Brody Clinical Trials

Study Design, Endpoints and Biomarkers, Drug Safety, and FDA and ICH Guidelines
1. Auflage 2011
ISBN: 978-0-12-391913-7
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 638 Seiten

Clinical Trials: Study Design, Endpoints and Biomarkers, Drug Safety, and FDA and ICH Guidelines is a practical guidebook for those engaged in clinical trial design. This book details the organizations and content of clinical trials, including trial design, safety, endpoints, subgroups, HRQoL, consent forms and package inserts. It provides extensive information on both US and international regulatory guidelines and features concrete examples of study design from the medical literature. This book is intended to orient those new to clinical trial design and provide them with a better understanding of how to conduct clinical trials. It will also act as a guide for the more experienced by detailing endpoint selection and illustrating how to avoid unnecessary pitfalls. This book is a straightforward and valuable reference for all those involved in clinical trial design. - Provides extensive coverage of the 'study schema' and related features of study design - Offers a 'hands-on' reference that contains an overview of the process, but more importantly details a step-by-step account of clinical trial design - Features examples from the medical literature to highlight how investigators choose the most suitable endpoint(s) for clinical trial and includes graphs from real clinical trials to help explain each concept in study design - Integrates clinical trial design, pharmacology, biochemistry, cell biology and legal aspects to provide readers with a comprehensive look at all aspects of clinical trials - Includes chapters on core material and important ancillary topics, such as package inserts, consent forms, and safety reporting forms used in the United States, England and Europe - For complimentary access to our sample chapter (chapter 24), please copy and paste this link into your browser: http://tinyurl.com/awwutvn

Dr. Tom Brody received his PhD from the University of California at Berkeley in 1980, and conducted postdoctoral research at University of Wisconsin-Madison and also at U.C. Berkeley. His 20 research publications concern the metabolism and pharmacology of folates, cloning an anti-cancer gene (XPE gene), and the structure of an antibody (natalizumab) used for treating multiple sclerosis. The author has 15 years of pharmaceutical industry experience, acquired at Schering-Plough, Cerus Corporation, and Elan Pharmaceuticals, and has contributed to FDA submissions for the indications of multiple sclerosis, melanoma, head and neck cancer, liver cancer, pancreatic cancer, and hepatitis C. At an earlier time, he wrote two editions of Clinical Trials, published by Elsevier, Inc. The author has 16 years of training and experience in the Code of Federal regulations, as it applies to pharmaceuticals and clinical trial design.

Autoren/Hrsg.

Brody, Tom

Inhaltsverzeichnis

1;Front Cover;1
2;Clinical Trials: Study Design, Endpoints and Biomarkers, Drug Safety, FDA and ICH Guidelines;4
3;Copyright Page;5
4;Contents;8
5;Acknowledgments;16
6;Preface;18
6.1;The Study Schema and Study Design;18
6.2;Intent to Treat Analysis;18
6.3;How to Choose the Endpoints;18
6.4;Diagnostic Tests;19
6.5;Mechanism of Action;19
6.6;Standards;19
6.7;Methodology;19
6.8;Clinicaltrials.Gov and other Registries for Clinical Trials;20
7;Introduction;24
8;Abbreviations and Definitions;28
9;Biography;34
10;1 The Origins of Drugs;36
10.1;I. Introduction;36
10.2;II. Structures of Drugs;37
10.2.1;a. Origins of warfarin;37
10.2.2;b. Origins of methotrexate and 5-fluorouracil;38
10.2.3;c. Origins of ribavirin;39
10.2.4;d. Origins of paclitaxel;39
10.2.5;e. Origins of cladribine;40
10.2.6;f. Origins of drugs in high-throughput screening;42
10.2.7;g. Origins of therapeutic antibodies;42
10.3;III. The 20 Classical Amino Acids;44
10.4;IV. Animal Models;46
10.4.1;a. Introduction;46
10.4.2;b. Estimating human dose from animal studies;48
10.4.2.1;1. NOAEL approach;49
10.4.2.2;2. MABEL approach;49
10.4.3;c. Scaling up the drug dose, acquired from animal studies, for use in humans;49
11;2 Introduction to Regulated Clinical Trials;52
11.1;I. Introduction;52
11.2;II. Study Design;54
11.3;III. The Study Schema;55
11.3.1;a. Examples of schema from clinical trials;57
11.3.2;b. Sequential treatment versus concurrent treatment – the Perez schema;59
11.3.3;c. Neoadjuvant chemotherapy versus adjuvant chemotherapy – the Gianni schema;61
11.3.4;d. Neoadjuvant chemotherapy plus adjuvant chemotherapy – the Untch schema;61
11.3.5;e. Forwards sequence and reverse sequence – the Puhalla schema;62
11.3.6;f. Both arms received three drugs, each arm at a different schedule – the Sekine schema;63
11.3.7;g. Staging – the Blumenschein schema;63
11.3.8;h. Staging and restaging – the Czito schema;65
11.3.9;i. Methodology tip – staging;65
11.3.10;j. Decision tree – the Baselga schema;66
11.3.11;k. Decision tree – the Katsumata schema;66
11.3.12;l. Methodology tip – what is “tumor progression”?;70
11.3.13;m. Methodology tip – unit of drug dose expressed in terms of body surface area;70
11.3.14;n. Run-in period – the schema of Dy;71
11.3.15;o. Methodology tip – c-kit and imatinib;72
11.3.16;p. Run-in period – the Hanna schema;72
