Upton Richard N, Ludbrook Guy L
Anaesthesia and Intensive Care, Royal Adelaide Hospital, North Terrace, Adelaide, SA 5000, Australia.
BMC Pharmacol. 2005 Mar 10;5:5. doi: 10.1186/1471-2210-5-5.
There have been few reports of pharmacokinetic models that have been linked to models of the cardiovascular system. Such models could predict the cardiovascular effects of a drug under a variety of circumstances. Limiting factors may be the lack of a suitably simple cardiovascular model, the difficulty in managing extensive cardiovascular data sets, and the lack of physiologically based pharmacokinetic models that can account for blood flow changes that may be caused by a drug. An approach for addressing these limitations is proposed, and illustrated using data on the cardiovascular effects of magnesium given intravenously to sheep. The cardiovascular model was based on compartments for venous and arterial blood. Blood flowed from arterial to venous compartments via a passive flow through a systemic vascular resistance. Blood flowed from venous to arterial via a pump (the heart-lung system), the pumping rate was governed by the venous pressure (Frank-Starling mechanism). Heart rate was controlled via the difference between arterial blood pressure and a set point (Baroreceptor control). Constraints were made to pressure-volume relationships, pressure-stroke volume relationships, and physical limits were imposed to produce plausible cardiac function curves and baseline cardiovascular variables. "Cardiovascular radar plots" were developed for concisely displaying the cardiovascular status. A recirculatory kinetic model of magnesium was developed that could account for the large changes in cardiac output caused by this drug. Arterial concentrations predicted by the kinetic model were linked to the systemic vascular resistance and venous compliance terms of the cardiovascular model. The kinetic-dynamic model based on a training data set (30 mmol over 2 min) was used to predict the results for a separate validation data set (30 mmol over 5 min).
The kinetic-dynamic model was able to describe the training data set. A recirculatory kinetic model was a good description of the acute kinetics of magnesium in sheep. The volume of distribution of magnesium in the lungs was 0.89 L, and in the body was 4.02 L. A permeability term (0.59 L min-1) described the distribution of magnesium into a deeper (probably intracellular) compartment. The final kinetic-dynamic model was able to predict the validation data set. The mean prediction error for the arterial magnesium concentrations, cardiac output and mean arterial blood pressure for the validation data set were 0.02, 3.0 and 6.1%, respectively.
The combination of a recirculatory model and a simple two-compartment cardiovascular model was able to describe and predict the kinetics and cardiovascular effects of magnesium in sheep.
将药代动力学模型与心血管系统模型相联系的报告很少。此类模型能够预测药物在多种情况下的心血管效应。限制因素可能包括缺乏一个足够简单的心血管模型、处理大量心血管数据集的困难,以及缺乏能够解释药物可能引起的血流变化的基于生理的药代动力学模型。本文提出了一种解决这些限制的方法,并通过静脉注射镁对绵羊心血管效应的数据进行了说明。心血管模型基于静脉血和动脉血的房室。血液通过一个被动流动经过体循环血管阻力从动脉房室流向静脉房室。血液通过一个泵(心肺系统)从静脉流向动脉,泵血速率由静脉压力控制(Frank-Starling机制)。心率通过动脉血压与设定点之间的差值进行控制(压力感受器控制)。对压力-容积关系、压力-搏出量关系施加了约束,并设定了物理极限以生成合理的心脏功能曲线和基线心血管变量。开发了“心血管雷达图”以简洁地显示心血管状态。建立了一个镁的再循环动力学模型,该模型能够解释该药物引起的心输出量的巨大变化。动力学模型预测的动脉浓度与心血管模型的体循环血管阻力和静脉顺应性项相关联。基于一个训练数据集(2分钟内30毫摩尔)的动力学-动态模型被用于预测一个单独的验证数据集(5分钟内30毫摩尔)的结果。
动力学-动态模型能够描述训练数据集。一个再循环动力学模型很好地描述了绵羊体内镁的急性动力学。镁在肺中的分布容积为0.89升,在体内为4.02升。一个通透项(0.59升/分钟)描述了镁向更深(可能是细胞内)房室的分布。最终的动力学-动态模型能够预测验证数据集。验证数据集的动脉镁浓度、心输出量和平均动脉血压的平均预测误差分别为0.02%、3.0%和6.1%。
再循环模型与一个简单的双房室心血管模型相结合能够描述并预测绵羊体内镁的动力学和心血管效应。