Regulatory review of higher phase clinical trials


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  • 10/12/12
  • 10/12/12
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  • Regulatory review of higher phase clinical trials

    1. 1. Regulatory Review of Higher Phase Clinical Trials ICMR sponsored and Sat Kaival College of Pharmacy organizedNational Symposium on EMERGING TRENDS IN CLINICAL RESEARCH 29th February, 2008 Dr. Bhaswat S. Chakraborty Sr. VP, R&D, Cadila Pharmaceuticals 1
    2. 2. Order of the Topics Clinical Trials – Regulatory phases 1,2,3 & 4 Review of Clinical Trials Data Requirements Study Design Considerations Controls – Placebo, Active, NI Assay Sensitivity, Multiplicity of Analyses Interim Analysis Intent to Treat Analysis Final Analysis Conclusions 2
    3. 3. 3
    4. 4. Investigational New Drug The pharmaceutical industry sometimes provides advice to the FDA prior to submission of an IND Sponsors, research institutions, and other organizations that take responsibility for developing a drug must show the FDA results of preclinical testing theyve done in laboratory animals and what they propose to do for human testing At this stage, the FDA decides whether it is reasonably safe for the company to move forward with testing the drug in humans 4
    5. 5. Phase 1 Phase 1 studies are usually conducted in healthy volunteers The goal here is to determine what the drugs most frequent side effects are and, often, how the drug is metabolized and excreted The number of subjects typically ranges from 20 to 100 5
    6. 6. Early Phase Clinical Trials 6
    7. 7. Phase 2 Phase 2 studies begin if Phase 1 studies dont reveal unacceptable toxicity While the emphasis in Phase 1 is on safety, the emphasis in Phase 2 is on effectiveness This phase aims to obtain preliminary data on whether the drug works in people who have a certain disease or condition For controlled trials, patients receiving the drug are compared with similar patients receiving a different treatment--usually an inactive substance (placebo), or a different drug Safety continues to be evaluated, and short-term side effects are studied. Typically, the number of subjects in Phase 2 studies ranges from a few dozen to about 300 7
    8. 8. Phase 3 At the end of Phase 2, the FDA and sponsors try to come to an agreement on how the large-scale studies in Phase 3 should be done How often the FDA meets with a sponsor varies, but this is one of two most common meeting points prior to submission of a new drug application The other most common time is pre-NDA, right before a new drug application is submitted Phase 3 studies begin if evidence of effectiveness is shown in Phase 2 These studies gather more information about safety and effectiveness, studying different populations and different dosages and using the drug in combination with other drugs The number of subjects usually ranges from several hundred to about 3,000 people 8
    9. 9. 9
    10. 10. Phase 4 Postmarketing study commitments are called Phase 4 commitments – studies required of or agreed to by a sponsor that are conducted after the FDA has approved a product for marketing The FDA uses postmarketing study commitments to gather additional information about a products safety, efficacy, or optimal use 10
    11. 11. 11
    12. 12. Examples of Regulatory Approvals: USFDA 12
    13. 13. Goals of NDA Review Whether the drug is safe and effective in its proposed use(s), and whether the benefits of the drug outweigh the risks. Whether the drugs proposed labeling (package insert) is appropriate, and what it should contain. Whether the methods used in manufacturing the drug and the controls used to maintain the drugs quality are adequate to preserve the drugs identity, strength, quality, and purity. 13
    14. 14. Drug Review Steps 1. Preclinical (animal) testing. 2. An investigational new drug application (IND) outlines what the sponsor of a new drug proposes for human testing in clinical trials. 3. Phase 1 studies (typically involve 20 to 80 people). 4. Phase 2 studies (typically involve a few dozen to about 300 people). 5. Phase 3 studies (typically involve several hundred to about 3,000 people). 6. The pre-NDA period, just before a new drug application (NDA) is submitted. A common time for the FDA and drug sponsors to meet. 7. Submission of an NDA is the formal step asking the FDA to consider a drug for marketing approval. 8. After an NDA is received, the FDA has 60 days to decide whether to file it so it can be reviewed. 9. If the FDA files the NDA, an FDA review team is assigned to evaluate the sponsors research on the drugs safety and effectiveness. 10. The FDA reviews information that goes on a drugs professional labeling (information on how to use the drug). 11. The FDA inspects the facilities where the drug will be manufactured as part of the approval process. 12. FDA reviewers will approve the application or find it either "approvable" or "not approvable." 14
