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Estudo de casos sobre QbD
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QbD implementation in Generic Industry: Overview and Case-StudyInna Ben Anat QbD Strategy Leader Teva Pharmaceuticals R&D
IFPACJAN
2013Inna Ben-Anat, QbD Strategy Leader, Teva Pharmaceuticals R&D
Three Core Components of QbD and Generic Industry: How Do They Overlap
1 Cl l d fi i h i d d f 1 R d ibl M ki A d d t
Quality by Design Generic Industry
1. Clearly defining the intended purpose of the future developed product and design this product to fit its purpose
1. Reproducibly Making A drug product that is comparable to brand/reference listed drug product in dosage form, strength route of administration qualityon
is c
lear
2. Understanding what attributes of this product are critical so it (product) will keep serving its intended purpose
strength, route of administration, quality and performance characteristics, and intended use"
The
conn
ectio
g p p
3. Enhanced understanding what impacting the critical quality attributes and how
2. Providing uninterrupted supply of high quality and affordable medication to our patients
T(materials, process, packaging etc) ; define control strategies so that the intended purpose of the product will reproducibly
i t i it i t it
3. Efficiency and Speed
maintain its integrity
QbD for Generics: Finding the right balance between Speed, Efficiency and Excellence
Overview of QbD (GPhA, May 2012)
QbD Guide for Generics: Step 1-Product Design
RLD Characterization Quality Target Product Profile Critical Quality Attributes Critical Quality Attributes
GPhA/FDA CMC Workshop, May 2012
QbD Guide for Generics: Step 2 - What are the potential Risks
What are the Risks?... API
Risk Assessment Defines the Development Strategy
How do we stay efficient
API Excipients Formulation and Process
i Effective Prior Knowledge utilization
and management
Generic Industry has a lot of
Equipment Testing Packaging
Generic Industry has a lot of information and in-house knowledge available Data bases of pre created
Data bases of pre-created Ishikawa diagrams in order to harmonize and streamline the Risk Assessment processp
Historical data-mining
Historical Data Mining: Drug Layering of Pellets ExampleHistorical Data Mining: Drug Layering of Pellets Example
Example: Previously developed product, multiply batches are available for Data Mining:
In-Process Pellets Assay vs. Fines Correlation
Based on the found relationship AssayBased on the found relationship, Assay decreases ~0.6% with each % fines
How do we control low % finesby process parameters
(Drug Layering)(Drug Layering)
All examples are for illustration purposes only
Historical Data Mining: Drug Layering of Pellets ExampleHistorical Data Mining: Drug Layering of Pellets Example
Actual Processing Parameters from all available historical lots were Actual Processing Parameters from all available historical lots were collected and datacollected and data--mined mined
Partition per most critical factor affecting % FinesPartition per most critical factor affecting % Fines
1. Most Significant parameters affecting All RowsCountMean
313 666129 1 6232558
LogWorth1 98596
Difference
%Fines are Slit Temp and Exhaust Temp
2. Lower Slit Temperature (
QbD Guide for Generics: Step 3 - Plan the right/relevant Experiments
Efficient and Informative DOE: CQAs= f (CPPs, CMAs)
How do we stay efficiento Effective Prior Knowledge Utilization
What do we vary and what do we fix? What do we vary and what do we fix?
What target and range do we evaluate and why?
What statistical model do we use and why? (Can we assess what interactions are most likely to occur? Can we assess what factors would have non linear relationship with the response?)
o Modern DOE techniques for efficient yet powerful designs (D-Optimum, I-Optimum)
o Monte Carlo Simulations to assess the process robustness using historical data to assess expected variabilityy
Lets take a typical manufacturing process for tablets as an example to start withLets take a typical manufacturing process for tablets as an example to start with
Wet Granulation Fluid Bed Drying Milling Blending CompressionWet Granulation Fluid Bed Drying Milling Blending Compression
How many potentially Critical Process Parameters do we need to assess?
5? 10? 25?
High Shear Wet Granulation: > 40 potential CPPsHigh Shear Wet Granulation: > 40 potential CPPs
High Shear Wet Granulation
Fish-Bone Diagram
CQAs
40>40
Fluid Bed Drying: > 30 potential CPPsFluid Bed Drying: > 30 potential CPPs
Fluid Bed Drying
Fish-Bone Diagram
CQAsCQAs
A Typical Manufacturing Process for TabletsA Typical Manufacturing Process for Tablets
HS Wet Granulation Fluid Bed Drying Milling Blending CompressionHS Wet Granulation Fluid Bed Drying Milling Blending Compression
For a process involving the above unit operations we may end up withFor a process involving the above unit operations we may end up with over 100 potential CPPs.How do we manage it?g
Effective Knowledge Management ! Effective Knowledge Management !
