Manufacturing QC and QA

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Information about Manufacturing QC and QA
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Published on January 16, 2009

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Manufacturing QC and QA

EMERGING ISSUES AND CONSIDERATIONS IN MANUFACTURING QUALITY CONTROL AND ASSURANCE OF DRUG PRODUCTS Yi Tsong, Ph.D., Acting Deputy Director Quantitative Methods and Research Staff OB, OPaSS, CDER, FDA This presentation does not necessarily represent the official position of FDA

Yi Tsong, Ph.D., Acting Deputy Director

Quantitative Methods and Research Staff

OB, OPaSS, CDER, FDA

Three Dimensions of the Critical Path Assessment of Safety – how to predict if a potential product will be harmful? Proof of Efficacy -- how to determine if a potential product will have medical benefit? Industrialization – how to manufacture a product at commercial scale with consistently high quality?

Assessment of Safety – how to predict if a potential product will be harmful?

Proof of Efficacy -- how to determine if a potential product will have medical benefit?

Industrialization – how to manufacture a product at commercial scale with consistently high quality?

Working in Three Dimensions on the Critical Path

Statistical Chemical Manufacturing Control and Assurance Programs Shelf Life Determination & Stability Acceptance Tests of Finished Product PAT (Process Analytical Technology) In Vitro Equivalence Tests

Pre-Marketing Shelf Life Determination Single factor design -> Multiple Factor Design ICH Guidance (2001) Optimal matrix design (Lin & Chen, JBS 2003) Significance level (Chen & Tsong, JBS, 2003) Shelf life determination of multi-factor design (Tsong & Chen, JBS, 2003) Equivalence approach (Tsong, Chen, Lin & Chen, JBS, 2003) General Issues Statistical Methods in Pharmaceutical Industry, 3 rd edition, 2004; Encyclopedia of Biopharmaceutical Stat. 2004; Encyclopedia of Clinical trials, 2005) I. Shelf Life Determination & Stability

Pre-Marketing Shelf Life Determination

Single factor design -> Multiple Factor Design

ICH Guidance (2001)

Optimal matrix design (Lin & Chen, JBS 2003)

Significance level (Chen & Tsong, JBS, 2003)

Shelf life determination of multi-factor design (Tsong & Chen, JBS, 2003)

Equivalence approach (Tsong, Chen, Lin & Chen, JBS, 2003)

General Issues

Statistical Methods in Pharmaceutical Industry, 3 rd edition, 2004;

Encyclopedia of Biopharmaceutical Stat. 2004;

Encyclopedia of Clinical trials, 2005)

Postmarketing stability Scale up Mixed effect design (batch is random) Nested factor design (specific levels of factors within a batch) Compliance of stability batches Web tool User friendly stability analysis tool for FDA reviewers Shelf Life Determination & Stability (2)

Postmarketing stability

Scale up

Mixed effect design (batch is random)

Nested factor design (specific levels of factors within a batch)

Compliance of stability batches

Web tool

User friendly stability analysis tool for FDA reviewers

II. Acceptance Tests of Finished Product For general tablets: Blend uniformity Dose content uniformity Dissolution test Purity test For inhaler/unit dose delivery system Delivery dose uniformity test Single dose system Multiple dose system Almost all tests are established at 2 nd WW Without batch specification Sample size restricted Lack of inference consideration

For general tablets:

Blend uniformity

Dose content uniformity

Dissolution test

Purity test

For inhaler/unit dose delivery system

Delivery dose uniformity test

Single dose system

Multiple dose system

Almost all tests are established at 2 nd WW

Without batch specification

Sample size restricted

Lack of inference consideration

USPXXIII 3-stage Dissolution Test Acceptance Rule Step 1, 6 tablets No Accept Yes Step 2, additional 6 tablets Yes No Step 3, additional 12 tablets Yes No Reject Accept Accept Tsong, Shen, Shah, JBS, 2004

Accept

Accept

Accept

Japan 2-Stage Dissolution Test Rule Step 1, 6 tablets No Accept Yes Step 2, additional 6 tablets Yes Accept No Reject Tsong, Shen, Shah, JBS, 2004

