A global release model is proposed to study the drug release from porous materials for pharmaceutical applications. This model is defined by implementing a compartmental model where the release profile could be explained as the combination of mass transfer phenomena through three compartments as well as a desorption process or dissolution process from the support. This model was validated with five different systems produced with supercritical CO2 (aerogels, membranes, and fibers), showing different release processes. Numerical results indicate that this compartmental approach can be useful to determine adsorption and desorption constants as well as mass transfer resistances within the material. Likewise, this model can predict lag phases and imbibition phenomena. Therefore, the development of compartmental models can be an alternative to traditional models to successfully predict the drug profile of porous materials, achieving a complete understanding of the involved phenomena regardless of the material characteristics.

Validation of a compartmental model to predict drug release from porous structures produced by ScCO2 techniques

Guastaferro M.;
2023-01-01

Abstract

A global release model is proposed to study the drug release from porous materials for pharmaceutical applications. This model is defined by implementing a compartmental model where the release profile could be explained as the combination of mass transfer phenomena through three compartments as well as a desorption process or dissolution process from the support. This model was validated with five different systems produced with supercritical CO2 (aerogels, membranes, and fibers), showing different release processes. Numerical results indicate that this compartmental approach can be useful to determine adsorption and desorption constants as well as mass transfer resistances within the material. Likewise, this model can predict lag phases and imbibition phenomena. Therefore, the development of compartmental models can be an alternative to traditional models to successfully predict the drug profile of porous materials, achieving a complete understanding of the involved phenomena regardless of the material characteristics.
2023
Gonzalez-Garcinuno, A.; Baldino, L.; Tabernero, A.; Guastaferro, M.; Cardea, S.; Reverchon, E.; Martin del Valle, E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1191407
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