Despite being convenient and practical, current nanomedicine (NM) release kinetic models remain unscalable, non-specific and less descriptive of the underlying physicochemical determinants. However, a deterministic mathematical modelling could overcome these limitations. In this study, we develop a model, based on a system of two differential equations (accounting for nanoparticle (NP) degradation and then drug release from degraded NM), which enable us to estimate per capita rate constant α (#NP degraded/hr) and β (Drug Amount Released/NP), the net effect of the nanomedicine (NE factor ɣ= α.β) and the controlled release index (φ, ratio of drug release to NP degradation). The model analysis conducted with tenofovir loaded hyaluronidase sensitive NM clearly shows the α factor significantly increased with triggering stimuli due to its enzymatic action on its substrate (hyaluronic acid). However, the β factor remained relatively unchanged, due to intrinsic physicochemical properties of the drug as limiting factor. The application of the solutions of this model clearly enabled us to effectively screen among various nanoformulations and identified the best hyaluronidase-sensitive NM formulation, exhibiting the highest ratio (3.7-fold increase compared to no enzyme). The φ value confirmed the controlled release and stimuli sensitivity of the nanosystem. Moreover, the computed drug release rate (dM/dt) is consistent with other existing mathematical models (under valid assumption). The key advantages of this approach are i) relevancy to underlying physicochemical and biochemical process, ii) versatility and application to various NM kinetics, and iii) prediction of temporo-spatial distribution of the drug loaded NP that could potentially improve in-vitro/in vivo correlation study. This unique approach is applicable for a more specific and more meaningful/physicochemically relevant description of bioactive agents release from NM or NP for various applications.
Full Text Finder