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시장보고서
Companion Biomarkers in Drug Development
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Abstract
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.
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
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