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相关概念视频

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.1K
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...
1.1K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

147
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.
147
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

158
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...
158
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
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...
85
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

252
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
252
Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

78
Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
78

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相关实验视频

Updated: Sep 8, 2025

Author Spotlight: Developing a Simple and Robust Hepatic Model for Pharmacological and Toxicological Applications
07:23

Author Spotlight: Developing a Simple and Robust Hepatic Model for Pharmacological and Toxicological Applications

Published on: October 20, 2023

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机器学习和大型语言模型用于模拟复杂的毒性途径和预测类固醇生成.

Thomas R Lane1, Patricia A Vignaux1, Joshua S Harris1

  • 1Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States of America.

Environmental science & technology
|June 27, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了计算模型来预测化学物质如何影响类固醇生成,即激素生产过程. 这些模型为评估化学物质影响提供了一个快速系统,有助于制定监管决策.

关键词:
莫尔巴特 (MolBART) 是一个名为莫尔巴特 (MolBART) 的公司.符合规范的预测器内分泌干扰 干扰内分泌系统大型语言模型.机器学习是机器学习.类固醇的产生.

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07:23

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科学领域:

  • 内分泌学 在内分泌学.
  • 计算毒理学计算毒理学
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 雌激素和雄激素受体相互作用的模型很好,但类固醇生成的预测仍然有限.
  • 类固醇生成对激素调节至关重要,是化学破坏的目标.
  • 目前用于评估化学物质对类固醇生成的影响的方法不足以进行大规模查.

研究的目的:

  • 开发和验证用于预测类固醇生成的化学调制的计算模型.
  • 在受化学物质影响的类固醇生成途径中识别特定的分子标.
  • 为化学风险评估和监管评估提供一个可扩展的系统.

主要方法:

  • 利用在H295R细胞中选的约1800种化学物质的数据来构建随机森林模型.
  • 开发了使用IC50数据从ChEMBL获得关键类固醇酶的分类和回归模型.
  • 采用基于变压器的模型 (MolBART) 来同时预测多个终点.

主要成果:

  • 随机森林模型在一般类固醇生成调节的前性验证中实现了80%的准确性.
  • 开发了包括CYP17A1,CYP21A2和CYP19A1在内的关键酶的模型.
  • 变压器模型证明了用于同时预测所有终点的验证性能.

结论:

  • 开发的模型提供了一种快速可扩展的方法来评估化学物质对类固醇生成的影响.
  • 这些工具可以支持化学风险评估,产品管理和监管决策.
  • 这些模型可以预测一般的类固醇生成抑制和特定的酶标.