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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.

1. 1. Overview 13
2. 1.1 Statement of Report 13
3. 1.2 About This Report 13
4. 1.3 Scope of the Report 13
5. 1.4 Objectives 13
6. 1.5 Methodology 15
7. 1.6 Executive Summary 16
9. 2. Introduction: Companion Diagnostics in Drug Development 19
10. 2.1 Companion Diagnostics as Biomarkers 20
11. 2.1.1 Potential Benefits of Biomarkers as Companion Diagnostics 22
12. 2.2 Biomarkers in Different Phases of Drug Development 22
13. 2.2.1 Drug Discovery and Development Process 22
14. 2.2.2 Biomarkers in Drug Development 24
15. 2.3 Drug Targets 24
16. 2.3.1 Target Discovery Using Functional Genomics 26
17. 2.3.2 Functional Genomics 26
18. 2.3.3 Target Validation 28
19. 2.3.3.1 Target Discovery 28
20. 2.3.3.2 Lead Identification 28
21. 2.3.4 Target and Biomarker Discovery 29
22. 2.3.4.1 Biomarker Validation 29
23. 2.4 Biomarkers in Drug Discovery, Development and Clinical Diagnostics 29
24. 2.4.1 Role of Biomarkers in Drug Discovery, Preclinical, Clinical Development and Diagnostics 29
25. 2.4.2 The Pipeline Problem 31
26. 2.4.3 Biomarkers in the Drug Discovery Process 32
27. 2.4.4 Segmentation of Biomarker Usage 32
28. 2.4.5 Efficacy of Biomarkers as Surrogate Endpoints 33
29. 2.4.6 Biomarkers Used to Reduce the Cost of Drug Development 34
30. 2.4.7 Biomarkers: Challenges and Opportunities 34
31. 2.4.8 Biomarkers in Early Safety and Toxicity Assessment 35
32. 2.4.9 Biomarkers in Determining Validation Parameters 35
33. 2.4.10 Challenges in Development of Biomarkers 36
34. 2.4.11 Using Biomarkers in Early Clinical Development 36
35. 2.4.12 Translational Biomarkers 36
36. 2.4.13 Use of Biomarkers in "Go"/No-Go" Decisions 37
37. 2.4.14 Diagnostic Tests 37
38. 2.4.15 Biomarkers in Deal Making 37
39. 2.4.16 Payors Use Biomarkers in Decision-Making 37
40. 2.5 World Pharmaceutical Markets 38
41. 2.5.1 World Market Summary 38
42. 2.5.2 Company Performance in this Segment 40
43. 2.5.3 Forces Affecting the Structure of the Pharmaceutical Industry 41
44. 2.5.3.1 Threats 41
45. 2.5.3.2 Competitive Forces 42
46. 2.6.1 Industry Overview 42
47. 2.6.1.1 Pharmaceutical Industry Drug Pipeline 44
48. 2.6.1.2 Asia-Pacific to Replace United States and Europe as Pharmaceutical Industry Center 54
49. 2.6.1.3 The Changing Pharmaceutical Business Model 54
50. 2.6.2 Benefits for Companion Diagnostic Tests in Drug Development 55
51. 2.6.3 Strategies for the Creation of Partnerships - Predicting and Overcoming Challenges in Creating Drug Response Profiling Diagnostics 57
52. 2.6.4 Options and Applications 57
53. 2.6.4.1 Clinical Applications of Genomics: The Use of Evidence Based Frameworks by Decision-Makers 57
54. 2.6.5 Challenges, Drivers and Trends 58
55. 2.6.5.1 Macro Trends in Biomarkers 58
56. 2.6.5.2 Biomarkers: Industry SWOT Analysis 61
57. 2.6.6 Breakaway Technologies 62
58. 2.6.7 Collaboration for Companion Diagnostics 63
59. 2.6.8 Key Stake Holders in Companion Diagnostics 63
60. 2.9 Future Developments 65
62. 3. Biomarker Development Tools 66
63. 3.1 New Technologies in Functional Genomics 66
64. 3.1.1 Genomics-Derived Drug Pipeline 66
65. 3.1.2 Future of Genomics Technologies for Drug Target Identification 66
66. 3.2 Overview of Microarrays 67
67. 3.2.1 General Theory of Microarrays 68
68. 3.2.2 GeneChip Probe Array Technology 69
69. 3.2.3 DNA Microarrays 69
70. 3.2.3.1 DNA Microarray Market Size 71
71. 3.2.3.2 DNA Microarrays in SNP Analysis 72
72. 3.2.3.3 DNA Microarrays in Cancer 72
73. 3.2.4 Protein Microarrays 73
74. 3.2.4.1 Reasons Why Researchers Use Protein Microarrays 74
75. 3.2.4.2 Factors for Adoption of Protein Microarrays Technology 74
76. 3.2.4.3 Future Innovations in Protein Microarray Technology 74
77. 3.2.5 New Technologies 75
78. 3.2.5.1 Antibody Microarrays 75
79. 3.2.5.2 Peptide Microarrays 75
80. 3.2.5.3 Peptide MHC Microarrays 75
