perspectives of safety issues in drug development industry statistical perspective timothy costigan,...
Post on 27-Mar-2015
214 Views
Preview:
TRANSCRIPT
Perspectives of Safety Issues in Drug Development
Industry Statistical Perspective Timothy Costigan, Ph.D.
Wei Shen, Ph.D.Eli Lilly and CompanyIndianapolis, Indiana
2003 FDA/Industry Statistics Workshop
Abstract
We review the objectives and requirements of safety assessment through the phases of drug development. We summarize recent regulatory guidelines and initiatives. We discuss how to communicate results of safety analyses through efficient use of graphical displays and data reduction. We also discuss multiplicity issues relating to safety data.
Overview of Presentation
I. Background and General Considerations
II. Recent Regulatory Guidances and Initiatives
III. Presentation of Integrated Safety Data – Synthesis, Data Reduction, Graphical Displays
IV. Multiplicity Issues in the Interpretation of Safety Data
V. Appendix: Assessing safety during the Phases of Drug Development
I. Background and General Considerations
Risk Assessment and Management - Context
Safety Data Collected in Clinical Trials
Objectives of Safety Data Analysis
Major Safety Issues in Drug Development
Adequacy of Long Term Exposure and Breadth of Effects Studied
Risk Benefit Interpretation of Safety and Efficacy – General Safety Effects
Risk Assessment and Management
Risk assessment is the process of identifying, estimating, and evaluating the nature and severity of risks associated with a product (draft FDA concept paper 2003). Http://www.fda.gov/cder/meeting/riskManageII.htm• Occurs throughout a product’s lifecycle• Comprehensive description of safety required by the Food,
Drug and Cosmetic Act• Emphasis on a Management Plan
Safety Data Collected in Clinical Trials
Standard Safety Data (Gait et al, 2000 DIA J, see appendix)
Exposure and reasons for discontinuation
Adverse events (SAE, DCAE, TEAE)
Clinical laboratory measurements
Vital signs
ECGs
Special Safety Data
Based on indication, class of medication, findings
Objectives of Safety Data Analysis
Identify and understand safety issues as early as possible
Identify risk factors related to increased toxicity and lack of efficacy
Consequences
Allow clinicians to assess the risk/benefit of therapies for classes of patients
Patient safety maintained overall and within special populations
Responsibilities and Goals
Regulatory Agency and Sponsor(s) Joint Responsibility
Drugs used in clinical trials are safe
Marketed drugs are safe and efficacious
Avoid product recalls and major label changes whichcould have been foreseen
Goal: each patient receives the therapy relative to efficacy and dose with the best risk/benefit profile
Adhere to the Declaration of Helsinki
Some Major Safety Issues in Drug Development
Hepatotoxicity
QT interval prolongation – surrogate for a major safety issue
Adverse events with etiology indicative of toxicity
Adequacy of Long Term Exposure and Breadth of Effects Studied
It is important to have adequate safety data in clinical studies of sufficient duration prior to approval
Guidance – minimum required prior to submission
Extension active treatment phases
Continued long term safety assessment during regulatory review, post marketing clinical study commitments
Breadth of effects studied overall and in special populations– Clinical pharmacology package is a major component (see Appendix)
Risk Benefit Interpretation of Safety and Efficacy
Safety and efficacy need to be considered jointly in a risk benefit analysis, particularly when comparing therapies
Adherence for all communications to physicians
Lack of efficacy after switching from an effective therapy is often a greater general safety concern than direct side effects, particularly when comparing therapies
Risk Management may address
Managing side effects
Appropriately monitoring patients who switch therapies
II. Recent Regulatory Safety Guidance and Initiatives
Current - Final
Assessment of hepatotoxicity
Proposed - Draft
Assessment of QT interval prolongation
Risk management plan
Assessment of Hepatotoxicity
FDA PhRMA Sponsored Workshop• Feb 12-13, 2001
Http://www.fda.gov/cder/livertoxThree White papers
Pre and Non ClinicalClinicalPost Marketing Considerations
Model for standardization of analysis
Clinical interpretation of liver enzymes discussed
Special laboratory analysis (as opposed to standard)
platelets, neutrophils, immunogenicity, immunotoxicity
Liver Signals
Hy’s Law
Individual Studies
Cumulative Meta Analysis
Integration of Studies with > 2 weeks exposure• By Study Design
– Placebo Controlled– Active Control– Open Label
• By Duration of Exposure• By frequency of Evaluation
Assessment of QT Interval Prolongation
Assessed in clinical pharmacology and clinical studies
New draft guidance: perform a large clinical pharmacology study to assess Qt prolongation very early in product development
Http://www.fda.gov./cder/calendar/meeting/qt4jam.pdf
January 2003 workshop, “The Clinical Evaluation of QT Interval Prolongation and Proarrhythmic Potential for Non-antiarrhythmic Drugs” which was sponsored by DIA, in collaboration with NASPE, FDA, and Health Canada.
