Clinical Trials (eBook)

Front Cover 1
Clinical Trials: Study Design, Endpoints and Biomarkers, Drug Safety, FDA and ICH Guidelines 4
Copyright Page 5
Contents 8
Acknowledgments 16
Preface 18
The Study Schema and Study Design 18
Intent to Treat Analysis 18
How to Choose the Endpoints 18
Diagnostic Tests 19
Mechanism of Action 19
Standards 19
Methodology 19
Clinicaltrials.Gov and other Registries for Clinical Trials 20
Introduction 24
Abbreviations and Definitions 28
Biography 34
1 The Origins of Drugs 36
I. Introduction 36
II. Structures of Drugs 37
a. Origins of warfarin 37
b. Origins of methotrexate and 5-fluorouracil 38
c. Origins of ribavirin 39
d. Origins of paclitaxel 39
e. Origins of cladribine 40
f. Origins of drugs in high-throughput screening 42
g. Origins of therapeutic antibodies 42
III. The 20 Classical Amino Acids 44
IV. Animal Models 46
a. Introduction 46
b. Estimating human dose from animal studies 48
1. NOAEL approach 49
2. MABEL approach 49
c. Scaling up the drug dose, acquired from animal studies, for use in humans 49
2 Introduction to Regulated Clinical Trials 52
I. Introduction 52
II. Study Design 54
III. The Study Schema 55
a. Examples of schema from clinical trials 57
b. Sequential treatment versus concurrent treatment – the Perez schema 59
c. Neoadjuvant chemotherapy versus adjuvant chemotherapy – the Gianni schema 61
d. Neoadjuvant chemotherapy plus adjuvant chemotherapy – the Untch schema 61
e. Forwards sequence and reverse sequence – the Puhalla schema 62
f. Both arms received three drugs, each arm at a different schedule – the Sekine schema 63
g. Staging – the Blumenschein schema 63
h. Staging and restaging – the Czito schema 65
i. Methodology tip – staging 65
j. Decision tree – the Baselga schema 66
k. Decision tree – the Katsumata schema 66
l. Methodology tip – what is “tumor progression”? 70
m. Methodology tip – unit of drug dose expressed in terms of body surface area 70
n. Run-in period – the schema of Dy 71
o. Methodology tip – c-kit and imatinib 72
p. Run-in period – the Hanna schema 72
q. How to maintain blinding of the treatment, when the study drug and the control treatment are provided by different-sized pills (or by different volumes of solutions)… 73
r. Methodology tip – bevacizumab and VEGF 76
s. Dose escalation – the Moore schema 76
t. Pharmacokinetics – the Marshall schema 78
IV. Further Concepts In Study Design 79
a. Active control 79
b. Add-on design active control 81
c. Three-arm study – clinical trial with two different active control arms 82
V. Summary 82
VI. Amendments to the Clinical Study Protocol 83
3 Run-In Period 86
I. Introduction 86
a. Washout period 87
b. Detecting baseline adverse events 87
c. Excluding potential study subjects who have safety issues correlating with the study drug 87
d. To include only study subjects with controllable pain 88
e. To determine the maximal tolerable dose 89
f. To achieve and ensure steady state in vivo concentrations of study drug 89
g. To allow a period of adjustment of lifestyle of the study subjects, for example changes in dietary patterns 90
h. To ensure that metabolic characteristics of all study subjects are similar, prior to administering drugs 91
i. To ensure that potential study subjects can adhere to, or comply with, the study protocol 91
j. To confirm that all study subjects meet the inclusion and exclusion criteria 92
k. Detecting potential study subjects who show a predetermined desired response to the study drug, with the goal of including only these subjects 93
l. Methodology tip – anti-cancer drugs that inhibit tumor growth 94
m. Decision tree 94
n. To create a self-control group 95
II. Concluding Remarks 96
4 Inclusion/Exclusion Criteria, Stratification, and Subgroups – Part I 98
I. The Clinical Study Protocol Is a Manual that Provides the Study Design 98
a. Clinical study protocol provides the inclusion/exclusion criteria and stratification 99
b. Stratification of study subjects 100
c. Words of warning 102
d. Staging of the disease 103
e. The study schema 103
1. Inclusion criteria for the RTOG 0232 study of prostate cancer 105
2. Exclusion criteria for the RTOG 0232 study of prostate cancer 105
f. Stratification of subjects into subgroups 105
g. Examples of subgroups 105
h. Prior therapy 107
i. Poor performance status as a basis for exclusion 108
j. Irreversible and cumulative toxicity as a basis for exclusion 109
k. Drug resistance as a basis for exclusion 111
II. Biology of Drug Resistance 111
a. Biochemistry of the ABC drug transporters 111
