一个贝叶斯神经普通微分方程框架,用于研究化学混合物对生存的影响
Virgile Baudrot1, Nina Cedergreen2, Thomas Kleiber1
1Qonfluens, Montpellier, France.
预测植物保护产品 (PPP) 的联合毒性是一项挑战. 一个新的混合模型将毒动力学-毒动力学 (TKTD) 模型与神经网络 (NN) 整合在一起,准确预测混合物效应,改善非标生物体风险评估.
科学领域:
- 环境毒理学环境毒理学
- 计算化学计算化学
- 风险评估 风险评估
背景情况:
- 植物保护产品 (PPP) 经常结合多种活性成分,引发了对非向物种暴露于未经测试的化学混合物的担忧.
- 目前的毒动力学-毒动力学 (TKTD) 模型难以预测PPP混合物中复杂的协同作用或对抗作用.
- 准确预测混合物毒性对于对非目标生物体进行可靠的风险评估至关重要.
研究的目的:
- 开发和评估一种新的混合方法来预测化学混合物的毒性,特别是植物保护产品 (PPP).
- 通过将它们与神经网络 (NN) 集成,增强现有的毒动-毒动力学 (TKTD) 模型的预测能力.
- 改进对暴露于复杂PPP混合物的非标生物的风险评估框架.
主要方法:
- 用常规微分方程 (ODE) 描述的毒动力学-毒动力学 (TKTD) 模型与神经网络 (NN) 集成,以建模复杂物质相互作用.
- 贝叶斯推理的应用,以解决数据不确定性,完善模型参数,量化预测不确定性.
- 使用99项涉及各种PPP混合物的急性毒性研究验证混合ODE-NN-贝叶斯模型.
主要成果:
- 混合模型成功地识别并预测了PPP中预期的添加剂混合物效应的偏差.
- 虽然更简单的线性模型可以有效地预测附加效应,但神经网络组件在捕捉显著的非线性相互作用 (协同/对抗) 中表现出色.
- 该模型展示了一个可靠的框架,用于预测未经测试的化学混合物的联合作用.
结论:
- 开发的混合ODE-NN-贝叶斯模型为预测PPP混合物毒性提供了更负责任和更准确的方法.
- 这种方法通过考虑复杂的化学相互作用,提高了对非目标生物的风险评估过程.
- 该研究强调了简单的线性模型和复杂的神经网络在混合物毒性预测中的独特但互补的作用.
更多相关视频
13:54A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
Published on: August 18, 2023
13:31High-resolution Quantification of Odor-guided Behavior in Drosophila melanogaster Using the Flywalk Paradigm
Published on: December 11, 2015
相关概念视频
Mechanistic Models: Compartment Models in Individual and Population Analysis
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Assumptions of Survival Analysis
Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
