in-situ particle characterization: applications in formulation development · pdf...

In-Situ Particle Characterization: Applications in Formulation Development and Manufacturing –

Disintegration/Dissolution. Fluid Bed Layering, and High Shear Wet Granulation

Jeffrey W. Sherman, Ph.D.Des O’Grady PhD

The Cortona Conference

- Introduction to FBRM, PVM and the Formulations Workflow

- Case Study 1 – Mapping the Dissolution Design Space

- Case Study 2 – Optimizing Dissolution Through Root Cause Identification

- Case Study 3 – Correlating Upstream Parameters to Dissolution Behavior

- Case Study 4 – Understand Physical Properties to Improve Dissolution

- Conclusion

Executive Summary

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3

Dissolution and PAT

FBRM® is used to understand the impact a formulation step has on tablet disintegration and dissolution mechanisms

FBRM® can identify the root cause of dissolution inconsistency leading to a better understood process

FBRM® can speed process development by providing immediate understanding that allows formulators to make better decisions faster

This can save up to months of development time, target consistent final products, and ensure formulators have answers for regulatory questions

Where does particle size matter?

Wrong sizeMultiple mill passesIncomplete milling

Mill

Optimize

Varying raw materialsSegregation in feedVariable API particle size

API ExcipientsBinder

Fluid bed

High attrition ratesInconsistent endpointIntensive optimization studies

Granulator

Inconsistent endpoint (fine, coarse, bimodal)API agglomeration Form conversionScale-up issues

Poor flow properties SegregationVariable tablet properties

Tablet press

Blender

The significance of particle size and shape

Dr. Zhigang Sun, FDA, Particle Size Specifications for Solid Oral Dosage Forms, AAPS 2008, Atlanta

The significance of particle size and shape

Dr. Zhigang Sun, FDA, Particle Size Specifications for Solid Oral Dosage Forms, AAPS 2008, Atlanta

FDA Stance on Dissolution Testing

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http://www.fda.gov/cder/Offices/ONDQA/presentations/051024-MMN-Dissolution.pdf

FDA Stance on Dissolution Testing

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Moheb Nasr, FDA-CDER, Advisory Committee of Pharmaceutical Science (ACPS)

FDA Stance on Dissolution Testing

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Moheb Nasr, FDA-CDER, Advisory Committee of Pharmaceutical Science (ACPS)

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PVM® TechnologyParticle Video Microscope

Microscope quality image sin-process and in real time

Characterize particle systems from 2µm to 1mm

FBRM® TechnologyFocused Beam Reflectance Measurement

Track the rate and degree of change to particle dimension, count and shape as they naturally exist in process

Characterize particle systems from submicron to 3mm

FBRM® PVM®

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The FBRM® Method of Measurement

FBRM®Probe TubeFBRM®Probe Tube

SapphireWindowSapphireWindow

Beam splitterBeam splitter

Rotating opticsRotating optics

FBRM®Probe TubeFBRM®Probe Tube

SapphireWindowSapphireWindow

Laser source fiberLaser source fiber

Beam splitterBeam splitter

Rotating opticsRotating optics

Focused beamFocused beamFBRM®Probe TubeFBRM®Probe Tube

SapphireWindowSapphireWindow

Detection fiberDetection fiberLaser source fiberLaser source fiber

Beam splitterBeam splitter

Rotating opticsRotating optics

Focused beamFocused beamFBRM®Probe TubeFBRM®Probe Tube

SapphireWindowSapphireWindow

Cutaway view of FBRM® In-process Probe

PVM® image illustrating the view from the FBRM® Probe Window

Probe installed in process stream

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The FBRM® Method of MeasurementPVM® image illustrating the view from the FBRM® Probe Window

Probe detects pulses of Backscattered light

And records measured Chord Lengths

Enlarged view

Path of Focused Beam

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The FBRM® Method of Measurement

Path of Focused Beam

Enlarged view

Thousands of Chord Lengths are measured each second to produce the FBRM® Chord Length Distribution :

