1. A QbD Approach to Process Development: Defining Critical Quality Attributes and Evaluating Criticality Across Scales
Carl A. Anderson, Duquesne University
NIPTE – Scientific Design of Pharmaceutical Products,
October 3rd, 2016
FDA White Oaks Campus, Silver Spring, MD

2. Researchers and Acknowledgements
FDA
Sharmista Chatterjee, Ph.D.
Duquesne University
Graduate School of Pharmaceutical Sciences
Carl A. Anderson, Ph.D.
Ira S. Buckner, Ph.D.
James K. Drennen, Ph.D.
Peter L.D. Wildfong, Ph.D., B.Sc.
Yuxiang (Henry) Zhao
Natasha Velez-Rodriguez
Pradeep Valekar
Purdue University
Carl R. Wassgren
Siddhartha Agarwal
University of Connecticut
Bohdi Chaudhuri, Ph.D.
Robin Bogner, Ph.D.
Kim Tran
Koyel Sen
Acknowledgements
James D. Lister, Ph.D.
Daniel Peng, Ph.D.
Jayanth Bhupasamudram
Maitraye Sen
Enhanced technical detail available at poster session
See: “A QbD Approach to Process Development: …”
We are grateful to the National Institute for Pharmaceutical Technology and Education (NIPTE) and the U.S. Food and Drug Administration (FDA) for providing funds for this research. This study was funded by the FDA Grant to NIPTE titled "The Critical Path Manufacturing Sector Research Initiative (U01)"; Grant# 5U01FD004275

3. Project Description 1
The investigators propose a project that will provide an example of QbD used for effective scale‐up of a complex multistep manufacturing process of a high‐risk (narrow therapeutic window) API, while investigating the criticality of quality attributes (CQAs) and process parameters (CPPs) across scales.
Process
High Shear Granulation and drying
Polymer film coating of granules
Blending
Compression

4. Project Description 2 API – theophylline Formulation strategy
Narrow therapeutic window
Facile formation of hydrate including solvent mediated transformation1
Formulation strategy
Dosage form: tablet
Controlled drug delivery: coated granules
1Rodriguez‐Hornedo, N., et al., Int J Pharm, : p. 149‐162
Yuen and Grant, International Journal of Pharmaceutics, (1‐2): p. 151‐16
Ticehurst et al., International Journal of Pharmaceutics, (1‐2): p. 1‐10
Airaksinen, S. et al., J Pharm Sci, : p. 516‐528

5. Experimental Plan Granulation Coating Blending Compression
Variability from formulation and processing conditions
Variability from formulation and processing conditions
Variability from formulation and processing conditions
Variability from processing conditions
Granule
variant - high
Coated granule
variant - high
Blend
variant - high
Experimental Plan
Optimum
granule
Optimum
coated granule
Optimum
blend
Granule
variant - low
Coated granule
variant - low
Blend
variant - low
Each variant is the processing condition/input that is the most significant for that process
Product
Variability
Acceptable
Quality

6. Risk Identification for Granule Coating
Initial risk identification for all processes

7. Initial Risk Severity Assessment
Critical Quality Attributes and severity ranking
Content uniformity 5
Assay 5
Physical stability 2
Chemical stability/purity 4
Dissolution 5
Microbiology 4
Appearance 3
Tablet RTS 3
Severity scale
Has no appreciable consequences to quality (change mid-process, change next batch, root cause is well understood)
Batch loss
Batch loss and mild risk to patient
Between 3 and 5
Batch loss and severe risk to patient (potentially lethal)

8. Risk and Process Control
Process control models within each unit operation
Control
Predict quality
Analytical models to measure attributes during unit operation
Hierarchical models to use incoming material attributes.
Understand risk at a given scale
Understand additional risks that arise from scale-up
Risk within a scale of a PAT empirical measurement – Validated method
Additional risk of scale – representative sampling

9. Critical Initial Process/Formulation Optimization Characteristics
Granule size and density to facilitate coating
Coated granules must have appropriate flowability for blending and transfer
Granule coating to control drug release
Coating on the granules must stay intact during compression

10. HSWG Scale Up Scale up equipment Will use a regime map approach
1 liter scale (Diosna mixer/granulator P 1-6)
Current formulation
Process development
Next scale: 10 and 25 liter bowls (Diosna P Vac 10-60)
Will use a regime map approach
Described in Kayrak-Talay et al. (2013, Powder Technology, Vol. 240, pp. 7 – 18)
Empirical (little to no knowledge) => regime map (known parameters and processes) => mechanics-based (significant knowledge)
Process
Identify operating conditions at the lab scale
Ensure that significant dimensionless parameters result in operation in the same regime during scale-up

