Development of a Translational Model to Assess the Impact of Opioid Overdose and Naloxone Dosing on Respiratory Depression and Cardiac Arrest

Abstract

In response to a surge of deaths from synthetic opioid overdoses, there have been increased efforts to distribute naloxone products in community settings. Prior research has assessed the effectiveness of naloxone in the hospital setting; however, it is challenging to assess naloxone dosing regimens in the community/first‐responder setting, including reversal of respiratory depression effects of fentanyl and its derivatives (fentanyls). Here, we describe the development and validation of a mechanistic model that combines opioid mu receptor binding kinetics, opioid agonist and antagonist pharmacokinetics, and human respiratory and circulatory physiology, to evaluate naloxone dosing to reverse respiratory depression. Validation supports our model, which can quantitatively predict displacement of opioids by naloxone from opioid mu receptors in vitro, hypoxia‐induced cardiac arrest in vivo, and opioid‐induced respiratory depression in humans from different fentanyls. After validation, overdose simulations were performed with fentanyl and carfentanil followed by administration of different intramuscular naloxone products. Carfentanil induced more cardiac arrest events and was more difficult to reverse than fentanyl. Opioid receptor binding data indicated that carfentanil has substantially slower dissociation kinetics from the opioid receptor compared with nine other fentanyls tested, which likely contributes to the difficulty in reversing carfentanil. Administration of the same dose of naloxone intramuscularly from two different naloxone products with different formulations resulted in differences in the number of virtual patients experiencing cardiac arrest. This work provides a robust framework to evaluate dosing regimens of opioid receptor antagonists to reverse opioid‐induced respiratory depression, including those caused by newly emerging synthetic opioids.

Structure–Kinetics Relationships of Opioids from Metadynamics and Machine Learning Analysis

“…For the sake of clarity, we drop the Ballesteros–Weinstein numbering in the discussion that follows. The calculated τ with Hid297 is about 1 order of magnitude smaller than the recent experimental value of 4 min, which is not surprising, given that the simulation time scale is about 8 orders of magnitude smaller, and neither the transition or unbound state regions are thoroughly sampled …”

“…K i,NLX refers to the naloxone concentration that produces 50% reduction of the maximal agonist occupancy in mOR. The experimental values were obtained by Mann et al…”

“…Although metadynamics protocols have been applied to estimate ligand–protein dissociation kinetics in a large number of studies (e.g., in refs , , , ), the prediction accuracy for predicting the relative order of residence times of a large number of structurally related ligands remains unclear, although this task is practically more relevant than predicting the absolute residence time of a single ligand. Here, we tested the aforementioned metadynamics protocol to estimate the residence times of 15 fentanyl and 4 morphine analogs (Figure b–d) and correlated the calculations with the newly obtained experimental estimates for 12 compounds (10 fentanyls and 2 morphinans) . The Pearson’s r value between calculation and experiment is 0.65, and it increases to 0.82 when the highly similar R2 modified compounds (mainly derived from butyrfentanyl) are excluded .…”

Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning

“…From the kinetic rate constants, the residence times τexp (1/k off ) and dissociation constants K d (k off /kon) were estimated (Table S1). Additionally, the naloxone inhibitory constant (Ki,NLX) (naloxone concentration that produces 50% reduction in the maximal agonist occupancy in mOR) for a subset (10) of the 12 compounds were also obtained 11 (Table S1).…”

New science, drug regulation, and emergent public health issues: The work of FDA’s division of applied regulatory science

“…Division of Applied Regulatory Science developed a quantitative systems pharmacology computational model ( 23 ) to assess naloxone dosing strategies and applied it to the regulatory review of a new naloxone formulation for use by military personnel and chemical incident responders ( 24 , 25 ). The model connects the pharmacokinetics of different opioids and naloxone to in vitro receptor binding kinetics, and subsequently to effects on respiratory depression and cardiac arrest.…”