Square weighted Distribution

Optimizing Petroleum processing with In Situ Particle Characterization

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FBRM distributions at key points show a decrease in count and an increase in dimension - particle agglomeration

Applying a weighting to the FBRM distribution enhances the resolution to change in different size ranges

Unweighted distribution is sensitive to fine particles and particle population

Square weighted distributions is sensitive to coarse particles and particle dimension

Decrease in population

Increase in dimension

Unweighted Distribution

Decrease in population

Increase in dimension

Optimizing Petroleum processing with In Situ Particle Characterization

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Mean = 75µm

Mean = 82µm

Mean = 141µm

Unweighted Distribution

Square Weighted Distribution

#/s <50 µm

#/s 50-1000 µm

Where does particle size matter?

Wrong sizeMultiple mill passesIncomplete milling

Mill

Optimize

Varying raw materialsSegregation in feedVariable API particle size

API ExcipientsBinder

Fluid bed

High attrition ratesInconsistent endpointIntensive optimization studies

Granulator

Inconsistent endpoint (fine, coarse, bimodal)API agglomeration Form conversionScale-up issues

Poor flow properties SegregationVariable tablet properties

Tablet press

Blender

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Dissolution and PAT

Dissolution testing with in situ particle characterization is a powerful tool for Quality by Design

FBRM® can link tablet disintegration and dissolution mechanisms to upstream formulation process steps and to the initial API and excipient particle size

Other techniques such as UV/VIS or IR to track solute concentration in real time

Speed process development by understanding if dissolution inconsistency it due to the formulation, raw materials or process variability

FBRM®

S400

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MT-Distek – Dissolution inconsistency

In this study dissolution and disintegration kinetics are compared for two acetaminophen based painkillers

A comparison is made between innovator and generic rapid release gel tablets

The innovator API release rate (UV-vis) is significantly faster. Why?

FBRM trends show the innovator tablet breaks apart rapidly into fine particulates

The generic tablet breaks apart slowly into a smaller number of fine particulates

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MT-Distek – Dissolution inconsistency

The initial median dimension of the innovator particulates is ~40µm and it rapidly breaks apart to ~6µm

The initial median dimension of the generic particulates is ~52µm and it slowly breaks apart to ~10µm

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MT-Distek – Dissolution inconsistency

The difference in release rate is clearly NOT a result of a difference in the intrinsic API solubilization rate – (i.e. differences in API PSD or impurity profile)

The difference is clearly a result of the formulation conditionsOver granulation or incomplete milling results in large granules in tablet press?

Difference in formulation components influences the inter and intra granule forces?

Fragmentation and compaction on the tablet press results in granule agglomeration?

Tablet coating retards granule break-up?

Tablet and Granule Release Mechanisms

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Disintegration

Diffusion

Mark Menning, Amgen, FBRM User’s Conference 2000

Innovator

Generic

- Introduction to FBRM, PVM and the Formulations Workflow

- Case Study 1 – Mapping the Dissolution Design Space

- Case Study 2 – Optimizing Dissolution Through Root Cause Identification

- Case Study 3 – Correlating Upstream Parameters to Dissolution Behavior

- Case Study 4 – Understand Physical Properties to Improve Dissolution

- Conclusion

Executive Summary

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Understand Disintegration Kinetics Leads to Risk Assessment

23Novel process Analytical Methodology to Establish Design Space and Real Time Prediction of Dissolution. Jonas Johansson, Staffan Folestad et al, AZ PAT Centre of Excellence, AstraZeneca R&D, Mölndal, Sweden, AAPS Meeting May 2006

Understand Disintegration Kinetics Leads to Risk Assessment

24Novel process Analytical Methodology to Establish Design Space and Real Time Prediction of Dissolution. Jonas Johansson, Staffan Folestad et al, AZ PAT Centre of Excellence, AstraZeneca R&D, Mölndal, Sweden, AAPS Meeting May 2006

