QbD in PLCM

Presented By: Mr. Girish Sonar,Group Leader – R&D (Formulation),Ipca Laboratories Ltd.,Plot No. Plot 48, Kandivali (West), Mumbai, India.

Date : 13.11.2014

Disclaimer

Any views or opinions expressed herein are solely those

of the author and do not necessarily represent those of

any company. This presentation is solely for

educational purposes and provides only general

expectations of regulatory agencies. For a complete

requirements detail, please consult the relevant

regulatory agency.

Content

1. Introduction to QbD

2. Misconception about QbD

3. Product Life Cycle – Flow diagram

4. Product Development Strategy

5. Initial batches

6. QbD Strategy I, II and III

7. Conclusion

Introduction to QbD

Ref: ICH Q8

Systematic and proactive approach to pharmaceutical

development.

Begins with predefined objectives

Emphasizes product and process understanding and

process control

Based on sound science and quality risk management

Ensure higher level of assurance of product quality for

patient

Improved product and process design & understanding

Monitoring, tracking, trending of product & process.

More efficient regulatory oversight

Rapid introduction of state-of-the art science and

technology

Encouraged continuous manufacturing process

improvements

Benefits of QbD (1)

Real-time quality control and reduced end-product release

testing

Fewer lost batches

Fewer manufacturing deviations, saving costly investigative

hours

Reduced out-of-specification results, reducing rework

Reduce post approval changes/Variations

Benefits of QbD (2)

1. Introduction to QbD

2. Misconception about QbD

3. Product Life Cycle – Flow diagram

4. Product Development Strategy

5. Initial batches

6. QbD Strategy I, II and III

7. Conclusion

Misconception about QbD (1)

QbDSystemic development

with predetermine objective for quality product

QbD contains DoE No software used to establish QbD

DoE Statistical technique used in interpreting sets of experiments aimed at making sound decisions DoE may be part of QbD DoE is implemented using statistical software program

Conclusion : QbD and DoE are two different terminologies

Misconception about QbD (2)QTPP Desired target for

developmental work Components of QTPP may or

may not be in specificationNot in spec – Dosage form, strengthIn spec – Assay, impurities Does not include acceptance

criteria

Specifications Includes all of the CQAs Specification is a list of - tests, - references to analytical

procedures - acceptance criteria Establishes the set of criteria to

which DP should conform to be considered acceptable for its intended use

Conclusion : Defining a QTPP does not mean setting all acceptance criteria or the product specifications before development work begins

Misconception about QbD (3)

QbD based PDR is document mandatory for

regulatory submission and to make it for the sake to

fulfill the submission criteria

QbD is the USFDA requirement only

DoE is mandatory for QbD as mentioned in published

IR/MR product example

1. Introduction to QbD

2. Misconception about QbD

3. Product Life Cycle – Flow diagram

4. Product Development Strategy

5. Initial batches

6. QbD Strategy I, II and III

7. Conclusion

QTPP CQA Initial Risk Assessment

Drug Substance

Formulation Variables

Updated Risk

Assessment

Risk Mitigation

Process

Packing Design Space

Quality Risk Management

Control Strategy

PLCM – Flow Diagram

QbD Scheme

1. Introduction to QbD

2. Misconception about QbD

3. Product Life Cycle – Flow diagram

4. Product Development Strategy

5. Initial batches

6. QbD Strategy I, II and III

7. Conclusion

Product Development Strategy

1. Introduction to QbD

2. Misconception about QbD

3. Product Life Cycle – Flow diagram

4. Product Development Strategy

5. Initial batches

6. QbD Strategy I, II and III

7. Conclusion

Initial Development Batches

1. Introduction to QbD

2. Misconception about QbD

3. Product Life Cycle – Flow diagram

4. Product Development Strategy

5. Initial batches

6. QbD Strategy I, II and III

7. Conclusion

QbD Stage I Strategy

Case Study

Development Batches

Formula Optimization

Process Optimization

Process Optimization

Process Optimization

Process optimization planned based on knowledge of –

1.Scale dependent equipment/Process parameter2.Scale independent equipment/Process parameter

If R&D scale and commercial scale equipments have same mechanism, same geometry and scalable based on scientific basis, then process optimization batches can be perform in R&D scale equipment. If not, then process optimization batches will be performed in commercial scale equipment.

Process Optimization – Wurster

Scale independent

Process Optimization - Wurster

Steps Formulation Variables Process VariablesPELLETS AND MUPSDrug Loading/ Barrier Coating/ Functional Coating/ Over Coating

NPS – Composition, Size, Shape, Density and PorosityPolymer qtyPlasticizer qtyAntitacking anent/wetting agent Qty

Pellets CoatingAir flow rateSpray rateAtomization air pressureProduct temperatureDew pointCuring temperature and timeMUPS CompressionPre-compression forceMain compression forceFeeder speedTurret speedFeed frame designTooling design

Process Optimization - Granulation

Steps Formulation Variables Process VariablesWET GRANULATION

Granulation

1. Binder Qty2. Water/Solvent Qty3. Diluent/Superdisintegrant/ Polymer/ Wetting agent Qty …..

RMG Granulation1.Impeller speed2.Chopper speed3.Binder addition time4.Granulation time5.Wet millingFluid Bed Granulation1.Spray rate 2.atomization air pressure3.Air flow4.Product temperature5.Granulation time

