Block copolymer-dsRNA complexes for crop protection

The use of pesticides has allowed crop production to more than double over the last 60-years, reaching 9 billion tonnes in 2018 and keeping up with increasing food and plant-derived products demand due to population growth [1]. However, their excessive use has led to negative impacts on the environment and to human health and off-target species [1].

This has contributed to the development of alternative pest control measures that target only specific species while minimising off-target toxicity effects.

One such alternative is RNAi-based biopesticides that can be tuned to match specific pest genes and reduce the possibility of non-target damage. In this method, a double-stranded RNA molecule (dsRNA) is delivered to pests, stunting their growth or potentially causing pest death by selective gene silencing. Delivery vectors have been developed to protect RNA from degradation in the many environments it encounters before reaching the target sites within the pests.

Recently a hydrophilic di-block copolymer has been synthesised for this purpose, where the cationic PQDMAEMA block binds to the negatively charged dsRNA and the neutral PDMA block provides steric stabilisation to prevent aggregation of the resulting polymer-dsRNA complexes, which were shown to effectively bind to and protect dsRNA [2].

In addition to protecting dsRNA, complexes should allow dsRNA release once within the pest's cells. To this end, this project will build upon the previously synthesised copolymer to include pH-responsiveness into polymer design. More specifically, both permanently quaternised (QDMAEMA) and pH-dependently quaternised (DMAEMA) monomers will be used to synthesise the cationic block.

This block will then have a variable positive charge depending on the pH of its environment, which is expected to induce the triggered release of dsRNA under targeted conditions. Anticipated results include reproducing synthesis and characterisation techniques of previous polymers and complexes and testing a machine learning algorithm to relate polymer properties to complexes' performance based on collaborator data.