81. 3.2.5.4 Tissue Microarrays 75
82. 3.2.5.5 Key Points for Developing Microarray Based Applications 76
83. 3.2.5.6 Reasons Why Researchers use DNA Microarrays 77
84. 3.2.5.7 Factors for Difficulties Applying DNA Microarrays Technology 77
85. 3.2.5.8 Emerging Microarray Trends 78
86. 3.2.5.9 Emerging Microarray Applications 78
87. 3.2.5.10 Key Findings on Use of Microarrays 79
88. 3.2.5.11 Advantages and Drivers of Microarrays 79
89. 3.2.5.12 Limitations and Barriers to Use of Microarrays 81
90. 3.2.5.13 qRT-PCR Use in Biomarker Identification and Drug Development 83
91. 3.2.5.14 Microarray Quality Control (MAQC) Project 84
92. 3.3 Theranostics 84
93. 3.3.1 Theranostics in Drug Development 84
94. 3.3.2 Trends in Theranostics 85
95. 3.3.3 Timeline for Impact on Various Segments in Theranostics 85
96. 3.3.4 Challenges for Biomarker Based Therapeutics Development 87
97. 3.4 Pharmaceutical Development and Bioanalytical Services 88
98. 3.4.1 Wyeth Singulex's Erenna 88
99. 3.5 Metabolomics in Drug Discovery 88
100. 3.6 Bioinformatics 90
101. 3.6.1 Definition and Role of Bioinformatics 90
102. 3.6.2 Bioinformatics Sector Overview 93
103. 3.6.3 Future Status of Bioinformatics 93
104. 3.6.3.1 Future in Drug Discovery 93
105. 3.6.3.2 Mergers and Acquisitions Could Deter Bioinformatics Growth 94
106. 3.6.3.3 Barriers to Bioinformatics Growth 94
107. 3.6.3.4 Types of Data and Bioinformatics Applications 94
108. 3.6.3.5 Validated Core Modeling Technology 95
109. 3.6.3.6 Applicability of Bioinformatics for Biomarker Discovery 95
110. 3.6.3.7 Biomarker Data Management Compliant with Industry Standards 96
111. 3.6.3.8 Data Management for Biomarkers 96
112. 3.6.3.8.1 Data Transformation for Biomarker Development 96
113. 3.6.3.8.2 Biomarker Data Collaboration 96
114. 3.6.3.8.3 Interface for Online Data Sources for Genomic Structures 96
115. 3.6.3.8.4 Target Markets for Informatics Software 96
116. 3.6.3.8.5 Bioinformatics Drivers and Challenges in the Pharmaceutical Industry 97
117. 3.6.3.8.6 Products of Bioinformatics 100
118. 3.6.3.8.7 Informatics Tools and Functionalities 101
119. 3.6.3.8.8 Bioinformatics in Lead Identification and Optimization 101
120. 3.6.3.8.9 Bioinformatics in Drug Development and Formulation 102
121. 3.6.3.8.10 Role of Bioinformatics in the Drug Discovery Value Chain 102
122. 3.6.3.8.11 Bioinformatics Software for Drug Discovery and Biomarker Development 102
123. 3.6.3.8.12 Bioinformatics Services 104
124. 3.7 Biomarkers and Proteomics 105
125. 3.7.1 Scientific Background 105
126. 3.7.2 Applying Proteomics to Biomarker Discovery 106
127. 3.7.2.1 Challenges Facing Biomarker Developers 106
128. 3.7.3 Limitations of Proteomic Approaches to Biomarker Discovery 108
129. 3.7.4 Validation of Biomarkers Using LC-MS/MS Systems 109
130. 3.7.5 Use of Mass Spectrometry in Biomarker Discovery 109
131. 3.7.5.1 Multiple Reaction Monitoring Assays (MRMs) 110
132. 3.7.5.2 Gel-based Approaches 110
133. 3.7.5.3 Non-Gel-based Approaches 111
134. 3.7.5.4 SELDI-TOF MS 111
135. 3.7.5.5 SELDI and Prognosis 112
136. 3.7.5.6 SELDI and Treatment Monitoring 112
137. 3.7.5.7 Limitations of Mass Spectroscopy 112
138. 3.7.6 Partnerships for Developing Proteomic Biomarkers 114
139. 3.7.7 Proteomics in Developing a New Cancer Marker 114
140. 3.7.7.1 Translating Proteomic Oncology Discoveries to the Clinic: Development of Analytical Reference Materials, Reagents, Data, and Technology Assessment and Validation 115
141. 3.7.7.2 Challenges of Discovering and Validating Clinical Protein Biomarkers 115
142. 3.7.7.3 Importance of Proteomics in Biomarker Discovery 115
143. 3.8 Toxicogenomics 115
144. 3.8.1 Toxicogenomics Concerns in Drug Safety Data 116
145. 3.8.2 Toxicogenomics and Prioritization of Drug Candidates 116
146. 3.8.3 Genomic Biomarkers for Drug-Induced Nephrotoxicity 117
147. 3.8.4 Use of Biomarkers of Drug-Induced Cardiotoxicity 117
