Clinical Trials - Study Design, Endpoints and Biomarkers, Drug Safety, and FDA and ICH Guidelines

Front Cover

Clinical Trials: Study Design, Endpoints and Biomarkers, Drug Safety, FDA and ICH Guidelines

Copyright Page

Contents

Acknowledgments

Preface

The Study Schema and Study Design

Intent to Treat Analysis

How to Choose the Endpoints

Diagnostic Tests

Mechanism of Action

Standards

Methodology

Clinicaltrials.Gov and other Registries for Clinical Trials

Introduction

Abbreviations and Definitions

Biography

1 The Origins of Drugs

I. Introduction

II. Structures of Drugs

a. Origins of warfarin

b. Origins of methotrexate and 5-fluorouracil

c. Origins of ribavirin

d. Origins of paclitaxel

e. Origins of cladribine

f. Origins of drugs in high-throughput screening

g. Origins of therapeutic antibodies

III. The 20 Classical Amino Acids

IV. Animal Models

a. Introduction

b. Estimating human dose from animal studies

1. NOAEL approach

2. MABEL approach

c. Scaling up the drug dose, acquired from animal studies, for use in humans

2 Introduction to Regulated Clinical Trials

I. Introduction

II. Study Design

III. The Study Schema

a. Examples of schema from clinical trials

b. Sequential treatment versus concurrent treatment – the Perez schema

c. Neoadjuvant chemotherapy versus adjuvant chemotherapy – the Gianni schema

d. Neoadjuvant chemotherapy plus adjuvant chemotherapy – the Untch schema

e. Forwards sequence and reverse sequence – the Puhalla schema

f. Both arms received three drugs, each arm at a different schedule – the Sekine schema

g. Staging – the Blumenschein schema

h. Staging and restaging – the Czito schema

i. Methodology tip – staging

j. Decision tree – the Baselga schema

k. Decision tree – the Katsumata schema

l. Methodology tip – what is “tumor progression”?

m. Methodology tip – unit of drug dose expressed in terms of body surface area

n. Run-in period – the schema of Dy

o. Methodology tip – c-kit and imatinib

p. Run-in period – the Hanna schema

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)…

r. Methodology tip – bevacizumab and VEGF

s. Dose escalation – the Moore schema

t. Pharmacokinetics – the Marshall schema

IV. Further Concepts In Study Design

a. Active control

b. Add-on design active control

c. Three-arm study – clinical trial with two different active control arms

V. Summary

VI. Amendments to the Clinical Study Protocol

3 Run-In Period

I. Introduction

a. Washout period

b. Detecting baseline adverse events

c. Excluding potential study subjects who have safety issues correlating with the study drug

d. To include only study subjects with controllable pain

e. To determine the maximal tolerable dose

f. To achieve and ensure steady state in vivo concentrations of study drug

g. To allow a period of adjustment of lifestyle of the study subjects, for example changes in dietary patterns

h. To ensure that metabolic characteristics of all study subjects are similar, prior to administering drugs

i. To ensure that potential study subjects can adhere to, or comply with, the study protocol

j. To confirm that all study subjects meet the inclusion and exclusion criteria

k. Detecting potential study subjects who show a predetermined desired response to the study drug, with the goal of including only these subjects

l. Methodology tip – anti-cancer drugs that inhibit tumor growth

m. Decision tree

n. To create a self-control group

II. Concluding Remarks

4 Inclusion/Exclusion Criteria, Stratification, and Subgroups – Part I

I. The Clinical Study Protocol Is a Manual that Provides the Study Design

a. Clinical study protocol provides the inclusion/exclusion criteria and stratification

