Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics (LAP&P), Leiden, The Netherlands.
GSK, Collegeville, Pennsylvania, USA.
CPT Pharmacometrics Syst Pharmacol. 2023 Nov;12(11):1602-1618. doi: 10.1002/psp4.13027. Epub 2023 Sep 7.
Establishing a dosing regimen that maximizes clinical benefit and minimizes adverse effects for novel therapeutics is a key objective for drug developers. Finding an optimal dose and schedule can be particularly challenging for compounds with a narrow therapeutic window such as in oncology. Modeling and simulation tools can be valuable to conduct in silico evaluations of various dosing scenarios with the goal to identify those that could minimize toxicities, avoid unscheduled dose interruptions, or minimize premature discontinuations, which all could limit the potential for therapeutic benefit. In this tutorial, we present a stepwise development of an adaptive dose simulation framework that can be used for dose optimization simulations. The tutorial first describes the general workflow, followed by a technical description with basic to advanced practical examples of its implementation in mrgsolve and is concluded with examples on how to use this in decision-making around dose and schedule optimization. The adaptive simulation framework is built with pharmacokinetic, pharmacodynamic (i.e., biomarkers, activity markers, target engagement markers, efficacy markers), and safety models that include evaluations of unexplained interindividual and intraindividual variability and covariate impact, which can be replaced and expanded (e.g., combination setting, comparator setting) with user-defined models. Subsequent adaptive simulations allow investigation of the impact of starting dose, dosing intervals, and event-driven (exposure or effect) dose modifications on any end point. The resulting simulation-derived insights can be used in quantitatively proposing dose and regimens that better balance benefit and adverse effects for further evaluation, aiding dose selection discussions, and designing dose modification recommendations, among others.
为新药制定既能最大程度发挥临床疗效又能将不良反应最小化的给药方案是药物开发者的主要目标。对于治疗窗较窄的药物（如肿瘤学药物），找到最佳剂量和方案可能极具挑战性。建模和模拟工具可用于对各种给药方案进行虚拟评估，以确定那些可能最小化毒性、避免非计划剂量中断或最小化过早停药的方案，这些都可能限制治疗效果的潜力。在本教程中，我们介绍了一个逐步开发的自适应剂量模拟框架，可用于剂量优化模拟。本教程首先描述了一般工作流程，然后详细介绍了其在 mrgsolve 中的基本到高级实践示例的实现技术，并通过示例介绍了如何在剂量和方案优化方面做出决策时使用该框架。自适应模拟框架基于药代动力学、药效学（即生物标志物、活性标志物、靶标占有率标志物、疗效标志物）和安全性模型构建，这些模型包括对无法解释的个体间和个体内变异性以及协变量影响的评估，可通过用户定义的模型进行替换和扩展（例如，联合治疗设定、对照治疗设定）。随后的自适应模拟可以研究起始剂量、给药间隔以及基于事件（暴露或效应）的剂量调整对任何终点的影响。从模拟中获得的见解可用于定量提出更好地平衡疗效和不良反应的剂量和方案，以进一步评估，辅助剂量选择讨论和设计剂量调整建议等。
