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

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Abstract

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.

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Acknowledgements

This research was funded internally by CFD Research Corporation’s IR&D. The authors greatly appreciate the assistance of Dr. ZJ Chen and Mr. Alex Boyer in assisting with model development and implementation of our model in an online format, respectively. We also thank Ms. Lindsey Maurel for her contribution of artwork. We would also like to extend our gratitude to the reviewers for providing keen insights and suggestions with which the model was able to be improved.

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  1. CFD Research Corp., Huntsville, AL, 35806, USA

    Carrie German & Andrzej Przekwas

  2. School of Chemical Engineering, Oklahoma State University, Stillwater, OK, 74078, USA

    Minu Pilvankar

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German, C., Pilvankar, M. & Przekwas, A. Computational framework for predictive PBPK-PD-Tox simulations of opioids and antidotes. J Pharmacokinet Pharmacodyn 46, 513–529 (2019). https://doi.org/10.1007/s10928-019-09648-1

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