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Introduction to the Topics: Setting the Stage. Advisory Committee for Pharmaceutical Science and Clinical Pharmacology March 18-19, 2008 Rockville, Maryland. Lawrence J. Lesko, Ph.D., FCP Director, Office of Clinical Pharmacology Center for Drug Evaluation and Research

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slide1

Introduction to the Topics: Setting the Stage

Advisory Committee for Pharmaceutical Science and Clinical PharmacologyMarch 18-19, 2008Rockville, Maryland

Lawrence J. Lesko, Ph.D., FCP

Director, Office of Clinical Pharmacology

Center for Drug Evaluation and Research

Food and Drug Administration

Silver Spring, Maryland, USA

topic 1 clinical pgx rationale for exploratory analysis in early drug development
Topic 1. Clinical PGx – Rationale for Exploratory Analysis in Early Drug Development
  • Differences in in pharmacokinetics (PK) and systemic exposure of a drug, or its active metabolite, are related to genetic variation in one or more CYP450 enzymes or transporters
  • Differences in systemic exposure can potentially lead to significant changes in relevant biomarkers and/or clinical endpoints
  • Genetic polymorphisms in drug targets may potentially cause important differences in pharmacodynamics (PD)
  • There are various options available to evaluate differences in PK and/or PD due to gene variants
  • Genotyping may potentially be needed to stratify dosing in subsequent clinical trials
proposed draft clinical pgx guidance
Proposed Draft Clinical PGx Guidance
  • Objective To assist the pharmaceutical industry, who are conducting new drug development – or who are involved with relabeling of previously approved drugs – in how to assess the interindividual variability in PK and/or PD that may be caused by known polymorphisms in genes related to ADME and drug targets. Clinical PGx studies have implications for preparing informative drug labels.
why now
Why Now?
  • Numerous genomic biomarkers affecting PK and/or PD have now been well characterized
  • Cost-effective technology exists to explore lesser known gene variants affecting ADME and PD
  • Other FDA Clinical Pharmacology guidances refer to PK/PD related to PGx without being specific
  • European and Japanese regulatory authorities have published preliminary guidance on clinical PGx
  • ICH PGx WG at Step 5 on E15 and will continue to focus on harmonization topics
collection of dna for exploratory analysis in early drug development
Collection of DNA for Exploratory Analysis in Early Drug Development
  • Rationale
  • It is not always known why similar healthy volunteers or patients experience unusual exposures, serious adverse events or no clinical response following drug administration
  • Current thinking is that there may be a genetic basis for such differences
  • Clinical studies provide a major opportunity to collect and store biological samples for DNA analysis to investigate these differences
slide6
Topic 2. Quantitative Clinical Pharmacology: Drug-Disease Models As a Critical Path Research Opportunity
  • FDA is working with public-private partnerships, industry and academia to evaluate the feasibility of developing drug-disease models
  • The long-term goal is to develop models that can potentially be used to design better clinical trials, predict adverse events and optimize dosing
  • Drug-disease models have the potential to identify which subset of patients are most likely to receive benefit or be harmed
  • Drug-disease models may potentially improve productivity in therapeutic areas with high clinical trial failure rate
  • NSCLC represents one such model
pediatrics a pilot project for leveraging prior knowledge and quantitative clinical pharmacology
Pediatrics: A Pilot Project for Leveraging Prior Knowledge and Quantitative Clinical Pharmacology
  • ObjectiveTo find better ways to use existing knowledge about age-related PK and/or PD differences and quantitative methods to improve:1. dose selection for pivotal pediatric clinical studies2. other design features for pediatric clinical studies3. quality of pediatric written requests4. information about pediatrics in drug product labels
topic 3 renal impairment concept paper rationale for updating the 1998 guidance
Topic 3. Renal Impairment Concept Paper – Rationale for Updating the 1998 Guidance
  • Premarketing observations – renal impairment causes significant changes in systemic exposure beyond those drugs cleared only by renal pathways
  • Quantitative assessment of drug metabolism and/or transport in renal impairment may avoid potential advese events related to changes in drug PK and/or PD profiles
  • The 1998 guidance was silent on premarketing studies to assess how hemodialysis would influence drug exposure leading to uncertainty in dosing
  • An update of the guidance will assist the pharmaceutical industry, based on contemporary evidence and expert input, in conducting informative premarketing renal impairment studies
new concept paper guidance on clinical pharmacogenetics

