Spray drying: process development and optimization for the production of pharmaceutical powders

This PhD thesis encompasses three-year study dedicated to advancing spray drying processes and microencapsulation methodologies. The first project tackled the thermodynamic and computational modeling of a benchtop spray dryer's outlet temperature to establish predictive models critical for process scale-up. Integrating machine learning refined the predictive capacity of thermodynamic models over computational fluid dynamics, revealing essential process variables such as feed rate and gas flow interaction. This modeling framework underpins quality-by-design applications for spray drying processes, offering robust scale-up pathways. The second focus was on developing solid lipid microparticles (SLMp) using a novel spray congealing technique starting from a water-in-oil emulsion, enabling high drug loading with controlled release characteristics for hydrophilic drugs like metoclopramide hydrochloride. The approach demonstrated enhanced stability and versatility through a factorial design, optimizing particle size, morphology, and drug release profiles. The final study bridged laboratory-to-pilot scale spray drying for microencapsulated vegetable oils, optimizing feed parameters and drying conditions to maintain critical quality attributes and minimize oxidative degradation. Emulsion stability and feed conditions were pivotal in achieving high encapsulation efficiency and maintaining product stability. Together, these studies highlight the integration of formulation and process parameters to engineer particles with precise physicochemical properties, contributing to enhanced drug delivery systems and scalable spray drying strategies.