Abstract: Many drugs are administered in the form of liquid-dispersed nanoparticles. Frequently, one of the overlooked aspects in the development of this drug delivery system is the loss of efficacy and the degradation of the carried drugs. Estimating the shelf life of drug products implies the storage of samples under controlled conditions of temperature and humidity for different periods, ranging from months to years, delaying decisions during development, manufacturing, and commercialization. Adapting well-known isothermal and nonisothermal methods to nanoparticles would allow correlating kinetic parameters obtained in a single mathematical model and predicting the shelf life faster than traditional methods. Unlike the traditional approaches, the isoconversional method (i) considers drug products as heterogeneous systems, without a unique kinetic order, (ii) establishes a maximum percentage of degradation, (iii) assumes the same kinetics for all processes regardless of the conditions, and (iv) includes the influence of humidity by a modification of Arrhenius equation. This method serves in calculating the kinetic parameters and shelf life derived from them, in a few weeks. In the same way, nonisothermal treatments allow obtaining these parameters by differential scanning calorimetry. Samples are subjected to different heating rates to establish the temperature at which the thermal decomposition event occurs and, thus, to calculate in a few days the activation energy and the preexponential factor using the Kissinger method. But this approach has limitations: the isoconversional method does not consider crystalline state of the sample, while nonisothermal method ignores the effect of the storage conditions. Processing nanoparticles for isothermal and nonisothermal treatments would allow accurate and fast prediction of the drug-loaded nanoparticle shelf life correlating parameters obtained using a single mathematical model. The accuracy of the prediction would be assessed by comparison of estimated shelf life versus data coming from traditional stability studies.
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