New Concept Paper/ Guidance on Clinical Pharmacogenetics

Clinical Pharmacology Advisory Committee

Rockville, MD

March 18, 2008

Felix W. Frueh, PhD

Associate Director for Genomics

Office of Clinical Pharmacology

CDER/FDA

background
Background
  • Rapid increase in our understanding about the role of genetic variations in human germline DNA in inter-individual differences in exposure-response relationships:
    • while studying well characterized allelic variations in e.g. drug metabolizing enzymes, transporters, and certain drug targets allow us already today to address a sizeable amount of this variation,
    • new molecular technologies enable us to learn more about the pharmacokinetics (PK) and pharmacodynamics (PD) about therapeutics.
background cont d
Background, cont’d
  • FDA issued a “Guidance for Industry: Pharmacogenomic Data Submissions” in 2005. This guidance:
    • Creates a broadly applicable, general framework of regulatory aspects concerning the use of genomic and genetic biomarkers in drug development
    • Clarifies what type of genetic or genomic data needs to be submitted and when
    • Introduces a novel pathway (VGDS) for submitting early stage, exploratory data which is not ready for use in regulatory decision making
      • Encourages the conduct and submission of such data under the VGDS program
background cont d1
Background, cont’d
  • “Guidance for Industry: Pharmacogenomic Data Submissions” does not discuss in detail
    • The decision making process itself
    • The design of studies using pharmacogenetic information
    • The implications of the use of pharmacogenetic information on the drug label
scope
Scope
  • This new concept paper/ draft guidance on clinical pharmacogenetics will discuss
    • Our current view on whether or not clinical pharmacogenetic studies should be performed or not based on the amount of information at hand
    • General strategies to use pharmacogenetic information in drug development
    • The design of clinical pharmacogenetic studies
    • The implications of the results of these studies on the drug label
outline content
Outline/ Content
  • Introduction
  • General Strategies
  • Decision Tree for Integrating Pharmacogenetic Studies into the Drug Development Process
  • In Vitro Studies Evaluating Drug as Substrate for Polymorphic Genes to Guide Clinical Studies
  • Design of Clinical Pharmacogenetic Studies
  • Labeling Implications
  • References
outline content1
Outline/ Content
  • Introduction
  • General Strategies
  • Decision Tree for Integrating Pharmacogenetic Studies into the Drug Development Process
  • In Vitro Studies Evaluating Drug as Substrate for Polymorphic Genes to Guide Clinical Studies
  • Design of Clinical Pharmacogenetic Studies
  • Labeling Implications
  • References
general strategies
General Strategies
  • Exploring the feasibility of using pharmacogenetic information for adjusting the dose, or to identify responders/ non-responders requires the (prospective) collection of DNA samples
    • We recommend to collect and bank DNA samplesfrom all participants in clinical trials
  • This information should be used as early as possible and carried forward into the later stages of drug development as appropriate
    • We recommend to conduct pharmacogenetic studies in early drug development
considerations for conduct of pgt studies in early drug development
Considerations for Conduct of PGt Studies in Early Drug Development
  • Entry criteria based on genetics can be established as early as Phase 1 (e.g. when pre-clinical studies suggest that a molecule is metabolized by a polymorphic pathway, clinical pharmacokinetic studies should be performed in healthy volunteers to determine the differences in exposure related to genotype)
  • Phase 1 and 2A studies are often exploratory because of small sample sizes and lack of statistical significance
    • However, these can be important studies for hypothesis generation to define subsets for dosing or identify responders/ non-responders
  • Associations between a marker of interest and an outcome found in such studies generally require confirmation through study replication
  • Banked samples (from all stages of development) can also be important for exploring unexpected safety signals
decision tree
Decision Tree
  • Goal: To assist in the integration of pharmacogenetic studies early into the drug development process
slide19

Molecule (NME)

Pre-clinical: in vitro metabolism and/or transporter studies on well characterizedcandidate genes (e.g. CYP2C9, CYP2C19, CYP2D6, UGT1A1, etc.)

