Design of particle size distribution for custom dissolution profiles by solving the inverse problem

Dissolution testing is widely used to measure the rate of drug release and predict its in-vivo behavior. The release rate can be controlled by adjusting the particle size distribution (PSD). However, experimental investigation of various particle sizes requires many time-consuming experiments. To reduce the need for them, we propose an optimization framework to solve the inverse problem, i.e., design a PSD that results in a prescribed dissolution profile. The framework's computational core predicts a dissolution profile using a population balance model coupled with a mass balance equation, while the optimization algorithm obtains the inverse solution. The model was validated using mono-and multimodal particle populations of a reference compound (KCl). The validation resulted in a good agreement between the simulated and experimental data. This suggests that the usage of the framework can provide a fast determination of the required PSD, reducing the number of experiments needed. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Dissolution testing is widely used to measure drug release rate and behavior. An optimization framework is proposed to design a PSD that results in a prescribed dissolution profile, reducing experimental time. The model shows good agreement between simulated and experimental data, indicating the framework's potential to determine the required PSD efficiently.