Assessment of QT Interval Prolongation
Industry working group reviewing draft guidance
Recent advisory committee for two products
Midwestern Biompharm Workshop, May 2003 ‘Analysis of QT/QTc Interval Data’, Marilyn Agin chair
Shah A and Hajian G (2003), ‘A maximum likelihood approach to estimating the QT correction factor using mixed effects model’, Statis Med, 1901-1909
Draft QT Guidance: Summary
Super-therapeutic doses in healthy volunteers, above highest possible attainable exposure
Evaluation by clinical experts rather than computer readings
Evaluation at times covering maximum concentration of agent and metabolites
Inclusion of a positive control for assay sensitivity
Clinical relevant changes described (< 5 ms increase not associated with Torsades de Pointes)
Prominence given to outlier analysis and concentration effects
Upper 90% confidence interval of change versus placebo < 10ms
Draft QT Guidance: Technical Issues
QT interval must be corrected for heart rate changes
Standard corrections: Fridericia
Bazzett (for historical purposes)
Individual corrections
Population based corrections
Model based ANCOVA correction (Dmitrienko A, Smith B, “Repeated measures models analysis in the analysis of QT interval. Pharmaceutical Statistics, 2003, to appear)
Averaging using at least 3 measurements recommended
Issues: study timing, measurement methodology, positive control
Risk Management Plan
Proactively identify safety issues as soon as possible
Labeling (USPI and sometimes PPI) and monitoring spontaneous adverse events is usually adequate
Key additional component: Physician and patient education
Superiority over relying mainly on black box warnings
Metrics to evaluate performance
Sometimes establishes additional studies
Clinical Studies, Analyses and Components of Plan
Post marketing studies to investigate rare events
Post marketing studies to assess safety in special populations not adequately studied in clinical studies
Interaction studies to assess effects of administering contraindicated medications concurrently - clinical pharmacology studies with healthy volunteers
Assessment of potential impact of off-label use
Product naming, tamper proof packaging, counterfeit protection
III. Presentation of Integrated Safety Data
Context of Presentation
Transparency, Consistency, Detail
Data Reduction and Synthesis
Graphical displays
Descriptive versus inferential statistics
Context of Presentation of Safety Data
Degree of detail, sophistication of analysis and language used should be determined by target audience
Regulatory Authorities for Approval
Clinical Investigator Brochures
Physician Education
Patient Education
Information Based Medicine
Communication of risks is an important aspect of management
Data Reduction and Synthesis
Treatment Emergent Adverse Events Signal Detection (See Appendix: MedDRA) – large integrated database
Incidence > 2% for treated patients and > 1% greater for treated patients than for placebo patients
Simple and transparent
Consistency with other TEAE signal detection criteria (relatedness, statistical significance)
Adverse events within pre-specified clinically relevant clusters
Incidence of selected serious events per patient year exposure across studies of various designs
Supportive Analyses of Selected Target
Further focus on selected target (for selected audiences)
Related to study drug
Severity - % patients with severe targeted event
% targeted event classified as severe
Incidence and prevalence by visit
Concomitant medications taken for event
Presentation of Safety Data: Graphics
Typically, safety data are summarized in tabular forms by therapy groups
Individual listing by subject
One-page patient summary
Graphical presentation of safety data can add value • Kaplan-Meier curves commonly used to describe time to event
data
Onset of Adverse Events
05
101520253035404550
0 to 3 3 to 6 6 to 9 9 to 12
Placebo
Low dose
High dose
Onset of AE by time of dose (hours)
• High dose is associated with an early onset of AE
% o
f O
ccu
rren
ces
Prevalence and Incidence of AE by Visit
0
1
2
3
4
5
6
7
Placebo LowDose
HighDose
Placebo LowDose
HighDose
Placebo LowDose
HighDose
Visit 3(4 weeks) Visit 4(8 weeks) Visit 5(12 weeks)
Per
cent
Persistent OccurrenceFirst Occurrence
• AE decreases in frequency by visit, particularly for new occurrence.
Blood Pressure Change Over Time
90
100
110
120
130
140
0 6 12 18 24 30 36 42 48
Time relative to dosing (h)
Mea
n A
mbu
lato
ry s
ysto
lic
BP
(mm
Hg)
Placebo
Treatment
• Treatment decreases SBP, effect lasts 12 hours.