b. Biology of cross-resistance 112
c. A tumor’s genetic expression can provide guidance on drug resistance 113
1. Doxorubicin 113
2. Paclitaxel 113
3. Tamoxifen 114
4. Imatinib 114
d. Prior treatment with hormones as a basis for exclusion 115
e. Immune status for exclusion criteria 116
f. Example of earlier vaccination as an inclusion criterion 116
g. Ethical considerations as a basis for inclusion criteria 117
III. More Information on Subgroups 117
a. Subgroup of non-elderly subjects and subgroup of elderly subjects 118
b. Subgroup of subjects with no metastasis and subgroup of subjects with metastasis 119
c. Subgroup of smokers and subgroup of non-smokers 120
d. Subgroups set forth in a clinical study protocol can be used as a basis for FDA approval 120
e. Subgroup analysis enables recommendations for a specific course of treatment 121
f. Subgroup analysis can justify increases in drug dose for specific subgroups 122
g. Subgroups determined by an analysis of gene expression by microarray analysis 122
h. Recommending dropping one subgroup from the trial, rather than stopping the entire trial 123
IV. Concluding Remarks 124
5 Inclusion and Stratification Criteria – Part II 126
I. Introduction 126
II. Staging 126
a. History of tumor staging 127
b. Revising staging systems 127
c. Biology of tumors 128
d. Biology of the lymphatic system 128
e. Relation between the tumors and the lymphatic system 129
f. Metastasis of tumors 130
g. The sentinel node and distant lymph nodes 131
III. Staging Systems for Various Cancers 132
a. Colorectal cancer 132
b. TNM definitions for colorectal cancer 133
Stage 0 133
Stage I 134
Stage IIA 134
Stage IIB 134
Stage IIC 134
Stage IIIA 134
Stage IIIB 134
Stage IIIC 135
Stage IVA 135
Stage IVB 135
c. Breast cancer 135
d. Breast cancer in situ (DCIS and LCIS) 135
e. Invasive breast cancer 136
f. Definitions for breast cancer 136
g. Breast cancer staging 138
Stage 0 138
Stage IA 138
Stage IB 139
Stage IIA 139
Stage IIB 139
Stage IIIA 139
Stage IIIB 140
Stage IIIC 140
Stage IV 140
IV. Summary 141
V. The will Rogers Phenomenon 141
a. Will Rogers phenomenon for prostate cancer 141
b. Will Rogers phenomenon for non-small lung cancer 142
c. Will Rogers phenomenon for small cell lung cancer 142
d. Will Rogers phenomenon for rectal cancer 143
e. Will Rogers phenomenon for multiple sclerosis 143
VI. Other Sources of Artifacts in Data from Clinical Trials 144
VII. Concluding Remarks 144
6 Randomization, Allocation, and Blinding 146
I. Introduction 146
a. Allocation and allocation concealment 147
b. Simple randomization 149
c. Stratification 150
II. Manual Technique for Allocation 151
a. Allocation by coin-toss versus allocation by sealed envelope 153
III. Information on Randomization, Blinding, and Unblinding may be Included in the Clinical Study Protocol 154
a. Introduction 154
b. When to break the randomization code – clinical study protocol for trial on Alzheimer’s disease (27) 154
c. When to break the randomization code – clinical study protocol for trial on malaria vaccine (28) 155
d. When to break the randomization code – clinical study protocol for trial typhoid vaccine (29) 155
e. When to break the randomization code – clinical study protocol for trial on lung cancer (30) 155
f. When to break the randomization code – clinical study protocol for trial on sepsis (31) 156
g. When to break the randomization code – clinical study protocol for trial on melanoma (32) 156
h. When to break the randomization code – clinical study protocol for trial on multiple sclerosis (33) 156
IV. Summary 157
V. Subjects are Enrolled into Clinical Trials, One by One, Over the Course of Many Months 157
VI. Blocked Randomization 158
VII. Blinding 158
VIII. Interactive Voice Response Systems 160
IX. Concluding Remarks 164
7 Placebo Arm as Part of Clinical Study Design 166
I. Introduction 166
II. Hawthorne Effect 167
III. The No-Treatment Arm 167
IV. Physical Aspects of the Placebo 168
V. Active Placebo 168
VI. Subjects in the Placebo Arm May Receive Best Supportive Care Or Palliative Care 169
VII. Clash Between Best Supportive Care and the Endpoint of HRQoL 171
VIII. Ethics of Placebos 171
8 Intent to Treat Analysis vs. Per Protocol Analysis 178
I. Introduction 178
a. Definition of intent to treat analysis 178
b. Deviations and inconsistencies 179
II. ITT Analysis Contrasted with PP Analysis 181
a. ITT analysis vs. PP analysis – the Hosking study 182
b. ITT analysis vs. PP analysis – the Sethi study 183