148. 3.8.5 Use of Biomarkers of Drug-induced Hepatotoxicity 117
149. 3.8.6 Transgenic Biomarkers for Adverse Drug-Drug Interactions 117
150. 3.8.7 Challenges to Toxicogenomics 118
151. 3.8.8 The Future Use of Toxicogenomics in Drug Discovery 118
153. 4. Market for Biomarkers in Drug Development 119
154. 4.1 C-KIT (CD117) Expression 122
155. 4.2 CCR5 -Chemokine C-C Motif Receptor 122
156. 4.3 CYP2C19 Variants 123
157. 4.4 CYP2C9 Variants 123
158. 4.5 CYP2D6 Variants 124
159. 4.6 CYP2D6 Variants with Alternate Context 124
160. 4.7 Clinical Biomarkers 124
161. 4.8 Targeting Kidney Toxicity 125
162. 4.8.1 Proximal and Distal Tubular Injury (alpha-GST & Pi-GST) 125
163. 4.8.2 Collecting Duct and Loop of Henle Injury (RPA-1 and RPA-2) 126
164. 4.8.3 Glomerular Injury (Collagen IV) 126
165. 4.8.4 KIM-1 126
166. 4.9 Targeting Hepatotoxicity 127
167. 4.9.1 Breast Cancer 128
168. 4.9.2 Colorectal Cancer 128
169. 4.9.3 Prostate Cancer 128
170. 4.9.4 Cystic Fibrosis 128
171. 4.10 Biomarker Application in Oncology Clinical Development 128
172. 4.10.1 Specific Example of Companion Biomarkers in Clinical Oncology 135
173. 4.10.2 Integration of a Companion Diagnostic Strategy into Oncology Drug Development 135
174. 4.10.2.1 Lilly to Co-Develop Companion IVDs for Cancer Drugs 135
175. 4.10.2.2 Celera to Work on Companion Diagnostics for Merck Cancer Drugs 136
176. 4.10.2.3 BioMérieux to Develop Companion Test for Ipsen's New Breast Cancer Drug 136
177. 4.10.2.4 Perlegen and Roche's 454 Develop Companion Tests 136
178. 4.10.2.5 Ventana Medical Systems and the Critical Path Institute 136
179. 4.10.2.6 Biomarkers in Recentin/AZD 2171 Development 136
180. 4.10.2.7 Biomarkers in Development of Iressa 136
181. 4.10.2.8 Epigenomics' Methylation Biomarker Septin 136
182. 4.11 Targeting Diabetes Related Heart Disease 137
183. 4.12 Key Challenges and Opportunities in Developing Targeted Therapeutics 137
185. 5. Imaging Biomarkers in Drug Discovery 138
186. 5.1 Introduction 138
187. 5.1.1 Validation of Imaging Biomarkers 138
188. 5.1.2 Types of Imaging Used in Drug Development 138
189. 5.1.3 Development of Imaging Technologies 139
190. 5.2 Molecular Imaging 139
191. 5.2.1 Use in Drug Discovery 139
192. 5.2.2 Use in Clinical Applications 139
193. 5.2.3 Use in Clinical Trials 139
194. 5.2.4 Cell-based Screening Technologies in Drug Development 139
195. 5.2.5 Optical Biomarkers 140
196. 5.3 Magnetic Resonance Imaging 140
197. 5.4 Positron Emission Tomography 140
198. 5.5 FDG-PET Patient Phase I Studies 141
199. 5.6 Imaging Biomarkers as Study Endpoints 142
200. 5.6.1 Oncology 142
201. 5.6.2 Parkinson's Disease 142
202. 5.6.3 Cardiac Disease 142
203. 5.7 IT Solutions for Imaging Biomarkers in Biopharmaceutical Research and Development 144
205. 6. Clinical Biomarkers Improving Trial Design 145
206. 6.1 Strategies to Improve the Measurement of Biomarkers for Drug Trials 145
207. 6.2 Key Opportunities in Biomarker Discovery, Development and Commercialization 145
208. 6.2.1 Contract Research Companies 145
209. 6.3 What Strategies Help Translate Biomarkers from Preclinical to Clinical Development? 147
210. 6.4 How Should Biomarker Data Be Compared to "Traditional" Safety and Efficacy Data? 147
212. 7. Biomarkers as Surrogate Endpoints 148
213. 7.1 What is a Surrogate Endpoint? 148
214. 7.2 Benefits and Drawbacks of Surrogate Endpoints 148
215. 7.2.1 Benefits 148
216. 7.2.2 Drawbacks 148
217. 7.3 Improving the Efficacy of Clinical Surrogate End Points Using Biomarkers 148
218. 7.4 Surrogate Endpoint Validation 149
219. 7.5 Effective Use of Surrogates 149
220. 7.5.1 FDG-PET as a Surrogate Endpoint in Oncology Studies 149
221. 7.6 Conclusions 149
223. 8. Market Size, Collaborations and Future Directions for Companion Diagnostics in Drug Development 150
224. 8.1 Strategies to Improve the Measurement of Biomarkers for Drug Trials 150
225. 8.1.1 Key Opportunities in Biomarker Discovery, Development and Commercialization 150