11.3.17;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
11.3.18;r. Methodology tip – bevacizumab and VEGF;76
11.3.19;s. Dose escalation – the Moore schema;76
11.3.20;t. Pharmacokinetics – the Marshall schema;78
11.4;IV. Further Concepts In Study Design;79
11.4.1;a. Active control;79
11.4.2;b. Add-on design active control;81
11.4.3;c. Three-arm study – clinical trial with two different active control arms;82
11.5;V. Summary;82
11.6;VI. Amendments to the Clinical Study Protocol;83
12;3 Run-In Period;86
12.1;I. Introduction;86
12.1.1;a. Washout period;87
12.1.2;b. Detecting baseline adverse events;87
12.1.3;c. Excluding potential study subjects who have safety issues correlating with the study drug;87
12.1.4;d. To include only study subjects with controllable pain;88
12.1.5;e. To determine the maximal tolerable dose;89
12.1.6;f. To achieve and ensure steady state in vivo concentrations of study drug;89
12.1.7;g. To allow a period of adjustment of lifestyle of the study subjects, for example changes in dietary patterns;90
12.1.8;h. To ensure that metabolic characteristics of all study subjects are similar, prior to administering drugs;91
12.1.9;i. To ensure that potential study subjects can adhere to, or comply with, the study protocol;91
12.1.10;j. To confirm that all study subjects meet the inclusion and exclusion criteria;92
12.1.11;k. Detecting potential study subjects who show a predetermined desired response to the study drug, with the goal of including only these subjects;93
12.1.12;l. Methodology tip – anti-cancer drugs that inhibit tumor growth;94
12.1.13;m. Decision tree;94
12.1.14;n. To create a self-control group;95
12.2;II. Concluding Remarks;96
13;4 Inclusion/Exclusion Criteria, Stratification, and Subgroups – Part I;98
13.1;I. The Clinical Study Protocol Is a Manual that Provides the Study Design;98
13.1.1;a. Clinical study protocol provides the inclusion/exclusion criteria and stratification;99
13.1.2;b. Stratification of study subjects;100
13.1.3;c. Words of warning;102
13.1.4;d. Staging of the disease;103
13.1.5;e. The study schema;103
13.1.5.1;1. Inclusion criteria for the RTOG 0232 study of prostate cancer;105
13.1.5.2;2. Exclusion criteria for the RTOG 0232 study of prostate cancer;105
13.1.6;f. Stratification of subjects into subgroups;105
13.1.7;g. Examples of subgroups;105
13.1.8;h. Prior therapy;107
13.1.9;i. Poor performance status as a basis for exclusion;108
13.1.10;j. Irreversible and cumulative toxicity as a basis for exclusion;109
13.1.11;k. Drug resistance as a basis for exclusion;111
13.2;II. Biology of Drug Resistance;111
13.2.1;a. Biochemistry of the ABC drug transporters;111
13.2.2;b. Biology of cross-resistance;112
13.2.3;c. A tumor’s genetic expression can provide guidance on drug resistance;113
13.2.3.1;1. Doxorubicin;113
13.2.3.2;2. Paclitaxel;113
13.2.3.3;3. Tamoxifen;114
13.2.3.4;4. Imatinib;114
13.2.4;d. Prior treatment with hormones as a basis for exclusion;115
13.2.5;e. Immune status for exclusion criteria;116
13.2.6;f. Example of earlier vaccination as an inclusion criterion;116
13.2.7;g. Ethical considerations as a basis for inclusion criteria;117
13.3;III. More Information on Subgroups;117
13.3.1;a. Subgroup of non-elderly subjects and subgroup of elderly subjects;118
13.3.2;b. Subgroup of subjects with no metastasis and subgroup of subjects with metastasis;119
13.3.3;c. Subgroup of smokers and subgroup of non-smokers;120
13.3.4;d. Subgroups set forth in a clinical study protocol can be used as a basis for FDA approval;120
13.3.5;e. Subgroup analysis enables recommendations for a specific course of treatment;121
13.3.6;f. Subgroup analysis can justify increases in drug dose for specific subgroups;122
13.3.7;g. Subgroups determined by an analysis of gene expression by microarray analysis;122
13.3.8;h. Recommending dropping one subgroup from the trial, rather than stopping the entire trial;123
13.4;IV. Concluding Remarks;124
14;5 Inclusion and Stratification Criteria – Part II;126
14.1;I. Introduction;126
14.2;II. Staging;126
14.2.1;a. History of tumor staging;127
14.2.2;b. Revising staging systems;127
14.2.3;c. Biology of tumors;128
14.2.4;d. Biology of the lymphatic system;128
14.2.5;e. Relation between the tumors and the lymphatic system;129
14.2.6;f. Metastasis of tumors;130
14.2.7;g. The sentinel node and distant lymph nodes;131
14.3;III. Staging Systems for Various Cancers;132
14.3.1;a. Colorectal cancer;132
14.3.2;b. TNM definitions for colorectal cancer;133
14.3.2.1;Stage 0;133
14.3.2.2;Stage I;134
14.3.2.3;Stage IIA;134
14.3.2.4;Stage IIB;134
14.3.2.5;Stage IIC;134
14.3.2.6;Stage IIIA;134
14.3.2.7;Stage IIIB;134
14.3.2.8;Stage IIIC;135
14.3.2.9;Stage IVA;135
14.3.2.10;Stage IVB;135
14.3.3;c. Breast cancer;135
14.3.4;d. Breast cancer in situ (DCIS and LCIS);135
14.3.5;e. Invasive breast cancer;136
14.3.6;f. Definitions for breast cancer;136
14.3.7;g. Breast cancer staging;138
14.3.7.1;Stage 0;138
14.3.7.2;Stage IA;138
14.3.7.3;Stage IB;139
14.3.7.4;Stage IIA;139
14.3.7.5;Stage IIB;139
14.3.7.6;Stage IIIA;139
14.3.7.7;Stage IIIB;140
14.3.7.8;Stage IIIC;140
14.3.7.9;Stage IV;140
14.4;IV. Summary;141
14.5;V. The will Rogers Phenomenon;141
14.5.1;a. Will Rogers phenomenon for prostate cancer;141
14.5.2;b. Will Rogers phenomenon for non-small lung cancer;142
14.5.3;c. Will Rogers phenomenon for small cell lung cancer;142
