Unifying bioinformatics frameworks for risk assessment of dsRNA-based biopesticides

In silico characterizations of risk are predictive, critical components to ensure safety of biological sequence-based actives. The sequence-dependent and targeted nature of RNAi-based biopesticides can especially benefit from such directional bioinformatics assessments of risk. Given the rapidly evolving landscape of such biopesticides, a wide variety of disparate bioinformatics frameworks exist for predicting impacts to humans and Non Target Organisms (NTOs). We propose a unified and evidence-based bioinformatics risk assessment framework, that we seamlessly integrate into sequence design of RNAi-based biopesticides, to unlock a targeted and highly specific pesticidal mode of action.