NME is a known substrateof a polymorphic enzyme: <25%of total metabolism or clearanceaffected

NME is a known substrateof a polymorphic enzyme: >25% of total metabolism

or clearance affected

NME is not a substrate, or unknownto be a substrate of a well characterized polymorphic enzyme

Collect DNA in Phase 1 – 2 studies

STOP:

Label appropriately

Collect DNA in Phase 1 – 2 studies

Screen of DNA for panel of gene variants of metabolism and transport and sources of PK or PD variability allows to generate hypothesis

Is difference between genotypes (PM vs. EM,or from an inhibitiondrug-interaction study) in PK important and based on dose-response or PK/PD data?

Select genotype-driven doses for Phase 2B – 3 or dose adjustment in label based on genotype

Yes

Yes

No

Consider

VGDS

Label appropriately

Collect DNA in future clinical trialsto evaluate outliers, study adverseevents or efficacy failures

No

Collect/ bank DNA for future exploratory or hypothesis generating studies

STOP

STOP:

Label appropriately

design of clinical pharmacogenetic studies purpose of studies
Design of Clinical Pharmacogenetic Studies – Purpose of Studies
  • The objective of clinical pharmacogenetics studies is to understand the importance of genetic factors in explaining and predicting inter-individual differences in drug exposure and response
  • Usually, this requires well-designed prospective studies to assess the pharmacokinetic and pharmacodynamic properties associated with genetic variations
design of clinical pharmacogenetic studies general considerations
Design of Clinical Pharmacogenetic Studies – General Considerations
  • A clinical pharmacogenetics study can be performed as an independent study or as an add-on to a larger clinical trial
  • The sample size of the study will depend on the purpose of the study, and the acceptable error rates (type 1 and type 2 errors) to define the variability
  • A specific clinical pharmacogenetic study may be warranted based on pre-clinical data
  • Studies can be conducted sequentially during drug development to refine the question and maximize the understanding of the biomarker
  • To be meaningful, clinical pharmacogenetic studies should be performed using analytically validated methods and include appropriate controls
design of clinical pharmacogenetic studies study populations
Design of Clinical Pharmacogenetic Studies – Study Populations
  • Phase 1 studies are usually conducted in healthy volunteers
    • However, under certain circumstances safety considerations may preclude the use of healthy volunteers (e.g. anticancer drugs)
  • Exclusion of subjects from clinical trials may be appropriate when it is known that subjects with certain genotypes would not respond favorably, or if there is a specific safety concern (e.g. due to high exposure)
  • Ethnicity may be an important co-variate for consideration in cases where allele frequencies vary between ethnicities and the effect of these variations may influence biological processes related to the therapy
question to the committee
Question to the Committee
  • It is proposed to collect DNA samples from all participants in clinical trials.
    • What issues or barriers should be addressed to facilitate routing collection of DNA samples?
    • When (under what circumstances, to what degree) should DNA be collected during drug development for use in exploratory analysis?
  • A decision tree depicting the integration of pharmacogenetic studies into the drug development process is proposed.
    • What comments and/or recommendations does the committee have on the scientific rationale and thought process embodied in the proposed decision tree?
  • Different study types for clinical pharmacogenetic studies are proposed.
    • What comments and/or recommendations does the committee have on the design of clinical pharmacogenetic studies and their proposed impact on subsequent clinical trials?
leveraging prior quantitative knowledge to guide drug development decisions

