Investigation into the swelling and dissolution behaviour of Polymer-Excipient blends of PEO Utilising dissolution imaging.

The use of dissolution imaging in analysing the behaviourof hydrophilic matrices and various types of excipients is examined in this study.The main aim was to investigate how different ratios of excipients with different solubility properties, such as lactose, microcrystalline cellulose, and dicalcium phosphate impact on the swelling properties and propranolol hydrochloride (PPN) release characteristics of polyethylene oxide matrix compacts. The surface properties of the compacts were investigated using a focus variation microscope after which dissolution studies were conducted to determine compact swelling and drug release properties. Smr2, a surface parameter representing the percentage of deeper valley structures on the surface, was used to calculate the proportion of the compact surface available for retaining lubrication (dissolution media in this case). Smr2 values of 83 and 84 were measured for the 1:1 and 1:3 PEO lactose compacts, respectively. This parameter utilised in this experiment gives an indication of the compact surface available for the initial hydration process and suggests a higher rate of hydration for the 1:1 and 1:3 PEO lactose compacts. The swelling studies revealed that a higher PEO ratio (3:1) resulted in more extensive gel layer formation as compared to the 1:3 compacts. All PEO:excipient compacts exhibited faster drug release than the compacts comprising PEO as the sole excipient. The quantity of PEO present was thus crucial in influencing the capacity of the matrix to control the release of PPN. This study underscores the potential for modifying drug release by altering the quantity of the matrix gel-former (PEO in this case) as well as the type or ratio of excipient used. The study also highlights the novelty of using UV dissolution imaging to image and quantify swelling and drug dissolution processes as well as providing qualitative observations such as channel formation which can support formulation optimisation and mechanistic understanding.