14.5.4;d. Will Rogers phenomenon for rectal cancer;143
14.5.5;e. Will Rogers phenomenon for multiple sclerosis;143
14.6;VI. Other Sources of Artifacts in Data from Clinical Trials;144
14.7;VII. Concluding Remarks;144
15;6 Randomization, Allocation, and Blinding;146
15.1;I. Introduction;146
15.1.1;a. Allocation and allocation concealment;147
15.1.2;b. Simple randomization;149
15.1.3;c. Stratification;150
15.2;II. Manual Technique for Allocation;151
15.2.1;a. Allocation by coin-toss versus allocation by sealed envelope;153
15.3;III. Information on Randomization, Blinding, and Unblinding may be Included in the Clinical Study Protocol;154
15.3.1;a. Introduction;154
15.3.2;b. When to break the randomization code – clinical study protocol for trial on Alzheimer’s disease (27);154
15.3.3;c. When to break the randomization code – clinical study protocol for trial on malaria vaccine (28);155
15.3.4;d. When to break the randomization code – clinical study protocol for trial typhoid vaccine (29);155
15.3.5;e. When to break the randomization code – clinical study protocol for trial on lung cancer (30);155
15.3.6;f. When to break the randomization code – clinical study protocol for trial on sepsis (31);156
15.3.7;g. When to break the randomization code – clinical study protocol for trial on melanoma (32);156
15.3.8;h. When to break the randomization code – clinical study protocol for trial on multiple sclerosis (33);156
15.4;IV. Summary;157
15.5;V. Subjects are Enrolled into Clinical Trials, One by One, Over the Course of Many Months;157
15.6;VI. Blocked Randomization;158
15.7;VII. Blinding;158
15.8;VIII. Interactive Voice Response Systems;160
15.9;IX. Concluding Remarks;164
16;7 Placebo Arm as Part of Clinical Study Design;166
16.1;I. Introduction;166
16.2;II. Hawthorne Effect;167
16.3;III. The No-Treatment Arm;167
16.4;IV. Physical Aspects of the Placebo;168
16.5;V. Active Placebo;168
16.6;VI. Subjects in the Placebo Arm May Receive Best Supportive Care Or Palliative Care;169
16.7;VII. Clash Between Best Supportive Care and the Endpoint of HRQoL;171
16.8;VIII. Ethics of Placebos;171
17;8 Intent to Treat Analysis vs. Per Protocol Analysis;178
17.1;I. Introduction;178
17.1.1;a. Definition of intent to treat analysis;178
17.1.2;b. Deviations and inconsistencies;179
17.2;II. ITT Analysis Contrasted with PP Analysis;181
17.2.1;a. ITT analysis vs. PP analysis – the Hosking study;182
17.2.2;b. ITT analysis vs. PP analysis – the Sethi study;183
17.2.3;c. ITT analysis vs. PP analysis – the Abrial study;183
17.2.4;d. ITT analysis vs. PP analysis – the Berthold study;183
17.2.5;e. ITT analysis vs. PP analysis – the Geddes study;184
17.2.6;f. ITT analysis vs. PP analysis – the Chauffert study;184
17.3;III. Disadvantages of ITT Analysis;184
17.4;IV. Run-in Period, as Part of the Study Design, is Relevant to ITT Analysis and PP Analysis;185
17.5;V. Summary;186
17.6;VI. Hypothetical Example Where Study Drug and Control Drug have Same Efficacy;186
17.7;VII. Modified ITT Analysis;187
17.7.1;a. Flow chart showing subjects included in the ITT analysis, modified ITT analysis, and PP analysis;188
17.7.2;b. Reasons for using modified ITT analysis;190
17.7.3;c. Excluding subjects who failed to meet inclusion or exclusion criteria, or who failed to receive study drug – the Vaira study;190
17.7.4;d. Excluding subjects who failed to meet inclusion or exclusion criteria – the Weigelt study;191
17.7.5;e. Excluding subjects who failed to meet inclusion or exclusion criteria – the Pinchichero study;191
17.7.6;f. Excluding subjects who failed to meet inclusion or exclusion criteria – the Leroy study;192
17.7.7;g. Exclusion of study subjects because of failure to satisfy the inclusion criteria, and for withdrawing consent – the Dupont study;193
17.7.8;h. Exclusion of study subjects because of failure to satisfy the inclusion criteria – the Florescu study;194
17.7.9;i. Excluding subjects who took prohibited drugs during the clinical trial, or who withdrew consent – the Manegold study;194
17.7.10;j. Excluding subjects who failed to receive the assigned treatment because of a mistake by the health care provider – the Berek study;194
17.7.11;k. Exclusion of study subjects who failed to take drug long enough to have the expected efficacy – the Krainick-Strobel study;195
17.7.12;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
17.7.13;m. Excluding subjects who failed to receive the assigned treatment because of adverse events – the Caraceni study;196
17.7.14;n. Modified ITT group based on a subgroup of study subjects – the Gralla study;196
17.8;VIII. Start Date for Endpoints in Clinical Studies;197
17.9;IX. Summary and Conclusions;199
18;9 Biostatistics;200
18.1;I. Introduction;200
18.1.1;a. Kaplan-Meier plot;200
18.1.2;b. Examples of Kaplan-Meier plots – the Holm study;201
18.1.3;c. Censoring data;203
18.1.4;d. Hazard ratio;204
18.2;II. Definitions and Formulas;206
18.3;III. Data from the Study of Machin and Gardner;207
18.4;IV. Data Used for Constructing the Kaplan-Meier Plot are from Subjects Enrolling at Different Times;207
18.5;V. Sample Versus Population;209
18.6;VI. What can be Compared;210
18.7;VII. One-Tailed Test Versus Two-Tailed Test;211
18.8;VIII. P Value;212
18.9;IX. Calculating the P Value – a Working Example;215
18.10;X. Summary;221
18.11;XI. Theory Behind the Z Value and the Table of Areas in the Tail of the Standard Normal Distribution;221
