Department of Mathematics and Statistics, University of Konstanz, PO Box 195, 78457, Konstanz, Germany.
Pediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Spitalstrasse 33, 4056, Basel, Switzerland.
J Pharmacokinet Pharmacodyn. 2024 Dec;51(6):919-934. doi: 10.1007/s10928-024-09940-9. Epub 2024 Oct 8.
Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal drug doses for any pharmacometrics model for a given dosing scenario. In the present work, we enhance the OptiDose concept to compute optimal drug dosing with respect to both efficacy and safety targets. Usually, these are not of equal importance, but one is a top priority, that needs to be satisfied, whereas the other is a secondary target and should be achieved as good as possible without failing the top priority target. Mathematically, this leads to state-constrained optimal control problems. In this paper, we elaborate how to set up such problems and transform them into classical unconstrained optimal control problems which can be solved in NONMEM. Three different optimal dosing tasks illustrate the impact of the proposed enhanced OptiDose method.
最近,开发了一种最优剂量算法(OptiDose),用于为给定的给药方案计算任何药代动力学模型的最佳药物剂量。在本工作中,我们增强了 OptiDose 概念,以针对疗效和安全性目标计算最佳药物剂量。通常,这些目标的重要性并不相同,但一个是首要任务,需要满足,而另一个是次要目标,在不影响首要目标的情况下尽可能好地实现。从数学上讲,这导致了状态约束最优控制问题。本文详细阐述了如何建立这些问题,并将其转化为可以在 NONMEM 中解决的经典无约束最优控制问题。三个不同的最优剂量任务说明了所提出的增强型 OptiDose 方法的影响。