Make an OFERR: Optimization for Enhanced RNA Replication

PROJECT SUMMARY Self-amplifying RNAs (saRNAs) are derived from single-stranded RNA (ssRNA) viruses, with the genes for the viral structural proteins removed and replaced with a gene(s) of interest. The viral genes remaining in the self- amplifying RNA encode the non-structural proteins which form the machinery necessary to replicate the RNA.

With the RNA undergoing repeated rounds of replication in the host cell, there is increased production of the proteins of interest over a longer period compared with conventional mRNAs. The use of self-amplifying RNAs, as opposed to conventional mRNAs, reduces the amount of RNA required to achieve similar responses.

Already, there are saRNAs in clinical trials by major pharmaceutical companies. The self-amplifying RNAs do not further infect cells, as there are no structural proteins to form infectious progeny. During replication, the positive-strand RNA is used as a template to make a negative-strand product which is then used as a template

for subsequent generation of more positive-strands and, subsequently, more of the desired protein. The goal of our proposed study is to develop software that will optimize RNA replication when administering saRNA vaccines and therapeutics. We will use a machine learning approach to develop software optimize to the

replicability of an RNA. That way when using the viral replicative machinery in self-amplifying RNAs the RNA can replicate repeatedly, increasing the duration of expression for cargo genes of interest. We will test this system and present data for at least three individual RNA genes optimized. One of the major impacts of our

proposed study will be the optimization of self-amplifying RNA therapeutics, which have been used for multiple indications including infectious diseases and cancer. Self-amplifying RNAs require less RNA to be administered, as compared to non-amplifying messenger RNAs, and have prolonged responses. Optimization

of the genetic cargo of self-amplifying RNAs is a critical step in their development as therapeutics and vaccines. Yet, the only software to optimize protein production in self-amplifying RNAs is still anchored in translation optimization from a single RNA, not to increase replication of RNAs. Our product adds another tool

in the toolbox for the development of self-amplifying RNAs. Just as many different approaches were combined to make conventional RNA therapy possible, our methodology can synergize with other techniques to increase protein production by self-amplifying RNAs.