b. Stratification of study subjects

c. Words of warning

d. Staging of the disease

e. The study schema

1. Inclusion criteria for the RTOG 0232 study of prostate cancer

2. Exclusion criteria for the RTOG 0232 study of prostate cancer

f. Stratification of subjects into subgroups

g. Examples of subgroups

h. Prior therapy

i. Poor performance status as a basis for exclusion

j. Irreversible and cumulative toxicity as a basis for exclusion

k. Drug resistance as a basis for exclusion

II. Biology of Drug Resistance

a. Biochemistry of the ABC drug transporters

b. Biology of cross-resistance

c. A tumor’s genetic expression can provide guidance on drug resistance

1. Doxorubicin

2. Paclitaxel

3. Tamoxifen

4. Imatinib

d. Prior treatment with hormones as a basis for exclusion

e. Immune status for exclusion criteria

f. Example of earlier vaccination as an inclusion criterion

g. Ethical considerations as a basis for inclusion criteria

III. More Information on Subgroups

a. Subgroup of non-elderly subjects and subgroup of elderly subjects

b. Subgroup of subjects with no metastasis and subgroup of subjects with metastasis

c. Subgroup of smokers and subgroup of non-smokers

d. Subgroups set forth in a clinical study protocol can be used as a basis for FDA approval

e. Subgroup analysis enables recommendations for a specific course of treatment

f. Subgroup analysis can justify increases in drug dose for specific subgroups

g. Subgroups determined by an analysis of gene expression by microarray analysis

h. Recommending dropping one subgroup from the trial, rather than stopping the entire trial

IV. Concluding Remarks

5 Inclusion and Stratification Criteria – Part II

I. Introduction

II. Staging

a. History of tumor staging

b. Revising staging systems

c. Biology of tumors

d. Biology of the lymphatic system

e. Relation between the tumors and the lymphatic system

f. Metastasis of tumors

g. The sentinel node and distant lymph nodes

III. Staging Systems for Various Cancers

a. Colorectal cancer

b. TNM definitions for colorectal cancer

Stage 0

Stage I

Stage IIA

Stage IIB

Stage IIC

Stage IIIA

Stage IIIB

Stage IIIC

Stage IVA

Stage IVB

c. Breast cancer

d. Breast cancer in situ (DCIS and LCIS)

e. Invasive breast cancer

f. Definitions for breast cancer

g. Breast cancer staging

Stage 0

Stage IA

Stage IB

Stage IIA

Stage IIB

Stage IIIA

Stage IIIB

Stage IIIC

Stage IV

IV. Summary

V. The will Rogers Phenomenon

a. Will Rogers phenomenon for prostate cancer

b. Will Rogers phenomenon for non-small lung cancer

c. Will Rogers phenomenon for small cell lung cancer

d. Will Rogers phenomenon for rectal cancer

e. Will Rogers phenomenon for multiple sclerosis

VI. Other Sources of Artifacts in Data from Clinical Trials

VII. Concluding Remarks

6 Randomization, Allocation, and Blinding

I. Introduction

a. Allocation and allocation concealment

b. Simple randomization

c. Stratification

II. Manual Technique for Allocation

a. Allocation by coin-toss versus allocation by sealed envelope

III. Information on Randomization, Blinding, and Unblinding may be Included in the Clinical Study Protocol

a. Introduction

b. When to break the randomization code – clinical study protocol for trial on Alzheimer’s disease (27)

c. When to break the randomization code – clinical study protocol for trial on malaria vaccine (28)

d. When to break the randomization code – clinical study protocol for trial typhoid vaccine (29)

e. When to break the randomization code – clinical study protocol for trial on lung cancer (30)

f. When to break the randomization code – clinical study protocol for trial on sepsis (31)

g. When to break the randomization code – clinical study protocol for trial on melanoma (32)

h. When to break the randomization code – clinical study protocol for trial on multiple sclerosis (33)