Tsong, Shen, Shah, JBS, 2004

3-Stage Dissolution Acceptance Test Based on Sequential Tolerance Interval Step 1, 6 tablets No Accept Yes Step 2, additional 6 tablets Yes Accept Step 3, additional 12 tablets Yes No Reject Accept Tsong, Shen, Shah, JBS, 2004

Tsong, Shen, Shah, JBS, 2004

Tsong, Shen, Shah, JBS, 2004

Tsong, Shen, Shah, JBS, 2004

Tsong, Shen, Shah, JBS, 2004

Tsong, Shen, Shah, JBS, 2004

FDA 2-Stage Delivery Dose Uniformity Acceptance Test Tsong & Shen, 2004

Step 1, 10 tablets No Accept Yes NMT 1 outside 85-115% All 10 within 75-125% Yes Reject No Step 2, additional 20 tablets NMT 1 outside 85-115% All 30 within 75-125% RSD  7.8% Yes Reject Accept All 10 within 85-115% RSD  6% No USP <905>, Content Uniformity Test (n = 30 units) Tsong, Shen, JBS, 2006

Parametric Tolerance Interval Approach Adjusted for sequential tests Unified OC curve against coverage Various sample sizes Small sample – acceptance test Large sample – compliance study Very large sample size – process monitoring Delivery Dose uniformity Test Collaborating with IPAC Dose Content Uniformity Test Multivariate adjustment Repeated test adjustment & Process control chart Researches in Acceptance Tests of Finished Product

Parametric Tolerance Interval Approach

Adjusted for sequential tests

Unified OC curve against coverage

Various sample sizes

Small sample – acceptance test

Large sample – compliance study

Very large sample size – process monitoring

Delivery Dose uniformity Test

Collaborating with IPAC

Dose Content Uniformity Test

Multivariate adjustment

Repeated test adjustment & Process control chart

Hierarchy of Process Understanding Ajaz Hussain, AAPS 39 th Pharm. Technologies Conf., Jan. 2004 Current State: “ Trial-n-Error” Batch Processes ‘ silo’ conditions ‘ black-box’ controls Quality-by-Inspection III. Process Analysis Technology

“ Trial-n-Error”

Batch Processes

‘ silo’ conditions

‘ black-box’ controls

Quality-by-Inspection

Hierarchy of Process Understanding Ajaz Hussain, AAPS 39 th Pharm. Technologies Conf., Jan. 2004 Desired State: 1st Principles Understanding Robust Processes Total Quality Control

1st Principles Understanding

Robust Processes

Total Quality Control

Hierarchy of Process Understanding Ajaz Hussain, AAPS 39 th Pharm. Technologies Conf., Jan. 2004 DOE Optimization Mechanistic Understanding Process Analytical Technology (PAT) Feed-forward control Real-Time-Release (RTR) Quality-by-Design Intermediate State:

DOE Optimization

Mechanistic Understanding

Process Analytical Technology (PAT)

Feed-forward control

Real-Time-Release (RTR)

Quality-by-Design

Typical Solid Dosage Process Wet Granulation Milling/ Sizing Blending Tablet Press Coating Inspection & Release Cogdill, et al, Fall Tech. Conf., 2004 FB Drier Dispensory PAT PAT PAT PAT PAT PAT PAT

Fluidized Bed Drying Input factors: Input air volume, humidity, temperature Product moisture content Material properties Loading Output factors: Drying time Material properties Used for other operations such as coating and granulation Cogdill, et al, Fall Tech. Conf., 2004

Input factors:

Input air volume, humidity, temperature

Product moisture content

Material properties

Loading

Output factors:

Drying time

Material properties

Used for other operations such as coating and granulation

Wet Granulation Input factors: Rotational speed Process scale Product moisture content Binder fluid application Material properties Output factors: Granulation time Particle size distribution Material properties Tablet performance Cogdill, et al, Fall Tech. Conf., 2004

Input factors:

Rotational speed

Process scale

Product moisture content

Binder fluid application

Material properties

Output factors:

