Presented by Bo Olsson (AstraZeneca)

Slides:



Advertisements
Similar presentations
Statistics for Quantitative Analysis
Advertisements

Statistics Part II Math 416. Game Plan Creating Quintile Creating Quintile Decipher Quintile Decipher Quintile Per Centile Creation Per Centile Creation.
Lecture 8: Hypothesis Testing
Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 1 Chapter 12 Cross-Layer.
Multiplication X 1 1 x 1 = 1 2 x 1 = 2 3 x 1 = 3 4 x 1 = 4 5 x 1 = 5 6 x 1 = 6 7 x 1 = 7 8 x 1 = 8 9 x 1 = 9 10 x 1 = x 1 = x 1 = 12 X 2 1.
Division ÷ 1 1 ÷ 1 = 1 2 ÷ 1 = 2 3 ÷ 1 = 3 4 ÷ 1 = 4 5 ÷ 1 = 5 6 ÷ 1 = 6 7 ÷ 1 = 7 8 ÷ 1 = 8 9 ÷ 1 = 9 10 ÷ 1 = ÷ 1 = ÷ 1 = 12 ÷ 2 2 ÷ 2 =
Client Assessment and Other New Uses of Reliability Will G Hopkins Physiology and Physical Education University of Otago, Dunedin NZ Reliability: the Essentials.
Design of Dose Response Clinical Trials
The Application of Propensity Score Analysis to Non-randomized Medical Device Clinical Studies: A Regulatory Perspective Lilly Yue, Ph.D.* CDRH, FDA,
BUS 220: ELEMENTARY STATISTICS
/4/2010 Box and Whisker Plots Objective: Learn how to read and draw box and whisker plots Starter: Order these numbers.
Measurements and Their Uncertainty 3.1
1 1  1 =.
Overview of Lecture Parametric vs Non-Parametric Statistical Tests.
Chapter 7 Sampling and Sampling Distributions
Box and Whiskers with Outliers. Outlier…… An extremely high or an extremely low value in the data set when compared with the rest of the values. The IQR.
The basics for simulations
You will need Your text Your calculator
1 Challenge the future Subtitless On Lightweight Design of Submarine Pressure Hulls.
On Comparing Classifiers : Pitfalls to Avoid and Recommended Approach
Company Confidential © 2012 Eli Lilly and Company Beyond ICH Q1E Opening Remarks Rebecca Elliott Senior Research Scientist Eli Lilly and Company MBSW 2013.
Validation & Sample Size Selection
Chapter 7 Hypothesis Testing
9.4 t test and u test Hypothesis testing for population mean Example : Hemoglobin of 280 healthy male adults in a region: Question: Whether the population.
1 Slides revised The overwhelming majority of samples of n from a population of N can stand-in for the population.
Re-Order Point Problems Set 1: General
Hypothesis Tests: Two Independent Samples
Chapter 4 Inference About Process Quality
Statistics Review – Part I
Target Costing If you cannot find the time to do it right, how will you find the time to do it over?
Quantitative Analysis (Statistics Week 8)
Least Common Multiples and Greatest Common Factors
Presented to: Minnesota Chamber of Commerce October 1, 2012.
A tool to protect Minnesota's waters Minnesota Pollution Control Agency, Sept. 10, 2012.
Before Between After.
Issues of Simultaneous Tests for Non-Inferiority and Superiority Tie-Hua Ng*, Ph. D. U.S. Food and Drug Administration Presented at MCP.
Chapter 8: Introduction to Hypothesis Testing. 2 Hypothesis Testing An inferential procedure that uses sample data to evaluate the credibility of a hypothesis.
Subtraction: Adding UP
Putting Statistics to Work
Statistical Inferences Based on Two Samples
© The McGraw-Hill Companies, Inc., Chapter 10 Testing the Difference between Means and Variances.
©Brooks/Cole, 2001 Chapter 12 Derived Types-- Enumerated, Structure and Union.
Chapter Thirteen The One-Way Analysis of Variance.