IV. Summary

V. Subjects are Enrolled into Clinical Trials, One by One, Over the Course of Many Months

VI. Blocked Randomization

VII. Blinding

VIII. Interactive Voice Response Systems

IX. Concluding Remarks

7 Placebo Arm as Part of Clinical Study Design

I. Introduction

II. Hawthorne Effect

III. The No-Treatment Arm

IV. Physical Aspects of the Placebo

V. Active Placebo

VI. Subjects in the Placebo Arm May Receive Best Supportive Care Or Palliative Care

VII. Clash Between Best Supportive Care and the Endpoint of HRQoL

VIII. Ethics of Placebos

8 Intent to Treat Analysis vs. Per Protocol Analysis

I. Introduction

a. Definition of intent to treat analysis

b. Deviations and inconsistencies

II. ITT Analysis Contrasted with PP Analysis

a. ITT analysis vs. PP analysis – the Hosking study

b. ITT analysis vs. PP analysis – the Sethi study

c. ITT analysis vs. PP analysis – the Abrial study

d. ITT analysis vs. PP analysis – the Berthold study

e. ITT analysis vs. PP analysis – the Geddes study

f. ITT analysis vs. PP analysis – the Chauffert study

III. Disadvantages of ITT Analysis

IV. Run-in Period, as Part of the Study Design, is Relevant to ITT Analysis and PP Analysis

V. Summary

VI. Hypothetical Example Where Study Drug and Control Drug have Same Efficacy

VII. Modified ITT Analysis

a. Flow chart showing subjects included in the ITT analysis, modified ITT analysis, and PP analysis

b. Reasons for using modified ITT analysis

c. Excluding subjects who failed to meet inclusion or exclusion criteria, or who failed to receive study drug – the Vaira study

d. Excluding subjects who failed to meet inclusion or exclusion criteria – the Weigelt study

e. Excluding subjects who failed to meet inclusion or exclusion criteria – the Pinchichero study

f. Excluding subjects who failed to meet inclusion or exclusion criteria – the Leroy study

g. Exclusion of study subjects because of failure to satisfy the inclusion criteria, and for withdrawing consent – the Dupont study

h. Exclusion of study subjects because of failure to satisfy the inclusion criteria – the Florescu study

i. Excluding subjects who took prohibited drugs during the clinical trial, or who withdrew consent – the Manegold study

j. Excluding subjects who failed to receive the assigned treatment because of a mistake by the health care provider – the Berek study

k. Exclusion of study subjects who failed to take drug long enough to have the expected efficacy – the Krainick-Strobel study

l. Excluding subject who dropped out because of adverse events, and because of the bad flavor of the study drug – the Kreijkamp-Kaspers study

m. Excluding subjects who failed to receive the assigned treatment because of adverse events – the Caraceni study

n. Modified ITT group based on a subgroup of study subjects – the Gralla study

VIII. Start Date for Endpoints in Clinical Studies

IX. Summary and Conclusions

9 Biostatistics

I. Introduction

a. Kaplan-Meier plot

b. Examples of Kaplan-Meier plots – the Holm study

c. Censoring data

d. Hazard ratio

II. Definitions and Formulas

III. Data from the Study of Machin and Gardner

IV. Data Used for Constructing the Kaplan-Meier Plot are from Subjects Enrolling at Different Times

V. Sample Versus Population

VI. What can be Compared

VII. One-Tailed Test Versus Two-Tailed Test

VIII. P Value

IX. Calculating the P Value – a Working Example

X. Summary

XI. Theory Behind the Z Value and the Table of Areas in the Tail of the Standard Normal Distribution

XII. Statistical Analysis by Superiority Analysis Versus by Non-Inferiority Analysis

10 Introduction to Endpoints for Clinical Trials in Pharmacology

I. Introduction

a. Phase I clinical trial endpoints

b. Clinical endpoints

c. Surrogate endpoints

d. Relatively objective endpoints versus relatively subjective endpoints

e. Using multiple endpoints, and choosing the endpoint on which to base conclusions

f. In choosing endpoints keep in mind the eventual goals of the clinical trial

11 Endpoints In Clinical Trials on Solid Tumors – Objective Response

I. Introduction

a. Objective response using RECIST criteria

b. Objective response – Demetri’s example of partial response

c. Objective response – van Meerten’s example of partial response

d. Objective response – example of progressive disease

II. Studies Characterizing an Association Between Objective Response and Survival

III. Avoiding Confusion when Using Objective Response as an Endpoint

a. Date for beginning objective response measurements in two study arms, relative to start date of treatment

b. Where multiple measurements of objective response are taken, which measurement is used for analysis of efficacy?

c. How is it possible to obtain a meaningful value for objective response, or for endpoints (PFS, TTP) that comprise objective response?

d. Objective response is reported in terms of a “rate” and also as a “percent”

e. Drugs that are cytostatic and not cytotoxic may provide misleading results, where the endpoint of objective response is used

f. Use of different criteria (standards) for objective response, and the availability of updated criteria

