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Drug Accumulation During Multiple Dosing: Repetitive IV Injections01:21

Drug Accumulation During Multiple Dosing: Repetitive IV Injections

256
Calculating drug dosage and accumulation in multiple-dose regimens is crucial for achieving therapeutic efficacy while avoiding toxicity. This involves determining the plasma drug concentrations over time to optimize dosing schedules. The principle of superposition is fundamental in this process, allowing for the prediction of drug concentration in plasma following multiple doses based on single-dose data.The principle of superposition asserts that the plasma concentration-time curves from...
256
Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

234
Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though...
234
One-Compartment Open Model for Extravascular Administration: Zero-Order Absorption Model01:12

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

368
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...
368
One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution01:09

One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution

857
The one-compartment open model is a simplified approach used in pharmacokinetics to understand the distribution and elimination of a drug administered through an intravenous bolus. This model assumes rapid drug dispersal throughout the body and elimination using a first-order process. Key pharmacokinetic parameters, such as the elimination rate constant (k), half-life (t1/2), and the apparent volume of distribution (Vd), can be estimated from this model. The elimination rate is calculated...
857
Two-Compartment Open Model: Extravascular Administration01:12

Two-Compartment Open Model: Extravascular Administration

663
The two-compartment model for extravascular administration represents a drug's absorption and distribution process. It features a central compartment, where the drug is first absorbed, and a peripheral compartment, which illustrates the drug's distribution throughout the body. The rate of change in drug concentration in the central compartment is calculated by three exponents: absorption, distribution, and elimination.
The absorption exponent (ka) indicates the speed at which the drug...
663
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

292
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
292

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相关实验视频

Updated: Jan 18, 2026

Preparation and Characterization of Individual and Multi-drug Loaded Physically Entrapped Polymeric Micelles
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使用可解释的机器学习预测长效注射剂的早期和完整的药物释放.

Karla N Robles, Manar D Samad

    ArXiv
    |January 16, 2026
    PubMed
    概括
    此摘要是机器生成的。

    机器学习模型通过分析材料特性来预测聚合物基长效注射剂 (LAI) 的药物释放. 这种方法优化了慢性疾病的药物输送控制,改善了治疗结果.

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    相关实验视频

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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
    • 计算化学的计算化学

    背景情况:

    • 基于聚合物的长效注射剂 (LAI) 为慢性疾病提供可控的药物输送,减少剂量频率.
    • 优化LAI的物理化学特性以控制释放是复杂且耗时的.
    • 现有的机器学习 (ML) 研究缺乏针对LAI数据的量身定制分析,限制了对药物释放调节的洞察力.

    研究的目的:

    • 开发和应用一种新的可解释的ML方法来进行LAI配方分析.
    • 从LAI配方中预测早期和完整的药物释放概况.
    • 确定影响药物释放动态的关键材料特征.

    主要方法:

    • 使用了321个LAI配方的数据集.
    • 采用了数据转换和可解释的ML技术.
    • 进行了24小时,48小时和72小时释放药物的预测,释放概况的分类和完全释放曲线的预测.

    主要成果:

    • 在72小时后预测和真实药物释放之间实现了强烈的相关性 (>0.65).
    • 在分类释放型类型方面获得0.87的F1得分.
    • 开发了一个时间独立的ML框架,其性能优于时间依赖的方法,用于预测复杂的释放配置文件 (双相,三相).
    • 沙普利的添加解释确定了影响早期和完整药物释放的关键材料特性.

    结论:

    • 新的ML方法为LAI材料特性和药物释放提供了可操作的见解.
    • 这种定量策略可以指导科学家优化LAI药物释放动态.
    • 这项研究解决了先前对LAI的体外和基于ML的分析的局限性.