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相关概念视频

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

49
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.
49
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

83
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
83
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

609
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...
609
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

36
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
36
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

103
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
103
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

64
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...
64

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Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish
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一种用于预测复合大脑度-时间概况的实用制方法:PK建模和机器学习的结合.

Koichi Handa1, Daichi Fujita1, Mariko Hirano1

  • 1Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan.

Molecular pharmaceutics
|September 26, 2024
PubMed
概括

开发一种新的in silico方法,将建模和模拟与机器学习相结合,以预测大脑中的药物度. 这种方法减少了对动物进行广泛测试的需求,为研究中枢神经系统药物提供了更有效的方法.

关键词:
在CNS中,CNS是CNS.的PK参数PK参数在QSAR中使用QSAR.复合大脑度-时间概况.复合设计是一种复合设计.机器学习是机器学习.建模和模拟的模型和模拟.小鼠的药理动力学随机的森林随机的森林

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科学领域:

  • 药理动力学和药理动力学
  • 计算机化药物发现技术
  • 神经科学是一个神经科学.

背景情况:

  • 全球人口老龄化推动了对新型中枢神经系统 (CNS) 药物的需求.
  • 血脑屏障对中枢神经系统的药物输送构成了重大挑战.
  • 目前用于评估大脑中药物分布的方法通常是昂贵的,耗时的,需要进行广泛的动物研究.

研究的目的:

  • 开发一种用于脑药物度的in silico预测方法.
  • 减少中枢神经系统药物开发所需的实验数据和动物使用.
  • 将建模和模拟 (M&S) 与机器学习 (ML) 整合起来,以提高预测准确度.

主要方法:

  • 构建了一个混合模型,将血度-时间概况与大脑区的动态联系起来,并考虑过渡时间和分布.
  • 机器学习模型是使用化学结构描述器构建的,以预测运动参数.
  • 评估了三个情景:情景I (全脑度-时间数据),情景II (ML预测被输入混合模型) 和情景III (使用单个时间点进行参数重定).

主要成果:

  • 场景II实现了0.445/0.517的RMSE/R2值,用于预测大脑化合物度-时间概况.
  • 场景III,使用单个时间点,显著提高了预测准确度,RMSE/R2值为0.246/0.805.
  • 开发的方法证明了高准确性和实用性,用于预测大脑化合物度-时间概况.

结论:

  • 综合M&S和ML方法为预测中枢神经系统药物药理动力学提供了强大的工具.
  • 该方法显著减少了对广泛实验数据和动物试验的需求.
  • 这种in silico策略为中枢神经系统药物发现和开发提供了实用和准确的解决方案.