18.12;XII. Statistical Analysis by Superiority Analysis Versus by Non-Inferiority Analysis;222
19;10 Introduction to Endpoints for Clinical Trials in Pharmacology;226
19.1;I. Introduction;226
19.1.1;a. Phase I clinical trial endpoints;226
19.1.2;b. Clinical endpoints;226
19.1.3;c. Surrogate endpoints;226
19.1.4;d. Relatively objective endpoints versus relatively subjective endpoints;228
19.1.5;e. Using multiple endpoints, and choosing the endpoint on which to base conclusions;229
19.1.6;f. In choosing endpoints keep in mind the eventual goals of the clinical trial;230
20;11 Endpoints In Clinical Trials on Solid Tumors – Objective Response;232
20.1;I. Introduction;232
20.1.1;a. Objective response using RECIST criteria;233
20.1.2;b. Objective response – Demetri’s example of partial response;238
20.1.3;c. Objective response – van Meerten’s example of partial response;240
20.1.4;d. Objective response – example of progressive disease;241
20.2;II. Studies Characterizing an Association Between Objective Response and Survival;242
20.3;III. Avoiding Confusion when Using Objective Response as an Endpoint;243
20.3.1;a. Date for beginning objective response measurements in two study arms, relative to start date of treatment;243
20.3.2;b. Where multiple measurements of objective response are taken, which measurement is used for analysis of efficacy?;244
20.3.3;c. How is it possible to obtain a meaningful value for objective response, or for endpoints (PFS, TTP) that comprise objective response?;245
20.3.4;d. Objective response is reported in terms of a “rate” and also as a “percent”;245
20.3.5;e. Drugs that are cytostatic and not cytotoxic may provide misleading results, where the endpoint of objective response is used;245
20.3.6;f. Use of different criteria (standards) for objective response, and the availability of updated criteria;246
21;12 Oncology Endpoints: Overall Survival and Progression-free Survival;248
21.1;I. Introduction;248
21.2;II. Comparing Contexts of Use and Advantages of Various Endpoints;249
21.2.1;a. Contrast between PFS and TTP;249
21.2.2;b. Excellence of PFS as an endpoint;250
21.2.3;c. Why progression-free survival may be preferred over overall survival;251
21.2.3.1;1. Confusion from effects of non-study drugs given to subjects who leave the trial;251
21.2.3.2;2. Collecting data on overall survival may require an extended follow-up period;251
21.2.3.3;3. Weakened conclusions, regarding efficacy of study drug, when the endpoint is keyed to a longer timeframe;252
21.2.3.4;4. Confusion from the multiplicity of causes of death;252
21.2.3.5;5. Ethical reasons;253
21.2.3.6;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
21.2.3.7;7. Conclusions arising from data on overall survival may be redundant with conclusions made from data on PFS;253
21.2.4;d. Why overall survival may be preferred over PFS;254
21.2.4.1;1. Overall survival is the gold standard;254
21.2.4.2;2. The date of the event that triggers PFS may be ambiguous, while the date that triggers overall survival is not ambiguous;254
21.2.5;e. Endpoint keyed to one specific time point – 6-month PFS;255
21.2.6;f. Use of the word “rate”;255
21.3;III. Data on Overall Survival and PFS from Clinical Trials;256
21.3.1;a. Utilities of the endpoints of objective response, PFS, and overall survival;256
21.3.1.1;1. Data on PFS may be more significant than data on overall survival – the Maemondo study;256
21.3.1.2;2. Methodology tip – shapes of Kaplan-Meier plots in the Maemondo study;259
21.3.1.3;3. Methodology tips – independent radiology assessments in the Gradishar study;259
21.3.1.4;4. The endpoint of PFS may have an advantage, where PFS data are more statistically significant than overall survival data – the Robert study;260
21.3.1.5;5. Rationale for combining trastuzumab with a platinum drug;262
21.3.1.6;6. Data on PFS can present earlier, and can be more dramatic, than data on overall survival – the Slamon study;263
21.3.1.7;7. Progression-free survival and subgroup analysis – the Van Cutsem study;266
21.3.1.8;8. Anti-sense drug for melanoma and subgroup analysis – the Bedikian study;269
21.4;IV. Summary;270
22;13 Oncology Endpoints: Time to Progression;272
22.1;I. Introduction;272
22.2;II. Agreement of Results from Objective Response, Time to Progression, and Overall Survival – the Paccagnella Study;273
22.3;III. Can the Value for PFS be Less than the Value for TTP?;273
22.4;IV. Time to Progression may be the Preferred Endpoint where, Once the Trial is Concluded, Patients Receive Additional Chemotherapy – the Park Study;274
22.5;V. The Endpoint of TTP may be Preferred Over Survival Endpoints, where Deaths Result from Causes Other than Cancer – the Llovet Study;275
22.6;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
22.7;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
22.8;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
22.9;IX. Methodology TIP – Advantage of Using an Endpoint that Incorporates a “Median” Time;281
22.10;X. Summary;282
22.11;XI. Thymidine Phosphorylase as a biomarker for Survival – the Meropol Study;282
22.12;XII. Drug Combinations that Include Capecitabine;284
22.13;XIII. Methodology TIP – do Changes in mRNA Expression Result in Corresponding Changes in Expression of Polypeptide?;284
22.14;XIV. Conclusions;285