226. 8.1.2 The Rationale Behind Biomarker Strategy 150
227. 8.1.3 New Development Strategies and Their Implications for Deal Making 151
228. 8.1.4 How Biomarkers Are Being Used To Reduce Attrition in Development 151
229. 8.1.5 Combined Therapeutics and Diagnostics Biomarker Business Makes Sense 152
230. 8.1.6 Use of Biomarkers In House or Partner with a Diagnostics Company 152
231. 8.2 What is the Best Balance of Resources to Have the Most Efficient Pathway to Develop Biomarkers? 152
232. 8.3 Current and Future Trends in Drug Development 152
233. 8.4 Future Role of Biomarkers in Healthcare 153
234. 8.5 What are the Current Organizational Obstacles in Biomarker Implementation? 154
236. 9. Regulatory Issues for Biomarkers in Drug Development 155
237. 9.1 Introduction 155
238. 9.1.1 Role of Regulatory Agencies in Development of Biomarkers 156
239. 9.2 FDA Perspective of Biomarkers in Clinical Trials 156
240. 9.2.1 FDA as a Gatekeeper of Companion Biomarkers 156
241. 9.2.2 FDA Criteria for a Valid Biomarker 157
242. 9.2.3 FDA Product Submission and Review Process 158
243. 9.2.4 FDA Pipeline for Biomarker Tests 158
244. 9.2.5 Adaptive Clinical Trial Design 159
245. 9.2.6 Orphan Drug Act and Biomarkers: Options and Opportunities 159
246. 9.3 Role of StaRT-PCR™ in Increasing Value of Pharmacogenomic Data 160
247. 9.4 Supporting IND, NDA, and BLA Submissions 161
248. 9.5 Performance Characteristics of Biomarker Tools 163
249. 9.6 Biomarker Initiative and VGDs 164
250. 9.7 Biomarker Qualification Pilot Process at the FDA 165
251. 9.7.1 Introduction 165
252. 9.7.2 Biomarker is Validity 166
253. 9.7.3 Biomarker Qualification Process Map 166
254. 9.7.4 Biomarker Qualification Pilot Process 166
255. 9.7.5 The Pipeline Problem 168
256. 9.7.6 FDA Critical Path 169
257. 9.7.6.1 Challenge and Opportunity on the Critical Path to New Medical Products 170
258. 9.7.6.2 The NIH Roadmap 171
259. 9.7.6.3 Predictive Safety Testing Consortium 171
260. 9.7.7 Negotiating the Critical Path 171
261. 9.7.8 Technical Dimensions along the Critical Path 172
262. 9.7.9 Product Development Toolkit 173
263. 9.7.10 Tools for Assessing Safety 174
264. 9.7.11 Tools for Demonstrating Medical Utility 176
265. 9.7.12 Tools for Manufacturing 179
266. 9.7.13 Orphan Products Grant Program 179
267. 9.7.14 Slowdown in New Medical Products 180
268. 9.7.15 Factors Contributing to the Decline in New Product Applications 182
269. 9.7.16 Factors that Cause Unnecessary Delays in New Product Approvals 184
270. 9.7.17 Reducing Avoidable Delays in Time to Approval 186
271. 9.7.18 Reducing Delays in Medical Device Reviews 187
272. 9.7.19 Reducing Delays in Animal Drug Reviews 187
273. 9.7.20 Quality Systems Approach to Medical Product Review 187
274. 9.7.20.1 Instituting Quality Systems in Review of New Drugs and Biologics 188
275. 9.7.20.2 Implementing of the Common Technical Document (CTD) and the electronic CTD 189
276. 9.7.20.3 Implementing Medical Device Quality Initiatives 189
277. 9.7.21 Case Study: Nephrotoxicity Biomarkers 189
278. 9.7.22 Role of the FDA 189
279. 9.8 CMS Regulatory Responsibilities 190
280. 9.9 Role of National Institute of Standards and Technology in Validation of Biomarkers 191
281. 9.10 Biomarkers and FDA's Voluntary Genomic Data Submission 191
282. 9.11 Federal Health Oncology Biomarker Qualification Initiative 193
283. 9.12 Orphan Drug Act and Pharmacogenomics: Options and Opportunities 194
284. 9.13 Post-market Covigilance Programs 195
285. 9.14 Technology Options, Potential Diagnostic Partners and Regulatory Hurdles 196
286. 9.15 What Regulatory Guidance Is Needed for Companion Biomarkers? 197
287. 9.16 U.S. Patent and Trademark Office (USPTO) 198
288. 9.17 IRB Approval in Clinical Trials 198
290. 10. Business Decisions Using Companion Biomarkers in Drug Development 199