Leveraging Prior Quantitative Knowledge to Guide Drug Development Decisions

Joga Gobburu

Director, Pharmacometrics

Office of Clinical Pharmacology

OTS/CDER/FDA

jogarao.gobburu@fda.hhs.gov

Gobburu, Pharmacometrics

quantitative clinical pharmacology critical path opportunity
Quantitative Clinical Pharmacology: Critical Path Opportunity

Gobburu, Pharmacometrics

pharmacometrics or quantitative experimental medicine
Pharmacometrics (or Quantitative Experimental Medicine)
  • Science that deals with quantifying disease and pharmacology to influence drug development and regulatory decisions
  • Focus is on ‘learning’ rather than ‘confirmation’
  • Diverse Expertise Needed
      • Quantitative (Clinical) Pharmacologists, Clinicians, Statisticians, Bioengineers

Gobburu, Pharmacometrics

prior quantitative knowledge

Diverse

Expertise

FDA Data

  • Biology
  • Natural Progression
  • Placebo
  • Biomarker-Outcome
  • Pharmacology
    • Effectiveness
    • Safety
      • Early-Late
  • Preclinical-Healthy-Patient
  • Patient Population
  • Drop-out
  • Compliance

Physiology

Prior Quantitative Knowledge

Disease

Model

Drug

Model

Trial

Model

Disease-Drug-Trial models are collectively called as Disease Models

Gobburu, Pharmacometrics

prior quantitative knowledge1

Diverse

Expertise

FDA Data

Dose

Selection

Patient

Selection

Physiology

Prior Quantitative Knowledge

Disease

Model

Drug

Model

Trial

Model

Molecule

Screening

Trial Design

Gobburu, Pharmacometrics

fda disease models highlights
FDA Disease Models: Highlights

Gobburu, Pharmacometrics

fda disease models highlights1
FDA Disease Models: Highlights

Gobburu, Pharmacometrics

disease models questions
Disease Models: Questions
  • 1.  What comments or suggestions does the committee have for improving the mathematical, statistical or clinical concepts in the model?
  • 2.  How does the committee envision such a model can be best utilized to improve drug development?
  • 3.  Does the committee have any general recommendations for further exploratory research into drug disease models? 

Gobburu, Pharmacometrics

pediatric initiative more efficient trials
Pediatric Initiative: More Efficient Trials
  • Experience dictates that pediatric trials could be designed to render more useful information
  • Goal is to employ prior knowledge from adults/pediatrics (using disease models) to design future pediatrics written request studies

Gobburu, Pharmacometrics

pediatric initiative conceptual framework

Disease

Model

Drug

Model

Trial

Model

Inter-disciplinary Team

Governance

Committee

WR

Data

(adult/ped)

Industry

E-Library

Communication

Pediatric Initiative: Conceptual Framework

Gobburu, Pharmacometrics

designing pediatric trials questions
Designing Pediatric Trials: Questions
  • 1. Do you think that such an approach will render pediatric trials more informative with respect to better dosing and study designs given the difficulties in conducting pediatric clinical trials?
  • 2. Given limited resources, please advice us on how to prioritize pediatrics programs for applying model-based trial design?
  • 3. Do you have any suggestions on how to improve the approach with respect to closing our knowledge gaps in pediatric pharmacotherapy?

Gobburu, Pharmacometrics

slide40

An Example of Disease Model (NSCLC)

Clinical Pharmacology Advisory Committee (CPAC)

March 18-19, 2008

Yaning Wang, Cynthia Sung, Celine Dartois, Roshni Ramchandani, Brian Booth, Ed Rock and Joga Gobburu

Office of Clinical Pharmacology

Office of Translational Sciences

Center for Drug Evaluation and Research

Food and Drug Administration

basic facts
Basic Facts

Cancer Death Rates in US

Female

Male

Cancer Facts & Figures 2006, American Cancer Society

low success rate in oncology
Low Success Rate in Oncology

Kola I, Landis J.Can the pharmaceutical industry reduce attrition rates? Nat.Rev.Drug.Disc. Aug 2004.

high failure rate even phase iii
High Failure Rate Even Phase III

Kola I, Landis J.Can the pharmaceutical industry reduce attrition rates? Nat.Rev.Drug.Disc. Aug 2004.

