随着现代医学逐步迈向精准用药时代，对个体化药物治疗也提出了更高的要求。在明确血药浓度影响因素的基础上，了解预测模型的构建方法以及基于模型引导的精准用药的应用对于临床患者特别是特殊患者群体的治疗具有重要的意义。通过对常用的血药浓度预测模型构建方法（如回归法、群体药动学、生理药动学以及机器学习）进行介绍，探讨各种方法的特点及适用范围，以期为模型引导的精准用药在临床血药浓度预测中的相关应用提供参考。

With modern medicine stepping into the era of precision medication, higher requirements have been put forward for individualized drug treatment. On the basis of clarifying the influencing factors of serum concentration, understanding construction of prediction model and its application of precision dosing are important for clinical patients, especially for treatment of special patient groups. In this review, some commonly used methods such as regression, population pharmacokinetics, physiologically based pharmacokinetic, machine learning and their application in prediction of serum concentration were introduced. The characteristics and applicability of these methods were also discussed in order to provide references for application of model-informed precision dosing in prediction of serum concentration.