QTc Change vs Plasma Concentration
-60
-40
-20
0
20
40
0 50 100 150 200 250 300 350 400 450 500
Serum Concentration (ng/mL)
QTC
Cha
nge
(mse
c)
• Lack of association between QTc change and serum concentration
Advantage of Graphical Analysis
Visual simplicity
Describe the entire distribution of data, including outliers - Box plots
Graphical summary of safety data is complimentary to tables and listings – use to illustrate most important features of data (observed patterns, lack of pattern)
Descriptive versus Inferential Statistics
Always present easily interpretable clinical summaries based on descriptive statistics (with basic statistical analyses such as Fisher’s exact test if appropriate)
More sophisticated inferential statistical analyses are sometimes warranted (with appropriately large sample size - reduce type II error rate)
Special safety studies
Newly proposed QT pharmacology study
Special laboratory analysis
IV. Multiplicity Issues Interpretation of Safety Data
Correction for multiplicity philosophy and strategy for efficacy and safety outcomes
QTc Example, Hypothetical example
Gate keeping strategy
Assessment of safety in subgroups
Multiplicity issues in meta analysis
Multiplicity adjustments for tolerance and adverse events
Identifying Optimal Treated Population
References
Multiplicity adjustment philosophy
Dunnett C and Goldsmith C “When and how to do multiple comparisons”, in Statistics in the Pharmaceutical industry, Buncher and Tsai Eds, 1981, Marcel Decker
Efficacy – often Safety – usually not
Still need to ask the question
Efficacy – always Safety – never Not correct
When multiplicity adjustments are made for safety outcomes present both unadjusted and adjusted p-values – aids in synthesis (type II error still primary concern)
When?
Usually not for standard safety data
More frequently for special safety data – pre-specified hypotheses in special safety studies – with adequate sample size to control type II error
Determining factors
Active versus placebo controlled
Whether non-inferiority design
Nature of outcome
Phase of Study
Hypothetical example
Special safety study undertaken due to non-clinical finding
Regulatory agency and sponsor design a large study
Primary endpoint main analysis (p>.5)
Primary endpoint supportive analysis (p>.3)
Secondary endpoints 2, 3 main analysis (p>.2)
Secondary endpoint 2 supportive analysis (p>.3)
Secondary endpoint 3 supportive analysis (p=.045)
Gate keeping strategy
Dmintrinko A, Offen W and Westfall P (2003) ‘Gate keeping strategies for clinical trials that do not require all primary effects to be significant’, Statist Med, 2387-2400
Specify a hierarchy of outcomes in the protocol and use a stage-wise testing strategy which controls the false error rate by the closed testing principle
Main use is assessing secondary efficacy outcomes
Is applicable in some special safety scenarios
QT example
Many ways of analyzing one outcome; QTc
Multiplicity adjustments probably not appropriate, but multiple testing can result in false positives
Synthesis is still needed
Pre-specify a primary correction
Present results based on all corrections in the same table
Emphasize pre-specified primary correction and the method that fits the data best (Correlation with RR closest to 0)
Give less emphasis to Bazzett (historical correction) – explain outliers due to large RR changes
Assessment of Safety in Subgroups
The correlation coefficients for test statistics for a single outcome in overlapping high (low) risk subgroups depends only on the sample sizes of the subgroups and the sample size of their intersection
Moreover in these situations multivariate probabilities can be easily calculated
Consequently model based multiplicity adjustments can be obtained to complement adjustments based on resampling
Multiplicity Considerations in Meta Analysis
Separate meta analyses should be performed based on study design and study duration
Consistency of results across studies should be examined
When meta analysis is performed in which not all studies contain all doses then a conservative test procedure based on direct comparisons is obtained when one includes study in the model and uses Dunnett’s test in SAS
Multiplicity adjustments for tolerance and adverse events
Novel Approaches for analyzing Clinical Safety/ Adverse event Data, Midwestern Biompharm Workshop, May 20, 2003 Devan Mehrotra organizer
“Multiplicity considerations in evaluating safety in Clinical Trials”, Joe Heyse and Devan Mehrotra
Active control setting
1. SAE
2. Pre-specified TEAE with expected high incidence
3. Remaining (multiplicity adjustment based on resampling and false discovery rate)
Identifying the optimal treated population
Therapy A and Therapy B are only treatments of a disease
Populations A Therapy A superior
B Therapy B superior
E Both equally efficacious
N Neither superior to placebo
Goal: Subscribe therapy A to all population A patients and to no population B or N patients
Identifying the optimal treated population: strategies
Competitive non-inferiority for efficacy and superiority for selected TEAE
Clinically relevant efficacy overall
Clinically relevant efficacy in selected subgroups
Diabetes, hypertension, hyperlipedemia
Various indicators of less severe disease
Competitive non-inferiority for efficacy and superiority for selected TEAE in selected subgroups
Conclusion
Recent guidelines will help ensure patient safety, especially with respect to hepatotoxicity and QT interval prolongation
Risk management occurs throughout a product’s life cycle and involves the effective communication of risks
Special safety data should be analyzed and interpreted differently than standard safety data, including multiplicity considerations
Joint assessment of safety and efficacy: references
Bryant J and Day R (1995) ‘Incorporating toxicity considerations into the design of two-stage phase II trials, Biometrics, 1372-1383
Jennison C and Turnbull B (1993), ‘Group sequential tests for bivariate response: interim analyses of clinical trials with both safety and efficacy endpoints, Biometrics, 741-752
Letierce A, Tubert-Bitter P, Kramar A and Maccario J (2003) ‘Two treatment comparison based on joint toxicity and efficacy ordered alternatives in cancer trials’ Statist Med, 859-868
Identifying the optimal treated population: references
Bristol D, ‘p-value adjustments for subgroup analyses’, J Biopharm Stat, 1997, 313-321.
