personalized Medicine on Various Therapeutic Areas 86
• Table 3.6: Hurdles in Biomarkers Development in Therapeutic Areas 87
• Table 3.7: Data Source and Bioinformatic Investigations 95
• Table 3.8: Drivers and Challenges of the Bioinformatics Industry 98
• Table 3.9: Bioinformatics Activities, Sub-Activities and Key Players 104
• Table 3.10: Concentration of Some Abundant Proteins, New Cancer Biomarkers Identified by SELDI-TOF, and Classical Cancer Biomarkers in Serum 113
• Table 3.11: Device Submission Elements for the FDA 113
• Table 3.12: Toxicogenomic Standards and Their Organizations 117
• Table 3.13: Genomic and Proteomic Technologies 118
• Table 4.1: Companion Biomarker Market Size, 2008 - 2013. 119
• Table 4.2: Kidney Biomarkers 126
• Table 4.3: Herceptin Worldwide Sales, 1999 - 2007 129
• Table 4.4: Characteristics of Different Cancer Biomarker Types and Associated Market Opportunities 130
• Table 4.5: Segmentation of the Cancer Biomarker Market by Type of Cancer Biomarkers and Market Size 131
• Table 4.6: Cancer Biomarker Market Estimates by Tissue of Origin 132
• Table 4.7: Companies Developing New Proteomic Cancer Biomarker Technology Platforms 133
• Table 4.8: Cancer Biomarkers Used to Maximize Likelihood of Response 134
• Table 4.9: Biomarkers for Monitoring Therapeutic Effectiveness and Resistance 135
• Table 6.1: Contract Research Companies 146
• Table 8.1: Stakeholders in Biomarker Development 154
• Table 9.1: Structure of the Critical Path 172
• Table 9.2: Device Submission Elements for the FDA 184