The Rami Five-Checkpoint Framework for AI-Guided mRNA-Encoded Neutralizers: A Unified Computational Platform for Snake and Scorpion Venom Antidote Design

Envenomation from venomous snakes and scorpions remains a major global health challenge, disproportionately affecting populations in low-resource settings where access to conventional antivenoms is limited by cost, cold-chain requirements, and species specificity. This work introduces the Rami Five-Checkpoint Framework, a unified computational methodology for the design of mRNA-encoded neutralizing proteins targeting venom toxins. The framework integrates five sequential design checkpoints: (1) toxin target selection and lethality scoring, (2) human codon optimization of mRNA sequences, (3) RNA secondary structure validation, (4) immunogenicity and toxin-binding prediction, and (5) economic feasibility assessment. The approach is demonstrated through parallel in silico case studies targeting two mechanistically distinct toxins: beta-taipoxin from the Inland Taipan (Oxyuranus microlepidotus) and the CsEv5 alpha-toxin from the Arizona bark scorpion (Centruroides sculpturatus). AI-guided de novo neutralizer designs exhibited predicted nanomolar binding affinities, high thermostability, favorable mRNA expression characteristics, and broad HLA population coverage. Cost modeling suggests the potential for substantial reductions in production cost relative to traditional animal-derived antivenoms. This study is exclusively computational and does not include experimental protocols, toxin synthesis, or clinical procedures. The framework is intended as a reusable design-time platform to accelerate antivenom research, support future experimental validation, and advance next-generation therapeutic strategies for envenomation preparedness.