Menzel D B
Duke University Medical Center, Department of Pharmacology, Durham, NC 27710.
Toxicol Lett. 1988 Oct;43(1-3):67-83. doi: 10.1016/0378-4274(88)90021-5.
A general method is presented for the use of mathematical modeling in the design, execution, and interpretation of toxicology experiments. To illustrate the use of mathematical modeling toxicology, a case study is presented of how a dosimetry model for inhaled nickel was developed for use in cancer risk estimation. A physiologically based pharmacokinetic (PB-PK) dosimetry model is used to plan animal experiments and to extrapolate nickel kinetics from animals to humans. These data are then used to estimate human lung cancer risks from human exposure to nickel aerosols. To achieve this goal, a PB-PK dosimetry model for the lung was integrated with a PB-PK dosimetry model for the internal organs. Nickel removal from the lung was found to be saturable and to follow Michaelis-Menten kinetics. The PB-PK lung dosimetry model was used to design both short-term (single exposures) and long-term (multiple intermittent exposures) needed to validate the parameters (Km and Vmax) of the lung dosimetry model. A constant infusion experiment was planned using the PB-PK modeling approach to measure the distribution and elimination of intravenously administered nickel. The two PB-PK models were integrated to estimate the fate of nickel after inhalation and are being used to plan experiments for other routes of exposure such as ingestion of drinking water and dermal contact. The integrated model has been used to calculate a human cancer risk estimate in combination with short-term genotoxic experiments. Using PB-PK models in toxicology, as illustrated here, conserves experimental animals, aids in understanding new physiological phenomena (such as saturable clearance from the lung), incorporates in vitro tests with in vivo experiments, and provides a means of extrapolation to human health risks from multiple routes of exposure. Introducing the concepts of mathematical modeling into toxicity experiments at the beginning of the experiment improves the usefulness of the experiments in risk estimation. PB-PK models are suggested as a new basis for experimental design in toxicology.
本文介绍了一种在毒理学实验的设计、实施和解释中使用数学建模的通用方法。为说明数学建模在毒理学中的应用，给出了一个案例研究，即如何开发用于吸入镍的剂量学模型以进行癌症风险评估。基于生理学的药代动力学（PB-PK）剂量学模型用于规划动物实验，并将镍的动力学从动物外推至人类。然后，这些数据用于估计人类因接触镍气溶胶而患肺癌的风险。为实现这一目标，将肺部的PB-PK剂量学模型与内脏器官的PB-PK剂量学模型相结合。发现肺部对镍的清除是可饱和的，并遵循米氏动力学。PB-PK肺部剂量学模型用于设计验证肺部剂量学模型参数（Km和Vmax）所需的短期（单次暴露）和长期（多次间歇暴露）实验。计划采用PB-PK建模方法进行恒速输注实验，以测量静脉注射镍的分布和消除情况。将这两个PB-PK模型相结合，以估计吸入后镍的归宿，并用于规划其他暴露途径（如饮用水摄入和皮肤接触）的实验。该综合模型已与短期遗传毒性实验相结合，用于计算人类癌症风险估计值。如此处所示，在毒理学中使用PB-PK模型可节省实验动物，有助于理解新的生理现象（如肺部的可饱和清除），将体外试验与体内实验相结合，并提供一种从多种暴露途径外推至人类健康风险的方法。在实验开始时将数学建模的概念引入毒性实验，可提高实验在风险评估中的有用性。建议将PB-PK模型作为毒理学实验设计的新基础。
