Computational framework for predictive PBPK-PD-Tox simulations of opioids and antidotes

The primary goal of this work was to develop a computational tool to enable personalized prediction of pharmacological disposition and associated responses for opioids and antidotes. Here we present a computational framework for physiologically-based pharmacokinetic (PBPK) modeling of an opioid (morphine) and an antidote (naloxone). At present, the model is solely personalized according to an individual's mass. These PK models are integrated with a minimal pharmacodynamic model of respiratory depression induction (associated with opioid administration) and reversal (associated with antidote administration). The model was developed and validated on human data for IV administration of morphine and naloxone. The model can be further extended to consider different routes of administration, as well as to study different combinations of opioid receptor agonists and antagonists. This work provides the framework for a tool that could be used in model-based management of pain, pharmacological treatment of opioid addiction, appropriate use of antidotes for opioid overdose and evaluation of abuse deterrent formulations.