Sriram Chandrasekaran, Assistant Professor, Biomedical Engineering, University of Michigan, Ann Arbor

Seminar abstract:
By 2050, we may lose 10 million people a year to drug-resistant infections. Unfortunately, the pace of drug discovery has not kept up with the rapid emergence of these pathogens. Drug combinations have great potential to reduce the spread of drug-resistant bacteria. However, current drug-discovery approaches are unable to screen an astronomical number of drug combinations and do not account for pathogen heterogeneity and the complex in vivo environment. We have developed mechanistic AI tools – INDIGO, MAGENTA, and CARAMeL, which predict the efficacy of drug regimens based on the properties of the drugs, the pathogen, and the immune and infection environment. Using these methods, we have identified highly synergistic drugs to treat drug resistant infections including Tuberculosis, the world’s deadliest bacterial infection. INDIGO also accurately predicts the outcome of past clinical trials of drug regimens. Our ultimate goal is to create a personalized approach to treat infections using AI.

https://bme.umich.edu/people/sriram-chandrasekaran/
https://www.sriramlab.org/
https://systemsbiologylab.org/

Frontiers in Genomics