23;14 Oncology Endpoint: Disease-free Survival;286
23.1;I. Introduction;286
23.2;II. Difference Between Disease-Free Survival and Progression-Free Survival;287
23.3;III. Ambiguity in the Name of the Endpoint, “Disease-Free Survival”;288
23.4;IV. Disease-Free Survival Provides Earlier Results on Efficacy than Overall Survival – the Add-on Breast Cancer Study of Romond;289
23.5;V. Disease-Free Survival as an Endpoint in the Analysis of Subgroups – the Add-on Breast Cancer Study of Hayes;290
23.6;VI. Neoadjuvant Therapy Versus Adjuvant Therapy for Rectal Cancer – the Roh Study;292
23.7;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
23.8;VIII. Disease-Free Survival and Overall Survival are Useful Tools for Testing and Validating Prognostic Biomarkers – the Bepler Study;294
23.9;IX. Summary;295
24;15 Oncology Endpoint: Time to Distant Metastasis;298
24.1;I. Introduction;298
24.2;II. Time to Distant Metastasis Data are Acquired Before Overall Survival Data are Acquired – the Wee Study;299
24.3;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
24.4;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
24.5;V. Use of Micro-RNA Expression Data as a Prognostic Factor for Breast Cancer Patients – the Foekens Study;303
24.6;VI. Biology of Micro-RNA;304
24.7;VII. Conclusions;305
25;16 Neoadjuvant Therapy versus Adjuvant Therapy;306
25.1;I. Introduction;306
25.2;II. Advantages of Neoadjuvant Therapy;307
25.2.1;a. Killing micrometastases;308
25.2.2;b. Making surgery easier;308
25.2.3;c. Preserving functions, or cosmetic issues, of organs;308
25.2.4;d. Enables the physician to perform an experiment that enables a decision regarding subsequent therapy;309
25.2.5;e. Better ability of patient to tolerate chemotherapy;310
25.3;III. Advantages of Adjuvant Therapy;310
25.3.1;a. Immediate surgery and reduced risk of metastasis;310
25.3.2;b. More accurate staging;311
25.3.3;c. Drugs that require chronic treatment, for example for 5 years;311
25.4;IV. Two Meanings of the Term Adjuvant;311
25.5;V. Concluding Remarks;312
26;17 Hematological Cancers;314
26.1;I. Introduction;314
26.1.1;a. Classification of hematological cancers;314
26.1.2;b. Hematopoietic stem cells give rise to the lymphoid lineage and myeloid lineage;317
26.1.3;c. Locations of leukemic cells in the body;319
26.1.4;d. Lymphoid neoplasms;319
26.1.4.1;1. Acute lymphocytic leukemia;319
26.1.4.2;2. Chronic lymphocytic leukemia;322
26.1.4.3;3. Hairy cell leukemia;323
26.1.5;e. Myeloid neoplasms;324
26.1.5.1;1. Acute myeloid leukemia;324
26.1.5.2;2. Acute promyelocytic leukemia;326
26.1.5.3;3. Methodology tip – platelets and blood clotting;327
26.1.5.4;4. Chronic myeloid leukemia;328
26.2;II. Myelodysplastic Syndromes;329
26.2.1;a. Classifying MDS and scoring MDS;330
26.2.2;b. Treating MDS;331
26.2.3;c. Transfusions in MDS;332
26.2.4;d. Chromosome 5 abnormality and lenalidomide for treating MDS;333
26.2.5;e. Mechanism of action of lenalidomide;333
26.2.6;f. Mechanism of action of 5-aza-deoxycytidine;334
26.3;III. Summary;334
26.4;IV. Cytogenetics and the Hematological Cancers;334
26.4.1;a. Cytogenetics for diagnosis and prediction – AML;335
26.4.2;b. Cytogenetics for diagnosis and prediction – ALL;335
26.4.2.1;1. Numeric abnormalities in ALL;337
26.4.2.2;2. Structural abnormality t(9;22) (Philadelphia chromosome) in ALL;337
26.4.2.3;3. Structural abnormality t(1;19) in ALL;338
26.4.2.4;4. Structural abnormality t(12:21) in ALL;339
26.4.3;c. Cytogenetics for diagnosis and prediction – CML;339
26.4.4;d. Utility of the Philadelphia chromosome in diagnosis, drug target, and for assessing response;340
26.4.5;e. Cytogenetics for diagnosis and prediction – CLL;342
26.4.6;f. Cytogenetics for diagnosis and prediction – myelodysplastic syndromes;343
26.5;V. Chromosomal Abnormalities in Solid Tumors;345
26.6;VI. Clinical Endpoints and Examples from Clinical Trials;345
26.6.1;a. Endpoint of event-free survival;346
26.6.2;b. Endpoint of relapse-free interval;348
26.7;VII. Cytogenetics as a Prognostic Marker – The Grever Study of CLL;349
26.8;VIII. Minimal Residual Disease;351
26.8.1;a. Example of use of minimal residual disease and relapse – the scheuring study of philadelphia chromosome positive ALL;352
26.8.2;b. Example of use of minimal residual disease and event-free survival – the basso study of philadelphia chromosome negative ALL;353
26.8.3;c. Methodology tip – flow cytometry for assessing minimal residual disease;355
26.8.4;d. Using cells acquired after chemotherapy (not before chemotherapy) as a prognostic factor for long-term relapse – the cilloni study;355
26.8.5;e. Methodology tip – should biomarkers be measured before or after chemotherapy?;357
26.8.6;f. Example of use of minimal residual disease – the Grimwade study using PML-RAR-alpha fusion protein;357
26.9;IX. Confluence of Cytogenetics and Gene Expression;358
26.10;X. Conclusions;359
27;18 Biomarkers and Personalized Medicine;362
27.1;I. Introduction;362
27.1.1;a. Predictive markers versus prognostic markers;363
27.1.2;b. Including biomarker tests in the study design;365
27.1.3;c. Criteria for surrogate markers;366
27.1.4;d. Clinical trials focusing on utility of a biomarker;367
27.1.4.1;1. Biomarkers in breast cancer – the Stratton study;367
27.1.4.2;2. Biomarkers in breast cancer – the Vogel study;369
27.1.4.3;3. Methodology tip – fluorescent in situ hybridization (FISH) technique;371