    15. 15. Review Process Once a new drug application is filed – an FDA review team evaluates whether the studies the sponsor submitted show that the drug is safe and effective for its proposed use Team consists of medical doctors, chemists, statisticians, microbiologists, pharmacologists, and other experts No drug is absolutely safe; all drugs have side effects – "Safe" in this sense above means that the benefits of the drug appear to outweigh the risks. The review team – analyzes study results – looks for possible issues with the application e.g., weaknesses of the study design or analyses may agree with the sponsors results and conclusions, or may need additional information to make a decision Each reviewer prepares a written evaluation containing conclusions and recommendations about the application These evaluations are then considered by team leaders, division directors, and office directors, depending on the type of application 15
    16. 16. Clinical Trials: Testing Medical Products in Humans Clinical studies, test potential treatments in human volunteers to see whether they should be approved for wider use in the general population – A treatment could be a drug, medical device, or biologic, such as a vaccine, blood product, or gene therapy – A new treatment may or may not be “better” – Complete and accurate research – Protection and well being of participants Ethics, consent, audit – Documentation 16
    17. 17. Clinical Trials.. Drug studies in humans can begin only after an IND is reviewed by the FDA and a local institutional review board (IRB) The board is a panel of scientists and non- scientists in hospitals and research institutions that oversees clinical research 17
    18. 18. Institutional Review IRBs approve the clinical trial protocols – the type of people who may participate in the clinical trial – the schedule of tests and procedures – the medications and dosages to be studied – the length of the study – the studys objectives – other details IRBs make sure the study is – acceptable – participants have given consent – Participants are fully informed of their risks – researchers take appropriate steps to protect patients from harm 18
    19. 19. Data There are legal and ethical reasons for reporting all relevant data collected during the drug development process Some reporting strategies already exist in the 1988 Guidelines, ICH E3 and E9 Electronic Submissions and desktop review capabilities will help all of us make better use of clinical data in NDA’s There may be better strategies and these should be considered 19
    20. 20. Source: MDS, New Zealand 20
    21. 21. What does FDA Look for? FDA approves a drug application based on – Substantial evidence of efficacy & safety from “adequate and well-controlled investigations” – A valid comparison to a control – Quantitative assessment of the drug’s effect (21 CFR 314.126.) The design of cancer trials intended to support drug approval is very important 21
    22. 22. Adequate and Well-Controlled Studies Because the course of most diseases is variable, you need a control group, a group treated just like the test group, except that they don’t get the drug, to distinguish the effect of the drug from spontaneous change, placebo effect, observer expectations 21 CFR 314.126 describes the following controls – Placebo – No treatment – Dose response – Active control Superiority of non-inferiority – Historical Placebo, dose response or superiority are usually convincing studies 22
    23. 23. Adequate and Well-Controlled Studies.. Minimization of Bias: a unidirectional tilt favoring one group, i.e., a non-random difference in how test and control group are selected, treated, observed, and analyzed – These are the 4 main places bias can enter Remedies: – Blinding (patient and observer bias) – Randomization (treatment and control start out equal) – Careful specification of procedures and analyzes in a protocol to avoid Choosing the most favorable analysis out of many (bias) Having so many analyses that one is favorable by chance (multiplicity) Source: RJ Temple, US FDA, Unapproved Drugs Workshop January 2007 23