12 Oncology Endpoints: Overall Survival and Progression-free Survival

I. Introduction

II. Comparing Contexts of Use and Advantages of Various Endpoints

a. Contrast between PFS and TTP

b. Excellence of PFS as an endpoint

c. Why progression-free survival may be preferred over overall survival

1. Confusion from effects of non-study drugs given to subjects who leave the trial

2. Collecting data on overall survival may require an extended follow-up period

3. Weakened conclusions, regarding efficacy of study drug, when the endpoint is keyed to a longer timeframe

4. Confusion from the multiplicity of causes of death

5. Ethical reasons

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

7. Conclusions arising from data on overall survival may be redundant with conclusions made from data on PFS

d. Why overall survival may be preferred over PFS

1. Overall survival is the gold standard

2. The date of the event that triggers PFS may be ambiguous, while the date that triggers overall survival is not ambiguous

e. Endpoint keyed to one specific time point – 6-month PFS

f. Use of the word “rate”

III. Data on Overall Survival and PFS from Clinical Trials

a. Utilities of the endpoints of objective response, PFS, and overall survival

1. Data on PFS may be more significant than data on overall survival – the Maemondo study

2. Methodology tip – shapes of Kaplan-Meier plots in the Maemondo study

3. Methodology tips – independent radiology assessments in the Gradishar study

4. The endpoint of PFS may have an advantage, where PFS data are more statistically significant than overall survival data – the Robert study

5. Rationale for combining trastuzumab with a platinum drug

6. Data on PFS can present earlier, and can be more dramatic, than data on overall survival – the Slamon study

7. Progression-free survival and subgroup analysis – the Van Cutsem study

8. Anti-sense drug for melanoma and subgroup analysis – the Bedikian study

IV. Summary

13 Oncology Endpoints: Time to Progression

I. Introduction

II. Agreement of Results from Objective Response, Time to Progression, and Overall Survival – the Paccagnella Study

III. Can the Value for PFS be Less than the Value for TTP?

IV. Time to Progression may be the Preferred Endpoint where, Once the Trial is Concluded, Patients Receive Additional Chemotherapy – the Park Study

V. The Endpoint of TTP may be Preferred Over Survival Endpoints, where Deaths Result from Causes Other than Cancer – the Llovet Study

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

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

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

IX. Methodology TIP – Advantage of Using an Endpoint that Incorporates a “Median” Time

X. Summary

XI. Thymidine Phosphorylase as a biomarker for Survival – the Meropol Study

XII. Drug Combinations that Include Capecitabine

XIII. Methodology TIP – do Changes in mRNA Expression Result in Corresponding Changes in Expression of Polypeptide?

XIV. Conclusions

14 Oncology Endpoint: Disease-free Survival

I. Introduction

II. Difference Between Disease-Free Survival and Progression-Free Survival

III. Ambiguity in the Name of the Endpoint, “Disease-Free Survival”

IV. Disease-Free Survival Provides Earlier Results on Efficacy than Overall Survival – the Add-on Breast Cancer Study of Romond

V. Disease-Free Survival as an Endpoint in the Analysis of Subgroups – the Add-on Breast Cancer Study of Hayes

VI. Neoadjuvant Therapy Versus Adjuvant Therapy for Rectal Cancer – the Roh Study

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

VIII. Disease-Free Survival and Overall Survival are Useful Tools for Testing and Validating Prognostic Biomarkers – the Bepler Study

IX. Summary

15 Oncology Endpoint: Time to Distant Metastasis

I. Introduction

II. Time to Distant Metastasis Data are Acquired Before Overall Survival Data are Acquired – the Wee Study

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

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

V. Use of Micro-RNA Expression Data as a Prognostic Factor for Breast Cancer Patients – the Foekens Study

VI. Biology of Micro-RNA

VII. Conclusions

16 Neoadjuvant Therapy versus Adjuvant Therapy

I. Introduction

II. Advantages of Neoadjuvant Therapy

a. Killing micrometastases

b. Making surgery easier

c. Preserving functions, or cosmetic issues, of organs

d. Enables the physician to perform an experiment that enables a decision regarding subsequent therapy

