Proteolytic activity is pivotal in maintaining cell homeostasis and function. In pathological conditions such as cancer, it covers a key role in tumor cell viability, spreading to distant organs, and response to the treatment. Endosomes represent one of the major sites of cellular proteolytic activity and very often represent the final destination of internalized nanoformulations. However, little information about nanoparticle impact on the biology of these organelles is available even though they represent the major location of drug release. In this work, we generated albumin nanoparticles with a different resistance to proteolysis by finely tuning the amount of cross-linker used to stabilize the carriers. After careful characterization of the particles and measurement of their degradation in proteolytic conditions, we determined a relationship between their sensitivity to proteases and their drug delivery properties. These phenomena were characterized by an overall increase in the expression of cathepsin proteases regardless of the different sensitivity of the particles to proteolytic degradation.

Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.