(1)statistics In Qbd Stats Ws 09-06
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Role of Statistics in Pharmaceutical Development Using Quality-by-Design Approach – an FDA Perspective Chi-wan Chen, Ph.D. Christine Moore, Ph.D. Office of New Drug Quality Assessment CDER/FDA FDA/Industry Statistics Workshop Washington D.C. September 27-29, 2006
Outline
FDA initiatives for quality
Application of statistical tools in QbD
Pharmaceutical CGMPs for the 21st Century ONDQA’s PQAS The desired state Quality by design (QbD) and design space (ICH Q8) Design of experiments Model building & evaluation Statistical process control
FDA CMC Pilot Program Concluding remarks 2
21st Century Initiatives
Pharmaceutical CGMPs for the 21st Century – a risk-based approach (9/04)
http://www.fda.gov/cder/gmp/gmp2004/GMP_finalrepor
ONDQA White Paper on Pharmaceutical Quality Assessment System (PQAS)
http://www.fda.gov/cder/gmp/gmp2004/ondc_reorg.htm
3
The Desired State
(Janet Woodcock, October 2005) A maximally efficient, agile, flexible pharmaceutical manufacturing sector that reliably produces high-quality drug products without extensive regulatory oversight A mutual goal of industry, society, and regulator 4
FDA’s Initiative on Quality by Design
In a Quality-by-Design system:
The product is designed to meet patient requirements The process is designed to consistently meet product critical quality attributes The impact of formulation components and process parameters on product quality is understood Critical sources of process variability are identified and controlled The process is continually monitored and updated to assure consistent quality over time 5
Quality by Design
FDA’s view on QbD, Moheb Nasr, 2006
6
Design Space (ICH Q8)
Definition: The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality Working within the design space is not considered as a change. Movement out of the design space is considered to be a change and would normally initiate a regulatory post-approval change process. Design space is proposed by the applicant and is subject to regulatory assessment and approval 7
Current vs. QbD Approach to Pharmaceutical Development Current Approach Quality assured by testing and inspection
QbD Approach Quality built into product & process by design, based on scientific understanding
Data intensive submission – disjointed Knowledge rich submission – showing information without “big picture” product knowledge & process understanding Specifications based on batch history
Specifications based on product performance requirements
“Frozen process,” discouraging changes
Flexible process within design space, allowing continuous improvement
Focus on reproducibility – often avoiding or ignoring variation
Focus on robustness – understanding and controlling variation 8
Pharmaceutical Development & Product Lifecycle Pr od uc t Desig n & Devel op ment
Proc ess Desig n & Devel op me nt
Manuf act uri ng Dev elo pment Cont inuous Im pro vem ent
Ca ndida te Sele cti on
Product Approv al 9
Pharmaceutical Development & Product Lifecycle
Statistical Tool
Produ ct Des ign & Deve lopm en t: Initial Scoping Product Characterization Product Optimization
Des ign of Ex per im en ts (DOE)
Proc es s Des ign & Deve lopm en t: Initial Scoping Process Characterization Process Optimization Process Robustness
Mode l Bu ild in g And Eva lu ati on
Manufactu ri ng Deve lopm en t and Conti nuou s Im pr ove me nt: Develop Control Systems Scale-up Prediction Tracking and trending
Statis tic al Proces s Con tr ol
Process Terminology Cr iti cal Qua li ty Att ribu tes Ou tp ut Ma teri als
Inp ut Mate ri als
Pr oce ss Ste p
(Prod uct or Intermed iate )
Desi gn Spa ce Inpu t Proces s Paramet er s Control Model
Measured Parame ters or Att ribu te s
Pro cess Me asu reme nts an d Co ntrols 11
Design Space Determination
First-principles approach
Statistically designed experiments (DOEs)
combination of experimental data and mechanistic knowledge of chemistry, physics, and engineering to model and predict performance efficient method for determining impact of multiple parameters and their interactions
Scale-up correlation
a semi-empirical approach to translate operating conditions between different scales or pieces of equipment 12
Design of Experiments (DOE)
Structured, organized method for determining the relationship between factors affecting a process and the response of that process Application of DOEs:
Scope out initial formulation or process design Optimize product or process Determine design space, including multivariate relationships 13
DOE Methodology (1) Choos e exp erim enta l desig n (e.g., full factorial, d-optimal) A
(2) Cond uct randomiz ed exp eri ments Experiment
Factor A
Factor B
Factor C
1
+ + +
+ + -
+ +
2 3 B
4
C
(4) Crea te mu lti di men sion al sur fac e mod el (for optimization or control)
(3) Anal yz e data
www.minitab.com
14
Model Building & Evaluation Examples
Models for process development
Models for manufacturing development
Computational fluid dynamics Scale-up correlations
Models for process monitoring or control
Kinetic models – rates of reaction or degradation Transport models – movement and mixing of mass or heat
Chemometric models Control models
All models require verification through statistical analysis 15
Model Building & Evaluation Chemometrics
Chemometrics is the science of relating measurements made on a chemical system or process to the state of the system via application of mathematical or statistical methods (ICS definition) Aspects of chemometric analysis:
Empirical method Relates multivariate data to single or multiple responses Utilizes multiple linear regressions
Applicable to any multivariate data:
Spectroscopic data Manufacturing data 16
Statistical Process Control Definitions
Statistical process control (SPC) is the application of statistical methods to identify and control the special cause of variation in a process.
