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

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

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

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, 1992. 85: p. 149‐162 Yuen and Grant, International Journal of Pharmaceutics, 1996. 135(1‐2): p. 151‐16 Ticehurst et al., International Journal of Pharmaceutics, 2002. 247(1‐2): p. 1‐10 Airaksinen, S. et al., J Pharm Sci, 2003. 92: p. 516‐528

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

Risk Identification for Granule Coating Initial risk identification for all processes

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)

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

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

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

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)

Granule Coating Process Model

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

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)

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

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

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.

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

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.

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

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