Byar D and Corle R,’Selecting optimal treatments in clinical trials using covariate information’, J Chron Dis, 1977, 445-459
Gail M and Simon R, ‘Testing for quantitative interactions between treatment effects and patient subsets’ JASA, 1985, 361-372
Koch GG, Stuart A and Gansky MS, “Statistical considerations for multiplicity in confirmatory protocols, DIA J, 1996, 523-533
Pocock S, Assmann S, Enos L and Kasten L (2002) ‘Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems, Stat Med, 2917-30
V. Appendix: Assessing Safety during the phases of development
Non-clinical and Toxicology Studies
Clinical Pharmacology Studies
Clinical Studies
Post Approval Studies
Interconnectedness of phases
Predominance of Safety
Pre-clinical and Toxicology Studies
Animal and Laboratory Studies
Target Organ Toxicity
Liver is a major target organ
Target reaction
QT prolongation – HERG assay study
Consequences of findings
End development, clinical hold, design special safely study, implement special monitoring (additional laboratory measurements, post marketing study/surveillance)
Standard Clinical Pharmacology Studies
PK and PD Studies
characterize exposure (Cmax, tmax, half life, accumulation)
Food effect study
Alcohol effect study
PK in special populations – hepatic, renal, elderly
Interaction of therapy with concomitant medications which inhibit or induce enzymes involved in metabolism
Objective - characterize maximum exposure (increased toxicity)
Objective - characterize minimum exposure (lack of efficacy)
Clinical Pharmacology Studies with Safety Outcomes
Effects on safety outcome (adverse event, laboratory analyte,vital sign)
Aspirin interaction study– Bleeding time- Healthy volunteers
Antihypertensive medication interaction study – Blood pressure outliers
Exercise tolerance – Patients with stable angina
Clinical Pharmacology Studies under Extreme Conditions
Super-therapeutic dose –signal detection
Healthy volunteers, no need to match target disease population
Interpret in relation to phase 2/3 studies
Additional clinical pharmacology studies under less extreme conditions may be useful
Clinical Studies Prior to ApprovalReference
Gait JE, Smith S and Brown S, “Evaluation of safety data from controlled clinical trials: the clinical principles explained”, DIA Journal, Vol 34, 273-287, 2000.
Disposition
Exposure
Adverse events: SAE, discontinuations due to AE, treatment emergent adverse events (TEAE)
Laboratory safety data
Safety profile for patients with targeted TEAE versus without
Adverse Events: MedDRA (new standard)
MedDRA ,a hierarchical dictionary which combines features of several dictionaries, is superior to COSTART which has inadequate specificity – actual term, LT, PT, HLT, HLGT, SOC
Requires adequate site training for actual term recording
Review of coding hierarchy relevant to each therapeutic area (TA) for subtleties in hierarchy
Sometimes coding is too specific for required purpose and TA specific clustering required
Regularly updated – logistical challenges (integration)
A listing of all TEAE sorted by preferred term is helpful (in addition to sorted by patient)
Adverse Event Analysis References
1996 FDA/Industry Workshop, 1996 Biopharmaceutical Report, Vol 4, No 3
1996 Biopharmaceutical Report, Vol 4, No 2, B Northington, ‘A review of issues in the collection and reporting of adverse events’ and L Tremmel ‘Describing Risk in Long-Term Clinical Trials’
O'Neil R “Statistical analysis of adverse events from clinical trials, with emphasis to serious adverse events’ Drug Inf J, 1987, 9-20
Post Approval DataTypes of Studies
Spontaneous Adverse Event Reporting
Case Series Analysis, Registry for rare events
Case Crossover Study -Within patient analysis based on exposure status while experiencing a targeted event relative to overall extent of exposure (Encyclopedia of Biostatistics, Wiley; Pharmacoepidemiology, Overview)
Post marketing regulatory commitments
dictated from results of earlier phases
Other post-marketing studies - useful safety assessment in real world clinical practice (even when not primary objective)
top related