Common cause variation – random fluctuation of response caused by unknown factors Special cause variation – non-random variation caused by a specific factor Upper Specification Limit
Upper Control Limit
3σ
Lower Control Limit
Target Lower Specification Limit
Special cause variation?
17
Process Capability Index (Cpk)
Cpk = 1.33
Cpk =
min (X − SL)
Cpk = 0.33
3σ
Cpk
|X - SL|
Expected Avg. OOS%*
2
6σ
∼0
1.7
5σ
∼0
1.33
4σ
0.003%
1
3σ
0.135%
0.7
2σ
2.28%
0.33
1σ
15.9%
*Percent out of specification beyond the high risk specification limit.
Industry Practice is to consider processes with Cpk below 1.33 as “not capable” of meeting specifications.
Quality by Design & Statistics
Statistical analysis has multiple roles in the Quality by Design approach
Statistically designed experiments (DOEs) Model building & evaluation Statistical process control Sampling plans (not discussed here) 19
CMC Pilot Program
Objectives: to provide an opportunity for
participating firms to submit CMC information based on QbD FDA to implement Q8, Q9, PAT, PQAS
Timeframe: began in fall 2005; to end in spring 2008 Goal: 12 original or supplemental NDAs Status: 1 approved; 3 under review; 7 to be submitted Submission criteria
More relevant scientific information demonstrating use of QbD approach, product knowledge and process understanding, risk assessment, control strategy 20
CMC Pilot - Application of QbD
All pilot NDAs to date contained some elements of QbD, including use of appropriate statistical tools
DOEs for formulation or process optimization (i.e., determining target conditions) DOEs for determining ranges of design space Multivariate chemometric analysis for in-line/at-line measurement using such technology as near-infrared
Statistical data presentation and usefulness
Concise summary data acceptable for submission and review Generally used by reviewers to understand how optimization or design space was determined
21
Concluding Remarks
Successful implementation of QbD will require multi-disciplinary and multi-functional teams
Development, manufacturing, quality personnel Engineers, analysts, chemists, industrial pharmacists & statisticians working together
FDA’s CMC Pilot Program provides an opportunity for applicants to share their QbD approaches and associated statistical tools FDA looks forward to working with industry to facilitate the implementation of QbD 22
Outline
FDA initiatives for quality
Application of statistical tools in QbD
Pharmaceutical CGMPs for the 21st Century ONDQA’s PQAS The desired state Quality by design (QbD) and design space (ICH Q8) Design of experiments Model building & evaluation Statistical process control
FDA CMC Pilot Program Concluding remarks 2
21st Century Initiatives
Pharmaceutical CGMPs for the 21st Century – a risk-based approach (9/04)
http://www.fda.gov/cder/gmp/gmp2004/GMP_finalrepor
ONDQA White Paper on Pharmaceutical Quality Assessment System (PQAS)
http://www.fda.gov/cder/gmp/gmp2004/ondc_reorg.htm
3
The Desired State
(Janet Woodcock, October 2005) A maximally efficient, agile, flexible pharmaceutical manufacturing sector that reliably produces high-quality drug products without extensive regulatory oversight A mutual goal of industry, society, and regulator 4
FDA’s Initiative on Quality by Design
In a Quality-by-Design system:
The product is designed to meet patient requirements The process is designed to consistently meet product critical quality attributes The impact of formulation components and process parameters on product quality is understood Critical sources of process variability are identified and controlled The process is continually monitored and updated to assure consistent quality over time 5
Quality by Design
FDA’s view on QbD, Moheb Nasr, 2006
6
Design Space (ICH Q8)
Definition: The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality Working within the design space is not considered as a change. Movement out of the design space is considered to be a change and would normally initiate a regulatory post-approval change process. Design space is proposed by the applicant and is subject to regulatory assessment and approval 7
Current vs. QbD Approach to Pharmaceutical Development Current Approach Quality assured by testing and inspection
QbD Approach Quality built into product & process by design, based on scientific understanding
Data intensive submission – disjointed Knowledge rich submission – showing information without “big picture” product knowledge & process understanding Specifications based on batch history
Specifications based on product performance requirements
“Frozen process,” discouraging changes
Flexible process within design space, allowing continuous improvement
Focus on reproducibility – often avoiding or ignoring variation
Focus on robustness – understanding and controlling variation 8
Pharmaceutical Development & Product Lifecycle Pr od uc t Desig n & Devel op ment
Proc ess Desig n & Devel op me nt
Manuf act uri ng Dev elo pment Cont inuous Im pro vem ent
Ca ndida te Sele cti on
Product Approv al 9
Pharmaceutical Development & Product Lifecycle
Statistical Tool
Produ ct Des ign & Deve lopm en t: Initial Scoping Product Characterization Product Optimization
