Gomeni R, Pineau G, Mentré F
SIMED, Créteil, France.
Anticancer Res. 1994 Nov-Dec;14(6A):2321-6.
The adjustment of individual dosage regimen is an adaptive control process based upon an individual response to a pharmacokinetic model. To attain this objective, it is very helpful to know the characteristics of the population to which the subject belongs, in terms of mean parameters and interindividual variability. Usually the available information consists of incomplete and sparse data. For this reason it is essential to employ a computational methodology based on non-linear mixed-effect procedures in order to obtain a population parameter estimate. A Bayesian methodology can then be applied from the population parameters to the specific data for the individual requiring a dosage adjustment (such data includes drug concentration(s) of the active drug, demographic data, etc). The result of the Bayesian calculation supplies the required individual pharmacokinetic parameters. An optimal dosage regimen can be defined on the basis of therapeutical criteria (concentration ranges) as well as practical constraints such as: the size of available unitary drug dosages, feasible drug intake times, penalties associated with expected concentrations falling outside the therapeutic concentration ranges. In this paper we present the methodology and results obtained using the P-Pharm software tool. P-Pharm implements a non-linear mixed-effect population parameter estimation algorithm based on the EM algorithm. This method allows the inclusion of explicit variables into the calculations, it implements an individual Bayesian parameter estimation procedure and also an algorithm for the conditional assessment of the optimal dosage regimen given a list of practical constraints.
个体给药方案的调整是一个基于个体对药代动力学模型反应的自适应控制过程。为实现这一目标,了解个体所属人群在平均参数和个体间变异性方面的特征非常有帮助。通常,可用信息由不完整和稀疏的数据组成。因此,采用基于非线性混合效应程序的计算方法来获得群体参数估计至关重要。然后可以将贝叶斯方法从群体参数应用于需要调整剂量的个体的特定数据(此类数据包括活性药物的药物浓度、人口统计学数据等)。贝叶斯计算的结果提供所需的个体药代动力学参数。可以根据治疗标准(浓度范围)以及实际限制来定义最佳给药方案,例如:可用单一药物剂量的大小、可行的药物服用时间、与预期浓度超出治疗浓度范围相关的惩罚。在本文中,我们展示了使用P-Pharm软件工具获得的方法和结果。P-Pharm实现了基于期望最大化(EM)算法的非线性混合效应群体参数估计算法。该方法允许在计算中纳入显式变量,实现个体贝叶斯参数估计程序以及在给定一系列实际限制的情况下对最佳给药方案进行条件评估的算法。