291. 10.1 Advantages of a Pharmacogenomic Assessment of Biomarkers to Determine Clinical Dose 199
292. 10.2 Key Opportunities in Biomarker Discovery, Development and Commercialization 199
293. 10.3 What Are the Current Obstacles in Biomarker Implementation? 199
294. 10.4 How Do Business Strategies, Such as Those Relating to Acquisition, Drive Biomarker Strategies? 200
295. 10.5 What is the Right Balance Between Using External Partnerships and Developing Internal Infrastructure? 200
296. 10.6 How Might Novel Biomarker Development Lead to Acquisition Strategies and Their Implications For Deal Making? 200
297. 10.7 Which Types of Biomarkers Should Be Developed at Various Stages in the Drug Pipeline? 200
298. 10.8 What Strategies Help Translate Biomarkers From Preclinical to Clinical Development? 200
299. 10.9 In What Class of Drugs Is the Value of Using Biomarkers in Decision Making the Highest? 201
300. 10.10 Increased Clinical Trial Costs in Targeted Phase I Trials 202
301. 10.11 How Can Big Pharma Co-develop Biomarkers in a Cost-sharing Model for Regulatory Acceptance? 202
302. 10.12 How Are Biomarkers Being Used to Reduce the Attrition Rate in Drug Development? 202
303. 10.13 How Is ROI Measured Using Biomarkers in Drug Development? 202
304. 10.14 How Might Organizational Structures Limit the Use of Biomarkers in Drug Development and How Should R&D Organizations Address This Problem? 202
305. 10.15 How to Maximize Business Development through Biomarker Strategies 203
306. 10.16 What Is the Best Type of Business Model for Developing Biomarkers? 203
307. 10.17 What Are Organizational Impediments Limiting the Use of Biomarkers in Drug Development? 203
308. 10.18 What Are Internal Capabilities for Novel Biomarker Development and Application? 203
309. 10.19 How Can Key Biomarker Technical Expertise Be Applied Across a Complex and Highly-Stratified R&D Value Chain? 204
310. 10.20 At What Stage of Drug Development Have Biomarkers Provided the Most Benefit? 204
311. 10.21 What Companies Are the most Innovative in Development of Biomarkers? 204
312. 10.22 Best Values for Biomarkers in Drug Development and in Diagnostics 204
313. 10.23 Companion Biomarkers Can Increase Value in an Associated Drug 205
315. 11. Company Profiles 206
316. 11.1 Abbott Laboratories 206
317. 11.2 Accelrys 207
318. 11.3 Affymetrix 208
319. 11.4 Agilent Technologies 211
320. 11.5 Amgen 213
321. 11.6 Ananomouse 214
322. 11.7 Applied Maths 215
323. 11.8 Ariadne Genomics 215
324. 11.9 ArrayIt (Integrated Media Holdings) 215
325. 11.10 AstraZeneca 216
326. 11.11 AutoGenomics 217
327. 11.12 Axontologic 217
328. 11.13 Beckman Coulter 218
329. 11.14 BD 224
330. 11.15 Bender MedSystems 225
331. 11.16 Bioalma 225
332. 11.17 BioAnalytics Group 226
333. 11.18 BioCat GmbH 226
334. 11.19 Biocept 226
335. 11.20 BioChain 226
336. 11.21 BioData 227
337. 11.22 BioDiscovery 227
338. 11.23 BioForce Nanosciences 227
339. 11.24 BioGenex 228
340. 11.25 Bioinformatics Solutions 228
341. 11.26 Biomax Informatics 228
342. 11.27 BioMérieux 229
343. 11.28 Biomind 229
344. 11.29 Bio-Rad Laboratories 229
345. 11.30 Biosite 230
346. 11.31 BioSystems International 230
347. 11.32 Biotrin 230
348. 11.33 BioWisdom 230
349. 11.34 Bristol-Myers Squibb Company 231
350. 11.35 Caliper Life Sciences 232
351. 11.36 Caprion Proteomics 235
352. 11.37 Carestream Health 237
353. 11.38 Celera 237
354. 11.39 Cepheid 239
355. 11.40 Chang Bioscience 241
356. 11.41 Clontech Laboratories 241
357. 11.42 CombiMatrix 241
358. 11.43 Compugen 243
359. 11.44 Corimbia 244
360. 11.45 Covance 244
361. 11.46 Cybrdi 244
362. 11.47 CyVera 244
363. 11.48 Dako A/S 244
364. 11.49 Decodon 245
365. 11.50 Definiens 245
366. 11.51 DiagnoSwiss 246
367. 11.52 Discerna 246
368. 11.53 DNAStar 246
369. 11.54 DNATools 246
370. 11.55 Eidogen-Sertanty 247
371. 11.56 Electric Genetics 247
372. 11.57 Eli Lilly and Company 247
373. 11.58 Entelos 248
374. 11.59 ePitope Informatics 248