Prior Knowledge Utilization
Blending Unit Operation
CQAs
4 critical variables are left for assessment, the rest are kept atconstant and monitored
Design Variable Prior Experience/Fixed Justify!!
Effective Knowledge Management ! Effective Knowledge Management !
With efficient Prior Knowledge utilization, we can end up with8-16 trials for Experimental Design- feasible!
JMP Statistical Software from SAS
Main effectsMain effects
Interactions
Prior Knowledge
Efficient and Informative Design of ExperimentsEfficient and Informative Design of Experiments
Brainstorming sessions will identify the design factors and their
ranges, while previous knowledge should be effectively utilized to identify those and limit them to the most critical ones
While conducting DoE, all parameters that are not studied should
be kept constant at their optimum fixed level (justify!) in order tobe kept constant at their optimum fixed level (justify!) in order to eliminate the noise and additional variation and increase the
effectiveness of the study
Prior to DoE execution, measurements system integrity and sensitivity must be verified
There is a lot to learn from every DoE: if a factor was found to have
no effect, it can be used to minimize cost or increase robustness by having it set on convenient levelrobustness by having it set on convenient level
DOE and Modeling: Process Robustness and Monte Carlo SimulationDOE and Modeling: Process Robustness and Monte Carlo Simulation
Monte Carlo Simulation: Predicted OOS Rate: ~0.02%
Distribution of the predicted output
Predicted OOS rate
Estimated Process Variability
All examples are for illustration purposes only
Estimated Analytical Variability
QbD Guide for Generics: Step 4 - Define Control Strategies
Questions to ask ourselves:1. Did we evaluate the impact of CMAs and CPPs on CQAs? Did we find
any interactions? What do they mean for us?any interactions? What do they mean for us?
2. Do we have a robust and reproducible process? Do we know the impact of raw materials variability? Did we identify potential sources of variation?
3. Did we establish meaningful In Process and Release specifications?
4. Did we address scale-up challenges?p g
5. ..
Case-Study
IR Tablet Dry Granulation ProcessIR Tablet, Dry Granulation Process
Product Development Outline
Analysis of the reference listed drug (RLD) f Q f (Q ) Defining Quality Target Product Profile (QTPP) Identification of Critical Quality Attributes (CQAs) Identification and evaluation of potential risks related to Drug p g
Product Components (DS and Excipients stability and compatibility), Formulation and Manufacturing Process, etc.
Screening and optimization of formulation Screening and optimization of formulation Development of a robust process (DOE for high risk
parameters) Manufacture of the exhibit batch Establishment of control strategies
QTPPQTPP
Component Target JustificationComponent Target JustificationDosage Form Tablet
Pharmaceutical equivalence to RLDAdministration Route Oral
Dosage Design Immediate release tabletDosage Design Immediate release tablet
Strength X and Y mgs
Bioequivalence AUC and Cmax match RLD under food Bioequivalent to RLD
AppearanceBoth: Brown to orange elegant film coated tablet. Dimensions similar to RLD Marketing requirement; Appearance Dimensions similar to RLD. X mg: round; Y mg: oval
g qNeeded for patient acceptability
Identity Positive for API Needed for labeled claim & therapeutic efficacy
Assay 100% of label claim Needed for therapeutic efficacyAssay 100% of label claim Needed for therapeutic efficacy
Impurities Specified and unspecified impurities meet ICH Q3B. Needed to ensure safety
Disintegration Comparable disintegration time as RLD in appropriate media at room temperaturePharmaceutical equivalence to RLD (possible route of administration as suspension)
Content Uniformity AV
CQAsCQAs
CQA Justification Potentially affected by
Assay Needed for therapeutic efficacy Process
Impurity Needed to ensure safety Formulation & Process
Content Uniformity
Needed for therapeutic efficacy of each unit
Formulation & Process
Dissolution Presumptive qualification for in vivo release and therapeutic efficacy
Formulation & Process
Disintegration Needed to ensure patient Formulation & ProcessDisintegration Needed to ensure patient compliance (suspension)
Formulation & Process
Formulation: Initial Risk Assessment and studies conducted
Formulation AttributeFiller Glid t Disintegrant Lubricant C tiDP CQA type