objective
Objective
  • Generate quantitative information that can be shared to improve oncology drug development
    • Risk factors for survival
    • Disease model for tumor size change over time
    • Quantitative relationship between tumor size related metrics (early biomarker) and overall survival

Note: Exploratory tool, not intended for surrogate endpoint

perceived utility of models
Perceived Utility of Models
  • Decrease attrition rate
    • by allowing better screening of compounds early in development
  • Optimize dose selection
    • by targeting meaningful changes in tumor size & balancing toxicity
  • Increase trial success rate
    • by aiding in designing better survival trials
database
Database
  • Four registration trials (A, B, C, D) for non-small cell lung cancer (NSCLC)
  • Eight active treatments and one best supportive care (placebo)
  • First-line or second line treatment for locally advanced or metastatic NSCLC (stage IIIA/B, IV)
  • N=243-488/arm
risk factors evaluation
Risk Factors Evaluation
  • Risk factors tested
    • Degree of weight loss over the previous 6 months (< 5% vs. ≥ 5%)
    • ECOG performance status (0 + 1 vs 2 + 3 or 0 vs 1)
    • Prior surgery (Yes or No)
    • Prior radiation (Yes or No)
    • Prior chemotherapy (Yes or No)
    • Best response to prior therapy (complete response [CR] or partial response [PR] vs stable disease [SD] vs PD)
    • Sex
    • Age
    • Baseline tumor size (sum of longest dimensions)
    • Number of prior chemotherapy
    • Lactate dehydrogenase (LDH>ULN)
  • Method
    • Cox regression (stepwise, inclusion 0.1, exclusion 0.05)
disease model for tumor size
Disease Model for Tumor Size

A mixed exponential and linear model

f(t): sum of longest dimensions (SLD) at time t

A: SLD at time 0, cm

k: SLD decay rate (treatment dependent), 1/week

B: SLD growth rate (treatment dependent), cm/week

Between subject variability for A, k and B (random effect):

Note: dose/exposure effect can be added to k and B if data are available

database for tumor model
Database for Tumor Model
  • 20-30% patients without post-baseline tumor measurements
  • Distribution of time for tumor measurements
model fits for individuals
Model Fits for Individuals

Observed

Mean Prediction

Indiv. Prediction

Sum of Longest Dimensions (cm)

Time (week)

tumor size survival model
Tumor Size – Survival Model
  • ECOG (0/1), baseline tumor size (centered at 8.5 cm) as covariates
  • Tumor size predictors (early biomarker)
    • Individual predicted tumor size percent reduction at 4, 6 or 8 weeks relative to baseline (TPRwkx)
    • Balance among time, predictive power and patient
  • Model development
    • Based on drug A1
    • Parametric survival model (log-normal)
  • Model evaluation
    • Model from drug A1 is used to predict survival curves for other drugs (different trials, different mechanism of actions)
a1 model predicts other drugs reasonably well week 8 tumor size percent reduction as predictor

Predicted

Observed

A1 Model Predicts Other Drugs Reasonably WellWeek 8 tumor size percent reduction as predictor

A2

B1

B2

B3

slide54

Predicted

Observed

A1 Model Predicts Other Drugs Reasonably WellWeek 8 tumor size percent reduction as predictor

C1

C2

D1

D2

contribution of tpr wk8 a1 model predicts c1

Predicted

Observed

Contribution of TPRwk8A1 Model Predicts C1

ECOG + baseline

ECOG+baseline+TPRwk8

tumor size survival relationship
Tumor Size – Survival Relationship

1st line treatment

2nd line treatment

Expected Survival Time (month)