    24. 24. Design Concepts Difference in Clinical Efficacy (Є) Non-Inferiority Superiority +δ 0 Equivalence -δ Inferiority Non-Superiority Equality δ = Meaningful Difference 24
    25. 25. Purposes of Active Trials The purpose of an active control trial could be to demonstrate that a new experimental treatment is either superior to the control equivalent to the control, or non-inferior to the control superior to a virtual placebo 25
    26. 26. Study Design: Approaches Randomised Controlled Trials (RCT) most preferred approach – Demonstrating superiority of the new therapy Other approaches – Single arm studies (e.g., Phase II) e.g., when many complete responses were observed or when toxicity was minimal or modest – Equivalence Trials – No Treatment or Placebo Control Studies – Isolating Drug Effect in Combinations – Studies for Radiotherapy Protectants and Chemotherapy Protectants 26
    27. 27. Randomized Clinical Trials Gold standard in Phase III Single centre CT – Primary and secondary indications – Safety profile in patients – Pharmacological / toxicological characteristics Multi-centre CT – Confirmation of the above – Effect size – Site, care and demographic differences – Epidemiological determination – Complexity – Far superior to meta-analyzed determination of effect 27
    28. 28. Placebo Control Equality Trials No anticancer drug treatment in the control arm is unethical Sometimes acceptable – E.g., in early stage cancer when standard practice is to give no treatment – Add-on design (also for adjuvants) all patients receive standard treatment plus either no additional treatment or the experimental drug – Placebos preferred to no-treatment controls because they permit blinding – Unless very low toxicity, blinding may not be feasible because of a relatively high rate of recognizable toxicities 28
    29. 29. Reasons for Active Control 1. Ethics – For trials involving mortality or serious morbidity outcome, it is unethical to use placebo when there are available active drugs on the market 2. Assay sensitivity – In trials involving psychotropic drugs, placebo often has large effect. An active control is sometime used to demonstrate that the trial has assay sensitivity 3. Comparative purpose – To show how the experimental drug compares to another known active drug or a competitor 29
    30. 30. Non-Inferiority Trials New drug not less effective by a predefined amount, the noninferiority (NI) margin – NI margin cannot be larger than the effect of the control drug in the new study – If the new drug is inferior by more than the NI margin, it would have no effect at all – NI margin is some fraction of (e.g., 50 percent) of the control drug effect 30
    31. 31. Drug or Therapy Combinations Use the add-on design – Standard + Placebo – Standard + Drug X Effects seen in early phases of development – Establish the contribution of a drug to a standard regimen – Particularly if the combination is more effective than any of the individual components 31
    32. 32. What to Measure? Primary outcome measure: The health parameter measured in all study participants to detect a response to treatment. Conclusions about the effectiveness of treatment should focus on this measurement. Secondary outcomes measure: Other parameters that are measured in all study participants to help describe the effect of treatment. Baseline variables: The characteristics of each participant measured at the time of random allocation. – This information is documented to allow the trial results to be generalised to the appropriate population/s – Specific characteristics associated with the patient’s response to treatment (such as age and sex) are known as prognostic factors 32
    33. 33. What to Measure? E.g., Cancer Trials Time to event end points – Survival – Disease free survival – Progress (of disease) free survival Objective response rates – Complete – Partial – Stable disease – Progressive disease Symptom end points Palliation QoL 33
    34. 34. Cancer Trials – End PointsEndpoint Evidence Assessment Some Advantages Some DisadvantagesSurvival Clinical benefit • RCT needed • Direct measure of • Requires larger and • Blinding not benefit longer studies essential • Easily measured • Potentially affected by • Precisely crossover therapy measured • Does not capture symptom benefit • Includes noncancer deathsDisease-Free Surrogate for • RCT needed • Considered to be • Not a validated survivalSurvival (DFS) accelerated • Blinding clinical benefit by surrogate in most settings approval or preferred some • Subject to assessment regular • Needs fewer bias approval* patients and shorter • Various definitions exist studies than survival 34
