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 TriMark Publications 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.
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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 BioMérieux 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 BioMérieux 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
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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

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
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