Modeling Insights into Potential Mechanisms of Opioid-Induced Respiratory Depression within Medullary and Pontine Networks

ABSTRACT The opioid epidemic is a pervasive health issue and continues to have a drastic impact on the healthcare system and United States. This is primarily because opioids cause respiratory suppression and the leading cause of death in opioid overdose is respiratory failure (i.e., opioid-induced respiratory depression, OIRD). Opioid administration can affect the frequency and magnitude of inspiratory motor drive by activating µ-opioid receptors that are located throughout the respiratory control network in the brainstem. This can significantly affect ventilation and blunt CO2 responsiveness, but the precise neural mechanisms that suppress breathing are not fully understood. Previous research has suggested that opioids affect medullary and pontine inspiratory neuron activity by disrupting upstream elements within this circuit. Inspiratory neurons within this network exhibit synchrony consistent with shared excitation from other neuron populations and recurrent mechanisms. One possible target for opioid suppression of inspiratory drive are excitatory synapses. Reduced excitability of these synaptic elements may result in disfacilitation and reduced synchrony among inspiratory neurons. Downstream effects of disfacilitation may result in abnormal output from phrenic motoneurons resulting in distressed breathing. We tested the plausibility of this hypothesis with a computational model of the respiratory network by targeting the synaptic excitability in fictive medullary and pontine populations. The synaptic conductances were systematically decreased while monitoring the overall respiratory motor pattern and aggregate firing rates of subsets of cell populations. Simulations suggest that highly selective, rather than generalized, actions of opioids on synapses within the inspiratory network may account for different observed breathing mechanics. Competing Interest Statement The authors have declared no competing interest. Footnotes Funding: Supported by NIH 1R01HL155721-01, 1R01HL163008, and T32HL134621. This research was supported by an MBI Accelerator Award from the Evelyn F. and William L. McKnight Brain Institute and UF Health at the University of Florida. Figures, tables and text have been revised to link simulated results to reported in vivo results. New code has been developed to support features of the uflsim model and the supporting equations have been emphasized to provide clarity. All code has been provided in the supplementary materials. https://github.com/jahayes-ns/uflsim/releases/download/neuroscience/uflsim_win_1.0.36.zip)