    35. 35. Interim Analysis 35
    36. 36. Interim analysis after each new response or group of responses an interim analysis is performed ⇓ enough evidence to stop the trial or continue the trial → continuous sequential or group sequential analysis 36
    37. 37. Why interim analyses? Ethics: superiority of a treatment Safety: inferiority of a treatment / toxicity of a treatment Economy: = costly therapy = no clinically relevant difference in effect between treatments 37
    38. 38. False Positives in Interim Analyses Interim analysis for a trial in non-Hodgkins lymphoma; n=130, IA after enrolment of each 25 patients Response Rate Response Rate CP CVP Analysis 1 3/14 5/11 1.63 Analysis 2 11/27 13/24 0.92 Analysis 3 18/40 17/36 0.04 Analysis 4 18/54 24/48 3.25 0.05<P<0.1 Analysis 5 23/67 31/59 4.25 0.016<P<0.05 CP=Cytoxan-prednisone CVP=Cytoxan-vincristine-prednisone Source: Stuart J. Pocock, Clinical Trials 38
    39. 39. Interim Analysis of Data 2 Looks 3 Looks How many times 0.05 0.05 can you look into Nominal Pvalue Nominal Pvalue the data? 0.03 0.03 o pocock o ob+fle 0.01 0.01 o fle+har+ob 0.0 0.0 1 2 1 2 3 Look Look 4 Looks 5 Looks 0.05 0.05 Type 1 error at kth Nominal Pvalue Nominal Pvalue test is NOT the 0.03 0.03 same as the 0.01 0.01 nominal p value 0.0 0.0 for the kth test 1 2 3 4 1 2 3 4 5 Look Look 39
    40. 40. Considerations for IA – Stopping rules for significant efficacy – Stopping rules for futility – Measures taken to minimize bias – A procedure/method for preparation of data for analysis – Data has to be centrally pooled, cleaned and locked – Data analysis - blinded or unblinded? – To whom the interim results will be submitted? DSMB Expert Steering Group – What is the scope of recommendations from IA results? – Safety? Efficacy? Both? Futility? Sample size readjustment for borderline results? 40
    41. 41. 41
    42. 42. Equality Designs(e.g., 2-Sample, Parallel) H0 : Є = 0 HA : Є ≠ 0 Reject H0 when p1— p2 ˆ ˆ > z/2 √p1(1—p1)/n1 + p2(1— p2)/n2 ˆ ˆ ˆ ˆ Where p1— p2 are true mean response rates ˆ ˆ from Test & Control 42
    43. 43. Superiority/Non-Inferiority Designs(e.g., 2-Sample, Parallel) H0 : Є ≤ δ HA : Є > δ … Superiority H0 : Є ≥ δ HA : Є < δ … Non-Inferiority Reject H0 when p1— p2 – δ ˆ ˆ > z √p1(1—p1)/n1 + p2(1— p2)/n2 ˆ ˆ ˆ ˆ 43
    44. 44. Survival Data – The Kaplan-Meier Estimator 1.0 0.75 Survival 0.50 0.25 ~80% Patient will survive beyond 0.35 0.0 years Time (Year) 0.0 0.2 0.4 0.6 0.8 1.0 44
    45. 45. Include all Patients: ITT It can be justified to look at data and drop the “outliers”, poor compliers, inappropriately entered patients It is even plausible and acceptable as an academic principle But if not rigorously planned, such exclusion can lead to bias Even when planned, it can lead to imbalances that also introduce bias 45
    46. 46. Intent-to-Treat Principle All randomized patients Exclusions on prespecified baseline criteria permissible – also known as Modified Intent-to-Treat Confusion regarding intent-to-treat population: define and agree upon in advance based upon desired indication Advantages: – Comparison protected by randomization Guards against bias when dropping out is realted to outcome – Can be interpreted as comparison of two strategies – Failure to take drug is informative – Refects the way treatments will perform in population Concerns: – “Difference detecting ability” 46
    47. 47. Per Protocol Analyses Focuses on the outcome data Addresses what happens to patients who remain on therapy Typically excludes patients with missing or problematic data Statistical concerns: – Selection bias – Bias difficult to assess 47
    48. 48. Intent to Treat & Per Protocol Analyses Both types of analyses are important for approval Results should be logically consistent Design protocol and monitor trial to minimize exclusions Substantial missing data and poor drug compliance weaken trial’s ability to demonstrate efficacy 48
    49. 49. Missing Data Protocol should specify preferred method for dealing with missing primary endpoint – ITT e.g., treat missing as failures e.g., assign outcome based on blinded case-by-case review – Per Protocol e.g., exclusion of patients with missing endpoint 49
