Browsing Theses and Dissertations School of Pharmacy by Title "In silico formulation: Application of artificial intelligence in support of hard gelatin capsule formulation of biopharmaceutics classification system class II drugs"
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In silico formulation: Application of artificial intelligence in support of hard gelatin capsule formulation of biopharmaceutics classification system class II drugsGuo et al have demonstrated that a prototype hybrid expert network (PEN) for capsule formulation is capable of yielding formulations that meet specific manufacturing and performance criteria for the model Biopharmaceutics Classification System (BCS) Class II drug piroxicam (Pharm Tech NA (26:9) 2002, p. 44). Purpose. The overall objective of this project is to assess the application of artificial intelligence in capsule formulation support of BCS Class If drugs. The phases of this project include: (1) Characterize model BCS Class II drugs based on physiochemical properties associated with solubility; (2) Evaluate the dissolution performance of the capsule formulations; (3) Generalize the PEN for formulation of model drugs; (4) Apply a Bayesian Network (BN) to formulation development of the model drugs. Methods. The model drugs used in this project were carbamazepine (CAR), chlorpropamide (CHL), diazepam (DIA), ibuprofen (IBU), ketoprofen (KET), naproxen (NAP) and piroxicam (PIR). The micromeritic properties, aqueous solubilities, contact angles, specific surface areas (SSA) and intrinsic dissolution rates (IDR) of these actives were characterized. Capsule formulations of each active were manufactured based on a Box-Behnken experimental design, varying the filler type/ratio and the amounts of lubricant, wetting agent and disintegrant. Dissolution performance of these capsules was characterized using USP standard dissolution media. This data was used to generalize the PEN and to develop a BN. Results. The model drugs were subdivided into weak acids and weak bases. A dataset containing SSA, contact angles, IDRs and percent drug dissolved at 10, 30 and 45 minutes for the model drugs was used as the basis for training the PEN. The system was successfully able to predict the dissolution performance of the model drugs. A BN was successfully developed to model the relationships between formulation variables and dissolution performance. Conclusions. Testing of the PEN indicates that the system can predict the dissolution performance of BCS Class II drugs and can be successfully generalized. The BN has proven to be a useful tool for formulation development. Within the scope of this research, this project proves that artificial intelligence can be successfully applied to pharmaceutical research and development.