Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

328
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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.
328
Mutagenicity and Carcinogenicity01:25

Mutagenicity and Carcinogenicity

1.9K
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...
1.9K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

241
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...
241
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.8K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.8K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

355
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
355
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

238
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and 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...
238

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Development of a deep learning based framework for classification of Indian venomous snakes integrated with explainable artificial intelligence for primary and emergency care providers.

PLoS neglected tropical diseases·2026
Same author

Paper-based microextraction tool for the <i>in vitro</i> and eco-friendly detection and degradation of malathion pesticide from soybean seeds.

RSC advances·2025
Same author

Characterization of a putative metal-dependent PTP-like phosphatase from Lactobacillus helveticus 2126.

International microbiology : the official journal of the Spanish Society for Microbiology·2023
Same author

Characterization of Biomineralizing and Plant Growth-Promoting Attributes of Lithobiontic Bacteria.

Current microbiology·2023
Same author

Morpho-physiological and demographic responses of three threatened Ilex species to changing climate aligned with species distribution models in future climate scenarios.

Environmental monitoring and assessment·2022
Same author

Training recurrent neural networks as generative neural networks for molecular structures: how does it impact drug discovery?

Expert opinion on drug discovery·2022
Same journal

Low electric charge loading in a sequencing batch electro-membrane bioreactor: influence of aeration intensity on treatment performance, biomass activity, and membrane fouling.

Environmental science and pollution research international·2026
Same journal

Life cycle assessment of foam concrete incorporating quarry microfines and sugarcane bagasse ash for low-carbon construction.

Environmental science and pollution research international·2026
Same journal

Retraction Note: Identification of rainfall homogenous regions in Saudi Arabia for experimenting and improving trend detection techniques.

Environmental science and pollution research international·2026
Same journal

Graphene-based composites for heavy metal adsorption: a review on synthesis, mechanisms, and influencing factors.

Environmental science and pollution research international·2026
Same journal

Application of non-destructive and chemical-free short-wave infrared hyperspectral imaging (SWIR-HSI) coupled with machine learning regression for rapid estimation of deoxynivalenol (DON) in individual corn kernels.

Environmental science and pollution research international·2026
Same journal

A predictive framework for land subsidence risk in Silakhor: integrating machine and deep learning.

Environmental science and pollution research international·2026
查看所有相关文章

相关实验视频

Updated: Jan 14, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.6K

基于AI/ML的计算模型用于毒性预测.

Sushmita Barua1, Badhrinarayanan Balaji2, Seetharaman Balaji3

  • 1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Environmental science and pollution research international
|January 12, 2026
PubMed
概括
此摘要是机器生成的。

计算毒理学和AI/ML模型正在推进化学安全评估. 这些工具可以预测毒性,帮助监管工作,减少动物试验,以更好地评估化学安全.

关键词:
在这里,我们可以看到AIAIAI.动物毒性 动物毒性计算性毒性 计算性毒性生态毒性 生态毒性人类毒性毒性人类毒性ML ML 在 ML可持续发展目标3,6,9,12,14 这些目标包括:

更多相关视频

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
09:01

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

7.6K
Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.2K

相关实验视频

Last Updated: Jan 14, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.6K
A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
09:01

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

7.6K
Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.2K

科学领域:

  • 计算毒理学计算毒理学
  • 人工智能 (AI) 是一种人工智能.
  • 机器学习 (ML) 是指机器学习.

背景情况:

  • 越来越多的对准确毒性评估和减少动物试验的需求推动了计算模型的开发.
  • 人工智能/ML模型和在线资源对于现代计算毒理学研究至关重要.

研究的目的:

  • 审查用于毒性预测和化学安全评估的计算模型和数据覆盖范围.
  • 强调AI/ML工具用于预测各种毒性终点,并讨论监管相关性.

主要方法:

  • 专注于计算模型,分子描述器,定量结构-活动关系 (QSAR) 模型.
  • 包括基于AI/ML的方法,可解释AI (XAI) 和预测方法.
  • 对数据覆盖范围,可访问性和监管考虑的分析.

主要成果:

  • 计算模型和AI/ML工具能够识别,预测和分析跨生物终点的化学毒性.
  • 人工智能/ML工具对于预测神经毒性,肝毒性,心脏毒性,基因毒性和环境毒性的有效.
  • 观察到大量的监管限制和化学品安全评估中缺乏全球合规性.

结论:

  • 由于人工智能的快速发展,监管适应性至关重要.
  • 整合AI/ML工具和可互操作框架可以显著推进预测毒理学.
  • 对监管规范的全球合规性是未来化学品安全评估的关键重点.