    50. 50. Multiple Analyses The two main problems introduced by multiple analyses are – firstly, the increased probability of detecting intervention effects where none exist (“false positives” owing to multiple comparisons — type I errors) – secondly, the limited capability (“power”) of trials to detect a true treatment effect in secondary outcomes if not enough participants are enrolled to show a statistically significant difference in these outcomes (“false negatives” — type II errors) 50
    51. 51. Assay Sensitivity The critical question is whether a non-inferiority trial, for example, could distinguish the control from placebo and shown an effect of the non-inferiority margin If it could – the trial is said to have said to have “assay sensitivity” If a trial a trial has assay sensitivity – then if C-T < M, T had an effect If the trial did not have assay sensitivity – then even if C-T < M, we have learned nothing If you don’t know whether the trial had assay sensitivity, finding no difference between C and T means – Both drugs were effective – Neither drug was effective Source: RJ Temple, US FDA, Unapproved Drugs Workshop 51
    52. 52. Assay Sensitivity: Major Problem in NI In a non a non--inferiority trial, the trial itself does not show the study’s ability to distinguish active from inactive therapy. Assay ability to distinguish active from inactive therapy Assay sensitivity must, therefore, be deduced or assumed, based on 1. historical experience showing sensitivity to drug effects 2. a close evaluation of study quality and, particularly important 3. the similarity of the current trial to trials that were able to distinguish the active control drug from placebo In many symptomatic conditions, such as depression, pain, allergic rhinitis, IBS, angina, the assumption of assay sensitivity cannot be made Source: RJ Temple, US FDA, Unapproved Drugs Workshop January 2007 52
    53. 53. Data Safety and Monitoring Board (DSMB) All trials may not need a DSMB DSMB Membership – Medical Oncologist, Biostatistician and Ethicist Statistical expertise is a key constituent of a DSMB Three Critical Issues – Risk to participants – Practicality of Periodic Review of a Trial – Scientific Validity of the Trial 53
    54. 54. Conclusions Randomized Clinical Trilas are very sophisticated and complex Principal Investigators’, Trial Monitors’and Biostatisticians’ roles are invaluable Higher Phase (Phases 2, 3) Clinical Trials provide for the main evidence of efficacy and safety Clinical data is very complex (confounded, censored, skewed, often fraught with missing data point), therefore, proper hypothesization and statistical treatment of data are required Prospective RCTs are usually the preferred approach for evaluation of new therapies 54
    55. 55. Conclusions Clinically meaningful margins must be well defined in Control trials prospectively – Superiority and non-inferiority margins must not be confused Two or one-sidedness of α should also be prospectively defined Power must be adequate Variance must be analysed using the right model Strategy for dealing with multiple end points must be prespecified – Too many end points ot tests will increase the false positive (α) error Sometimes (e.g., in equality trials) statistically significant results may not be medically significant Data censoring or skewed data must be well defined – E.g., time to event data 55
    56. 56. Conclusions Randomisation and blinding offer a robust way to remove bias in end-point estimations Data must be accurately captured without any bias and analysed by prospectively described methods Interim analysis should carefully plan ‘ spending’ function and the outcome measure to be analyzed Final analysis should be done carefully, independently and meaningfully (medical as well as scientific) Choose clinically relevant delta Design, conduct, and monitor trials to minimize missing data and poor compliance to drug Analysis – Both intent-to-treat and per protocol analyses should be conducted – Sensitivity analyses Outstanding medical and statistical issues must be brought to the fore. 56
    57. 57. Thank you 57
    58. 58. Order of the Topics Clinical Trials – Regulatory phases 1,2,3 & 4 Review of Clinical Trials Data Requirements Study Design Considerations Controls – Placebo, Active, NI Assay Sensitivity, Multiplicity of Analyses Interim Analysis Intent to Treat Analysis Final Analysis Conclusions 58