e. Better ability of patient to tolerate chemotherapy

III. Advantages of Adjuvant Therapy

a. Immediate surgery and reduced risk of metastasis

b. More accurate staging

c. Drugs that require chronic treatment, for example for 5 years

IV. Two Meanings of the Term Adjuvant

V. Concluding Remarks

17 Hematological Cancers

I. Introduction

a. Classification of hematological cancers

b. Hematopoietic stem cells give rise to the lymphoid lineage and myeloid lineage

c. Locations of leukemic cells in the body

d. Lymphoid neoplasms

1. Acute lymphocytic leukemia

2. Chronic lymphocytic leukemia

3. Hairy cell leukemia

e. Myeloid neoplasms

1. Acute myeloid leukemia

2. Acute promyelocytic leukemia

3. Methodology tip – platelets and blood clotting

4. Chronic myeloid leukemia

II. Myelodysplastic Syndromes

a. Classifying MDS and scoring MDS

b. Treating MDS

c. Transfusions in MDS

d. Chromosome 5 abnormality and lenalidomide for treating MDS

e. Mechanism of action of lenalidomide

f. Mechanism of action of 5-aza-deoxycytidine

III. Summary

IV. Cytogenetics and the Hematological Cancers

a. Cytogenetics for diagnosis and prediction – AML

b. Cytogenetics for diagnosis and prediction – ALL

1. Numeric abnormalities in ALL

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

c. Cytogenetics for diagnosis and prediction – CML

d. Utility of the Philadelphia chromosome in diagnosis, drug target, and for assessing response

e. Cytogenetics for diagnosis and prediction – CLL

f. Cytogenetics for diagnosis and prediction – myelodysplastic syndromes

V. Chromosomal Abnormalities in Solid Tumors

VI. Clinical Endpoints and Examples from Clinical Trials

a. Endpoint of event-free survival

b. Endpoint of relapse-free interval

VII. Cytogenetics as a Prognostic Marker – The Grever Study of CLL

VIII. Minimal Residual Disease

a. Example of use of minimal residual disease and relapse – the scheuring study of philadelphia chromosome positive ALL

b. Example of use of minimal residual disease and event-free survival – the basso study of philadelphia chromosome negative ALL

c. Methodology tip – flow cytometry for assessing minimal residual disease

d. Using cells acquired after chemotherapy (not before chemotherapy) as a prognostic factor for long-term relapse – the cilloni study

e. Methodology tip – should biomarkers be measured before or after chemotherapy?

f. Example of use of minimal residual disease – the Grimwade study using PML-RAR-alpha fusion protein

