In silico prediction of the in vitro behavior of polymeric gene delivery vectors

Non-viral gene delivery vectors have increasingly come under the spotlight, but their performaces are still far from being satisfactory. Therefore, there is an urgent need for forecasting tools and screening methods to enable the development of ever more effective transfectants. Here, coarse-grained (CG) models of gold standard transfectant poly(ethylene imine)s (PEIs) have been profitably used to investigate and highlight the effect of experimentally-relevant parameters, namely molecular weight (2 vs. 10 kDa) and topologies (linear vs. branched), protonation state, and ammine-to-phosphate ratios (N/Ps), on the complexation and the gene silencing efficiency of siRNA molecules. The results from the in vitro screening of cationic polymers and conditions were used to validate the in silico platform that we developed, such that the hits which came out of the CG models were of high practical relevance. We show that our in silico platform enables to foresee the most suitable conditions for the complexation of relevant siRNA-polycation assemblies, thereby providing a reliable predictive tool to test bench transfectants in silico, and foster the design and development of gene delivery vectors.