11. Regime Maps Nucleation Regime Map Growth Regime Map
granule deformation Stokes #
deformation strength)
(impact pressure /
dimensionless drop penetration time
(time for drop to penetrate/
bed circulation time)
dimensionless spray flux
(rate at which drop area is generated /
rate at which fresh powder area appears)
granule pore saturation
(fraction of granule volume filled with liquid)

12. Granule Coating
Process Model

13. Granule Coating Process Details
Material and
Environmental Variability
Granule Coating Process Details
Engineering Control
FFC
Analytical Control
Coating thickness / Moisture level
Deviation
Air Volume
Atomization pressure
Spray rate
Inlet air temperature
Set points
Target
Basic Control
some delay
Heater on/off
Flap position
Pump speed
FBC
FBC
FBC
Process
Measurement
Air Volume
Atomization pressure
Spray rate
Inlet air temperature
Adjustments
Temperature / Air Volume
Target
Deviation
nearly no delay
Deviation
Dissolution / Flowability / Mechanical Properties
Target
long delay and RTRT model required

14. Granule Attrition Example
Low-density granules
Labelled Particle Size: 355 – 710
Attrition Resistance: 96% ± 0.49% (n = 3)
High-density granules
Labelled Particle Size: 355 – 710
Attrition Resistance: 98% ± 0.04% (n = 3)

15. Coating Thickness Determination and Attrition Resistance
Attrition Resistance: 98.8% ± 0.01% (n = 3)
Attrition Resistance: 95.0% ± 0.74% (n = 3)

16. Blending and Compaction Formulation and Process Development
Formulation Optimization Study
Formulation Development Study
Identify initial formulation system
Define formulation requirements in TPP
Is the formulation manufacturable?
The development or improvement of pharmaceutical formulations typically involves many materials and process variables that interact in a complex way, making control and optimization a complex, time consuming and costly task.
Designing and implementing formulation development studies at small-scale can be the most effective and fast approach to understanding the formulation system and to optimizing the formulation according to formulation requirements defined by the TPP.
Process feasibility studies at small-scale for unit operation (Blending)
Identification of relevant formulation variables (type of brittle excipient, multiple pharmaceutical grades)
Identification of critical responses (Blend stability)
Design of Experiments to select best combination of excipients.
Evaluate excipients capabilities to meet critical quality attributes (compressibility, compatibility)
Selecting final excipients and optimal levels
A small-scale process feasibility study demonstrated to be a fast and effective approach to selecting the combination of excipients that better advances blending of the formulation system.
It provides the opportunity for continuous formulation optimization by assessing formulation manufacturability and quality across unit operations.
Process feasibility studies at small-scale for unit operation
Identification of relevant formulation variables
Identification of critical responses
Product Description: Extended release tablet
Formulation Goals: must be manufacturable and facilitate compression, while avoiding any disruption to the release coating membrane.
Compatibility and preliminary feasibility studies
Coated active granules + extragranular excipients
Evaluate excipients capabilities to meet critical quality attributes
Selecting final excipients and optimal levels

17. Small-Scale Formulation Development Study
Evaluate the effect on blend stability (NIR)
Brittle excipient
MCC grades
A small-scale process feasibility study demonstrated to be a fast and effective approach to selecting the combination of excipients that better advances blending of the formulation system.
It provides the opportunity for continuous formulation optimization by assessing formulation manufacturability and quality across unit operations.

18. Developing Compaction Process Understanding
Characterize Formulation Components
Compressibility
Strength
Disintegration
Mean Yield Pressure/Indentation Hardness
Strain rate sensitivity
Intergranular Excipients
Coated Granules
Predict and Model
Optimum Formulation
Strength
Release
Scale-up changes
DEFORMATION

19. Compaction: Process Understanding
Strength as a function of solid fraction can be predicted from the behaviors of formulation components. The insensitivity of this relationship to changes in compression speed allows adjustments to be made during scale-up.

20. Release of API from Coated Particle Systems
Coating Damaged – Faster release
Matrix Formed – Slower release

21. Anticipated Outcomes
Process models and/or analytical end-points for individual unit operations at small scale
First principle and empirical models
Scale-up
Adjustments to process models
Sampling verification and robustness of analytical end-points
Prediction of final product CQAs throughout process train