Chapter 8 Estimation Understandable Statistics Ninth Edition
©2006 Prentice Hall Business Publishing, Auditing 11/e, Arens/Beasley/Elder Audit Sampling for Tests of Controls and Substantive Tests of Transactions.
PSSA Preparation.
Copyright © 2013 Pearson Education, Inc. All rights reserved Chapter 11 Simple Linear Regression.
Experimental Design and Analysis of Variance
Lial/Hungerford/Holcomb/Mullins: Mathematics with Applications 11e Finite Mathematics with Applications 11e Copyright ©2015 Pearson Education, Inc. All.
Simple Linear Regression Analysis
January Structure of the book Section 1 (Ch 1 – 10) Basic concepts and techniques Section 2 (Ch 11 – 15): Inference for quantitative outcomes Section.
Slide Slide 1 Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley. Section 7-2 Estimating a Population Proportion Created by Erin.
4/4/2015Slide 1 SOLVING THE PROBLEM A one-sample t-test of a population mean requires that the variable be quantitative. A one-sample test of a population.
Commonly Used Distributions
Parametric Tolerance Interval (PTI) Test for Delivered Dose Uniformity (DDU) for Orally Inhaled and Nasal Drug Products (OINDP) Michael Golden On behalf.
Pharmaceutical Product Quality Assurance Through CMC Drug Development Process Presented by Darlene Rosario (Aradigm) 21 October 2003 Meeting of the Advisory.
FDA Nasal BA/BE Guidance Overview
1 Process to Address Specifications for Delivered Dose Uniformity of Inhaled and Nasal Drug Products Presented by Robert O’Neill, Ph.D. Chair ACPS Working.
ITFG/IPAC Collaboration CMC Specifications Technical Team ITFG/IPAC TECHNICAL TEAM: CMC SPECIFICATIONS Presented by: Bo Olsson, PhD 26 April 2000 Rockville,
2015 MBSW1 Quality Assurance Test of Delivered Dose Uniformity of Multi-dose Spray and Inhalation Drug Products Drs. Yi Tsong 1, Xiaoyu (Cassie) Dong*
Parametric Tolerance Interval Test for Delivered Dose Uniformity (DDU) Working Group Update Moheb M. Nasr, Ph.D. Office of New Quality Assessment (ONDQA,
Bioequivalence Studies and Other Recommendations for Orally Inhaled and Nasal Drug Products: Work of the ITFG/IPAC-RS Collaboration Presented by Cynthia.
1 Dose Content Uniformity for Aerosol Products Wallace P. Adams, Ph.D. OPS/IO Advisory Committee for Pharmaceutical Science 13 March 2003 Rockville, MD.
1 PTIT for DCU of OINDP: Approaches to Resolution of Identified Issues Wallace P. Adams, Ph.D. OPS/IO Advisory Committee for Pharmaceutical Science 21.
ITFG/IPAC Collaboration Introduction OVERVIEW OF ITFG/IPAC COLLABORATION Presented by: Harris Cummings, PhD 26 April 2000 Rockville, MD.
Orally Inhaled and Nasal Drug Products Subcommittee Introduction and Objectives Eric B. Sheinin Deputy Director Office of Pharmaceutical Science Center.
Orally Inhaled and Nasal Drug Products (OINDP) Subcommittee Report to the Advisory Committee for Pharmaceutical Sciences Rockville, Maryland November 15,
Ajaz S. Hussain, Ph.D. Office of Pharmaceutical Sciences CDER, FDA October 21, 2003 Dose Content Uniformity: Parametric Tolerance Interval Approach.
Parametric Tolerance Interval (PTI) Test for Delivered Dose Uniformity (DDU) for Orally Inhaled and Nasal Drug Products (OINDP) Michael Golden On behalf.
An Assessment of IPAC-RS’ Proposal Walter W. Hauck, Ph.D. Biostatistics Section Division of Clinical Pharmacology Thomas Jefferson University Philadelphia,
Presentation transcript:

Presented by Bo Olsson (AstraZeneca) Parametric Tolerance Interval (PTI) Test for Improved Control of Delivered Dose Uniformity (DDU) in Orally Inhaled and Nasal Drug Products (OINDP) Presented by Bo Olsson (AstraZeneca) on behalf of International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS) 13 March 2003 Rockville, MD

Purpose of Delivered Dose Uniformity Test DDU, which is one of several quality attributes for OINDP, combines performance of delivery device and formulation DDU verifies delivered (emitted) dose in the batch dose uniformity between containers dose uniformity within multi-dose containers closeness of mean dose to target - label claim March 2003 IPAC-RS

Scope of Products Oral inhalation and nasal drug products (OINDP) are either single-dose or multi-dose pressurized metered dose inhalers (pMDIs) dry powder inhalers (DPIs) nasal sprays inhalation solutions deliver medication to the respiratory tract lung nasal passages in the form of aerosol to treat diseases and conditions respiratory (e.g., asthma, COPD, rhinitis) systemic (e.g., diabetes, migraine) March 2003 IPAC-RS

Inhalation Product History First pressurized Metered Dose Inhaler (pMDI) introduced in 1955 Until early 1990 pMDI technology based on chlorofluorocarbons (CFC) CFCs linked to ozone depletion and banned by international environmental treaty (Montreal Protocol), phased out in medical use applications With phase-out of CFC for medical use, reformulation of pMDIs with hydrofluorocarbons (HFCs) and other new types of OINDP became necessary March 2003 IPAC-RS

OINDP DDU Test History Regulatory requirements for DDU evolved based on FDA experience with CFC products DDU testing requirements became more stringent over time from multiple actuations per test to minimal clinical dose per test from testing beginning of container only to testing beginning, middle, end from USP limits to tighter limits DDU requirements are more challenging for new technology from CMC perspective Formulation flexibility significantly reduced with HFC’s due to their physico-chemical characteristics and compatibility with excipients Challenging flow characteristics of powders March 2003 IPAC-RS

Traditional DDU Tests USP: counting test, e.g. 10 doses from one inhaler (3 beginning + 4 middle + 3 end) no more than 1 of 10 doses outside 75-125% of label claim (LC) none of 10 doses outside 65-135% LC (zero tolerance) under certain conditions 2nd tier allowed with larger sample FDA: counting test with tighter limits, e.g. (through-container-life DDU test for multi-dose pMDIs/DPIs): 3 doses from each of 3 inhalers (3 beginning + 3 middle + 3 end) no more than 1 of 9 doses outside 80-120% LC none of 9 doses outside 75-125% LC (zero tolerance) each of 3 means (B, M, E) within 85-115% LC March 2003 IPAC-RS

Reason for DDU Replacement Parametric Tolerance Interval (PTI) test is proposed as replacement of current FDA DDU tests BECAUSE PTI test is more powerful and discriminating than current tests simultaneously uses mean and standard deviation to make quality assessment The FDA counting test is less efficient in use of data unnecessarily rejects good batches The FDA counting test penalizes increased testing e.g., stability testing increases chances to fail not due to product quality change OINDP cannot routinely meet expectations in draft Guidances e.g., many products have been approved with exceptions to DDU test and acceptance criteria in published Guidances March 2003 IPAC-RS

Origins of PTI Test Statistical design built on previous work 1999 AAPS/FDA/USP Workshop presentation by Dr. Walter Hauck Williams, Adams, Poochikian and Hauck, Pharm. Res. (2002) JP/EP/USP parametric test for dose uniformity in tablets Features of FDA Draft Guidance test (e.g., target interval and mean criteria) Acceptance criteria designed to match or exceed statistical consumer protection implied by published Draft Guidances Metered Dose Inhaler (MDI) and Dry Powder Inhaler (DPI) Drug Products 1998 Nasal Spray and Inhalation Solution, Suspension, and Spray Drug Products Draft: 1999, Final: 2002 March 2003 IPAC-RS

Define Batch Quality With Coverage Coverage = Proportion of doses in the batch that are within target interval Batches having same coverage of given target interval are considered to be of equal quality Emitted Dose Target Interval March 2003 IPAC-RS