27.1.4.4;4. Circulating tumor cells as a prognostic biomarker for colon cancer – the Cohen study;372
27.1.4.5;5. Methodology tip – circulating tumor cells as a biomarker;373
27.1.4.6;6. Cytokeratin as a soluble protein biomarker for colon cancer – the Koelink study;373
27.1.4.7;7. Tumor infiltrating T cells as a prognostic biomarker for colon cancer – the Galon study;374
27.1.4.8;8. Tumor infiltrating T cells as a prognostic biomarker for colon cancer – the Morris study;375
27.1.5;e. Lymphocytes can kill cancer cells, but lymphocytes can also cause cancer;375
27.2;II. Microarrays;376
27.2.1;a. Microarray used in ovarian cancer – the Spentzos study;377
27.2.2;b. Microarray used in colon cancer – the Wang study;378
27.2.3;c. Microarray used in liver cancer – the Hoshida study;379
27.3;III. C-Reactive Protein;380
27.3.1;a. Biology of C-reactive protein;380
27.3.2;b. C-reactive protein as a cancer biomarker;382
27.3.2.1;1. C-reactive protein and lung cancer – the Allin study;382
27.3.2.2;2. C-reactive protein and liver cancer – the Wong study;382
27.3.2.3;3. C-reactive protein and melanoma – the Findeisen study;383
27.3.3;c. Methodology tip – identifying new biomarkers by mass spectroscopy;384
27.3.4;d. C-reactive protein and atherosclerosis;384
27.4;IV. Concluding Remarks;387
28;19 Endpoints in Immune Diseases;390
28.1;I. Introduction;390
28.2;II. Multiple Sclerosis;390
28.2.1;a. Diagnosis;391
28.2.2;b. Endpoints;392
28.2.3;c. Timing for measuring endpoints;394
28.2.4;d. Primary endpoint;394
28.2.5;e. Multiple sclerosis functional composite (MSFC) score;395
28.2.6;f. Secondary endpoints;395
28.2.7;g. Introduction to MRI and detecting the onset of brain lesions;397
28.2.7.1;1. Example of MRI photograph;399
28.2.7.2;2. T2-weighted MRI;399
28.2.7.3;3. T1-weighted MRI;400
28.2.8;h. Results from the kappos study;401
28.3;III. Concluding Remarks;401
29;20 Endpoints in Clinical Trials on Infections;404
29.1;I. Introduction;404
29.2;II. Clinical and Immunological Features of Hepatitis C Virus Infections;404
29.3;III. Acute HCV Versus Chronic HCV;405
29.4;IV. Drugs Against Hepatitis C Virus;406
29.5;V. Immune Responses Against Hepatitis C Virus;408
29.6;VI. Kinetics of Hepatitis C Virus Infections;408
29.7;VII. Responders Versus Non-Responders;412
29.8;VIII. Endpoints in Clinical Trials Against Hepatitis C Virus;413
29.8.1;a. Endpoints of the McHutchison study;414
29.8.2;b. Endpoints of the Di Bisceglie study;414
29.9;IX. Biomarkers and Hepatitis C Virus;416
29.10;X. Concluding Remarks;417
30;21 Health-related Quality of Life;418
30.1;I. Introduction;418
30.2;II. Summary;420
30.3;III. HRQoL Instruments Take on Increased Importance, When Capturing Data on Adverse Events, or in Trials on Palliative Treatments;420
30.4;IV. Scheduling the Administration of HRQoL Instruments;421
30.5;V. HRQoL Instruments in Oncology;422
30.5.1;a. Introduction;422
30.5.2;b. Symptoms and functioning;423
30.5.3;c. Formats for disclosing HRQoL results;424
30.5.4;d. Colorectal cancer;425
30.5.5;e. Melanoma;428
30.5.6;f. Non-small cell lung cancer;430
30.5.6.1;1. The Shepherd study;430
30.5.6.2;2. The Bezjak study;431
30.5.6.3;3. The Bonomi study;431
30.5.6.4;4. Representative list of clinical trials;432
30.5.7;g. HRQoL in breast cancer;433
30.5.7.1;1. Where survival data are identical in both study arms, HRQoL data turn the tide – the Watanabe study;433
30.5.7.2;2. HRQoL data demonstrate that long-term treatment is well tolerated – the Muss clinical trial;433
30.6;VI. Decisions on Counseling; Decisions on Chemotherapy Versus Surgery;434
30.7;VII. Conclusions;434
31;22 Health-related Quality of Life Instruments for Immune Disorders;436
31.1;I. Introduction;436
31.2;II. Short Form SF-36 Questionnaire;436
31.2.1;a. Arthritis;439
31.2.2;b. Psoriasis;439
31.2.3;c. Crohn’s disease;439
31.2.4;d. Chronic obstructive pulmonary disease;440
31.2.5;e. Multiple sclerosis;440
31.3;III. HRQoL Instruments Specific for Multiple Sclerosis;440
31.3.1;a. The Rudick study;441
31.3.2;b. EDSS score versus HRQoL score;442
31.3.3;c. Interferon-alpha-2a – the Nortvedt study;442
31.3.4;d. Interferon-beta-1a – the Jongen study;443
31.3.5;e. Glatiramer acetate – the Zwibel study;443
31.3.6;f. Meditation training – the Grossman study;444
31.4;IV. Conclusions;444
32;23 Health-related Quality of Life Instruments and Infections;446
32.1;I. Introduction;446
32.2;II. Health-Related Quality of Life Instruments with Chronic Hepatitis C Virus;446
32.2.1;a. Example of hepatitis C virus HRQoL – the Mathew study;447
32.2.2;b. Concluding remarks;448
33;24 Drug Safety;450
33.1;I. Introduction;450
33.1.1;a. Overview of drug safety;451
33.1.2;b. Examples of adverse events;453
33.1.3;c. Anticipating adverse events in the design of clinical studies;454
33.1.4;d. Dose modification;455
33.2;II. Safety Definitions;458
33.2.1;a. Definitions from U.S. and European regulatory agencies;458
33.2.1.1;1. Adverse events;459
33.2.1.2;2. Serious adverse event;459
33.2.1.3;3. Adverse drug reaction (ADR);460
33.2.1.4;4. Unexpected adverse drug reaction;460
33.2.1.5;5. Potential confusion in defining adverse events;460
33.2.2;b. Classification of adverse events as induced by disease versus induced by the study drug;461
33.2.3;c. Classification of adverse events by considerations used by statisticians;462
33.2.4;d. ITT analysis versus per protocol analysis;462
