模型知情的精确剂量（MIPD）是个性化医疗的一项重大发展，可根据个体患者的特征定制药物剂量。 MIPD 通过整合剂量的数学预测并考虑患者特定因素（患者特征、药物测量）以及不同的变异源，超越了传统的治疗药物监测 (TDM)。为此，MIPD 在患者中的应用需要严格的模型鉴定。这篇综述深入探讨了模型选择和验证的新方法，还强调了机器学习在改善 MIPD 中的作用、利用生物传感器进行实时监测，以及整合生物标志物以实现精确剂量的功效或毒性的模型的潜力。 TDM 和 MIPD 的临床证据在感染医学、肿瘤学、移植医学和炎症性肠病等各个医学领域进行了讨论，从而强调了药代动力学/药效学和特定生物标志物的作用。需要进一步的研究，特别是随机临床试验，来证实 MIPD 在改善患者治疗结果和推进个性化医疗方面的价值。版权所有 © 2024 作者。由 Elsevier B.V. 出版。保留所有权利。

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing, and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.