Jelliffe R W, Schumitzky A, Bayard D, Milman M, Van Guilder M, Wang X, Jiang F, Barbaut X, Maire P
Laboratory of Applied Pharmacokinetics, University of Southern California School of Medicine, Los Angeles, USA.
Clin Pharmacokinet. 1998 Jan;34(1):57-77. doi: 10.2165/00003088-199834010-00003.
This article examines the use of population pharmacokinetic models to store experiences about drugs in patients and to apply that experience to the care of new patients. Population models are the Bayesian prior. For truly individualised therapy, it is necessary first to select a specific target goal, such as a desired serum or peripheral compartment concentration, and then to develop the dosage regimen individualised to best hit that target in that patient. One must monitor the behaviour of the drug by measuring serum concentrations or other responses, hopefully obtained at optimally chosen times, not only to see the raw results, but to also make an individualised (Bayesian posterior) model of how the drug is behaving in that patient. Only then can one see the relationship between the dose and the absorption, distribution, effect and elimination of the drug, and the patient's clinical sensitivity to it; one must always look at the patient. Only by looking at both the patient and the model can it be judged whether the target goal was correct or needs to be changed. The adjusted dosage regimen is again developed to hit that target most precisely starting with the very next dose, not just for some future steady state. Nonparametric population models have discrete, not continuous, parameter distributions. These lead naturally into the multiple model method of dosage design, specifically to hit a desired target with the greatest possible precision for whatever past experience and present data are available on that drug--a new feature for this goal-oriented, model-based, individualised drug therapy. As clinical versions of this new approach become available from several centers, it should lead to further improvements in patient care, especially for bacterial and viral infections, cardiovascular therapy, and cancer and transplant situations.
本文探讨了群体药代动力学模型在存储患者用药经验并将该经验应用于新患者治疗中的应用。群体模型是贝叶斯先验。对于真正的个体化治疗，首先有必要选择一个特定的目标，例如期望的血清或外周室浓度，然后制定个体化的给药方案，以最佳地达到该患者的目标。必须通过测量血清浓度或其他反应来监测药物的行为，这些反应最好在最佳选择的时间获得，不仅要查看原始结果，还要建立一个关于药物在该患者体内行为的个体化（贝叶斯后验）模型。只有这样，才能了解剂量与药物吸收、分布、效应和消除之间的关系，以及患者对药物的临床敏感性；必须始终关注患者。只有同时查看患者和模型，才能判断目标是否正确或是否需要更改。再次制定调整后的给药方案，以便从下一次给药开始就最精确地达到该目标，而不仅仅是为了某个未来的稳态。非参数群体模型具有离散而非连续的参数分布。这些自然地引出了剂量设计的多模型方法，特别是在有关于该药物的任何既往经验和当前数据的情况下，以尽可能高的精度达到期望的目标——这是这种以目标为导向、基于模型的个体化药物治疗的一个新特点。随着几个中心推出这种新方法的临床版本，它应该会进一步改善患者护理，特别是在细菌和病毒感染、心血管治疗以及癌症和移植情况方面。