33.2.5;e. Summary;466
33.2.6;f. Classification of adverse events as anticipated versus unanticipated;466
33.2.7;g. Using raw data on adverse events to acquire cause-and-effect data on adverse drug reactions;470
33.3;III. Paradoxical Adverse Drug Reactions;471
33.3.1;a. Paradox with chemotherapy for cancer;472
33.3.2;b. Paradox with growth factors for cancer;473
33.3.3;c. Paradox with anti-depressants and depression;474
33.3.4;d. Paradoxes with drugs for treating bronchial constriction;475
33.4;IV. Monitoring and Evaluating Adverse Events;475
33.4.1;a. The data manager’s tasks include documenting missing data;476
33.4.2;b. CTCAE dictionary;477
33.4.3;c. Examples of missing data in documents submitted to the FDA;478
33.4.4;d. Writing style in case report forms;479
33.5;V. Adverse Events – Capturing, Transmitting, and Evaluating Data on Adverse Events;480
33.6;VI. Post-Marketing Report of Adverse Events;484
33.6.1;a. The MedWatch form, the yellow card, and the CIOMS I form;485
33.6.1.1;1. CIOMS;485
33.6.1.2;2. The CIOMS I form;486
33.6.2;b. Post-marketing surveillance;486
33.7;VII. Risk Minimization Tools;487
33.7.1;a. Introduction;487
33.7.2;b. Dear Healthcare Professional letter regarding birth control pills;490
33.7.3;c. Dear Healthcare Professional letter regarding acne medicine;491
33.7.4;d. Dear Healthcare Professional letter regarding appetite suppressants;492
33.8;VIII. Patient-Reported Outcomes;492
33.8.1;a. Introduction;492
33.8.2;b. PROs – example of head and neck cancer;493
33.9;IX. Summary of Reporting Systems Suitable for Capturing Adverse Events;495
33.10;X. Data and Safety Monitoring Committee;495
33.10.1;a. The DMC Charter;497
33.10.2;Data Safety Monitoring Board Charter;497
33.10.2.1;INTRODUCTION;498
33.10.2.2;ROLE OF THE BOARD;498
33.10.2.3;BOARD MEMBERSHIP;498
33.10.2.4;TERM;498
33.10.2.5;CONFLICT OF INTEREST AND FINANCIAL DISCLOSURE;499
33.10.2.6;COMPENSATION;499
33.10.2.7;BOARD MEETINGS AND REPORTS;499
33.10.2.8;ORGANIZATIONAL MEETING;499
33.10.2.9;INTERIM REVIEW MEETINGS;499
33.10.2.10;FORMAT;499
33.10.2.11;PARTICIPANTS;500
33.10.2.12;REVIEW MATERIALS;500
33.10.2.13;PERIODIC REPORTS TO THE DMC;501
33.10.2.14;UNSCHEDULED MEETINGS;501
33.10.2.15;DMC RECOMMENDATIONS;501
33.10.2.16;OUTSIDE EXPERTS;501
33.10.2.17;ACCESS TO INTERIM RESULTS;502
33.10.2.18;STOPPING RULES;502
33.10.2.19;COMMUNICATIONS;502
33.10.2.19.1;MEETING MINUTES;502
33.10.2.20;OTHER COMMUNICATIONS;502
33.10.2.21;SPONSOR’S DECISIONS AND ANNOUNCEMENTS;503
33.10.2.22;TIMETABLE;503
33.10.2.23;CONTACT INFORMATION;503
33.11;XI. Concluding Remarks;503
34;25 Mechanism of Action, Part I;506
34.1;I. Introduction;506
34.2;II. MOA and the Package Insert;507
34.3;III. MOA and Surrogate Endpoints;508
34.4;IV. MOA and Expected Adverse Drug Reactions;508
34.5;V. MOA and Drug Combinations;509
34.5.1;a. Drug combinations that are complementary or synergic;509
34.5.2;b. Drug combinations that avoid inducing cross-resistance;509
34.6;VI. Mechanism of Action of Diseases with an Immune Component;510
34.6.1;a. Introduction;510
34.6.2;b. Diseases with an immune component;511
34.6.3;c. Messengers in the immune system;511
34.6.4;d. Cells of the immune system;513
34.6.5;e. Processing and presentation of antigens, T cell activation, and T cell maturation;517
34.6.6;f. Drugs that modulate the immune system;517
34.6.6.1;1. Vaccines;518
34.6.6.2;2. Cytokines;518
34.6.6.3;3. TLR-agonists;519
34.6.6.4;4. Methodology tip – fine tuning of immune adjuvants when treating cancer;520
34.6.6.5;5. Antibodies;521
34.6.6.6;6. Treg inhibitors;521
34.7;VII. Immunology can be Organized as Pairs of Concepts;522
34.7.1;a. Myeloid DCs and plasmacytoid DCs;523
34.7.2;b. Th1-type response and Th2-type response;523
34.7.3;c. Externally acquired antigens and internally acquired antigens;523
34.7.4;d. Polypeptide antigens can contain both MHC class I and MHC class II epitopes;524
34.7.5;e. CD4+ T cells and CD8+ T cells;524
34.7.6;f. Two different mechanisms of CTL response;524
34.7.7;g. Naive response and memory response;524
34.7.8;h. Specific immunity and innate immunity;525
34.8;VIII. Conclusions;525
35;26 Mechanisms of Action, Part II – Cancer;528
35.1;I. Immune Response Against Cancer;528
35.1.1;a. Mechanisms of immune response against tumors;529
35.1.2;b. Natural killer cells and antibody-dependent cell cytotoxity;532
35.1.3;c. Regulatory T cells;534
35.1.4;d. Concluding remarks;536
36;27 Mechanisms of Action, Part III – Immune Disorders;538
36.1;I. Introduction;538
36.1.1;a. Mechanisms of action summaries for various immune disorders;539
36.1.1.1;1. Rheumatoid arthritis;540
36.1.1.2;2. Psoriasis;540
36.1.1.3;3. Lupus;540
36.1.1.4;4. Crohn’s disease and ulcerative colitis;541
36.1.1.5;5. Asthma;541
36.1.1.6;6. Chronic obstructive pulmonary disease (COPD);541
36.2;II. Detailed Example of Multiple Sclerosis Mechanism of Action;541
36.2.1;a. Natalizumab;542
36.2.2;b. Fingolimod;542
36.2.3;c. Interferon-beta1 (IFNbeta1);543
36.2.4;d. Cladribine;544
36.2.5;e. Animal model for multiple sclerosis;545
36.2.6;f. Mechanisms leading to multiple sclerosis are complex and not firmly established;546
36.2.7;g. Lesions of multiple sclerosis in humans;546
36.2.7.1;1. Initiating events in multiple sclerosis;546
36.2.7.2;2. CD8+ T cells attack nerves;546
36.2.7.3;3. Contributions of CD4+ T cells;547