c. ITT analysis vs. PP analysis – the Abrial study 183
d. ITT analysis vs. PP analysis – the Berthold study 183
e. ITT analysis vs. PP analysis – the Geddes study 184
f. ITT analysis vs. PP analysis – the Chauffert study 184
III. Disadvantages of ITT Analysis 184
IV. Run-in Period, as Part of the Study Design, is Relevant to ITT Analysis and PP Analysis 185
V. Summary 186
VI. Hypothetical Example Where Study Drug and Control Drug have Same Efficacy 186
VII. Modified ITT Analysis 187
a. Flow chart showing subjects included in the ITT analysis, modified ITT analysis, and PP analysis 188
b. Reasons for using modified ITT analysis 190
c. Excluding subjects who failed to meet inclusion or exclusion criteria, or who failed to receive study drug – the Vaira study 190
d. Excluding subjects who failed to meet inclusion or exclusion criteria – the Weigelt study 191
e. Excluding subjects who failed to meet inclusion or exclusion criteria – the Pinchichero study 191
f. Excluding subjects who failed to meet inclusion or exclusion criteria – the Leroy study 192
g. Exclusion of study subjects because of failure to satisfy the inclusion criteria, and for withdrawing consent – the Dupont study 193
h. Exclusion of study subjects because of failure to satisfy the inclusion criteria – the Florescu study 194
i. Excluding subjects who took prohibited drugs during the clinical trial, or who withdrew consent – the Manegold study 194
j. Excluding subjects who failed to receive the assigned treatment because of a mistake by the health care provider – the Berek study 194
k. Exclusion of study subjects who failed to take drug long enough to have the expected efficacy – the Krainick-Strobel study 195
l. Excluding subject who dropped out because of adverse events, and because of the bad flavor of the study drug – the Kreijkamp-Kaspers study 195
m. Excluding subjects who failed to receive the assigned treatment because of adverse events – the Caraceni study 196
n. Modified ITT group based on a subgroup of study subjects – the Gralla study 196
VIII. Start Date for Endpoints in Clinical Studies 197
IX. Summary and Conclusions 199
9 Biostatistics 200
I. Introduction 200
a. Kaplan-Meier plot 200
b. Examples of Kaplan-Meier plots – the Holm study 201
c. Censoring data 203
d. Hazard ratio 204
II. Definitions and Formulas 206
III. Data from the Study of Machin and Gardner 207
IV. Data Used for Constructing the Kaplan-Meier Plot are from Subjects Enrolling at Different Times 207
V. Sample Versus Population 209
VI. What can be Compared 210
VII. One-Tailed Test Versus Two-Tailed Test 211
VIII. P Value 212
IX. Calculating the P Value – a Working Example 215
X. Summary 221
XI. Theory Behind the Z Value and the Table of Areas in the Tail of the Standard Normal Distribution 221
XII. Statistical Analysis by Superiority Analysis Versus by Non-Inferiority Analysis 222
10 Introduction to Endpoints for Clinical Trials in Pharmacology 226
I. Introduction 226
a. Phase I clinical trial endpoints 226
b. Clinical endpoints 226
c. Surrogate endpoints 226
d. Relatively objective endpoints versus relatively subjective endpoints 228
e. Using multiple endpoints, and choosing the endpoint on which to base conclusions 229
f. In choosing endpoints keep in mind the eventual goals of the clinical trial 230
11 Endpoints In Clinical Trials on Solid Tumors – Objective Response 232
I. Introduction 232
a. Objective response using RECIST criteria 233
b. Objective response – Demetri’s example of partial response 238
c. Objective response – van Meerten’s example of partial response 240
d. Objective response – example of progressive disease 241
II. Studies Characterizing an Association Between Objective Response and Survival 242
III. Avoiding Confusion when Using Objective Response as an Endpoint 243
a. Date for beginning objective response measurements in two study arms, relative to start date of treatment 243
b. Where multiple measurements of objective response are taken, which measurement is used for analysis of efficacy? 244
c. How is it possible to obtain a meaningful value for objective response, or for endpoints (PFS, TTP) that comprise objective response? 245
d. Objective response is reported in terms of a “rate” and also as a “percent” 245
e. Drugs that are cytostatic and not cytotoxic may provide misleading results, where the endpoint of objective response is used 245
f. Use of different criteria (standards) for objective response, and the availability of updated criteria 246