Shrink

Grow

Tumor Size Percent Reduction at Week 8

excluded patients 20 30
Excluded Patients (20-30%)
  • Shorter survival times
  • Only ECOG is significant for survival
    • ECOG as a categorical variable
    • Two levels (0/1) for 1st line treatment
    • Four levels (0/1/2/3) for 2nd line treatment
summary
Summary
  • ECOG and baseline tumor size are consistent prognostic factors for survival across various trials for various treatments
  • Simple disease model for tumor size describes observed data well
  • The tumor size-survival model shows reasonable consistency across various drugs within the defined population
  • Less sensitivity for 2nd line therapy
  • Two populations are needed for full clinical trial simulation
perceived utility of models1
Perceived Utility of Models
  • Decrease attrition rate
    • by allowing better screening of compounds early in development
  • Optimize dose selection
    • by targeting meaningful changes in tumor size & balancing toxicity
  • Increase trial success rate
    • by aiding in designing better survival trials
acknowledgment
Acknowledgment
  • Office of New Drug
    • Ed Rock
  • Biostatistics
    • Mark Rothmann
  • Former Fellows
    • Cynthia Sung
    • Celine Dartois
  • Clinical Pharmacology
    • Division V
      • Roshni Ramchandani
      • Leslie Kenna
      • Brian Booth
    • Pharmacometrics
      • Joga Gobburu
  • OTS: Robert Powell
  • External
  • - Laurent Claret, Pharsight
  • - Edward F. Vonesh, Baxer
  • This project is funded by OWH funding
slide63

Pediatric Studies under Pharmaceuticals for Children Act (BPCA) and the Pediatric Research Equity Act (PREA)

Lisa L. Mathis, M.D.

Pediatric and Maternal Health Staff

Office of New Drugs

overview of presentation
Overview of Presentation
  • History of Legislation
    • Why we do pediatric studies
    • Tools we use to obtain data
  • What has been accomplished
    • Study results
    • Lessons learned
slide65

General Principles*

  • Pediatric patients should be given medicines that have been properly evaluated for their use in the intended population
  • Product development programs should include pediatric studies when pediatric use is anticipated
  • Pediatric development should not delay adult studies nor adult availability
  • Shared responsibility among companies, regulatory authorities, health professionals, and society as a whole

*from ICH E-11

historical overview pediatric benchmarks
Historical Overview: Pediatric Benchmarks
  • 1979 Labeling Requirement
  • 1994 Pediatric Labeling Rule
  • 1997 FDA Modernization Act (FDAMA)
  • 1998 Pediatric Rule
  • 2002 Best Pharmaceuticals for Children Act (BPCA)
  • 2002 Pediatric Rule Enjoined
  • 2003 Pediatric Research Equity Act (PREA)
  • 2007 FDA Amendments Act of 2007
    • Pediatric Medical Device Safety and Improvement Act
    • Pediatric Research Equity Act (PREA)
    • Best Pharmaceuticals for Children Act (BPCA)
slide67

BPCA

  • Reauthorized 2007
  • Written Request (WR) may be issued if studies are needed in the pediatric population
  • Sponsor is eligible for 6 months marketing exclusivity if studies are performed as described in the WR
    • Entire product line is protected
    • Can be substantial incentive for companies
slide68

PREA

  • Reauthorized in 2007
  • Drugs and Biologics affected
  • Pediatric Assessment required for certain applications unless waived or deferred
  • Established the Internal Review Committee
  • Public posting of pediatric study results
  • Reporting of all AE’s for 1 year after approved labeling change
efficacy safety not established
Efficacy/Safety NOT established
  • What leads to failed studies? Reasons include:
    • Smaller sample size
      • Limited number of patients
      • Cannot use normal volunteers
    • Endpoints not well defined in pediatrics
      • Metabolism may differ, sensitivity may be a function of development
    • Dose for Phase 3 studies not correctly identified
      • Simple fractionation of adult dose is not sufficient
solutions
Solutions
  • Review results of previous failed studies to try and advance the approach for the next drug in the same or related class
  • Leverage adult data to model appropriate doses to investigate
    • Now have the capability to make an educated guess of where to start with studies
  • Application of statistical methods to ensure interpretability
conclusion
Conclusion
  • Efforts have been made to obtain data to support appropriate medication use in the pediatric population
  • Over 800,000 pediatric patients are anticipated to have participated in the program
  • Many lessons learned that have advanced the science and the public health
pediatric written requests for antihypertensive drugs