IX. Confluence of Cytogenetics and Gene Expression

X. Conclusions

18 Biomarkers and Personalized Medicine

I. Introduction

a. Predictive markers versus prognostic markers

b. Including biomarker tests in the study design

c. Criteria for surrogate markers

d. Clinical trials focusing on utility of a biomarker

1. Biomarkers in breast cancer – the Stratton study

2. Biomarkers in breast cancer – the Vogel study

3. Methodology tip – fluorescent in situ hybridization (FISH) technique

4. Circulating tumor cells as a prognostic biomarker for colon cancer – the Cohen study

5. Methodology tip – circulating tumor cells as a biomarker

6. Cytokeratin as a soluble protein biomarker for colon cancer – the Koelink study

7. Tumor infiltrating T cells as a prognostic biomarker for colon cancer – the Galon study

8. Tumor infiltrating T cells as a prognostic biomarker for colon cancer – the Morris study

e. Lymphocytes can kill cancer cells, but lymphocytes can also cause cancer

II. Microarrays

a. Microarray used in ovarian cancer – the Spentzos study

b. Microarray used in colon cancer – the Wang study

c. Microarray used in liver cancer – the Hoshida study

III. C-Reactive Protein

a. Biology of C-reactive protein

b. C-reactive protein as a cancer biomarker

1. C-reactive protein and lung cancer – the Allin study

2. C-reactive protein and liver cancer – the Wong study

3. C-reactive protein and melanoma – the Findeisen study

c. Methodology tip – identifying new biomarkers by mass spectroscopy

d. C-reactive protein and atherosclerosis

IV. Concluding Remarks

19 Endpoints in Immune Diseases

I. Introduction

II. Multiple Sclerosis

a. Diagnosis

b. Endpoints

c. Timing for measuring endpoints

d. Primary endpoint

e. Multiple sclerosis functional composite (MSFC) score

f. Secondary endpoints

g. Introduction to MRI and detecting the onset of brain lesions

1. Example of MRI photograph

2. T2-weighted MRI

3. T1-weighted MRI

h. Results from the kappos study

III. Concluding Remarks

20 Endpoints in Clinical Trials on Infections

I. Introduction

II. Clinical and Immunological Features of Hepatitis C Virus Infections

III. Acute HCV Versus Chronic HCV

IV. Drugs Against Hepatitis C Virus

V. Immune Responses Against Hepatitis C Virus

VI. Kinetics of Hepatitis C Virus Infections

VII. Responders Versus Non-Responders

VIII. Endpoints in Clinical Trials Against Hepatitis C Virus

a. Endpoints of the McHutchison study

b. Endpoints of the Di Bisceglie study

IX. Biomarkers and Hepatitis C Virus

X. Concluding Remarks

21 Health-related Quality of Life

I. Introduction

II. Summary

III. HRQoL Instruments Take on Increased Importance, When Capturing Data on Adverse Events, or in Trials on Palliative Treatments

IV. Scheduling the Administration of HRQoL Instruments

V. HRQoL Instruments in Oncology

a. Introduction

b. Symptoms and functioning

c. Formats for disclosing HRQoL results

d. Colorectal cancer

e. Melanoma

f. Non-small cell lung cancer

1. The Shepherd study

2. The Bezjak study

3. The Bonomi study

4. Representative list of clinical trials

g. HRQoL in breast cancer

1. Where survival data are identical in both study arms, HRQoL data turn the tide – the Watanabe study

2. HRQoL data demonstrate that long-term treatment is well tolerated – the Muss clinical trial

VI. Decisions on Counseling; Decisions on Chemotherapy Versus Surgery

VII. Conclusions

22 Health-related Quality of Life Instruments for Immune Disorders

I. Introduction

II. Short Form SF-36 Questionnaire

a. Arthritis

b. Psoriasis

c. Crohn’s disease

d. Chronic obstructive pulmonary disease

e. Multiple sclerosis

III. HRQoL Instruments Specific for Multiple Sclerosis

a. The Rudick study

b. EDSS score versus HRQoL score

c. Interferon-alpha-2a – the Nortvedt study

d. Interferon-beta-1a – the Jongen study

e. Glatiramer acetate – the Zwibel study

f. Meditation training – the Grossman study

IV. Conclusions

23 Health-related Quality of Life Instruments and Infections

I. Introduction

II. Health-Related Quality of Life Instruments with Chronic Hepatitis C Virus

a. Example of hepatitis C virus HRQoL – the Mathew study

b. Concluding remarks

24 Drug Safety

I. Introduction

a. Overview of drug safety

b. Examples of adverse events

c. Anticipating adverse events in the design of clinical studies

d. Dose modification

II. Safety Definitions

a. Definitions from U.S. and European regulatory agencies

1. Adverse events

2. Serious adverse event

3. Adverse drug reaction (ADR)

4. Unexpected adverse drug reaction

5. Potential confusion in defining adverse events

b. Classification of adverse events as induced by disease versus induced by the study drug

c. Classification of adverse events by considerations used by statisticians

d. ITT analysis versus per protocol analysis

e. Summary

f. Classification of adverse events as anticipated versus unanticipated

g. Using raw data on adverse events to acquire cause-and-effect data on adverse drug reactions

