考虑混合物中的相互作用的机器学习衍生剂量反应关系:对颗粒物氧化潜力的应用
Charles O Esu1, JongCheol Pyo1, Kuk Cho2
1Department of Environmental Engineering, Pusan National University, Republic of Korea.
Journal of hazardous materials
|June 14, 2024
概括
这项研究引入了一种新的机器学习方法,FLIT-SHAP,用于分析复杂的污染物混合物及其健康风险. 它揭示了协同作用和对抗作用,改善了对环境健康决策的毒性预测.
科学领域:
- 环境健康科学科学 环境健康科学
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
背景情况:
- 传统的研究往往忽略了污染物的综合作用,错过了关键的毒性相互作用.
- 了解污染物混合物对于准确的环境风险评估至关重要.
- 现有的方法在多污染物场景中难以破译复杂的剂量反应关系.
研究的目的:
- 引入一种可解释的机器学习 (ML) 方法,FLIT-SHAP,用于分析污染物混合物.
- 阐明剂量反应关系并确定协同效应/对抗效应.
- 改进对环境颗粒物 (PM) 的氧化潜力 (OP) 的预测.
主要方法:
- 开发和应用了特征局部化截面变形-沙普利增量解释 (FLIT-SHAP).
- 使用FLIT-SHAP分析多污染物氧化潜力 (OP) 数据.
- 将FLIT-SHAP预测与使用实验室和真实世界PM样本的传统添加剂模型进行比较.
主要成果:
- 在受控的OP数据中,FLIT-SHAP发现了显著的协同效应 (55-63%) 和对抗效应 (25-42%).
- 在环境PM OP中观察到对抗作用 (33-66%).
- FLIT-SHAP的预测准确度 (R2=0.99) 比添加模型 (R2=0.89) 的预测准确度更高.
- 发现类,像类类,是PM2.5毒性的重要贡献者.
结论:
- FLIT-SHAP有效地捕捉复杂的混合效应,优于传统模型.
- 该方法提高了对污染物相互作用及其对氧化潜力的影响的理解.
- FLIT-SHAP为改善毒性预测和为环境健康政策提供信息提供了一个强大的工具.
相关概念视频
Mutagenicity and Carcinogenicity
1.2K
Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
1.2K
Dose-Response Relationship: Overview
3.1K
Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
3.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis
36
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
36
Analysis of Population Pharmacokinetic Data
252
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
252
Dose-Response Relationship: Potency and Efficacy
4.3K
The potency of a drug is the measure of its ability to produce a biological response and can be compared by looking at the half-maximum effective concentration or EC50 values of different drugs. A lower EC50 value indicates higher potency of the drug. In the dose–response curve of two antihypertensive drugs, candesartan and irbesartan, a significant difference is observed in their EC50 values. A lower EC50 value for candesartan indicates that it is more potent than irbesartan, as it...
4.3K
Statistical Methods for Analyzing Epidemiological Data
349
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
349


