A predictive mechanistic model of drug release from surface eroding polymeric nanoparticles

• Published in: Journal of controlled release Vol. 351; pp. 883 - 895
• Main Authors: Stiepel, Rebeca T., Pena, Erik S., Ehrenzeller, Stephen A., Gallovic, Matthew D., Lifshits, Liubov M., Genito, Christopher J., Bachelder, Eric M., Ainslie, Kristy M.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.11.2022
• Subjects: Acetalated dextran; Dextrans; Drug Delivery Systems - methods; Drug Liberation; Fickian diffusion; Machine learning; Nanoparticles; Neural network; pH responsive; Pharmaceutical Preparations; Polyanhydride; Polymers; Polyanhydride; Acetalated dextran; Fickian diffusion; Neural network; pH responsive; Machine learning
• ISSN: 0168-3659; 1873-4995
• DOI: 10.1016/j.jconrel.2022.09.067

Effective drug delivery requires ample dosing at the target tissue while minimizing negative side effects. Drug delivery vehicles such as polymeric nanoparticles (NPs) are often employed to accomplish this challenge. In this work, drug release of numerous drugs from surface eroding polymeric NPs was evaluated in vitro in physiologically relevant pH 5 and neutral buffers. NPs were loaded with paclitaxel, rapamycin, resiquimod, or doxorubicin and made from an FDA approved polyanhydride or from acetalated dextran (Ace-DEX), which has tunable degradation rates based on cyclic acetal coverage (CAC). By varying encapsulate, pH condition, and polymer, a range of distinct drug release profiles were achieved. To model the obtained drug release curves, a mechanistic mathematical model was constructed based on drug diffusion and polymer degradation. The resulting diffusion-erosion model accurately described drug release from the variety of surface eroding NPs. For drug release from varied CAC Ace-DEX NPs, the goodness of fit of the developed diffusion-erosion model was compared to several conventional drug release models. The diffusion-erosion model maintained optimal fit compared to conventional models across a range of conditions. Machine learning was then employed to estimate effective diffusion coefficients for the diffusion-erosion model, resulting in accurate prediction of in vitro release of dexamethasone and 3′3’-cyclic guanosine monophosphate–adenosine monophosphate from Ace-DEX NPs. This predictive modeling has potential to aid in the design of future Ace-DEX formulations where optimized drug release kinetics can lead to a desired therapeutic effect. [Display omitted]