375. 11.60 Eurogentec 248
376. 11.61 Exiqon A/S 249
377. 11.62 Forensic Bioinformatics 249
378. 11.63 Fujitsu 249
379. 11.64 Future Diagnostics 250
380. 11.65 Genaissance Pharmaceuticals 250
381. 11.66 Gene Codes 250
382. 11.67 Genedata 250
383. 11.68 GeneGo 250
384. 11.69 Gene Network Sciences 251
385. 11.70 Geneva Bioinformatics 251
386. 11.71 Genomatica 251
387. 11.72 Genomic Solutions 251
388. 11.73 Genomining 252
389. 11.74 Gen-Probe 252
390. 11.75 GE Healthcare 256
391. 11.76 GeneStudio 256
392. 11.77 Genomatix Software 256
393. 11.78 GenomeQuest 257
394. 11.79 Genus BioSystems 257
395. 11.80 Genzyme 257
396. 11.81 Geospiza 258
397. 11.82 GlaxoSmithKline 259
398. 11.83 Golden Helix 259
399. 11.84 Grace Bio-Labs 260
400. 11.85 Gyros AB 260
401. 11.86 HealthCare IT 260
402. 11.87 High Throughput Genomics 260
403. 11.88 Human Genome Sciences 261
404. 11.89 Illumina 261
405. 11.90 Imgenex 264
406. 11.91 Imaxia 264
407. 11.92 INCOGEN 264
408. 11.93 Incyte 265
409. 11.94 InforSense 265
410. 11.95 Ingenuity Systems 265
411. 11.96 InPharmix 266
412. 11.97 Insightful Corporation 266
413. 11.98 Integromics, S.L 266
414. 11.99 IBM 266
415. 11.100 IO Informatics 267
416. 11.101 Ipsen 268
417. 11.102 Jerini AG 268
418. 11.103 Johnson & Johnson 268
419. 11.104 Koada Technology 269
420. 11.105 KOOPrime 269
421. 11.106 Life Technologies Corporation 269
422. 11.107 LINCO Research 270
423. 11.108 Luminex 270
424. 11.109 Marligen Biosciences 271
425. 11.110 Matrix Science 271
426. 11.111 MDS 272
427. 11.112 Merck & Company 272
428. 11.113 Merck KGaA 273
429. 11.114 Meso Scale Discovery 273
430. 11.115 Metabolon 274
431. 11.116 Microbionix 274
432. 11.117 MicroDiscovery 274
433. 11.118 Millennium Pharmaceuticals 275
434. 11.119 Millipore 275
435. 11.120 MiraiBio 276
436. 11.121 Molecular Connections 276
437. 11.122 MolMine AS 276
438. 11.123 Molsoft 277
439. 11.124 Monogram Biosciences 277
440. 11.125 MTR Scientific 278
441. 11.126 Multimetrix 278
442. 11.127 Nanogen 278
443. 11.128 Nanosphere 280
444. 11.129 NetGenics 280
445. 11.130 NextGen Sciences 280
446. 11.131 NimbleGen Systems 281
447. 11.132 Nonlinear Dynamics 281
448. 11.133 Novartis 281
449. 11.134 Nuvera Biosciences 282
450. 11.135 Ocimum Biosolutions 282
451. 11.136 OmniViz 282
452. 11.137 One Lambda 282
453. 11.138 Oracle 283
454. 11.139 Ore Pharmaceuticals 284
455. 11.140 Orla Protein Technologies 285
456. 11.141 Osmetech 285
457. 11.142 Oxonica 285
458. 11.143 PamGene BV 286
459. 11.144 Panomics 286
460. 11.145 Partek 286
461. 11.146 Pepscan 287
462. 11.147 Perbio Science 287
463. 11.148 Perlegen Sciences 287
464. 11.149 Pfizer 287
465. 11.150 PharmaSeq 288
466. 11.151 Pierce Biotechnology 288
467. 11.152 Platypus Technologies 288
468. 11.153 Predictive Patterns Software 288
469. 11.154 Proceryon 288
470. 11.155 Protagen AG 289
471. 11.156 ProteinOne 289
472. 11.157 Proteome Sciences 289
473. 11.158 PubGene 289
474. 11.159 Qiagen 290
475. 11.160 Radix BioSolutions 293
476. 11.161 Randox Laboratories 294
477. 11.162 RayBiotech 294
478. 11.163 Redasoft 294
479. 11.164 RedStorm Scientific 294
480. 11.165 Reel Two 294
481. 11.166 Rescentris 295
482. 11.167 Roche 295
483. 11.168 Rosetta Biosoftware 296
484. 11.169 Rules-Based Medicine 296
485. 11.170 SAS 296
486. 11.171 Schleicher & Schuell BioScience 297
487. 11.172 SciTegic 297
488. 11.173 Semantx Life Sciences 297
489. 11.174 Sequenom 297
490. 11.175 Sigma-Aldrich 298
491. 11.176 Silicon Genetics 299
492. 11.177 Singulex 299
493. 11.178 Softberry 299
494. 11.179 SoftGenetics 299
495. 11.180 SomaLogic 299
496. 11.181 Spotfire 300
497. 11.182 SPSS 300
498. 11.183 Strand Life Sciences 301
499. 11.184 Stratagene 301
500. 11.185 SuperBioChips Laboratories 301
501. 11.186 SurroMed 301
502. 11.187 Sun Microsystems 301
503. 11.188 Sygnis Pharma AG 302
504. 11.189 Techne Corporation 302
505. 11.190 Tepnel Life Sciences 303
506. 11.191 Teranode 303