12 Oncology Endpoints: Overall Survival and Progression-free Survival 248
I. Introduction 248
II. Comparing Contexts of Use and Advantages of Various Endpoints 249
a. Contrast between PFS and TTP 249
b. Excellence of PFS as an endpoint 250
c. Why progression-free survival may be preferred over overall survival 251
1. Confusion from effects of non-study drugs given to subjects who leave the trial 251
2. Collecting data on overall survival may require an extended follow-up period 251
3. Weakened conclusions, regarding efficacy of study drug, when the endpoint is keyed to a longer timeframe 252
4. Confusion from the multiplicity of causes of death 252
5. Ethical reasons 253
6. Need for premature halt of the trial, where the halt allows collection of data on progression-free survival, but prevents collection of data on overall survival 253
7. Conclusions arising from data on overall survival may be redundant with conclusions made from data on PFS 253
d. Why overall survival may be preferred over PFS 254
1. Overall survival is the gold standard 254
2. The date of the event that triggers PFS may be ambiguous, while the date that triggers overall survival is not ambiguous 254
e. Endpoint keyed to one specific time point – 6-month PFS 255
f. Use of the word “rate” 255
III. Data on Overall Survival and PFS from Clinical Trials 256
a. Utilities of the endpoints of objective response, PFS, and overall survival 256
1. Data on PFS may be more significant than data on overall survival – the Maemondo study 256
2. Methodology tip – shapes of Kaplan-Meier plots in the Maemondo study 259
3. Methodology tips – independent radiology assessments in the Gradishar study 259
4. The endpoint of PFS may have an advantage, where PFS data are more statistically significant than overall survival data – the Robert study 260
5. Rationale for combining trastuzumab with a platinum drug 262
6. Data on PFS can present earlier, and can be more dramatic, than data on overall survival – the Slamon study 263
7. Progression-free survival and subgroup analysis – the Van Cutsem study 266
8. Anti-sense drug for melanoma and subgroup analysis – the Bedikian study 269
IV. Summary 270
13 Oncology Endpoints: Time to Progression 272
I. Introduction 272
II. Agreement of Results from Objective Response, Time to Progression, and Overall Survival – the Paccagnella Study 273
III. Can the Value for PFS be Less than the Value for TTP? 273
IV. Time to Progression may be the Preferred Endpoint where, Once the Trial is Concluded, Patients Receive Additional Chemotherapy – the Park Study 274
V. The Endpoint of TTP may be Preferred Over Survival Endpoints, where Deaths Result from Causes Other than Cancer – the Llovet Study 275
VI. The Endpoint of Overall Survival may be Preferred Over Objective Response or Over TTP, where the Drug Is Classed as a Cytostatic Drug – the Llovet Study 277
VII. Time to Progression may Show Efficacy, where the Endpoint of Overall Survival Fails to Show Efficacy, where the Number of Subjects is Small – the McDermott Study 279
VIII. Time to Progression may Show Efficacy, where the Endpoint of Overall Survival Failed to Show Efficacy, where the duration of the Trial was too Short – the Cappuzzo Study 280
IX. Methodology TIP – Advantage of Using an Endpoint that Incorporates a “Median” Time 281
X. Summary 282
XI. Thymidine Phosphorylase as a biomarker for Survival – the Meropol Study 282
XII. Drug Combinations that Include Capecitabine 284
XIII. Methodology TIP – do Changes in mRNA Expression Result in Corresponding Changes in Expression of Polypeptide? 284
XIV. Conclusions 285
14 Oncology Endpoint: Disease-free Survival 286
I. Introduction 286
II. Difference Between Disease-Free Survival and Progression-Free Survival 287
III. Ambiguity in the Name of the Endpoint, “Disease-Free Survival” 288
IV. Disease-Free Survival Provides Earlier Results on Efficacy than Overall Survival – the Add-on Breast Cancer Study of Romond 289
V. Disease-Free Survival as an Endpoint in the Analysis of Subgroups – the Add-on Breast Cancer Study of Hayes 290
VI. Neoadjuvant Therapy Versus Adjuvant Therapy for Rectal Cancer – the Roh Study 292
VII. Where Efficacy of Two Different Treatments is the Same, Choice of Treatment Shifts to the Treatment that Improves Quality of Life – the Ring Study 293
VIII. Disease-Free Survival and Overall Survival are Useful Tools for Testing and Validating Prognostic Biomarkers – the Bepler Study 294
IX. Summary 295
15 Oncology Endpoint: Time to Distant Metastasis 298
I. Introduction 298