III. Paradoxical Adverse Drug Reactions

a. Paradox with chemotherapy for cancer

b. Paradox with growth factors for cancer

c. Paradox with anti-depressants and depression

d. Paradoxes with drugs for treating bronchial constriction

IV. Monitoring and Evaluating Adverse Events

a. The data manager’s tasks include documenting missing data

b. CTCAE dictionary

c. Examples of missing data in documents submitted to the FDA

d. Writing style in case report forms

V. Adverse Events – Capturing, Transmitting, and Evaluating Data on Adverse Events

VI. Post-Marketing Report of Adverse Events

a. The MedWatch form, the yellow card, and the CIOMS I form

1. CIOMS

2. The CIOMS I form

b. Post-marketing surveillance

VII. Risk Minimization Tools

a. Introduction

b. Dear Healthcare Professional letter regarding birth control pills

c. Dear Healthcare Professional letter regarding acne medicine

d. Dear Healthcare Professional letter regarding appetite suppressants

VIII. Patient-Reported Outcomes

a. Introduction

b. PROs – example of head and neck cancer

IX. Summary of Reporting Systems Suitable for Capturing Adverse Events

X. Data and Safety Monitoring Committee

a. The DMC Charter

Data Safety Monitoring Board Charter

INTRODUCTION

ROLE OF THE BOARD

BOARD MEMBERSHIP

TERM

CONFLICT OF INTEREST AND FINANCIAL DISCLOSURE

COMPENSATION

BOARD MEETINGS AND REPORTS

ORGANIZATIONAL MEETING

INTERIM REVIEW MEETINGS

FORMAT

PARTICIPANTS

REVIEW MATERIALS

PERIODIC REPORTS TO THE DMC

UNSCHEDULED MEETINGS

DMC RECOMMENDATIONS

OUTSIDE EXPERTS

ACCESS TO INTERIM RESULTS

STOPPING RULES

COMMUNICATIONS

MEETING MINUTES

OTHER COMMUNICATIONS

SPONSOR’S DECISIONS AND ANNOUNCEMENTS

TIMETABLE

CONTACT INFORMATION

XI. Concluding Remarks

25 Mechanism of Action, Part I

I. Introduction

II. MOA and the Package Insert

III. MOA and Surrogate Endpoints

IV. MOA and Expected Adverse Drug Reactions

V. MOA and Drug Combinations

a. Drug combinations that are complementary or synergic

b. Drug combinations that avoid inducing cross-resistance

VI. Mechanism of Action of Diseases with an Immune Component

a. Introduction

b. Diseases with an immune component

c. Messengers in the immune system

d. Cells of the immune system

e. Processing and presentation of antigens, T cell activation, and T cell maturation

f. Drugs that modulate the immune system

1. Vaccines

2. Cytokines

3. TLR-agonists

4. Methodology tip – fine tuning of immune adjuvants when treating cancer

5. Antibodies

6. Treg inhibitors

VII. Immunology can be Organized as Pairs of Concepts

a. Myeloid DCs and plasmacytoid DCs

b. Th1-type response and Th2-type response

c. Externally acquired antigens and internally acquired antigens

d. Polypeptide antigens can contain both MHC class I and MHC class II epitopes

e. CD4+ T cells and CD8+ T cells

f. Two different mechanisms of CTL response

g. Naive response and memory response

h. Specific immunity and innate immunity

VIII. Conclusions

26 Mechanisms of Action, Part II – Cancer

I. Immune Response Against Cancer

a. Mechanisms of immune response against tumors

b. Natural killer cells and antibody-dependent cell cytotoxity

c. Regulatory T cells

d. Concluding remarks

27 Mechanisms of Action, Part III – Immune Disorders

I. Introduction

a. Mechanisms of action summaries for various immune disorders

1. Rheumatoid arthritis

2. Psoriasis

3. Lupus

4. Crohn’s disease and ulcerative colitis

5. Asthma

6. Chronic obstructive pulmonary disease (COPD)

II. Detailed Example of Multiple Sclerosis Mechanism of Action

a. Natalizumab

b. Fingolimod

c. Interferon-beta1 (IFNbeta1)

d. Cladribine

e. Animal model for multiple sclerosis

f. Mechanisms leading to multiple sclerosis are complex and not firmly established

