Predictive Model for Delivery Efficiency: Erythrocyte Membrane-Camouflaged Magnetofluorescent Nanocarriers Study

• Autor(a) principal: Sousa-Junior, Ailton A.
• Data de Publicação: 2020
• Outros Autores: Mendanha, Sebastião A., Carrião, Marcus S., Capistrano, Gustavo, Próspero, André G. [UNESP], Soares, Guilherme A. [UNESP], Cintra, Emílio R., Santos, Sônia F.O., Zufelato, Nicholas, Alonso, Antônio, Lima, Eliana M., Miranda, José Ricardo A. [UNESP], Silveira-Lacerda, Elisângela De P., Cardoso, Cléver G., Bakuzis, Andris F.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1021/acs.molpharmaceut.9b01094; http://hdl.handle.net/11449/198599
• topic: iron oxide-based nanoparticles; magnetic hyperthermia; membrane-coated nanoparticles; near-infrared dye; pharmacokinetic model; photothermal therapy; tumor delivery efficiency

Delivery efficiencies of theranostic nanoparticles (NPs) based on passive tumor targeting strongly depend either on their blood circulation time or on appropriate modulations of the tumor microenvironment. Therefore, predicting the NP delivery efficiency before and after a tumor microenvironment modulation is highly desirable. Here, we present a new erythrocyte membrane-camouflaged magnetofluorescent nanocarrier (MMFn) with long blood circulation time (92 h) and high delivery efficiency (10% ID for Ehrlich murine tumor model). MMFns owe their magnetic and fluorescent properties to the incorporation of manganese ferrite nanoparticles (MnFe2O4 NPs) and IR-780 (a lipophilic indocyanine fluorescent dye), respectively, to their erythrocyte membrane-derived camouflage. MMFn composition, morphology, and size, as well as optical absorption, zeta potential, and fluorescent, magnetic, and magnetothermal properties, are thoroughly examined in vitro. We then present an analytical pharmacokinetic (PK) model capable of predicting the delivery efficiency (DE) and the time of peak tumor uptake (tmax), as well as changes in DE and tmax due to modulations of the tumor microenvironment, for potentially any nanocarrier. Experimental PK data sets (blood and tumor amounts of MMFns) are simultaneously fit to the model equations using the PK modeling software Monolix. We then validate our model analytical solutions with the numerical solutions provided by Monolix. We also demonstrate how our a priori nonmechanistic model for passive targeting relates to a previously reported mechanistic model for active targeting. All in vivo PK studies, as well as in vivo and ex vivo biodistribution studies, were conducted using two noninvasive techniques, namely, fluorescence molecular tomography (FMT) and alternating current biosusceptometry (ACB). Finally, histopathology corroborates our PK and biodistribution results.