Hypothesis Testing Framework To assure with high confidence that sub-standard batches are rejected, set statistical hypothesis as: H0: batch quality is out of specification Ha: batch quality is in specification Type I error: batch released but is outside specification Type II error: batch rejected but is within specification March 2003 IPAC-RS

Hypothesis Testing Usefulness Quality of batches released to consumer is of greatest importance Need to control risk of releasing substandard batches to consumer (i.e., Type I error) Type I error is controlled independently of sample size Therefore UNACCEPTABLE (limiting) quality is defined as baseline standard March 2003 IPAC-RS

Proposed Standard of Batch Quality The proposed limiting quality is set at 85% batch coverage of the 75-125% LC target interval corresponds to 5% acceptance for FDA multi-dose test This means that commercial batches must far exceed 85% coverage with high confidence March 2003 IPAC-RS

Comparison of Coverage at Limiting Quality between FDA and PTI tests DDU Test Approach Minimum BATCH Coverage (95% confidence) FDA for single-dose products (Counting Test) 78% coverage of 75-125%LC FDA for multi-dose products (Counting Test) 85% coverage of 75-125%LC IPAC-RS for all OINDP (Parametric Tolerance Interval Test) 85% coverage of 75-125%LC March 2003 IPAC-RS

PTI Test Mechanics SAMPLE: pre-defined number of units (n), from different portions in container (Life Stages - LS), one dose from each unit CALCULATE: mean (m), standard deviation (s), Acceptance Value (AV) =100-mLS + ks COMPARE: AV  25, 100-mLS 15, and s  25f/k (Maximum Sample Standard Deviation) IF NOT ACCEPTED, GO TO SECOND TIER Target (100%LC) Mean (m) Standard Deviation (s) Emitted Dose k and f pre-defined based on n n determined as appropriate for each product March 2003 IPAC-RS

Revised PTI Test Coefficients Ensure less than 5.1% Type I error for all batch means n - sample size in tier 1/ total sample size for tiers 1+ 2 k1 and k2 - acceptability coefficients in 1st and 2nd tier f - factor for maximum sample standard deviation Test plan (sample size) selected as appropriate for each product Consumer protection the same for all sample sizes by design Producer risk decreases with increasing sample size March 2003 IPAC-RS

Normal distribution at limiting quality Maximum Type I Error is 5.1% (occurs for smallest sample size at mean deviation of ±9 %LC) Sample size Normal distribution at limiting quality (85% coverage of 100±25% LC) March 2003 IPAC-RS

Issues Discussed in Previous Meetings with FDA “Gap” in OC curves Quality Standard “Zero tolerance” Performance for normal and non-normal distributions Representative sampling from batch Differences between product types March 2003 IPAC-RS

Progress to Date for DDU Replacement FDA has stated that conceptually, PTI approach is acceptable Need to resolve: acceptance criteria to be used by PTI test March 2003 IPAC-RS

“Gap”

Operating Characteristic Curve Producer protection region 100 90 80 70 60 Area of uncertainty Probability to Accept (%) 50 40 30 20 10 5% Consumer protection region Variability (either standard deviation at a given batch mean or coverage) March 2003 IPAC-RS

Comparison of Operating Characteristic Curves 21 “Gap” LQ FDA test: as in Draft MDI/DPI Guidance Consumer protection (Limiting Quality, LQ) same PTI test’s curve is sharper (narrowed area of uncertainty) Fewer acceptable batches rejected (lower producer risk) “Gap”: Fewer rejections does not mean lower quality of accepted batches (see simulated production illustration below)

Examples of Effect of Deviations on current FDA OC Curve 22 FDA DCU & TCL test as in Draft MDI/DPI Guidance Majority of OINDP products approved by FDA in 1990-2001 have DDU test or acceptance criteria or both that deviate from draft Guidances (IPAC-RS 2001 survey) The ”gap” between FDA and PTI OC curves decreases with such deviations, and consumer protection is eroded PTIT provides reduction of producer risk without compromising consumer protection

Simulated Illustration* (see figures in next two slides) Unacceptable quality: FDA and PTI tests have comparable performance Acceptable quality: PTI test rejects fewer acceptable batches than FDA test * Used 5000 simulations, normal distributions March 2003 IPAC-RS