II. Time to Distant Metastasis Data are Acquired Before Overall Survival Data are Acquired – the Wee Study 299
III. Time to Distant Metastasis Data Can Reveal a Dramatic Advantage of the Study Drug, in a Situation Where Overall Survival Fails to Show Any Advantage – the Roach Study 301
IV. Use of a Gene Array as a Prognostic Factor for Breast Cancer Patients, Using the Endpoint of Time to Distant Metastasis – the Loi Study 302
V. Use of Micro-RNA Expression Data as a Prognostic Factor for Breast Cancer Patients – the Foekens Study 303
VI. Biology of Micro-RNA 304
VII. Conclusions 305
16 Neoadjuvant Therapy versus Adjuvant Therapy 306
I. Introduction 306
II. Advantages of Neoadjuvant Therapy 307
a. Killing micrometastases 308
b. Making surgery easier 308
c. Preserving functions, or cosmetic issues, of organs 308
d. Enables the physician to perform an experiment that enables a decision regarding subsequent therapy 309
e. Better ability of patient to tolerate chemotherapy 310
III. Advantages of Adjuvant Therapy 310
a. Immediate surgery and reduced risk of metastasis 310
b. More accurate staging 311
c. Drugs that require chronic treatment, for example for 5 years 311
IV. Two Meanings of the Term Adjuvant 311
V. Concluding Remarks 312
17 Hematological Cancers 314
I. Introduction 314
a. Classification of hematological cancers 314
b. Hematopoietic stem cells give rise to the lymphoid lineage and myeloid lineage 317
c. Locations of leukemic cells in the body 319
d. Lymphoid neoplasms 319
1. Acute lymphocytic leukemia 319
2. Chronic lymphocytic leukemia 322
3. Hairy cell leukemia 323
e. Myeloid neoplasms 324
1. Acute myeloid leukemia 324
2. Acute promyelocytic leukemia 326
3. Methodology tip – platelets and blood clotting 327
4. Chronic myeloid leukemia 328
II. Myelodysplastic Syndromes 329
a. Classifying MDS and scoring MDS 330
b. Treating MDS 331
c. Transfusions in MDS 332
d. Chromosome 5 abnormality and lenalidomide for treating MDS 333
e. Mechanism of action of lenalidomide 333
f. Mechanism of action of 5-aza-deoxycytidine 334
III. Summary 334
IV. Cytogenetics and the Hematological Cancers 334
a. Cytogenetics for diagnosis and prediction – AML 335
b. Cytogenetics for diagnosis and prediction – ALL 335
1. Numeric abnormalities in ALL 337
2. Structural abnormality t(9 22) (Philadelphia chromosome) in ALL
3. Structural abnormality t(1 19) in ALL
4. Structural abnormality t(12:21) in ALL 339
c. Cytogenetics for diagnosis and prediction – CML 339
d. Utility of the Philadelphia chromosome in diagnosis, drug target, and for assessing response 340
e. Cytogenetics for diagnosis and prediction – CLL 342
f. Cytogenetics for diagnosis and prediction – myelodysplastic syndromes 343
V. Chromosomal Abnormalities in Solid Tumors 345
VI. Clinical Endpoints and Examples from Clinical Trials 345
a. Endpoint of event-free survival 346
b. Endpoint of relapse-free interval 348
VII. Cytogenetics as a Prognostic Marker – The Grever Study of CLL 349
VIII. Minimal Residual Disease 351
a. Example of use of minimal residual disease and relapse – the scheuring study of philadelphia chromosome positive ALL 352
b. Example of use of minimal residual disease and event-free survival – the basso study of philadelphia chromosome negative ALL 353
c. Methodology tip – flow cytometry for assessing minimal residual disease 355
d. Using cells acquired after chemotherapy (not before chemotherapy) as a prognostic factor for long-term relapse – the cilloni study 355
e. Methodology tip – should biomarkers be measured before or after chemotherapy? 357
f. Example of use of minimal residual disease – the Grimwade study using PML-RAR-alpha fusion protein 357
IX. Confluence of Cytogenetics and Gene Expression 358
X. Conclusions 359
18 Biomarkers and Personalized Medicine 362
I. Introduction 362
a. Predictive markers versus prognostic markers 363
b. Including biomarker tests in the study design 365
c. Criteria for surrogate markers 366
d. Clinical trials focusing on utility of a biomarker 367
1. Biomarkers in breast cancer – the Stratton study 367
2. Biomarkers in breast cancer – the Vogel study 369
3. Methodology tip – fluorescent in situ hybridization (FISH) technique 371
4. Circulating tumor cells as a prognostic biomarker for colon cancer – the Cohen study 372
5. Methodology tip – circulating tumor cells as a biomarker 373
6. Cytokeratin as a soluble protein biomarker for colon cancer – the Koelink study 373
7. Tumor infiltrating T cells as a prognostic biomarker for colon cancer – the Galon study 374
