DEVELOPMENT OF QUALITY BY DESIGN (QBD) GUIDANCE ELEMENTS ON DESIGN SPACE SPECIFICATIONS ACROSS SCALES WITH STABILITY CONSIDERATIONS Fluid Bed Drying Small Scale Experimentation Benoit Igne, Sameer Talwar, Brian Zacour, Carl Anderson, James Drennen Duquesne University Center for Pharmaceutical Technology

Fluid Bed Drying An optimal point from the wet granulation DOE was chosen from the data available and was produced repetitively to study drying. Drying was controlled using a hybrid method that combines first principles (thermodynamic) modeling with empirical measurements, process models, and real time data management.

Hybrid Controls (First-Principles and Empirical) Establish a thermodynamic environment inside the dryer that ensured a constant drying mechanism and product properties. –Managed environmental fluctuation by adjusting process parameters that are easily controlled to maintain a constant drying environment Used empirical measurements to define meaningful phase endpoints and observe process changes quickly Enabled the reduction of input variables for efficient experimental designs –The thermodynamic environment variable (environmental equivalency factor) accounts for 4 of the input variables (airflow, input temperature, environmental humidity, starting material moisture content) to reduce the necessary number of experiments to 16 (2 4 ). The thermodynamic environment was directly scaleable.

Environmental Equivalency Factor A single calculated value that represents the environmental condition in the bowl at which the process take place 2 mechanisms control drying –Heat Transfer – driven by temperature –Mass Transfer – driven by differences in vapor pressure EEF is equal to the ratio of the heat-transfer surface area to the mass-transfer surface area –Regressed against quality attributes. –Potentially useful scale factor 1 Ebey GC A thermodynamic model for aqueous film-coating. Pharmaceutical Technology 11(4):

Hybrid Controls PLS Calibration Cooling Begins EEF Calc. Temp. Calc. Univariate Model 1.The thermodynamic (EEF) calculations use 9 input variables to predict the necessary inlet air temperature to maintain a constant drying environment. 2.The NIR spectra are used to identify a moisture endpoint for drying. 3.A differential pressure transmitter measures the pressure drop across the powder bed to control airflow velocity.

Fluid Bed Drying DOE Two-level 4-Factor Full-Factorial Design RunCombination Factors EEF (a)Target Mois. End pt (b)Batch Size (% Capacity) (c)Prod. Temp at completion (d) a b ab c ac bc abc d ad bd abd cd acd bcd abcd

FBD Process Models Median PS Cohesion Blend Pre-Stressed Lactam

Summary of Dried Granule Models The EEF (temperature) had no effect on the drying time. The end moisture target (EMT) is a strong factor for most responses. Batch Size along with EMT correlated to particle size statistics (Could result from mixing properties in wet granulation) Weak correlations b/t drying factors (EMT and EEF) and in-process stability results. EEF, EMT, EPTT, and interaction terms all have correlations to flow properties.

Summary of Tablet Drying Models After accounting for compression factors, the EEF factor is a significant predictor of compact crushing strength for both rotary press and presster data. –EMT is correlated to rotary press crushing strength, while BS and EPTT are correlated to presster crushing strength. EMT and BS are correlated to rotary press weight variability.

FBD Design Space Development

Summary of Drying Tablet Design Spaces Residual moisture in the granules results in harder tablets and lower weight variability. The higher temperature EEF value results in harder tablets on the rotary press. Better flow properties results in harder tablets. Residual moisture stabilizes granules and blends from lactam formation. High temperature drying results in greater lactam formation.

Summary of Drying Granule Specs EEF is not related to drying time. Residual moisture allows for more efficient drying times and higher mass yield. Residual moisture results in larger particles, but wider PS distributions. Residual moisture results in poorer flow properties. The EEF factor does not have a clear relationship with flow properties. Higher bulk densities show better flow properties. –High temperature drying results in a lower bulk densities.

Overall Drying Design Space Summary High temperature drying does not result in more efficient drying, but it does show increases in lactam formation. –Low temperature EEF is ideal This decision renders the EPTT variable irrelevant A trade-off must be made for the End Moisture Target –Residual moisture in the granules results in poorer flow properties, but also results in harder tablets and less lactam formation.