(M1230-07-41) Mass Transport Analysis of the Particle Drifting Effect in a Biphasic Diffusion Apparatus

Purpose: Colloidal species, such as nanocrystals and amorphous drug nanoparticles, have been widely used to improve a drug’s bioavailability. However, such bioavailability enhancement cannot be sufficiently explained by increased dissolution rate due to increased surface area. Alternatively, the particle drifting effect, where colloidal particles, serving as drug reservoirs, dissolve and replenish the free drug concentration in the aqueous boundary layer to reduce its diffusional resistance, can effectively increase the drug’s membrane permeation rate and absorption. While many studies have reported enhanced permeation rates provided by these colloidal formulations and linked them to the particle drifting effect, few have attempted to predict the extent of the particle drifting effect, with most of these efforts being empirical. It remains challenging to appropriately interpret experimental data or to accurately predict enhanced permeation rate provided by particle drifting effect. Therefore, a mechanistic mass transport model more accurately describing the role of diffusion boundary layer in the particle drifting effect, and its validation using a variety of drugs and particles, is needed.

Methods: In this study, we integrated the two kinetic processes involved, dissolution in the aqueous boundary layer and free drug permeation across the liquid interface, in the mass transport model (Figure 1), and performed mass transport analysis of the particle drifting effect. The particle drifting effect was modeled by coupling the Wang-Flanagan particle dissolution model with a stagnant-film permeation model. Amorphous drug nanoparticle was used as a model system due to its simplicity in preparation and particle size control. Experiments were performed using a biphasic diffusion setup. Anacetrapib, atazanavir, clotrimazole, enzalutamide, and felodipine were used as model drugs to evaluate the impact of particle concentration and size on mass transport.

Results: Results suggested that drug nanoparticles at low concentrations did not alter the diffusional profile or the fitted permeability coefficient. At high particle concentrations, a flux plateau was observed (Figure 2), signifying non-sink dissolution of the particles. The fitted interfacial permeability coefficient increased with increasing particle concentration, confirming reduced diffusional resistance of the aqueous boundary layer by the presence of drug particles. High number of particles also altered the fitted partition coefficient of the drug due to saturation of the free drug in the aqueous boundary layer adjacent to the liquid interface. The mass transport model was able to predict the particle drifting effect for systems with high particle concentrations or extremely poorly soluble drugs where experimental evaluations become challenging. It also confirms the nonlinear nature of the particle drifting effect, where nonlinear pharmacokinetics would be expected if drug absorption is mainly attributed to particles. Combined with a direct differential equation-based pharmacokinetic model, in vivo drug absorption in mice was predicted at different doses. The mass transport model was used to calculate the diffusional profile of enzalutamide at different doses. This was then used as model input for the pharmacokinetic model. Data obtained at doses below in vivo amorphous solubility (2.764 mg/kg) was used to develop the model, and the model was then used to make predictions at doses above in vivo amorphous solubility. Excellent agreements between the predicted and observed plasma drug concentrations across all doses were observed (Figure 3), confirming the potential of the proposed model to establish in vitro-in vivo correlations for nanoparticle-containing formulations that exhibit the particle drifting effect.

Conclusion: This study presents the theory and derivation of a mechanistic dissolution–permeation model for solute transport provided by amorphous drug nanoparticles in a biphasic setup. Experimental data and mass transport analysis revealed saturated mass transport at high particle concentrations, due to non-sink dissolution of the particles in the aqueous diffusion boundary layer. Fitted drug partition coefficient also showed a large decline at high particle concentrations, confirming saturation of the drug in the aqueous boundary layer. Flux enhancement provided by the particle drifting effect increases nonlinearly with increasing drug concentration. The model revealed a log-linear relationship between permeability and saturation index, which can be utilized for flux predictions. Combined with pharmacokinetic models, it was demonstrated that the biphasic experimental setup combined with the mass transport model may be appropriate tools to predict oral drug absorption in vivo.

Acknowledgements: The authors gratefully acknowledge financial support from the University of Connecticut and the National Institutes of Health (R35GM155235).


Figure 1. Schematic showing the structure of the dissolution-permeation model


Figure 2. Impact of particle concentration on the particle drifting effect: representative diffusional profiles showing good agreements between model fitting and experimental data


Figure 3. Predicted vs experimental plasma drug concentration after oral administration of enzalutamide amorphous solid dispersions in mice