g. Lesions of multiple sclerosis in humans

1. Initiating events in multiple sclerosis

2. CD8+ T cells attack nerves

3. Contributions of CD4+ T cells

4. Dendritic cells present antigen to T cells and activate the T cells

5. Breakdown of blood–brain barrier

6. Toxic oxygen from microglia

7. Diagram of multiple sclerosis

III. Concluding Remarks

28 Mechanisms of Action, Part IV – Infections

I. Introduction

II. Hepatitis C Virus Infections

a. Protease inhibitors used as drugs against anti-hepatitis C virus

b. Mechanisms of immune response against hepatitis C virus antigens

c. Methodology tip – GenBank

d. Dendritic cells and antigens of hepatitis C virus

e. Hepatitis C, chronic inflammation, and liver cancer

f. Dendritic cells

g. Sources of interferons during HCV infections

h. What IFN-gamma does during HCV infections

i. What T cells do during HCV infections where the patient spontaneously recovers

j. What immune cells do during HCV infections where the patient develops a chronic HCV infection

k. In HCV infections, IL-12 stImulates NK cells to express IFN-gamma

l. In HCV infections, IFN-alpha stimulates NK cells (or CD8+ T cells) to express IFN-gamma

m. Influence of IFN-alpha on gene expression as measured by microarrays

n. Diagrams of the immune network in immune response against HCV

o. Methodology tip – populations of leukocytes in the bloodstream

III. Concluding Remarks

29 Consent Forms

I. Introduction

a. An early clinical study using a consent form – yellow fever study

b. The consent form of the Yellow Fever Commission

c. Summary

II. Sources of the Law in the United States

III. Guidance for Industry

IV. Ethical Doctrines

V. The Case Law

VI. Basis for Consent Forms in the Code of Federal Regulations

VII. Summary

VIII. Examples of Contemporary Consent Forms

a. Example of a contemporary consent form (reproduced in full) (39,40)

b. Another example of a contemporary consent form (reproduced in part)

c. Comparison of standard consent form with the more elementary consent form

d. Analysis of consent forms by the medical community

e. Most consent forms are written at a level that is too advanced

IX. Ethical Issues Specific to Phase I Clinical Trials in Oncology

X. Decision Aids

XI. Distinction Between Stopping Treatment and Withdrawing from the Study

XII. Concluding Remarks

30 Package Inserts

I. Introduction

a. FDA’s Guidance for Industry documents relating to package inserts

b. Classes of drugs

c. Black box warning

d. Summary

II. Potential Ambiguity of Writing in Package Inserts

III. Package Insert may Protect Manufacturer from Liability

a. Opinion concerning dicumarol

b. Opinion regarding kanamycin

c. Opinion regarding dilantin

d. Opinion concerning oxytocin

e. Opinion regarding oral polio vaccine

f. Opinion regarding norethindrone

g. Summary

IV. Package Insert Compared with Consent Form

V. Relation between Package Inserts to the Standard of Care, and to off-Label Uses

VI. Conclusions

31 Regulatory Approval

I. Introduction

a. Origins of the Federal Food, Drug and Cosmetic Act and its amendments

b. Federal Food, Drug and Cosmetic Act of 1938

c. Drug Amendments Act of 1962

d. Food and Drug Administration Modernization Act of 1997 and Phase IV clinical trials

II. History of the European Medicines Agency

III. International Conference on Harmonisation

IV. History of the Medicines and Healthcare Products Regulatory Agency

V. Outline of Regulatory Approval in the United States

a. The Investigational New Drug

b. The Investigational New Drug and the Common Technical Document

VI. Process of Administering Clinical Trials

VII. Process of Medical Writing

a. Grammatical issues

b. Formatting issues

VIII. Meetings with the U.S. Food and Drug Administration

a. Introduction

b. Paper trail of FDA’s decision-making process for individual drugs

c. Clinical review

d. Pharmacology review

e. Approval letter

f. Snapshots of the FDA’s regulatory review process

1. Example showing transition from an open-label Phase I trial to a blinded Phase II trial

2. Example showing how FDA uses data from Phase I trial to arrive at a dose for using in a Phase II trial

32 Patents

I. Introduction

a. History of patenting

b. Outline of the patenting process

c. Summary

II. Types of Patent Documents

III. Structure of Patents

a. Introduction

b. The claims

IV. Timeline for Patenting

V. Sources of the Law for Patenting

VI. Intersections between the FDA Review Process and Patents

a. Introduction

b. Using patent as source documents when writing regulatory submissions

Index