& amount
Glidant amount
Disintegrant type
& amount
Lubricant type
& amount
Coating formulation
Assay Low Low Low Low Low
Impurities Low Low Low Low Low
Content Uniformity Low Medium Low Low Lowy
Dissolution Medium Low High Medium Low
Disintegration Medium Low High Low Low
Vary type & amount (control strategy: optimized and
fixed) Vary type & amount (control t t ti i d d fi d)
)Fix on high level based on
prior knowledgestrategy: optimized and fixed)
Process Scheme
Mixing II+IIIMilling I
(De-lumping)Mixing IPharmacy
Mixing IV & VCompression I
(slugs)Milling II
Compression II (cores)
C ti C tiCosmetic Coating
Initial Risk Assessment: Process
Unit OperationsUnit OperationsDP CQA Mixing I Milling I
(De-lumping)Mixing II+III Compression I
(Slugs)
Assay Medium Medium Low Lowy
Impurities Low Medium Low Medium
Content Uniformity
Low Medium Medium Medium
L M di L Hi hDissolution Low Medium Low High
Disintegration Low Low Low High
Unit Operations cont'dUnit Operations-cont'd
DP CQA Milling II Mixing IV+V Compression II(Tablets)
Coating
Assay Low Low Low Lowy
Impurities Medium Low Low Medium
Content Uniformity
Low Low Low Low
Dissolution High Low High Medium
Disintegration High Low High Medium
Process Optimization DOE
Based on prior knowledge, previous experience and initial feasibility studies, the most potentially critical process parameters were chosen for further evaluation in DOE study. Additional parameters were set at h i i fi d l l i d d ll d itheir optimum fixed constant level in order to reduce uncontrolled noise and variability
(13 runs including 2 centers, D-Optimum Design using JMP software from SAS)
Unit Operation DOE FactorsLevels Used
Responses -1 0 +1
Compression Low Medium HighCompression I
(Slugs)force Low Medium High 1. Slug weight /RSD
2. Slug hardnessCompression speed Low Medium High
Mill type Quadro NA Frewitt 1. PSD Milling II
type Quad o e tt S2. Bulk & tap density 3. Hausner ratio/FlowMill screen 0.6 NA 0.8
Compression II
Compression force Low Medium High
1. Assay & impurities2. Dissolution Compression II
(Tablets) 3. Content Uniformity 4. Disintegration time5. Tablet Hardness
Compression speed Low Medium High
Prediction Profilers: Factors/Responses relationship-% on PAN (Fines)
Interaction: Mill screen impact is low for Frewitt Type Mill
Prediction Profilers: Factors/Responses relationship (Dissolution)
DOE Model Prediction vs. actual Exhibit Batch data
Selected Response Exhibit Batch Value
Model Predicted
ValueValueHausner Ratio 1.31 1.28% Fines 19% 17%Dissolution T1 AVG 36% 36%Dissolution-T1 AVG (N=6)
36% 36%
Dissolution-T1 RSD (N=6)
10.1% 8.8 %
Dissolution-T3 AVG (N=6)
69% 68 %
UoC RSD(N=10)
1.69 % 1.45 %(N=10)
Good Correlation between Values predicted by DOE Model & p yActual Responses
Process-Risk Mitigation, 1/2
Unit Operations
CompressionDP CQA Mixing I Milling I Mixing II+III Compression I (Slugs)
AssayControlled by
mixing
Controlled by
S Low LowAssay mixing time/speed Screen size
Low Low
Impurities LowLow (Was
found not LowLow (Was
found not pcritical) critical)
CU Low
Controlled by
Screen
Controlled by mixing
Low (Was found not Screen
size time/speed critical)
Dissolution LowLow (Was
found not Low Controlled by critical) slug
hardnessDisintegration Low Low Low
Process-Risk Mitigation, 2/2
Unit Operations-cont'd
DP CQA Milling II Mixing IV+V Compression II(Tablets)
Coating
Assay Low Low Low Lowy
ImpuritiesLow (Was found not critical)
Low Low
Low (Was found not critical)
Content Uniformity Low Low Low Low
Dissolution Controlled by mill
type/ mill screen
Low Controlled by core hardness and
compression
Controlled by fixed
coating levelDisintegration Lowscreen speedg
Summary
Despite all of the challenges, the Generics Industry acknowledges that implementing QbD is the way forward, gainingp g Q y , g g
o Enhanced product and process understanding- robust products and processes
Id tifi ti d t l f f i ti f t do Identification and control of sources of variation- faster and efficient tech transfers, greater process capability
Efficient utilization of prior knowledge is a key to successful QbD implementation in generics
Real change will come if and when
o The risk/cost benefits are realizedo The risk/cost benefits are realized
o Playing field is leveled
o FDA review of the applications shows the benefits of QbD
32
Wh h ld d ?-What should I do next?
-Create an action plan, Adopt the Big Q ConceptCreate an action plan, Adopt the Big Q Concept
Juran on Quality by Design