NJIT
Dissolution kinetics of model api in molten polymer excipients during batch processing
Abstract
dc:description.abstractIn the pharmaceutical industrial application field, hot-melt extrusion (HME) has been recently introduced to develop new solid dosage forms and products. By dissolving the poorly-soluble active pharmaceutical ingredients (API) into water-soluble polymers, the bioavailability of the Class II (with low solubility and high permeability in water) API in Biopharmaceutical Classification System (BCS) could be significantly improved in the body. For readily water-soluble API, HME provides a new approach to produce a controlled release drug system. Hence, pharmaceutical HME is a promising processing method in the pharmaceutical industry. However, HME has not been widely applied into the pharmaceutical industry. The thermal degradation of the polymer (and/or other excipients) and API are major concerns in the pharmaceutical HME process: researchers aim to dissolve the total loading of the API into the excipient within the short residence time with minimal API degradation. Therefore, the kinetics of the dissolving process should be known. In this work, the expression of dissolution process and the impact of shear rate, API concentration and API species in dissolution kinetics are determined. The viscosities of the mixture at different shear rates are also measured. A model API shall be dissolved into a polymeric excipient by conducting melt-mixing experiments using the Brabender Batch Mixer.
Degree
thesis:*- Name thesis:degree_name
- Master of Science in Chemical Engineering - (M.S.)
- Discipline thesis:degree_discipline
- Chemical, Biological and Pharmaceutical Engineering
- Year
- 2016
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Ji, Shen
- Contributors dc:contributor
-
- Costas G. Gogos
- R. P. T. Tomkins
- Laurent Simon
Subjects
dc:subject × 4Identifiers
dc:identifier.*- Repository record dc:identifier
- https://digitalcommons.njit.edu/theses/273
- OAI identifier oai:identifier
- oai:digitalcommons.njit.edu:theses-1272