Sepsis occurs when the body’s normal immune response to injury and infection goes awry, causing systemic inflammation that kills up to half of sufferers. So far, researchers have struggled to find drugs that effectively combat sepsis, so patients are mainly treated with antibiotics to fight the underlying condition.
In a new study published in PLOS Computational Biology, Chase Cockrell and Gary An of the University of Chicago employed a computational model of the human immune system to explore the challenges of attacking the molecular processes underlying sepsis. The model, developed previously to investigate systemic inflammation, simulates how immune system cells and signaling molecules behave during sepsis, as well as the effects of disrupting these processes.
The model suggested successful treatment would require drugs targeting multiple immune system processes: disrupting a single signaling process would not be sufficient. This may explain why previous attempts that employed such a strategy have not been effective. The model also indicated that a “one-size-fits-all” multi-target approach would still be inadequate, demonstrating the need for treatment to adapt, perhaps using a machine-learning algorithm, to a patient’s individual response.
Cockrell believes that his findings may be “a reality check” for those working on sepsis treatments. “It will hopefully help focus research into those areas that will actually provide a path towards effective therapy, such as high-resolution diagnostics and sampling, and realizing that there is no ‘one-size-fits-all’ answer.”
Research Article: Cockrell RC, An G (2018) Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation. PLoS Comput Biol 14(2): e1005876. https://doi.org/10.1371/journal.pcbi.1005876
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