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.

中文翻译：

---

阿片类药物和解毒剂的预测性PBPK-PD-Tox模拟的计算框架。

这项工作的主要目标是开发一种计算工具，以实现对阿片类药物和解毒剂的药理作用以及相关反应的个性化预测。在这里，我们为阿片类药物（吗啡）和解毒剂（纳洛酮）的基于生理学的药代动力学（PBPK）建模提供了一个计算框架。目前，该模型仅根据个人的质量进行个性化设置。这些PK模型与呼吸抑制诱导（与阿片类药物相关）和逆转（与解毒剂相关）的最小药效学模型集成在一起。开发了该模型，并根据人类数据进行了吗啡和纳洛酮的静脉给药验证。该模型可以进一步扩展以考虑不同的管理途径，以及研究阿片受体激动剂和拮抗剂的不同组合。这项工作提供了一个工具的框架，该工具可用于基于模型的疼痛管理，阿片类药物成瘾的药理治疗，阿片类药物过量的解毒剂的合理使用以及滥用威慑制剂的评估。