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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

54
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...
54
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

545
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...
545
Drug Delivery: Overview01:16

Drug Delivery: Overview

271
The selection of a drug's delivery route depends upon its physicochemical properties, including lipid or water solubility and ionization, as well as the therapeutic requirement, such as immediate or sustained effect. These routes can be divided into three primary categories: enteral, parenteral, and topical.
Enteral delivery involves administering drugs directly through swallowing, sublingual placement, or buccal application. Orally administered drugs predominantly navigate the...
271
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

38
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.
38
One-Compartment Open Model for Extravascular Administration: Zero-Order Absorption Model01:12

One-Compartment Open Model for Extravascular Administration: Zero-Order Absorption Model

50
Extravascular administration, such as oral or intramuscular routes, is a non-invasive drug delivery method, often preferred for ease and patient compliance. A key factor here is absorption, which dictates how quickly and effectively the drug enters the bloodstream from the administration site. Absorption follows either zero-order or first-order kinetics.
Zero-order absorption maintains a steady rate irrespective of the amount of drug left to be absorbed, making it a constant process. In the...
50
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

72
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...
72

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相关实验视频

Updated: May 27, 2025

Preparation and Characterization of Individual and Multi-drug Loaded Physically Entrapped Polymeric Micelles
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Published on: August 28, 2015

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利用机器学习来预测聚合物药物输送系统中的药物释放.

Sareh Aghajanpour1, Hamid Amiriara2, Mehdi Esfandyari-Manesh3

  • 1Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran; Department of Pharmaceutics, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran.

Computers in biology and medicine
|February 20, 2025
PubMed
概括

机器学习 (ML) 通过准确预测药物释放配置文件来增强聚合物药物递送系统 (PDDS). 人工神经网络对复杂的系统,如3D打印的剂量表格,特别有希望.

关键词:
人工神经网络的人工神经网络深度学习是一种深度学习.药物释放预测模型的模型智能制药是一个智能制药.机器学习是机器学习.聚合物药物递送系统的多重物质.

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科学领域:

  • 制药科学 制药科学
  • 材料科学 材料科学 材料科学
  • 计算化学计算化学

背景情况:

  • 聚合物药物递送系统 (PDDS) 对于受控释放至关重要,但面临着配方和预测挑战.
  • 传统的方法与PDDS的复杂性和众多影响变量作斗争.
  • 机器学习 (ML) 提供了一种有希望的方法来克服药物输送中的这些局限性.

研究的目的:

  • 审查ML策略来预测PDDS中的药物释放.
  • 突出七个持续释放系统中的关键ML应用.
  • 讨论基于ML的药物释放预测中的挑战,解决方案和未来方向.

主要方法:

  • 对PDDS应用的基本ML原则的概述.
  • 对用于矩阵平板,微球,植入物,水凝,薄膜和3D打印形式的ML的当前研究的分析.
  • 评估人工神经网络 (ANN) 和组合模型用于释放预测.

主要成果:

  • 人工神经网络在PDDS释放预测方面表现优越,与其他ML方法相比.
  • 集成模型对于具有多个参数的复杂释放配置文件是有效的.
  • ML显示了3D打印剂型的巨大潜力,使个性化医疗成为可能.

结论:

  • ML,特别是ANN,为预测各种PDDS药物释放提供了强大的工具.
  • 机器学习促进了复杂药物输送系统的进步,包括3D打印的形式.
  • 基于ML的预测为个性化医疗和精确的药物输送铺平了道路.