The effect of polymer architecture, composition, and molecular weight on the properties of glycopolymer-based non-viral gene delivery systems

Although a variety of non-viral gene delivery vectors has been synthesized and used for gene delivery purposes, well-defined glycopolymer-based gene delivery carriers is not well explored. Reversible Addition-Fragmentation Chain Transfer (RAFT) polymerization technique allows successful and facile synthesis of cationic glycopolymers containing pendant sugar moieties in the absence of protecting group chemistry. A library of cationic glycopolymers of pre-determined molar masses and narrow polydispersities ranging from 3 to 30 kDa has been synthesized using RAFT polymerization technique. These polymers differ from each other in their architectures (block versus random), molecular weights, and monomer ratios (carbohydrate to cationic segment). It is shown that the above-mentioned parameters can largely affect the toxicity, DNA condensation ability and gene delivery efficacy of these polymers. Statistical copolymers of high degree of polymerization are found to be the ideal vector for gene delivery purposes. These statistical copolymers show lower toxicity and higher gene expression in the presence and absence of serum, as compared to the corresponding diblock copolymers. This is the first example of well-defined synthetic glycopolymers as DNA carriers that works both in the presence and absence of serum proteins. The critical composition of carbohydrate segment in copolymers for enhanced gene delivery and low toxicity was determined and an increase in carbohydrate residues in copolymers resulted in a decrease in transfection efficiencies of these polymers. The effect of serum proteins on statistical and diblock copolymer based polyplexes and hence gene delivery efficacy was studied. The results showed that the diblock copolymer-based polyplexes showed lower interactions with serum proteins, lower cellular uptake and very low gene expression in both Hep G2 and Hela cells in comparison to statistical copolymers. (C) 2011 Elsevier Ltd. All rights reserved.