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Sepsis is a life-threatening condition affecting one million people per yearin the US in which dysregulation of the body's own immune system causes damageto its tissues, resulting in a 28 - 50% mortality rate. Clinical trials forsepsis treatment over the last 20 years have failed to produce a singlecurrently FDA approved drug treatment. In this study, we attempt to discover aneffective cytokine mediation treatment strategy for sepsis using a previouslydeveloped agent-based model that simulates the innate immune response toinfection: the Innate Immune Response agent-based model (IIRABM). Previousattempts at reducing mortality with multi-cytokine mediation using the IIRABMhave failed to reduce mortality across all patient parameterizations andmotivated us to investigate whether adaptive, personalized multi-cytokinemediation can control the trajectory of sepsis and lower patient mortality. Weused the IIRABM to compute a treatment policy in which systemic patientmeasurements are used in a feedback loop to inform future treatment. Using deepreinforcement learning, we identified a policy that achieves 0% mortality onthe patient parameterization on which it was trained. More importantly, thispolicy also achieves 0.8% mortality over 500 randomly selected patientparameterizations with baseline mortalities ranging from 1 - 99% (with anaverage of 49%) spanning the entire clinically plausible parameter space of theIIRABM. These results suggest that adaptive, personalized multi-cytokinemediation therapy could be a promising approach for treating sepsis. We hopethat this work motivates researchers to consider such an approach as part offuture clinical trials. To the best of our knowledge, this work is the first toconsider adaptive, personalized multi-cytokine mediation therapy for sepsis,and is the first to exploit deep reinforcement learning on a biologicalsimulation.

Precision medicine as a control problem: Using simulation and deep  reinforcement learning to discover adaptive, personalized multi-cytokine  therapy for sepsis