PREPRINT Machine Learning for the Sensitivity Analysis of a Model of the Cellular Uptake of Nanoparticles for the Treatment of Cancer.

Experimental studies on the cellular uptake of nanoparticles (NPs), useful for the investigation of NP-based drug delivery systems, are often difficult to interpret due to the large number of parameters that can contribute to the phenomenon. It is therefore of great interest to identify insignificant parameters to reduce the number of variables used for the design of experiments. In this work, a model of the wrapping of elliptical NPs by the cell membrane is used to compare the influence of the aspect ratio of the NP, the membrane tension, the NP-membrane adhesion, and its variation during the interaction with the NP on the equilibrium state of the wrapping process.

Influence of the mechanical and geometrical parameters on the cellular uptake of nanoparticles: A stochastic approach.

Nanoparticles (NPs) are used for drug delivery with enhanced selectivity and reduced side-effect toxicity in cancer treatments. Based on the literature, the influence of the NPs mechanical and geometrical properties on their cellular uptake has been studied through experimental investigations. However, due to the difficulty to vary the parameters independently in such a complex system, it remains hard to efficiently conclude on the influence of each one of them on the cellular internalization of a NP.