8. Tumor infiltrating T cells as a prognostic biomarker for colon cancer – the Morris study 375
e. Lymphocytes can kill cancer cells, but lymphocytes can also cause cancer 375
II. Microarrays 376
a. Microarray used in ovarian cancer – the Spentzos study 377
b. Microarray used in colon cancer – the Wang study 378
c. Microarray used in liver cancer – the Hoshida study 379
III. C-Reactive Protein 380
a. Biology of C-reactive protein 380
b. C-reactive protein as a cancer biomarker 382
1. C-reactive protein and lung cancer – the Allin study 382
2. C-reactive protein and liver cancer – the Wong study 382
3. C-reactive protein and melanoma – the Findeisen study 383
c. Methodology tip – identifying new biomarkers by mass spectroscopy 384
d. C-reactive protein and atherosclerosis 384
IV. Concluding Remarks 387
19 Endpoints in Immune Diseases 390
I. Introduction 390
II. Multiple Sclerosis 390
a. Diagnosis 391
b. Endpoints 392
c. Timing for measuring endpoints 394
d. Primary endpoint 394
e. Multiple sclerosis functional composite (MSFC) score 395
f. Secondary endpoints 395
g. Introduction to MRI and detecting the onset of brain lesions 397
1. Example of MRI photograph 399
2. T2-weighted MRI 399
3. T1-weighted MRI 400
h. Results from the kappos study 401
III. Concluding Remarks 401
20 Endpoints in Clinical Trials on Infections 404
I. Introduction 404
II. Clinical and Immunological Features of Hepatitis C Virus Infections 404
III. Acute HCV Versus Chronic HCV 405
IV. Drugs Against Hepatitis C Virus 406
V. Immune Responses Against Hepatitis C Virus 408
VI. Kinetics of Hepatitis C Virus Infections 408
VII. Responders Versus Non-Responders 412
VIII. Endpoints in Clinical Trials Against Hepatitis C Virus 413
a. Endpoints of the McHutchison study 414
b. Endpoints of the Di Bisceglie study 414
IX. Biomarkers and Hepatitis C Virus 416
X. Concluding Remarks 417
21 Health-related Quality of Life 418
I. Introduction 418
II. Summary 420
III. HRQoL Instruments Take on Increased Importance, When Capturing Data on Adverse Events, or in Trials on Palliative Treatments 420
IV. Scheduling the Administration of HRQoL Instruments 421
V. HRQoL Instruments in Oncology 422
a. Introduction 422
b. Symptoms and functioning 423
c. Formats for disclosing HRQoL results 424
d. Colorectal cancer 425
e. Melanoma 428
f. Non-small cell lung cancer 430
1. The Shepherd study 430
2. The Bezjak study 431
3. The Bonomi study 431
4. Representative list of clinical trials 432
g. HRQoL in breast cancer 433
1. Where survival data are identical in both study arms, HRQoL data turn the tide – the Watanabe study 433
2. HRQoL data demonstrate that long-term treatment is well tolerated – the Muss clinical trial 433
VI. Decisions on Counseling Decisions on Chemotherapy Versus Surgery
VII. Conclusions 434
22 Health-related Quality of Life Instruments for Immune Disorders 436
I. Introduction 436
II. Short Form SF-36 Questionnaire 436
a. Arthritis 439
b. Psoriasis 439
c. Crohn’s disease 439
d. Chronic obstructive pulmonary disease 440
e. Multiple sclerosis 440
III. HRQoL Instruments Specific for Multiple Sclerosis 440
a. The Rudick study 441
b. EDSS score versus HRQoL score 442
c. Interferon-alpha-2a – the Nortvedt study 442
d. Interferon-beta-1a – the Jongen study 443
e. Glatiramer acetate – the Zwibel study 443
f. Meditation training – the Grossman study 444
IV. Conclusions 444
23 Health-related Quality of Life Instruments and Infections 446
I. Introduction 446
II. Health-Related Quality of Life Instruments with Chronic Hepatitis C Virus 446
a. Example of hepatitis C virus HRQoL – the Mathew study 447
b. Concluding remarks 448
24 Drug Safety 450
I. Introduction 450
a. Overview of drug safety 451
b. Examples of adverse events 453
c. Anticipating adverse events in the design of clinical studies 454
d. Dose modification 455
II. Safety Definitions 458
a. Definitions from U.S. and European regulatory agencies 458
1. Adverse events 459
2. Serious adverse event 459
3. Adverse drug reaction (ADR) 460
4. Unexpected adverse drug reaction 460
5. Potential confusion in defining adverse events 460
b. Classification of adverse events as induced by disease versus induced by the study drug 461
c. Classification of adverse events by considerations used by statisticians 462
d. ITT analysis versus per protocol analysis 462
e. Summary 466
f. Classification of adverse events as anticipated versus unanticipated 466
g. Using raw data on adverse events to acquire cause-and-effect data on adverse drug reactions 470
