Team:Harvard/Implementation

Proposed Implementation

Proposed End Users

Our iGEM project MOTbox aims to establish a novel therapeutic application of machine learning and DNA origami for the efficient, scalable, and lasting treatment of COVID-19. In a healthcare landscape, given expiated regulatory hurdles and pre-established efficacy of the mode of treatment, we envision that our primary beneficiaries will be COVID patients and the medical professionals responsible for delivering treatment. The machine learning component of our project encompasses principles of precision medicine and is designed to optimize host immunity against the virus, providing a fast-tracked path towards active immunity. In addition, MOTbox provides an advantage over traditional antibody regimens in that a smaller dosage of antibody may be required for efficient delivery, over nonspecific circulation of free antibody. All of these qualities will be beneficial to frontline health workers administering rapid point-of-care treatment, as well as help expedite the recovery of those currently suffering from COVID infections.

How Others Will Adapt Our Project

While our iGEM project is far from being an FDA-approved treatment, we believe that it will bring value to the scientific community by expanding the repertoire of tools that are available to deliver interim treatment during pandemic scenarios. Our machine learning algorithm is inherently tunable and can be continuously improved with new sequencing data. In addition, our DNA origami-based mRNA delivery vehicle can be adapted to transport antibody transcripts against any epitope. Thus, we envision that both components of MOTbox (ie. the machine learning-driven transcript optimization and the fabrication of the DNA origami chassis) are fully modular and can prove to be effective against a wide array of emerging diseases.