Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, United States; Department of Bioengineering, University of Pennsylvania, Philadelphia, United States
Benjamin A Harrison
Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, United States
Victoria M Bedell
Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, United States
Mathangi J Sridharan
College of Medicine, The Ohio State University, Columbus, United States
Jayce J Breig
Department of Medicine, Drexel University College of Medicine, Philadelphia, United States
Michael Pack
Department of Medicine, University of Pennsylvania, Philadelphia, United States
Max B Kelz
Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, United States; Department of Bioengineering, University of Pennsylvania, Philadelphia, United States
Traditionally, drug dosing is based on a concentration-response relationship estimated in a population. Yet, in specific individuals, decisions based on the population-level effects frequently result in over or under-dosing. Here, we interrogate the relationship between population-based and individual-based responses to anesthetics in mice and zebrafish. The anesthetic state was assessed by quantifying responses to simple stimuli. Individual responses dynamically fluctuated at a fixed drug concentration. These fluctuations exhibited resistance to state transitions. Drug sensitivity varied dramatically across individuals in both species. The amount of noise driving transitions between states, in contrast, was highly conserved in vertebrates separated by 400 million years of evolution. Individual differences in anesthetic sensitivity and stochastic fluctuations in responsiveness complicate the ability to appropriately dose anesthetics to each individual. Identifying the biological substrate of noise, however, may spur novel therapies, assure consistent drug responses, and encourage the shift from population-based to personalized medicine.