III. Paradoxical Adverse Drug Reactions 471
a. Paradox with chemotherapy for cancer 472
b. Paradox with growth factors for cancer 473
c. Paradox with anti-depressants and depression 474
d. Paradoxes with drugs for treating bronchial constriction 475
IV. Monitoring and Evaluating Adverse Events 475
a. The data manager’s tasks include documenting missing data 476
b. CTCAE dictionary 477
c. Examples of missing data in documents submitted to the FDA 478
d. Writing style in case report forms 479
V. Adverse Events – Capturing, Transmitting, and Evaluating Data on Adverse Events 480
VI. Post-Marketing Report of Adverse Events 484
a. The MedWatch form, the yellow card, and the CIOMS I form 485
1. CIOMS 485
2. The CIOMS I form 486
b. Post-marketing surveillance 486
VII. Risk Minimization Tools 487
a. Introduction 487
b. Dear Healthcare Professional letter regarding birth control pills 490
c. Dear Healthcare Professional letter regarding acne medicine 491
d. Dear Healthcare Professional letter regarding appetite suppressants 492
VIII. Patient-Reported Outcomes 492
a. Introduction 492
b. PROs – example of head and neck cancer 493
IX. Summary of Reporting Systems Suitable for Capturing Adverse Events 495
X. Data and Safety Monitoring Committee 495
a. The DMC Charter 497
Data Safety Monitoring Board Charter 497
INTRODUCTION 498
ROLE OF THE BOARD 498
BOARD MEMBERSHIP 498
TERM 498
CONFLICT OF INTEREST AND FINANCIAL DISCLOSURE 499
COMPENSATION 499
BOARD MEETINGS AND REPORTS 499
ORGANIZATIONAL MEETING 499
INTERIM REVIEW MEETINGS 499
FORMAT 499
PARTICIPANTS 500
REVIEW MATERIALS 500
PERIODIC REPORTS TO THE DMC 501
UNSCHEDULED MEETINGS 501
DMC RECOMMENDATIONS 501
OUTSIDE EXPERTS 501
ACCESS TO INTERIM RESULTS 502
STOPPING RULES 502
COMMUNICATIONS 502
MEETING MINUTES 502
OTHER COMMUNICATIONS 502
SPONSOR’S DECISIONS AND ANNOUNCEMENTS 503
TIMETABLE 503
CONTACT INFORMATION 503
XI. Concluding Remarks 503
25 Mechanism of Action, Part I 506
I. Introduction 506
II. MOA and the Package Insert 507
III. MOA and Surrogate Endpoints 508
IV. MOA and Expected Adverse Drug Reactions 508
V. MOA and Drug Combinations 509
a. Drug combinations that are complementary or synergic 509
b. Drug combinations that avoid inducing cross-resistance 509
VI. Mechanism of Action of Diseases with an Immune Component 510
a. Introduction 510
b. Diseases with an immune component 511
c. Messengers in the immune system 511
d. Cells of the immune system 513
e. Processing and presentation of antigens, T cell activation, and T cell maturation 517
f. Drugs that modulate the immune system 517
1. Vaccines 518
2. Cytokines 518
3. TLR-agonists 519
4. Methodology tip – fine tuning of immune adjuvants when treating cancer 520
5. Antibodies 521
6. Treg inhibitors 521
VII. Immunology can be Organized as Pairs of Concepts 522
a. Myeloid DCs and plasmacytoid DCs 523
b. Th1-type response and Th2-type response 523
c. Externally acquired antigens and internally acquired antigens 523
d. Polypeptide antigens can contain both MHC class I and MHC class II epitopes 524
e. CD4+ T cells and CD8+ T cells 524
f. Two different mechanisms of CTL response 524
g. Naive response and memory response 524
h. Specific immunity and innate immunity 525
VIII. Conclusions 525
26 Mechanisms of Action, Part II – Cancer 528
I. Immune Response Against Cancer 528
a. Mechanisms of immune response against tumors 529
b. Natural killer cells and antibody-dependent cell cytotoxity 532
c. Regulatory T cells 534
d. Concluding remarks 536
27 Mechanisms of Action, Part III – Immune Disorders 538
I. Introduction 538
a. Mechanisms of action summaries for various immune disorders 539
1. Rheumatoid arthritis 540
2. Psoriasis 540
3. Lupus 540
4. Crohn’s disease and ulcerative colitis 541
5. Asthma 541
6. Chronic obstructive pulmonary disease (COPD) 541
II. Detailed Example of Multiple Sclerosis Mechanism of Action 541
a. Natalizumab 542
b. Fingolimod 542
c. Interferon-beta1 (IFNbeta1) 543
d. Cladribine 544
e. Animal model for multiple sclerosis 545
f. Mechanisms leading to multiple sclerosis are complex and not firmly established 546
g. Lesions of multiple sclerosis in humans 546
1. Initiating events in multiple sclerosis 546
2. CD8+ T cells attack nerves 546
3. Contributions of CD4+ T cells 547
4. Dendritic cells present antigen to T cells and activate the T cells 547
5. Breakdown of blood–brain barrier 548
6. Toxic oxygen from microglia 548
7. Diagram of multiple sclerosis 548
III. Concluding Remarks 550
28 Mechanisms of Action, Part IV – Infections 552