Batch Mean ~10014, Batch SD ~ 203 Unacceptable Quality Batch Mean ~10014, Batch SD ~ 203 Batch SD, s (%LC) 1.2% Accepted by FDA test (median cov= 80.3%) 98.8% Rejected by FDA test (median cov= 76.5%) 0.3% Accepted by PTI test (median cov= 81.2%) 99.7% Rejected by PTI test (median cov= 76.6%) March 2003 IPAC-RS

Batch Mean ~1009, Batch SD ~103 Acceptable Quality Batch Mean ~1009, Batch SD ~103 65% Accepted by FDA test (median cov= 98.6%) Batch SD, s (%LC) 35% Rejected by FDA test (median cov= 96.5%) 95% Accepted by PTI test (median cov= 98.1%) 5% Rejected by PTI test (median cov= 91.5%) March 2003 IPAC-RS

Quality Standard

Quality Standard Quality of a batch should be judged against a specific standard. Within presented hypothesis framework that standard is Limiting (Unacceptable) Quality coverage corresponding to 5% acceptance probability consumer protection typical batch quality has to be far above the limiting quality to achieve reasonable batch acceptance probability not Typical Batch Quality coverage corresponding to e.g., greater than 95% acceptance producer risk is different for different products March 2003 IPAC-RS

Proposed Quality Standard IPAC-RS 2001 Proposal: Limiting quality set to 85% coverage of 75 -125% LC interval same limiting quality as implied by Draft Guidances demonstrated for each batch with high confidence FDA comment: tighter standard may be needed Significantly tighter standard will be problematic in setting the standard, both producer risk and consumer protection should be considered standard must be compatible with capability of current and pipeline products and analytical methodology if standard exceeds capability, it will create difficulties for manufacturing of current products, and development and approval of new products and generic versions March 2003 IPAC-RS

Non-Normal Distributions and Zero Tolerance Criterion

Non-Normal Distributions IPAC-RS database demonstrates that assumption of normality is appropriate Studied non-normal distributions: symmetric short-tailed (beta) or bimodal, asymmetric short-tailed (beta and gamma) or long-tailed (outliers) Revised PTI test assures < 5.1 % Type I error for all normal and most non-normal distributions For a few extreme distributions, 5% is exceeded at the limiting quality: Notably off-target, relatively symmetric distributions with extremely short tails Notably off-target, notably asymmetric distributions with longer tail in the off-target direction PTI test is appropriate for real products March 2003 IPAC-RS

Effect of Zero Tolerance (ZT) Criteria A ZT criterion does not protect from having values outside ZT limits in the batch A fixed ZT criterion degrades parametric tests; this effect escalates with sample size A ZT must scale with sample size in order to avoid degrading parametric test and to have no effect on producer risk A scaled ZT has little or no effect on consumer protection even for extreme non-normal distributions Any ZT penalizes thorough testing (stability, validations) Conclusion: ZT does not help control product quality March 2003 IPAC-RS

ZT Criterion Has No or Little Effect on Acceptance rate at 85% coverage The addition of ZT criterion does not materially improve consumer protection March 2003 IPAC-RS

Desired Outcome of DDU Effort for IPAC-RS Agree that PTI test is conceptually acceptable as a replacement Parametric (no Zero Tolerance) Coverage as quality definition Allow product-by-product justification of sample size n multiple sampling plans, e.g., 12/36 to 30/90 Maintain limiting quality standard implied by FDA Guidances 85% coverage of 75-125% LC target interval 5% acceptance (95% rejection) at limiting quality March 2003 IPAC-RS

Acknowledgements FDA / CDER / OPS IPAC-RS Members Aradigm AstraZeneca Aventis Boehringer Ingelheim Eli Lilly GlaxoSmithKline Members of IPAC-RS DDU Working Group IPAC-RS Secretariat IVAX Kos Pharmaceuticals Nektar Therapeutics Novartis Pfizer Schering-Plough March 2003 IPAC-RS