Granulation time

Particle size distribution

Material properties

Tablet performance

Factors varied: Drug concentration Rotational speed Humidity Factors held constant Material properties Temperature Fill level Loading scheme Powder Blending Cogdill, et al, Fall Tech. Conf., 2004

Factors varied:

Drug concentration

Rotational speed

Humidity

Factors held constant

Material properties

Temperature

Fill level

Loading scheme

Tablet Compression Input factors: Compression force Dwell time Tablet size & shape Material properties Output factors: Tablet hardness Friability Tablet performance Uniformity Cogdill, et al, Fall Tech. Conf., 2004

Input factors:

Compression force

Dwell time

Tablet size & shape

Material properties

Output factors:

Tablet hardness

Friability

Tablet performance

Uniformity

Blend Uniformity & PAT Univariate Testing to Document Quality Approach Multivariate Quality-by Design Approach Traditional test methods At-line test methods On- and/or At-line test methods for all critical components and processes Current PQRI proposal and draft Guidance Draft Guidance may include information on the use of NIR methods Proposed PAT Guidance Incentive? Higher efficiency Lower “risk” leading to lower regulatory concern Ajaz Hussain, AAPS 39th Pharm. Technologies Conf., Jan. 2004

8-qt plastic V-blender (Patterson-Kelly) Blend composition Salicyclic acid (SA), 30.5 mm particle size Lactose, 115.5 mm particle size Input factor levels Relative humidity: 20%, 60% SA concentration: 3%, 7%, 11% Rotation speed: 12.8, 20.3 rpm Powder Blending Cogdill, et al, Fall Tech. Conf., 2004

8-qt plastic V-blender (Patterson-Kelly)

Blend composition

Salicyclic acid (SA), 30.5 mm particle size

Lactose, 115.5 mm particle size

Input factor levels

Relative humidity: 20%, 60%

SA concentration: 3%, 7%, 11%

Rotation speed: 12.8, 20.3 rpm

Sampling method Blend process monitored for 50 minutes Stopped at pre-determined time intervals for sampling with thief probe and NIR analysis Thief samples analyzed via UV spectroscopy (296.9 nm) Powder Blending Cogdill, et al, Fall Tech. Conf., 2004

Sampling method

Blend process monitored for 50 minutes

Stopped at pre-determined time intervals for sampling with thief probe and NIR analysis

Thief samples analyzed via UV spectroscopy (296.9 nm)

Powder Blending Typical powder blend profiles Cogdill, et al, Fall Tech. Conf., 2004

Typical powder blend profiles

3 Factors Humidity Blender speed Salicylic acid Concentration Experimental design generated using JMP ND = 16 experiments D-Optimal Design of Experiment Cogdill, et al, Fall Tech. Conf., 2004

3 Factors

Humidity

Blender speed

Salicylic acid Concentration

Experimental design generated using JMP

ND = 16 experiments

Cogdill, et al, Fall Tech. Conf., 2004 * Blender speed measured in rpm 12.8 3% 60% XVI 12.8 7% 60% XV 20.3 3% 60% XIV 12.8 11% 60% XIII 20.3 7% 60% XII 20.3 7% 60% XI 12.8 7% 60% X 20.3 11% 60% IX 20.3 3% 60% VIII 12.8 11% 20% VII 20.3 11% 20% VI 20.3 7% 20% V 12.8 7% 20% IV 20.3 3% 20% III 12.8 11% 20% II 12.8 3% 20% I Blender Speed * Salicylic acid Concentration Humidity Experimental Conditions Order

Thief-Sample Position Dependency Outliers were flagged during UV analysis as samples exceeding 1.5x IQR Cogdill, et al, Fall Tech. Conf., 2004 R L 1 2 3 4 5 0 5 10 15 20 25 30 35 40 1 2 3 4 5 Location % Outliers B A

Outliers were flagged during UV analysis as samples exceeding 1.5x IQR

Results P = 0.0002 P = 0.002 P = 0.0331 Cogdill, et al, Fall Tech. Conf., 2004

Optimal Design of Experiment Collect Data to Establish Control Chart Univariate Multivariate PCA Profile Application of Multi-level Control Specification Trend Statistical Monitoring and Feedback System Similar concepts are applicable also to batch-to-batch control of finished products PAT (Process Analytical Technology)