507. 11.192 Textco BioSoftware 303
508. 11.193 TG Services 304
509. 11.194 Thermo Fisher Scientific 304
510. 11.195 Third Wave Technologies 305
511. 11.196 TIBCO Software 305
512. 11.197 TimeLogic 305
513. 11.198 TriStar Technology Group 305
514. 11.199 Tyrian Diagnostics (formerly Proteome Systems) 306
515. 11.200 VBC-Genomics Bioscience Research GmbH 306
516. 11.201 Ventana Medical Systems 306
517. 11.202 ViaLogy 307
518. 11.203 Wyeth 307
519. 11.204 Zeptosens 307
520. 11.205 Zeus Scientific 308
521. 11.206 Zyagen 308
523. Appendix 1: FDA Guidance for Industry: Pharmacogenomic Data Submission 309
524. A 1.1 Introduction 309
525. A 1.2 Background 309
526. A 1.3 Submission Policy 310
527. A 1.3.1 General Principles 310
528. A 1.3.2 Specific Uses of Pharmacogenomic Data in Drug Development and Labeling 311
529. A 1.3.3 Benefits of Voluntary Submissions to Sponsors and FDA 312
530. A 1.4 Submission of Pharmacogenomic Data 313
531. A 1.4.1 Submission of Pharmacogenomic Data during the IND Phase 313
532. A 1.4.2 Submission of Pharmacogenomic Data to a New NDA, BLA, or Supplement 314
533. A 1.4.3 Submission to a Previously Approved NDA or BLA 315
534. A 1.4.4 Compliance with 21 CFR Part 58 315
535. A 1.4.5 Submission of Voluntary Genomic Data from Application-Independent Research 316
536. A 1.5 Format and Content of a VGDS 316
537. A 1.6 Process for Submitting Pharmacogenomic Data 317
538. A 1.7 Agency Review of VGDSs 317
540. Glossary 319
543. INDEX OF FIGURES
545. Figure 2.1: Drug Discovery and Development Paradigm 24
546. Figure 2.2: Paradigm of Drug Discovery and Development Illustrating the Central and Essential Role of Biomarkers in Screening 25
547. Figure 2.3: Functional Genomic Process for Drug Development 26
548. Figure 2.4: Reimbursement for Diagnostics in Healthcare Decision Making 30
549. Figure 2.5: Market Growth and Evolution of Companion Biomarkers 31
550. Figure 2.6: Medical Product Development Models 32
551. Figure 2.7: Segmentation of the Biomarker Development Market 33
552. Figure 2.8: Medical Research in the U.S. Outpaces the Rest of the World 45
553. Figure 2.9: Worldwide Pharmaceutical Products Markets 48
554. Figure 2.10: Biomarkers Market Drivers 58
555. Figure 2.11: Challenges in the Biomarkers Space 59
556. Figure 2.12: FDA Co-Developed Products 64
557. Figure 3.1: Informatics Applications Along the Drug Discovery Value Chain 91
558. Figure 3.2: Bioinformatics Software Flow Chart 91
559. Figure 3.3: Growth of GenBank, 1982 - 2008 92
560. Figure 3.4: Role of Bioinformatics in the Drug Discovery Value Chain 102
561. Figure 3.5: Challenges in the Study or Utilization of Proteomic Biomarkers 107
562. Figure 3.6: Challenges in the Study or Utilization of Companion Diagnostic Biomarkers 107
563. Figure 3.7: Top Unmet Needs in Products in the Biomarkers Space 108
564. Figure 4.1: Growth and Evolution of the Biomarker Space 120
565. Figure 4.2: Revenue Forecast Projections for Global Biomarker Markets by Segments, 2005 - 2012 121
566. Figure 4.3: Biomarker Discovery by Therapeutic Area 122
567. Figure 4.4: Kidney Biomarker Paradigm 125
568. Figure 4.5: Hepatic Biomarker Paradigm 127
569. Figure 9.1: IPRG Biomarker Qualification Process 167
570. Figure 9.2: Critical Path for Drug Development 180
571. Figure 9.3: Path for R&D Product Development 181
572. Figure 9.4: Dimensions of the Critical Path 181
573. Figure 9.5: FDA Interactions During Drug Development 182
574. Figure 9.6: Problem Resolution During the FDA Review Process 182
575. Figure 9.7: VGDS Process Flow 193
576. Figure 10.1: Discovery, Validation and Use of Biomarkers 201
579. INDEX OF TABLES
581. Table 2.1: Utility of Biomarkers as Companion Diagnostics to Drug Development 20
582. Table 2.2: Biomarker End Points in Drug Development 22
583. Table 2.3: Value of Biomarkers in Phase II Clinical Trials 24
584. Table 2.4: Comparative Genome Sizes of Humans and Other Organisms 27