CountDisso

CountDisso

Understand Disintegration Kinetics Leads to Risk Assessment

25Novel process Analytical Methodology to Establish Design Space and Real Time Prediction of Dissolution. Jonas Johansson, Staffan Folestad et al, AZ PAT Centre of Excellence, AstraZeneca R&D, Mölndal, Sweden, AAPS Meeting May 2006

- Introduction to FBRM, PVM and the Formulations Workflow

- Case Study 1 – Mapping the Dissolution Design Space

- Case Study 2 – Optimizing Dissolution Through Root Cause Identification

- Case Study 3 – Correlating Upstream Parameters to Dissolution Behavior

- Case Study 4 – Understand Physical Properties to Improve Dissolution

- Conclusion

Executive Summary

26

Understand Disintegration Kinetics Leads to Risk Assessment

27Novel process Analytical Methodology to Establish Design Space and Real Time Prediction of Dissolution. Jonas Johansson, Staffan Folestad et al, AZ PAT Centre of Excellence, AstraZeneca R&D, Mölndal, Sweden, AAPS Meeting May 2006

Understand Disintegration Kinetics Leads to Risk Assessment

28Novel process Analytical Methodology to Establish Design Space and Real Time Prediction of Dissolution. Jonas Johansson, Staffan Folestad et al, AZ PAT Centre of Excellence, AstraZeneca R&D, Mölndal, Sweden, AAPS Meeting May 2006

Understand Disintegration Kinetics Leads to Risk Assessment

29Novel process Analytical Methodology to Establish Design Space and Real Time Prediction of Dissolution. Jonas Johansson, Staffan Folestad et al, AZ PAT Centre of Excellence, AstraZeneca R&D, Mölndal, Sweden, AAPS Meeting May 2006

- Introduction to FBRM, PVM and the Formulations Workflow

- Case Study 1 – Mapping the Dissolution Design Space

- Case Study 2 – Optimizing Dissolution Through Root Cause Identification

- Case Study 3 – Correlating Upstream Parameters to Dissolution Behavior

- Case Study 4 – Understand Physical Properties to Improve Dissolution

- Conclusion

Executive Summary

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Investigating Tablet Properties and Understanding Design Space

Understanding the Correlation Between Drug Dissolution Behavior and Key Formulation Parameters: A Vertex Case Study Kyle Bui Analytical Development Vertex Pharmaceuticals April 28, 2008 Dissolution AAPS Meeting

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Investigation of an Atypical Observation of Harder Tablets Having a Faster Dissolution Rate Zane Arp, GlaxoSmithKline, AAPS November 2007

Investigating Tablet Properties and Understanding Design Space

Statistic particle distribution results during dissolution of tablets at different compression forceTablet dissolution profile by Fiber Optical UV at

different compression force

Low compression tablet

High compression tablet

CompressForce

c50sq wt

c90sq wt

5kN 207µm 371µm

15kN 176µm 315µm

Chord length µmTime (minutes)

In the Vertex example - a higher compression force results in larger granules and a slower dissolution rate

In the GSK example - a higher compression force results in a smaller granules and faster dissolution

Monitoring granule dimension during dissolution helps understandinconsistencies and enables formulators to target consistent dissolution profiles with each new formulations step

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Investigating Tablet Properties and Understanding Design Space

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Conclusion

FBRM® is used to understand the impact a formulation step has on tablet disintegration and dissolution mechanisms

FBRM® can identify the root cause of dissolution inconsistency leading to a better understood process

FBRM® can speed process development by providing immediate understanding that allows formulation scientists to make better decisions faster

This can save up to months of development time, target consistent final products, and ensure formulators have answers for regulatory questions

Optimization of High Shear Wet Granulation using FBRM® and PVM®

Zane Arp, PhDGlaxoSmithKline

Investigator – Process Analytical ChemistryPD-MOST-PUC

Benjamin Smith Mettler Toledo Market Manager

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Agenda

Introduction to high shear wet granulation

DOE Study: Acetaminophen granulation on 6 liter scale- DOE Outline- Ideal batch conditions and process upsets - Extreme process conditions- Varying wet massing time- Optimization and control with varying spray rate

Conclusions

Where do Particles Play a Role?

