(162g) Dissolution Kinetics Modelling of a BCS Class II Active Pharmaceutical Ingredient

Mechanisms of dissolution kinetics of active pharmaceutical ingredient (API) crystals have been extensively studied in the pharmaceutical domain, as these have a significant impact upon the bioavailability of drugs within the body. In such studies, the pharmaceutical dosage form of the API is typically suspended in a stirred vessel (in which the hydrodynamic conditions are well controlled), allowing the rate of dissolution to be measured using either UV or conductivity methods, providing an insight into the drug's behavior within the body. Dissolution kinetics can also be used to improve industrial crystallization processes, as the selective dissolution of fines could lead to narrower crystal size distributions (termed kinetic ripening). However, while the dissolution of APIs is of critical importance in the pharmaceutical industry, very few studies have been conducted to date which combine experimental methods with numerical simulations in order to achieve a comprehensive understanding of the principle influencing factors. This study attempts to address this shortcoming by examining the dissolution performance of Ibuprofen, a BCS Class II API, with PAT techniques experimentally and mathematical modelling.

The dissolution rate of Ibuprofen with different particle size distribution (PSD) and crystal habit was measured experimentally at different agitation rates. In order to simulate the environment encountered by the drug in the human body, all experiments were carried out in water at 37˚C using a phosphate buffer to maintain a pH of 7.20 (in accordance with U.S. Pharmacopoeia requirements). Different crystal habits were obtained by recrystallizing Ibuprofen in methanol and hexane respectively. Crystals generated using methanol as the solvent produce polyhedral crystals while those generated using hexane are needlelike. Different agitation conditions have been investigated while the effect of particle size upon the dissolution rate was examined by using four different size fractions (sub 150µm, 150-300µm, 300-500µm and 500-850µm).

Experiments were carried out in a 100mL, temperature controlled Easymax vessel. Agitation in the vessel was achieved using a stainless-steel pitched blade impeller. Solvent and crystals were added into the glass vessel and the rate of dissolution was in-situ measured using on-line UV probe to track the liquid phase Ibuprofen concentration, while focused beam reflectance measurement (FBRM) was applied to track the solid phase particle size by measuring chord length distribution (CLD) and particle vision measurement (PVM) was used to monitor the shape and structure of the crystals. The initial and final PSD of Ibuprofen were also measured off-line using a Malvern Mastersizer 2000.

As dissolution is the reverse process of crystal growth, it involves two steps: (1) surface reaction and disintegration of the surface species; (2) mass transfer of these species into the bulk solution across the diffusion layer. It is typically assumed that the overall process is limited by the slower step. Based on this theory, a kinetic model has been devised to describe the dissolution behavior of a polydisperse powder under non-sink conditions. Two rate limiting regimes, surface detachment controlling and mass transfer controlling, were derived respectively. This model was solved using numerical methods and combined with a fraction of the collected experimental data in order to obtain a shape modulated dissolution rate and an overall correlation for the dissolution rate. The remaining experimental data was used to verify the accuracy of the model, demonstrating a very good agreement between theory and experiment. This dissolution kinetic model, which was derived from fundamental principles, is of great use to identify favorable crystal characteristics enabling higher rates of dissolution. It can be used to inform parameter selection during Quality by Design (QbD), for instance, the prediction of PSD of API crystals could be applied to guide the crystallization process for API manufacturing while the estimation of the API concentration changing with time provides useful information for formulation development.

Keywords: Dissolution kinetics; Mathematical modelling; API manufacturing and formulation