In silico models for nanotoxicity evaluation and prediction at the blood-brain barrier level: A mini-review

Abstract Understanding and predicting the potential cytotoxic effect of various nanoparticles (NPs) at the blood-brain barrier (BBB) is an important challenge in modern nanotoxicology and nanomedicine. Different experimental and theoretical tools have been developed and implemented as cost-effective approaches for efficient nanotoxicity testing an area, where experimental and nanotoxicological data are still very sparse. NPs, as drug delivery vectors, can enhance or diminish drug permeation across the BBB due to their hydrophobic or hydrophilic nature. They are also prone to form lipophilic aggregates and agglomerates, which damage cellular membranes and components, and can accumulate inside of the living cells. As a result, various computational techniques, including molecular docking, molecular dynamics simulations, quantitative structure-activity/property relationship, have paved the way for investigating NP-cell interactions, predicting BBB permeation rates, and evaluating the potentially harmful effects of NPs on cells. This review discusses these in silico methods and computational strategies in an attempt to provide new insights and directions in the development of novel neuroactive molecular formulations (known as “nanodrugs” with “nanocarriers”) for improving BBB permeation and minimizing cytotoxicity risks.