585. Table 2.5: Global Pharmaceutical Drug Sales, 2004 - 2012 38
586. Table 2.6: Worldwide Generic Pharmaceutical Drug Market, 2003 - 2012 39
587. Table 2.7: Worldwide OTC Pharmaceutical Drug Market, 2003 - 2012 39
588. Table 2.8: Worldwide Biopharmaceutical Drug Market, 2003 - 2012 40
589. Table 2.9: Top Ten Pharmaceutical Companies by Worldwide Sales, 2008 40
590. Table 2.10: Pharmaceutical Companies' Drug Sales as Percent of the Worldwide Market, 2008 41
591. Table 2.11: Threats to Pharmaceutical Industry Productivity 42
592. Table 2.12: Competitive Forces Governing the Pharmaceutical Industry 42
593. Table 2.13: Time Line for Development of Companion Diagnostics 43
594. Table 2.14: Leading Therapy Classes for R&D, 2008 44
595. Table 2.15: Global Pharmaceutical Industry R&D Spending, 1995 - 2008 46
596. Table 2.16: Pharmaceutical R&D Expenditures by World Region, 1990 - 2006 46
597. Table 2.17: U.S. Government NIH Research Budget, 1995 - 2008 47
598. Table 2.18: Pharmaceutical Companies Ranked by Total R&D Expenditures, 2006 47
599. Table 2.19: Global Pharmaceutical Sales by Region, 2007 48
600. Table 2.20: World's Top-Selling Drugs, 2007 49
601. Table 2.21: Top Pharmaceutical Companies by Healthcare Revenue, 2008 50
602. Table 2.22: Leading Therapy Classes by Global Pharmaceutical Sales, 2007 50
603. Table 2.23: Leading Ten Therapeutic Classes by U.S. Sales, 2003, 2006 and 2007 50
604. Table 2.24: Top Ten Therapeutic Classes by U.S. Dispensed Prescriptions, 2006 and 2007 51
605. Table 2.25: Top Ten Brand Drugs by Retail Dollars, 2007 51
606. Table 2.26: Pharmaceuticals Industry Challenges 54
607. Table 2.27: Reasons for Developing Phase I Biomarkers 55
608. Table 2.28: Percentage of Non-Responders in Various Drug Classes 56
609. Table 2.31: High Profile Drug Withdrawals from the Marketplace 56
610. Table 2.30: Market Opportunities in Biomarkers 59
611. Table 2.31: Challenges for Market Adoption of the Various Biomarkers Tests 60
612. Table 2.32: Biomarkers Industry SWOT 62
613. Table 3.1: Worldwide Microarray Market Size, 2004 - 2012 71
614. Table 3.2: List of DNA Array Manufacturers 78
615. Table 3.3: U.S. qRT-PCR Market, 2007 - 2013 84
616. Table 3.4: Theranostics Technology Platforms-Timeline of Impact 85
617. Table 3.5: Impact of Personalized Medicine on Various Therapeutic Areas 86
618. Table 3.6: Hurdles in Biomarkers Development in Therapeutic Areas 87
619. Table 3.7: Data Source and Bioinformatic Investigations 95
620. Table 3.8: Drivers and Challenges of the Bioinformatics Industry 98
621. Table 3.9: Bioinformatics Activities, Sub-Activities and Key Players 104
622. Table 3.10: Concentration of Some Abundant Proteins, New Cancer Biomarkers Identified by SELDI-TOF, and Classical Cancer Biomarkers in Serum 113
623. Table 3.11: Device Submission Elements for the FDA 113
624. Table 3.12: Toxicogenomic Standards and Their Organizations 117
625. Table 3.13: Genomic and Proteomic Technologies 118
626. Table 4.1: Companion Biomarker Market Size, 2008 - 2013. 119
627. Table 4.2: Kidney Biomarkers 126
628. Table 4.3: Herceptin Worldwide Sales, 1999 - 2007 129
629. Table 4.4: Characteristics of Different Cancer Biomarker Types and Associated Market Opportunities 130
630. Table 4.5: Segmentation of the Cancer Biomarker Market by Type of Cancer Biomarkers and Market Size 131
631. Table 4.6: Cancer Biomarker Market Estimates by Tissue of Origin 132
632. Table 4.7: Companies Developing New Proteomic Cancer Biomarker Technology Platforms 133
633. Table 4.8: Cancer Biomarkers Used to Maximize Likelihood of Response 134
634. Table 4.9: Biomarkers for Monitoring Therapeutic Effectiveness and Resistance 135
635. Table 6.1: Contract Research Companies 146
636. Table 8.1: Stakeholders in Biomarker Development 154
637. Table 9.1: Structure of the Critical Path 172
638. Table 9.2: Device Submission Elements for the FDA 184