• P Worth Longest, Dale Farkas, Amr Hassan, and Michael Hindle.
• Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, 401 West Main Street, P.O. Box 843015, Richmond, VA, 23284-3015, USA. pwlongest@vcu.edu.
• Pharm. Res. 2020 May 21; 37 (6): 101.

PurposeThe purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics within a laboratory-scale spray dryer, and relate CFD-predicted drying parameters to powder aerosolization metrics from a reference dry powder inhaler (DPI).MethodsA CFD model of the Buchi Nano Spray Dryer B-90 was developed that captured spray dryer conditions from a previous experimental study producing excipient enhanced growth powders with L-leucine as a dispersion enhancer. The CFD model accounted for two-way heat and mass transfer coupling between the phases and turbulent flow created by acoustic streaming from the mesh nebulizer. CFD-based drying parameters were averaged across all droplets in each spray dryer case and included droplet time-averaged drying rate (κavg), maximum instantaneous drying rate (κmax) and precipitation window.ResultsCFD results highlighted a chaotic drying environment in which time-averaged droplet drying rates (κavg) for each spray dryer case had high variability with coefficients of variation in the range of 60-70%. Maximum instantaneous droplet drying rates (κmax) were discovered that were two orders of magnitude above time-averaged drying rates. Comparing CFD-predicted drying parameters with experimentally determined mass median aerodynamic diameters (MMAD) and emitted doses (ED) from a reference DPI produced strong linear correlations with coefficients of determination as high as R2 = 0.98.ConclusionsFor the spray dryer system and conditions considered, reducing the CFD-predicted maximum drying rate experienced by droplets improved the aerosolization performance (both MMAD and ED) when the powders were aerosolized with a reference DPI.

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