Pediatric Written Requests for Antihypertensive Drugs

Norman Stockbridge

Division of Cardiovascular and Renal Products

who we are
Who we are
  • Division of Cardiovascular and Renal Products
    • One of 15 review divisions within the Office of New Drugs
    • Largest part of the portfolio is the anthypertensives
approval of antihypertensives
Approval of antihypertensives
  • Adults
    • Approval is based on a surrogate end point
    • Current labeling does not contain clinical outcome claims (but that is going change)
effect of bpca
Effect of BPCA
  • Prior to BPCA
    • No successful studies in children
    • Generally considered unethical (or at least infeasible)
  • Post BPCA
    • 24 Written Requests (2/3 of all C-R WRs)
    • Few abandoned
what is needed to approve use in children
What is needed to approve use in children?
  • Require demonstrating blood pressure effect in the target age group
    • Despite lack of any data that clinical outcomes are affected in children
  • Require only one study
    • Implies that we considered the diseases to be basically similar in adults and children
  • Summary – one PD study
other policy issues
Other policy issues
  • First member of class studies age 6-16
  • Second member of class studies age <6
  • Third member has to be creative
    • Specialized population
    • Novel claim
study design
Study design
  • Range of acceptable designs are suggested
  • Concern remains regarding initial randomization to placebo
  • Systolic or diastolic pressure at trough
  • Usual design is a multi-arm parallel design with no placebo followed by a (briefer) randomized placebo-controlled withdrawal
    • Two chances to win
formulation
Formulation
  • Age-appropriate formulation (may need BE studied in adults)
  • PK in children of target age range, but not necessarily same study as PD
    • Prefer early enough to inform PD dose selection
    • Sample size set based on experience
sample size for pd
Sample size for PD
  • Not explicitly set in WR!
  • WR sets minimal effect size of interest
  • Sponsor uses own estimate of variability to set sample size to detect (90% power)
  • Late, blinded interim analysis for variability used to resize study as needed
  • (Leaves all responsibility for design, power, and protocol adherence on sponsor)
  • Goal of labeling that definitively states drug is or is not useful
sponsor s escape clause
Sponsor’s escape clause
  • Can ignore advice about study power
  • If successful, can submit technically incomplete results and request amendment to WR
  • Same clause can be invoked to get WR amended for study stopped early because of safety issues (never happened)
learning from experience
Learning from experience
  • Collaboration between FDA and DCRI based upon limited data extraction
    • ACE and race
      • ACEI less effective as monotherapy in black adults
      • Trials optimized to look for this, but no power in individual studies
      • 6 studies pooled
      • Similar findings in children age 6-16
learning from experience1
Learning from experience
  • Collaboration between FDA and DCRI
    • ACE and race
    • Reasons for failure
      • Half of studies failed to find dose-response
      • 6 studies pooled (same design)
      • ACEI, ARB, CCB
      • Limited dose range most likely culprit
learning from experience2
Learning from experience
  • Collaboration between FDA and DCRI
    • ACE and race
    • Reasons for failure
    • Adverse events on placebo
      • Is randomization to placebo ethical?
      • Pool of 10 studies
      • No difference in adverse events discernible
learning from experience3
Learning from experience
  • Collaboration between FDA and SAS
    • Based upon
      • 12 drugs
      • 29 trials (including PK)
    • Broader scope of data integration
    • Homogenized to CDISC SDTM data standard
    • Two products
      • Resource for further research projects
      • Anonymized, mangled dataset with statistical properties similar to original
        • Fully releasable
        • Use to test tools for cross-study analysis
learning from experience4
Learning from experience
  • Several pediatric antihypertensive development programs failed, probably because of dosing issues
    • Target exposure selection
    • Limitations of existing formulations
  • Incentive for model-based advice given a sponsor for a recent program, as to be described by Dr. Jadhav