用符号回归网络学习药效学协变量模型结构。

IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-04-01 Epub Date: 2023-10-21 DOI:10.1007/s10928-023-09887-3
Ylva Wahlquist, Jesper Sundell, Kristian Soltesz
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引用次数: 0

摘要

有效地找到协变模型结构来最大限度地减少对随机效应的需求来描述药理学数据是具有挑战性的。标准方法侧重于相关协变量的识别,而目前的方法缺乏自动识别协变量模型结构的工具。尽管神经网络可能被用于近似协变参数关系,但这种近似不是人类可读的,并且由于模型复杂性高,存在可推广性差的风险。在本研究中,提出了一种同时选择协变模型结构和优化其参数的新方法。它是基于符号回归的,提出了一个具有光滑损失函数的优化问题。这使得能够通过使用有效梯度计算的反向传播来训练模型。通过应用于丙泊酚的临床药代动力学数据集,证明了可行性和有效性,该数据集包含1031名患者的输注和血样时间序列。将得到的模型与相同数据集的已发表的最先进的模型进行比较。我们的方法找到了一个协变量模型结构和相应的参数值,其拟合度略好,同时依赖的协变量明显少于最先进的模型。与当代实践不同,找到协变模型结构是在没有涉及手动交互的迭代过程的情况下实现的。
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Learning pharmacometric covariate model structures with symbolic regression networks.

Efficiently finding covariate model structures that minimize the need for random effects to describe pharmacological data is challenging. The standard approach focuses on identification of relevant covariates, and present methodology lacks tools for automatic identification of covariate model structures. Although neural networks could potentially be used to approximate covariate-parameter relationships, such approximations are not human-readable and come at the risk of poor generalizability due to high model complexity.In the present study, a novel methodology for the simultaneous selection of covariate model structure and optimization of its parameters is proposed. It is based on symbolic regression, posed as an optimization problem with a smooth loss function. This enables training of the model through back-propagation using efficient gradient computations.Feasibility and effectiveness are demonstrated by application to a clinical pharmacokinetic data set for propofol, containing infusion and blood sample time series from 1031 individuals. The resulting model is compared to a published state-of-the-art model for the same data set. Our methodology finds a covariate model structure and corresponding parameter values with a slightly better fit, while relying on notably fewer covariates than the state-of-the-art model. Unlike contemporary practice, finding the covariate model structure is achieved without an iterative procedure involving manual interactions.

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来源期刊
CiteScore
4.90
自引率
4.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Broadly speaking, the Journal of Pharmacokinetics and Pharmacodynamics covers the area of pharmacometrics. The journal is devoted to illustrating the importance of pharmacokinetics, pharmacodynamics, and pharmacometrics in drug development, clinical care, and the understanding of drug action. The journal publishes on a variety of topics related to pharmacometrics, including, but not limited to, clinical, experimental, and theoretical papers examining the kinetics of drug disposition and effects of drug action in humans, animals, in vitro, or in silico; modeling and simulation methodology, including optimal design; precision medicine; systems pharmacology; and mathematical pharmacology (including computational biology, bioengineering, and biophysics related to pharmacology, pharmacokinetics, orpharmacodynamics). Clinical papers that include population pharmacokinetic-pharmacodynamic relationships are welcome. The journal actively invites and promotes up-and-coming areas of pharmacometric research, such as real-world evidence, quality of life analyses, and artificial intelligence. The Journal of Pharmacokinetics and Pharmacodynamics is an official journal of the International Society of Pharmacometrics.
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