Prediction of surfactant-mediated dissolution of poorly soluble drugs from drug powder

Beyond rough "what if" estimation, in vitro dissolution is infrequently predicted. The objective was to assess the predictability of a powder dissolution model with a single diffusion layer thickness model, where dissolution of various drugs was facilitated by several surfactant micelles. Powder dissolution of three poorly water soluble drugs (i.e., posaconazole, ritonavir, and griseofulvin) was measured into buffer, as well as four surfactant solutions [i.e., sodium lauryl sulfate (SLS), polysorbate 80 (PS80), polyoxyethylene (10) lauryl ether (POE10), and cetyltrimethylammonium bromide (CTAB)]. Drug solubility, micelle sizing, and powder sizing were also performed. Prediction of drug dissolution employed the film dissolution model, applied to spherical drug particle fractions of the percent weight particle size distribution, and with a surfactant-mediated dissolution component. There were two competing models for diffusion layer thickness: fixed thickness (i.e., h_fixed) and radius-dependent thickness (i.e., h_max) models. SLS, PS80, POE10, and CTAB increased the dissolution of posaconazole, ritonavir, and griseofulvin, compared to no-surfactant buffer. Results show that in vitro drug dissolution from various polydisperse powders into several surfactant solutions was successfully predicted using a surfactant-mediated dissolution model. The best diffusion layer thickness for the fixed thickness model and the radius-dependent model were separately found to be h_fixed = 12 µm and h_max = 12 µm, respectively, with h_fixed = 12 µm being the more preferred. Also, the powder dissolution model where powder was parameterized in terms of its entire particle size distribution was successful in predicting observed dissolution profiles using each h_fixed = 12 µm and h_max = 12 µm; model use of a mean particle size was also successful in prediction using h_fixed = 12 µm. Credibility assessment of the in vitro dissolution model was performed, including model verification and validation considerations in light of the question of interest, the context of use, and model risk.