Fluid bedGranulator

Mill

Tablet press

API ExcipientsBinder

Blender

Why do we Granulate?

Objective is to produce larger, denser agglomerates from fine powders that improve downstream processing

- Improve handling- Improve product appearance- Enhance flow and mixing properties- Control of solubility and porosity- Increase bulk density and storage- Create of non-segregated blends of powder ingredients

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An Overview of Fine-Powder Granulation using a BinderGabriel I. Tardos, City College of New York

Factors Affecting Granule Growth

Binder concentration - Granule strength and tablet strength increase as binder concentration increases

Effects of raw material properties - Packing properties of the solids- Particle shape and surface morphology - Particle size distribution: The smaller the particle size of the raw material, the

more binder liquid required.

Process conditions- Wet massing/densification- High shear vs low shear mixer- Impeller rotation speed - Evaporation of the solvent (Temperature)- Contact angle of the binder liquid to the solids - Solubility of the particles in the binder liquid - Liquid addition: A narrow margin exists between the liquid required to obtain

granule growth and the amount that results in an over-wetted mass.

39Ensuring Better Control of Granulation

Rakesh P. Patel, PhD, and A.M. Suthar,

What is the Granulation Endpoint?

Endpoint can be defined by the formulator as a target particle size distribution, granule density, and rheology

- Wet Granulation: End-Point Determination and Scale-UpBy Michael Levin, Ph. D. “Granulation: End-Point Theory, Instrumentation, and Scale-Up,” AAPS 1999

- Ensuring Better Control of GranulationRakesh P. Patel, PhD, and A.M. Suthar, Gnpat University, India

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Effect of Varying Starting Distribution

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∆ (mean) ~100 µm during blend

Comparing trends highlights differences between the Batch 1 and Batch 2

Batch 1 exhibits a consistently larger granule dimension throughout the batch

The root cause for this appears to be a difference in the size of the raw materials

Effect of Varying Starting Distribution

Batch 1 product is coarser - likely due to larger starting particle distribution

Batch 2Sq Wt Mean = 317.45µm

Batch 1Sq Wt Mean = 476.11µm

Elements of Quality by Design

Process understanding- DOE

Risk management - Root cause analysis

Process analytical tools - NIR, FBRM, PVM, FTIR, UV-vis

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What Process Variables Affect Granulation Endpoint?

Final granule density and particle distribution predicted by: - Binder addition - Moisture addition- Chopper speed - Impeller speed- Spray position- Nozzle type- Spray flux- Product composition- Starting particle distribution

The following are variables based on the above criteria: - Batch time- Bowl type- Temperature

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Wet Granulation: End-Point Determination and Scale-UpMichael Levin, Ph. D. AAPS 1999

Choosing Identical Operating Conditions Does Not Always Lead to Batch to Batch Reproducibility

Three identical Placebo Batches?- Same formulation, same starting materials, same spray rate, same bowl,

same impeller speed, same probe location, same wet mixing time

Process Variability in granulation growth occurs during the wet mass

45Time (2 sec intervals)

Mea

n (s

quar

e w

eigh

t)

Sam

ple S

ampl

e

Batch to Batch Reproducibility at Endpoint?

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Time (2 sec intervals)

Mea

n (s

quar

e w

eigh

t)

Batch 4 EndBatch 4 End Batch 3 EndBatch 3 EndBatch 2 EndBatch 2 End

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Agenda

Introduction to high shear wet granulation

DOE Study: Acetaminophen granulation on 6 liter scale- DOE Outline- Ideal batch conditions and process upsets - Extreme process conditions- Varying wet massing time- Optimization and control with varying spray rate

Conclusions

DOE Characterization and Control of API Granulation

6 liter bowl

12 batch DOE- Vary moisture spray 20%, 25%, 30%- Vary impellor rpm: Low, high- Vary wet mass time 1 min, 2min, 4min

In-Process Understanding

FBRM® associates particle system dynamics with processing conditions in real time

Faster understanding and optimization

No sampling required.