Des ign of Ex per im en ts (DOE)
Proc es s Des ign & Deve lopm en t: Initial Scoping Process Characterization Process Optimization Process Robustness
Mode l Bu ild in g And Eva lu ati on
Manufactu ri ng Deve lopm en t and Conti nuou s Im pr ove me nt: Develop Control Systems Scale-up Prediction Tracking and trending
Statis tic al Proces s Con tr ol
Process Terminology Cr iti cal Qua li ty Att ribu tes Ou tp ut Ma teri als
Inp ut Mate ri als
Pr oce ss Ste p
(Prod uct or Intermed iate )
Desi gn Spa ce Inpu t Proces s Paramet er s Control Model
Measured Parame ters or Att ribu te s
Pro cess Me asu reme nts an d Co ntrols 11
Design Space Determination
First-principles approach
Statistically designed experiments (DOEs)
combination of experimental data and mechanistic knowledge of chemistry, physics, and engineering to model and predict performance efficient method for determining impact of multiple parameters and their interactions
Scale-up correlation
a semi-empirical approach to translate operating conditions between different scales or pieces of equipment 12
Design of Experiments (DOE)
Structured, organized method for determining the relationship between factors affecting a process and the response of that process Application of DOEs:
Scope out initial formulation or process design Optimize product or process Determine design space, including multivariate relationships 13
DOE Methodology (1) Choos e exp erim enta l desig n (e.g., full factorial, d-optimal) A
(2) Cond uct randomiz ed exp eri ments Experiment
Factor A
Factor B
Factor C
1
+ + +
+ + -
+ +
2 3 B
4
C
(4) Crea te mu lti di men sion al sur fac e mod el (for optimization or control)
(3) Anal yz e data
www.minitab.com
14
Model Building & Evaluation Examples
Models for process development
Models for manufacturing development
Computational fluid dynamics Scale-up correlations
Models for process monitoring or control
Kinetic models – rates of reaction or degradation Transport models – movement and mixing of mass or heat
Chemometric models Control models
All models require verification through statistical analysis 15
Model Building & Evaluation Chemometrics
Chemometrics is the science of relating measurements made on a chemical system or process to the state of the system via application of mathematical or statistical methods (ICS definition) Aspects of chemometric analysis:
Empirical method Relates multivariate data to single or multiple responses Utilizes multiple linear regressions
Applicable to any multivariate data:
Spectroscopic data Manufacturing data 16
Statistical Process Control Definitions
Statistical process control (SPC) is the application of statistical methods to identify and control the special cause of variation in a process.
Common cause variation – random fluctuation of response caused by unknown factors Special cause variation – non-random variation caused by a specific factor Upper Specification Limit
Upper Control Limit
3σ
Lower Control Limit
Target Lower Specification Limit
Special cause variation?
17
Process Capability Index (Cpk)
Cpk = 1.33
Cpk =
min (X − SL)
Cpk = 0.33
3σ
Cpk
|X - SL|
Expected Avg. OOS%*
2
6σ
∼0
1.7
5σ
∼0
1.33
4σ
0.003%
1
3σ
0.135%
0.7
2σ
2.28%
0.33
1σ
15.9%
*Percent out of specification beyond the high risk specification limit.
Industry Practice is to consider processes with Cpk below 1.33 as “not capable” of meeting specifications.
Quality by Design & Statistics
Statistical analysis has multiple roles in the Quality by Design approach
Statistically designed experiments (DOEs) Model building & evaluation Statistical process control Sampling plans (not discussed here) 19
CMC Pilot Program
Objectives: to provide an opportunity for
participating firms to submit CMC information based on QbD FDA to implement Q8, Q9, PAT, PQAS
Timeframe: began in fall 2005; to end in spring 2008 Goal: 12 original or supplemental NDAs Status: 1 approved; 3 under review; 7 to be submitted Submission criteria
More relevant scientific information demonstrating use of QbD approach, product knowledge and process understanding, risk assessment, control strategy 20
CMC Pilot - Application of QbD
All pilot NDAs to date contained some elements of QbD, including use of appropriate statistical tools
DOEs for formulation or process optimization (i.e., determining target conditions) DOEs for determining ranges of design space Multivariate chemometric analysis for in-line/at-line measurement using such technology as near-infrared
Statistical data presentation and usefulness
Concise summary data acceptable for submission and review Generally used by reviewers to understand how optimization or design space was determined
21
Concluding Remarks
Successful implementation of QbD will require multi-disciplinary and multi-functional teams
Development, manufacturing, quality personnel Engineers, analysts, chemists, industrial pharmacists & statisticians working together
FDA’s CMC Pilot Program provides an opportunity for applicants to share their QbD approaches and associated statistical tools FDA looks forward to working with industry to facilitate the implementation of QbD 22
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