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相关概念视频

Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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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...
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Nonlinear Pharmacokinetics: Bioavailability and Protein-Drug Binding01:22

Nonlinear Pharmacokinetics: Bioavailability and Protein-Drug Binding

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When a drug follows nonlinear pharmacokinetics, its bioavailability, the amount of the drug that reaches the systemic circulation, can change with different doses. This is due to the presence of a saturable pathway. The pathway becomes saturated as the drug concentration increases, decreasing the absorption rate. Consequently, the drug's bioavailability may be lower than expected at higher doses.
To quantify the extent of bioavailability, pharmacologists often use a parameter called .
276
Drug Distribution: Plasma Protein Binding01:29

Drug Distribution: Plasma Protein Binding

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Drugs predominantly attach to plasma proteins, with only a small percentage remaining unbound. The unbound portion can be calculated as one minus the bound fraction. Acidic drugs form large, inactive complexes by reversibly binding to plasma albumin, which prevents them from diffusing across biological barriers. These drug-protein complexes act as reservoirs for the drugs. As the concentration of unbound drugs decreases, these complexes quickly dissociate to release the free drug, maintaining...
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Bioavailability: Overview01:13

Bioavailability: Overview

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Bioavailability refers to the proportion of an unaltered drug that, after administration, enters the systemic circulation and can be distributed to the desired action site. Factors such as gastrointestinal (GI) absorption and liver biotransformation influence the bioavailability of a drug when it is administered orally. When a drug is administered intravenously, it enters the systemic circulation directly; by definition, its bioavailability is assumed to be 100%. The bioavailability of an...
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Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

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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...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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...
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相关实验视频

Updated: Sep 12, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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使用组合蛋白语言模型预测皮下抗体的生物可用性

Miles Cabreza1, William Hojegian1, I-En Wu1

  • 1Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, New Jersey United States.

Molecular pharmaceutics
|August 5, 2025
PubMed
概括
此摘要是机器生成的。

通过新的机器学习框架,预测单克隆抗体 (mAb) 皮下生物可用性变得更加容易. 这种方法使用蛋白质语言模型 (PLM) 准确预测生物可用性,加速治疗开发.

关键词:
生物可用性 生物可用性高度抗体配方的高度抗体机器学习是机器学习.蛋白质语言模型的模型皮下注射 皮下注射 皮下注射

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科学领域:

  • 生物技术是生物技术.
  • 计算生物学 计算生物学
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 单克隆抗体 (mAbs) 是重要的治疗方法.
  • 由于复杂的SC环境和当前实验模型的局限性,难以预测mAbs的皮下 (SC) 生物可用性.

研究的目的:

  • 开发一种新的机器学习框架,用于预测mAb的皮下生物可用性.
  • 利用蛋白质语言模型 (PLM) 提高预测准确性和可访问性.

主要方法:

  • 利用三个不同的PLM (antiBERTy,ABlang,ESM-2) 来从抗体序列中提取高维嵌入物.
  • 应用特征选择和维度减小来完善数值表示.
  • 开发了一个集体模型,使用一个调整的支持向量机器分类器,使用Leave-One-Out交叉验证.

主要成果:

  • 对于组合模型,实现了89%的验证准确性.
  • 整体方法,汇总抗体的预测,与以前的计算方法相比,显示出更高的性能.
  • 开发和部署了SubQAvail网络应用程序,用于可访问的生物可用性预测.

结论:

  • 整合PLM衍生功能的组合学习显著提高了mAb生物可用性评估的准确性和可扩展性.
  • 该 SubQAvail 应用程序促进了快速预测,加速了单克隆抗体的治疗开发管道.