36.2.7.4;4. Dendritic cells present antigen to T cells and activate the T cells;547
36.2.7.5;5. Breakdown of blood–brain barrier;548
36.2.7.6;6. Toxic oxygen from microglia;548
36.2.7.7;7. Diagram of multiple sclerosis;548
36.3;III. Concluding Remarks;550
37;28 Mechanisms of Action, Part IV – Infections;552
37.1;I. Introduction;552
37.2;II. Hepatitis C Virus Infections;552
37.2.1;a. Protease inhibitors used as drugs against anti-hepatitis C virus;553
37.2.2;b. Mechanisms of immune response against hepatitis C virus antigens;555
37.2.3;c. Methodology tip – GenBank;557
37.2.4;d. Dendritic cells and antigens of hepatitis C virus;558
37.2.5;e. Hepatitis C, chronic inflammation, and liver cancer;560
37.2.6;f. Dendritic cells;560
37.2.7;g. Sources of interferons during HCV infections;560
37.2.8;h. What IFN-gamma does during HCV infections;561
37.2.9;i. What T cells do during HCV infections where the patient spontaneously recovers;561
37.2.10;j. What immune cells do during HCV infections where the patient develops a chronic HCV infection;562
37.2.11;k. In HCV infections, IL-12 stImulates NK cells to express IFN-gamma;562
37.2.12;l. In HCV infections, IFN-alpha stimulates NK cells (or CD8+ T cells) to express IFN-gamma;563
37.2.13;m. Influence of IFN-alpha on gene expression as measured by microarrays;565
37.2.14;n. Diagrams of the immune network in immune response against HCV;566
37.2.15;o. Methodology tip – populations of leukocytes in the bloodstream;566
37.3;III. Concluding Remarks;568
38;29 Consent Forms;570
38.1;I. Introduction;570
38.1.1;a. An early clinical study using a consent form – yellow fever study;570
38.1.2;b. The consent form of the Yellow Fever Commission;572
38.1.3;c. Summary;573
38.2;II. Sources of the Law in the United States;573
38.3;III. Guidance for Industry;574
38.4;IV. Ethical Doctrines;575
38.5;V. The Case Law;576
38.6;VI. Basis for Consent Forms in the Code of Federal Regulations;576
38.7;VII. Summary;578
38.8;VIII. Examples of Contemporary Consent Forms;578
38.8.1;a. Example of a contemporary consent form (reproduced in full) (39,40);579
38.8.2;b. Another example of a contemporary consent form (reproduced in part);584
38.8.3;c. Comparison of standard consent form with the more elementary consent form;586
38.8.4;d. Analysis of consent forms by the medical community;587
38.8.5;e. Most consent forms are written at a level that is too advanced;588
38.9;IX. Ethical Issues Specific to Phase I Clinical Trials in Oncology;590
38.10;X. Decision Aids;591
38.11;XI. Distinction Between Stopping Treatment and Withdrawing from the Study;593
38.12;XII. Concluding Remarks;593
39;30 Package Inserts;596
39.1;I. Introduction;596
39.1.1;a. FDA’s Guidance for Industry documents relating to package inserts;597
39.1.2;b. Classes of drugs;600
39.1.3;c. Black box warning;600
39.1.4;d. Summary;602
39.2;II. Potential Ambiguity of Writing in Package Inserts;602
39.3;III. Package Insert may Protect Manufacturer from Liability;603
39.3.1;a. Opinion concerning dicumarol;604
39.3.2;b. Opinion regarding kanamycin;605
39.3.3;c. Opinion regarding dilantin;605
39.3.4;d. Opinion concerning oxytocin;605
39.3.5;e. Opinion regarding oral polio vaccine;606
39.3.6;f. Opinion regarding norethindrone;607
39.3.7;g. Summary;607
39.4;IV. Package Insert Compared with Consent Form;608
39.5;V. Relation between Package Inserts to the Standard of Care, and to off-Label Uses;608
39.6;VI. Conclusions;610
40;31 Regulatory Approval;612
40.1;I. Introduction;612
40.1.1;a. Origins of the Federal Food, Drug and Cosmetic Act and its amendments;612
40.1.2;b. Federal Food, Drug and Cosmetic Act of 1938;613
40.1.3;c. Drug Amendments Act of 1962;615
40.1.4;d. Food and Drug Administration Modernization Act of 1997 and Phase IV clinical trials;615
40.2;II. History of the European Medicines Agency;616
40.3;III. International Conference on Harmonisation;618
40.4;IV. History of the Medicines and Healthcare Products Regulatory Agency;620
40.5;V. Outline of Regulatory Approval in the United States;621
40.5.1;a. The Investigational New Drug;621
40.5.2;b. The Investigational New Drug and the Common Technical Document;626
40.6;VI. Process of Administering Clinical Trials;627
40.7;VII. Process of Medical Writing;630
40.7.1;a. Grammatical issues;631
40.7.2;b. Formatting issues;632
40.8;VIII. Meetings with the U.S. Food and Drug Administration;635
40.8.1;a. Introduction;635
40.8.2;b. Paper trail of FDA’s decision-making process for individual drugs;636
40.8.3;c. Clinical review;636
40.8.4;d. Pharmacology review;638
40.8.5;e. Approval letter;638
40.8.6;f. Snapshots of the FDA’s regulatory review process;639
40.8.6.1;1. Example showing transition from an open-label Phase I trial to a blinded Phase II trial;639
40.8.6.2;2. Example showing how FDA uses data from Phase I trial to arrive at a dose for using in a Phase II trial;640
41;32 Patents;642
41.1;I. Introduction;642
41.1.1;a. History of patenting;642
41.1.2;b. Outline of the patenting process;644
41.1.3;c. Summary;645
41.2;II. Types of Patent Documents;646
41.3;III. Structure of Patents;647
41.3.1;a. Introduction;647
41.3.2;b. The claims;648
41.4;IV. Timeline for Patenting;651
41.5;V. Sources of the Law for Patenting;654
41.6;VI. Intersections between the FDA Review Process and Patents;656
41.6.1;a. Introduction;656
41.6.2;b. Using patent as source documents when writing regulatory submissions;657
42;Index;660