PIPP Phase I: Community Informed Computational Prevention of Pandemics

The COVID-19 pandemic has revealed multiple gaps in our understanding of how viruses emerge to infect humans. This Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grant seeks to create a cohesive interdisciplinary research team to investigate three distinct yet related scientific challenges with the goal of predicting and preventing future pandemics. In the first theme, the project will develop computational and experimental methods that analyze the genomic sequences of viruses that currently infect animals to determine how they may evolve in order to cross species barriers and infect humans in the future. The second theme of the project will be to investigate and develop predictive models to find drugs that can be used to combat a virus identified in the first theme as having the potential to infect humans. A distinguishing characteristic of the approach will be to identify new uses for or to chemically modify drugs that have already been approved for other human diseases. Finally, in the third theme, the investigators will engage with the community at large to develop an understanding of societal and ethical concerns of the research. The COVID-19 pandemic has brought to the spotlight the urgent need to be prepared both on scientific and societal fronts to detect and curb novel zoonotic viral infections. The project seeks to control and prevent future viral pandemics through ethically-grounded and community-informed research that will (a) predict in silico cross-species virus infection and host adaptation and (b) perform computational drug redesign and repurposing. The deep learning methods to be developed will detect positive selection events in virus sequences that permit a zoonotic shift from an animal host to humans. These methods will be translatable to any viral pathogen whose genome has been sequenced. The project will experimentally validate predictions using single or integrated human organoids, which will enable a comprehensive understanding on how a pathogen could infect and harm a broad range of organs and tissues. Other explainable machine learning systems to be developed will predict interactions between viral and human proteins, repurpose drugs that target key host proteins required by a virus, and redesign high affinity antiviral drugs. The investigators will use innovative efforts to integrate community engagement into the scientific goals of the project. The community-informed framework addresses the challenges faced during pandemics related to public perception of preventive strategies, societal concerns, inequities in healthcare. It will result in a multifaceted strategy to prepare for and address the next pandemic. These efforts can be translated to best practices in future potential crises on how to effectively integrate disciplines and utilize feedback from diverse sources to successfully implement preventative measures. Team-building and peer-mentoring activities will enable participants to think outside the realms of traditional scientific boundaries and instill the importance of interdisciplinary research, thereby providing a pipeline to a workforce in the future that will value and thrive in a team science environment. This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.