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"

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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

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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