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Consistent Measurements

The FBRM® window stayed clean allowing the system to track particle agglomeration, compaction, and breakage even in the most cohesive API particle conditions.

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FBRM® Probe Installation

Probe location - Near wall, deep install, 0.5-1” above bottom blade

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Formulation:Material Weight (g)

Acetaminophen 625

PVP 20

AC-Di-Sol 20

Lactose Monohydrate 165

Avicel PH-101 135

Total Mass 965

Material Weight (%)

Water 20, 25, 30%

DOE Experimental Setup

DOE # Batch # Water addition (%)

Actual water addition

amount (g)

Addition rate (mg/min)

Wet massing

time (min)

Impeller speed (RPM)

1 15 25 190 90 4 271

2 16 20 190 90 1 542

3 17 20 190 90 4 542

4 6 30 288 90 4 271

5 7 25 240 90 2.5 271

6 8 30 288 90 4 542

7 9 25 240 90 2.5 542

8 10 30 288 90 4 271

9 11 25 240 90 2.5 271

10 12 30 289 90 1 542

11 13 25 243 90 2.5 542

12 14 20 193 90 1 271

13 21 25 246 125 2 542

14 22 25 255 125 2 542

15 23 25 242 25 1 542

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Agenda

Introduction to high shear wet granulation

DOE Study: Acetaminophen granulation on 6 liter scale- DOE Outline- Extreme process conditions- Varying wet massing time- Optimization and control with varying spray rate

Acetaminophen Batch Scale-up and Control in 75 liter vessel

Conclusions

Endpoints from Three Extremes in Water Addition Result in Drastically Different Endpoint Distributions

FBRM® distributions provide immediate target endpoint detection, ensuring consistent downstream particle flow, tableting and dissolution

5422.52575424306

5424203Impeller speed (RPM)Wet massing time (min)Water addition (%)DOE #Color

Endpoints from Three Extremes in Water Addition Result in Drastically Different Endpoint Distributions

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DOE #6

DOE #7

DOE #3

FBRM® distributions provide immediate target endpoint detection, ensuring consistent downstream particle flow, tableting and dissolution

Statistic DOE #3 DOE #7 DOE #6

Mean Lth Wt 120µm 187µm 336µm

DOE #3 Under Granulated

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DOE #7 Ideal

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DOE #6 Over Granulated

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Conclusions

Critical Process ParametersFBRM® helped immediately identify the critical process parameters:- % moisture addition- Wet massing time- Moisture addition rate

Control Batch to Batch Repeatability FBRM® trends were used to troubleshoot unexpected process changes. FBRM® can be used to minimize a-typical batches and improve batch to batch repeatability

Identify Batch EndpointFBRM® distributions provided immediate target endpoint detection, ensuring consistent downstream particle flow, tableting and dissolution

Ensure Consistent Measurements The FBRM® window stayed clean allowing the system to track particle agglomeration, compaction, and breakage even in the most cohesive API particle conditions.

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Acknowledgements/References

Moheb Nasr, CDER, FDA- http://www.fda.gov/cder/Offices/ONDQA/presentations/051024-MMN-Dissolution.pdf

Jonas Johansson, AstraZeneca, Sweden- http://www.aapspharmaceutica.com/meetings/files/63/Johansson.pdf

Kyle Bui Vertex Pharmaceuticals- http://www.aapspharmaceutica.com/meetings/files/126/bui.pdf

Zane Arp, GlaxoSmithKline, AAPS, November 2007

Michael Cheng, Amgen, AAPS, November 2007

Jeff Seely, Distek

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