I. Introduction 552
II. Hepatitis C Virus Infections 552
a. Protease inhibitors used as drugs against anti-hepatitis C virus 553
b. Mechanisms of immune response against hepatitis C virus antigens 555
c. Methodology tip – GenBank 557
d. Dendritic cells and antigens of hepatitis C virus 558
e. Hepatitis C, chronic inflammation, and liver cancer 560
f. Dendritic cells 560
g. Sources of interferons during HCV infections 560
h. What IFN-gamma does during HCV infections 561
i. What T cells do during HCV infections where the patient spontaneously recovers 561
j. What immune cells do during HCV infections where the patient develops a chronic HCV infection 562
k. In HCV infections, IL-12 stImulates NK cells to express IFN-gamma 562
l. In HCV infections, IFN-alpha stimulates NK cells (or CD8+ T cells) to express IFN-gamma 563
m. Influence of IFN-alpha on gene expression as measured by microarrays 565
n. Diagrams of the immune network in immune response against HCV 566
o. Methodology tip – populations of leukocytes in the bloodstream 566
III. Concluding Remarks 568
29 Consent Forms 570
I. Introduction 570
a. An early clinical study using a consent form – yellow fever study 570
b. The consent form of the Yellow Fever Commission 572
c. Summary 573
II. Sources of the Law in the United States 573
III. Guidance for Industry 574
IV. Ethical Doctrines 575
V. The Case Law 576
VI. Basis for Consent Forms in the Code of Federal Regulations 576
VII. Summary 578
VIII. Examples of Contemporary Consent Forms 578
a. Example of a contemporary consent form (reproduced in full) (39,40) 579
b. Another example of a contemporary consent form (reproduced in part) 584
c. Comparison of standard consent form with the more elementary consent form 586
d. Analysis of consent forms by the medical community 587
e. Most consent forms are written at a level that is too advanced 588
IX. Ethical Issues Specific to Phase I Clinical Trials in Oncology 590
X. Decision Aids 591
XI. Distinction Between Stopping Treatment and Withdrawing from the Study 593
XII. Concluding Remarks 593
30 Package Inserts 596
I. Introduction 596
a. FDA’s Guidance for Industry documents relating to package inserts 597
b. Classes of drugs 600
c. Black box warning 600
d. Summary 602
II. Potential Ambiguity of Writing in Package Inserts 602
III. Package Insert may Protect Manufacturer from Liability 603
a. Opinion concerning dicumarol 604
b. Opinion regarding kanamycin 605
c. Opinion regarding dilantin 605
d. Opinion concerning oxytocin 605
e. Opinion regarding oral polio vaccine 606
f. Opinion regarding norethindrone 607
g. Summary 607
IV. Package Insert Compared with Consent Form 608
V. Relation between Package Inserts to the Standard of Care, and to off-Label Uses 608
VI. Conclusions 610
31 Regulatory Approval 612
I. Introduction 612
a. Origins of the Federal Food, Drug and Cosmetic Act and its amendments 612
b. Federal Food, Drug and Cosmetic Act of 1938 613
c. Drug Amendments Act of 1962 615
d. Food and Drug Administration Modernization Act of 1997 and Phase IV clinical trials 615
II. History of the European Medicines Agency 616
III. International Conference on Harmonisation 618
IV. History of the Medicines and Healthcare Products Regulatory Agency 620
V. Outline of Regulatory Approval in the United States 621
a. The Investigational New Drug 621
b. The Investigational New Drug and the Common Technical Document 626
VI. Process of Administering Clinical Trials 627
VII. Process of Medical Writing 630
a. Grammatical issues 631
b. Formatting issues 632
VIII. Meetings with the U.S. Food and Drug Administration 635
a. Introduction 635
b. Paper trail of FDA’s decision-making process for individual drugs 636
c. Clinical review 636
d. Pharmacology review 638
e. Approval letter 638
f. Snapshots of the FDA’s regulatory review process 639
1. Example showing transition from an open-label Phase I trial to a blinded Phase II trial 639
2. Example showing how FDA uses data from Phase I trial to arrive at a dose for using in a Phase II trial 640
32 Patents 642
I. Introduction 642
a. History of patenting 642
b. Outline of the patenting process 644
c. Summary 645
II. Types of Patent Documents 646
III. Structure of Patents 647
a. Introduction 647
b. The claims 648
IV. Timeline for Patenting 651
V. Sources of the Law for Patenting 654
VI. Intersections between the FDA Review Process and Patents 656
a. Introduction 656
b. Using patent as source documents when writing regulatory submissions 657
Index 660