Drying1. Product temperature2. Air flow3. LOD

Process Optimization - Granulation

Steps Formulation Variables Process VariablesWET GRANULATIONSizing and Milling

1. Type of mill (Multimill/Co-mill)2. Type of screens (Plain/Grater)3. Screen size4. Mill speed

Blending and Lubrication

1. Superdisintegrant/Polymer Qty2. Glidant/Antiadhering

agent/Lubricant qty

1. Blending time2. Lubrication time3. Blender type

Process Optimization - Granulation

Steps Formulation Variables Process VariablesROLLER COMPACTION

Granulation1. Dry Binder Qty2. Diluent/Superdisintegrant/

Polymer/ Wetting agent Qty

1. Type of roller2. Compaction force3. Roller gap4. Roller speed5. Feed speed

Sizing and Milling

1. Type of mill (Multimill/Co-mill)2. Type of screens (Plain/Grater)3. Screen size4. Mill speed

Process Optimization – Study Plan (1)

Equipment Scalable process parameters

Recommended Remark

Wurster (Bottom spray)

Spray rate, atomization air pressure, air flow volume, dew point

ADP area is considered to calculate the scale up factor and apply to all critical process parameter except dew point and product temp

Scale independent

RMG Impeller speed , Chopper speed, Granulation time

Tip velocity : Low speed = 3.0-3.5m/Sec, High Speed = 6.0-7.0m/Sec at the R&D scale and commercial scale

Scale independent

FBP (Top Spray granulation)

Spray rate, atomization air pressure, air flow volume, dew point

Calculate the scale up factor based on vendor’s recommendation and apply for critical process parameters

Scale independent

Multimill Milling

screen opening, mill speed and direction

Screen size/impeller direction/ mill speed should be same

Scale independent

Co- mill screen opening, mill speed and direction

Screen size/impeller direction/ mill speed should be same. Apply scale factor as per vendor’s recommendation

Scale independent

Equipment Scalable process parameters

Recommended Remark

Blender No of revolutions, Blender geometry

Blending : 300 ± 10 revolutions,Lubrication: 50 ± 5 revolutions.Calculate the blender rpm and time based on Froude no calculation.

Scale independent

Roller Compaction

Roller speed, roller gap, compaction force, milling parameters

Scaling up factor varies from mechanism of roller compaction and follow vendor’s guideline for scale-up

Scale independent most of the time

Compression machine

Turret speed, feeder speed, pre-compression force, main compression force, dwell time

Optimize the process parameters wrt compression machine at manufacturing site

Scale dependent

Coating Spray rate, atomization air pressure, product temp, gun to bed distance, pan rpm

Optimize the process parameters wrt coating machine at manufacturing site

Scale dependent

Process Optimization – Study Plan (2)

QbD Stage II Strategy

QbD Stage II Strategy

QbD II strategy = Updated risk assessment with justification based on development batches results

Quality Risk Management

Quality Risk Management

Risk Review

Ris

k C

omm

unic

atio

n

Risk Assessment

Risk Evaluationunacceptable

Risk Control

Risk Analysis

Risk Reduction

Risk Identification

Review Events

Risk Acceptance

InitiateQuality Risk Management Process

Output / Result of theQuality Risk Management Process

Risk M

anagement tools

Ref: ICH Q9

Risk Management Tools

1. Basic risk management facilitation methods (flowcharts, check sheets etc.)

2. Failure Mode Effects Analysis (FMEA)3. Failure Mode, Effects and Criticality Analysis (FMECA)4. Fault Tree Analysis (FTA)5. Hazard Analysis and Critical Control Points (HACCP)6. Hazard Operability Analysis (HAZOP)7. Preliminary Hazard Analysis (PHA)8. Risk ranking and filtering9. Supporting statistical tools

Ref: ICH Q9

Basic Risk Management Facilitation Method

1. Flowcharts;2. Check Sheets;3. Process Mapping; Cause and Effect Diagrams (also called an Ishikawa diagram or fish bone diagram)

FMEA – Case Study (1)

RPN : Risk Priority Number

FMEA – Case Study (2)

QbD Stage III Strategy

QbD Stage III Strategy

QbD III Strategy = Control Strategy

Control strategy should be discussed with manufacturing

person before finalize for the best results

All critical attributes control should be mentioned clearly

in control strategy and mentioned the name of reporting

documents

Post submission Phase

Easy to perform

based on QbD

based Dossier contains

evaluated critical attributes

1. Introduction to QbD

2. Misconception about QbD

3. Product Life Cycle – Flow diagram

4. Product Development Strategy

5. Initial batches

6. QbD Strategy I, II and III

7. Conclusion

Conclusion

QbD is the effective tool, should be implement from the initial stage of the product development independent of target market Discuss QbD scheme with other groups and stake holder to achieve aim of QbD and keep future projection to avoid regulatory queries and post approval changes/Variation DoE is not mandatory for QbD based submission Try to cover maximum range of formulation and process variables during optimization study to make fastest and cost effective post approval changes/Variation

Girish SonarGroup Leader – R&D (Formulation)Ipca Laboratories Ltd.Plot No. Plot 48, Kandivali Industrial Estate,Kandivali (West), Mumbai 400 067, India.www.ipca.com