Date of Award
Ph.D. in Pharmaceutical Sciences
Particulate drug delivery systems are gaining considerable attention in recent years due to increasing advantages for an effective delivery of therapeutics. Application of advanced particle engineering technologies in polymeric drug delivery systems has shown to improve the efficacy of a drug substance by maintaining stable levels in the blood when compared to traditional parenteral drug products. However, development of these products is a complex multifaceted process with numerous challenges. Formulation development scientists need to comprehend and control a range of unit manufacturing operations along with the formulation composition for a successful product. Therefore, the objective of this project is to study the development of advanced micro-and nano-sized drug products with minimum number of experiments using multivariate statistical tools.
Chapter 1 provides background and literature on various polymers used in drug delivery and particle engineering technologies to manufacture micro- and nano-sized drug products. It also provides a comprehensive overview on various multivariate statistical tools that could be applied in product development.
Chapter 2 compares the capabilities of principal component regression (PCR) and multiple linear regression (MLR) to model and predict the impact of formulation and process parameters on the metronidazole benzoate–ethyl cellulose microsponge particle properties. The observations imply that MLR models showed relatively better predictability than PCR.
Chapter 3 reports the aerosolization of sildenafil citrate loaded polymeric microparticles engineered using multivariate statistical tools by spray-drying (SD) and spray freeze-drying (SFD) processes. Particles engineered by both SD and SFD demonstrate good aerosolization properties. However, particles engineered by SD demonstrated relatively superior aerodynamic characteristics than SFD
Chapters 4 and 5 reports gelatin nanoparticles (GNPs) engineered by desolvation using multivariate statistical tools. About 20-40% of the low molecular weight (25 kD) fraction could be eliminated by a desolvating ‘as is’ gelatin with acetone. Studies demonstrated statistically significant (p<0.05) roles of gelatin solution pH and incubation times on the size and size distribution of the nanoparticles prepared by desolvation. Irrespective of gelatin grades, desolvated gelatins produced GNPs with significantly (p=0.0287) lower size when compared to ‘as is’ gelatins. It is highly recommended to use freshly prepared gelatin solution to attain GNPs of reproducible size.
Overall, these experimental findings show that selection of statistical design for particle engineering is formulation and process dependent. Reproducibility of protein-based nanoparticles are greatly influenced by starting material properties and sample composition prior to synthesis. This study is anticipated to lay foundation for further exploration to develop a highly-controlled processing technologies to engineer polymeric particulate drug delivery systems.
Vinjamuri, Bhavani Prasad, "Engineer Micro- and Nano-Sized Polymeric Particles for Drug Delivery Using Advanced Multivariate Statistical Tools" (2021). Electronic Theses and Dissertations. 2180.