Optimal Design of Experiment

Collect Data to Establish Control Chart

Univariate

Multivariate

PCA

Profile

Application of Multi-level Control

Specification

Trend

Statistical Monitoring and Feedback System

Similar concepts are applicable also to batch-to-batch control of finished products

Generic Product Requirement SUPAC (Scale-up and Post Approval Changes) Requirement Biowaiver Comparability of new suppliers Formulation change Manufacturer site Change IV. In Vitro Equivalence Tests

Generic Product Requirement

SUPAC (Scale-up and Post Approval Changes) Requirement

Biowaiver

Comparability of new suppliers

Formulation change

Manufacturer site Change

Dissolution Profile Similarity Test Particle Size Distribution Profile Equivalence Pharmaceutical Equivalence In Vitro Equivalence Tests

Dissolution Profile Similarity Test

Particle Size Distribution Profile Equivalence

Pharmaceutical Equivalence

Dissolution Profile Similarity

Dissolution Profile Similarity The U.S. FDA Guidance, (SUPAC – IR), 1997 The U.S. FDA Guidance, (SUPAC – MR), 1997 The U.S. FDA Guidance, (SUPAC – ER), 1997 Sathe, Tsong, Shah, In Vitro-In Vivo Correlation, ed. Young D., Devane J.D., and Butler J., Plenum Publishing Corp., 1996. Tsong, Hammerstrom, Sathe, Shah. Proceedings of the Biopharmaceutical Section of ASA, pp. 129-134, 1996. Tsong, Hammerstrom, Sathe, Shah. DIJ, 30: 1105-1112, 1996. Shah, Tsong, Sathe, Liu. Pharmaceutical Research, 15: 889-896, 1998. Ma, Wang, Liu, Tsong. JBS, 10(2):229-249, 2000.

The U.S. FDA Guidance, (SUPAC – IR), 1997

The U.S. FDA Guidance, (SUPAC – MR), 1997

The U.S. FDA Guidance, (SUPAC – ER), 1997

Sathe, Tsong, Shah, In Vitro-In Vivo Correlation, ed. Young D., Devane J.D., and Butler J., Plenum Publishing Corp., 1996.

Tsong, Hammerstrom, Sathe, Shah. Proceedings of the Biopharmaceutical Section of ASA, pp. 129-134, 1996.

Tsong, Hammerstrom, Sathe, Shah. DIJ, 30: 1105-1112, 1996.

Shah, Tsong, Sathe, Liu. Pharmaceutical Research, 15: 889-896, 1998.

Ma, Wang, Liu, Tsong. JBS, 10(2):229-249, 2000.

Particle Size Distribution Profile Equivalence Test of Inhaler Products

Particle Size Distribution Profile Equivalence Test of Inhaler Products

Particle Size Distribution Profile Equivalence Test of Inhaler Products

Challenges and Opportunities in CMC Shelf Life and Stability Pooling batches by equivalence Pre-marketing to Scale-up, postmarketing Measurements difference between stability and compliance Quality of finished products WWII compendia to modern inference From mean and STD to tolerance interval Multiple and repeated tests Restricted sample size to unrestricted sample size Batch test versus test during process

Shelf Life and Stability

Pooling batches by equivalence

Pre-marketing to Scale-up, postmarketing

Measurements difference between stability and compliance

Quality of finished products

WWII compendia to modern inference

From mean and STD to tolerance interval

Multiple and repeated tests

Restricted sample size to unrestricted sample size

Batch test versus test during process

PAT From acceptance test to quality by design To identify, manage, monitor, and control critical variables of the manufacturing process Statistical expertise in process control In-vitro equivalence Variation between laboratories, technicians, and environmental conditions No conventional statistics and critical values Challenges and Opportunities in CMC

PAT

From acceptance test to quality by design

To identify, manage, monitor, and control critical variables of the manufacturing process

Statistical expertise in process control

In-vitro equivalence

Variation between laboratories, technicians, and environmental conditions

No conventional statistics and critical values

Thank You For Your Interest!!!

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