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相关概念视频

Formulation and Manufacturing Process: Physical Attributes of Generic Tablets and Capsules01:18

Formulation and Manufacturing Process: Physical Attributes of Generic Tablets and Capsules

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Bioequivalence in generic drugs, such as tablets and capsules, refers to their pharmaceutical equivalence to the brand-name counterparts. However, for therapeutic equivalence, manufacturers must also consider physical attributes like size, shape, and weight (FDA Guidance for Industry, December 2003). Discrepancies in these aspects could impact patient compliance and cause medication errors. For instance, swallowing difficulties, often experienced with larger tablets or capsules, can lead to...
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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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One-Compartment Open Model for IV Bolus Administration: General Considerations01:19

One-Compartment Open Model for IV Bolus Administration: General Considerations

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The one-compartment model is a pharmacokinetic tool that models the body as a single, uniform compartment, facilitating the understanding of drug distribution and elimination. This model is particularly beneficial for intravenous (IV) bolus administration, where the drug rapidly circulates throughout the body.
The drug's presence in the body is defined by an equation representing the difference between the rates of drug entry and exit. Key parameters—elimination rate constant,...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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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.
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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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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

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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...
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A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
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机器学习在药物制药过程建模中的实施现状,用于口服固体剂型.

Maryam Rezaeizadeh1, Sonia M Razavi2, Fernando J Muzzio3

  • 1Engineering Research Center for Structured Organic Particulate Systems (C-SOPS), East Windsor, NJ, USA; Rutgers University, Ernest Mario School of Pharmacy, Pharmaceutical Science, Piscataway, NJ, USA.

International journal of pharmaceutics
|December 10, 2025
PubMed
概括
此摘要是机器生成的。

人工智能 (AI) 和机器学习 (ML) 正在改变口服固体剂型的制药制造业. 这些技术使预测建模和实时优化成为可能,尽管数据和集成方面的挑战仍然存在.

关键词:
人工智能的人工智能是人工智能.亚斯科布酸 (PubChem CID: 5785). 亚斯科布酸是一种碳酸 (PubChem CID: 10112) 是一种碳酸.塞莱科克西布 (PubChem CID: 2662) 是一种药物.数据驱动的数据驱动.乙胺 (PubChem CID: 3282) 的使用情况乙烯-乙酸乙烯共聚合物 (PubChem CID: 32742) 的使用情况乳糖 (PubChem CID: 6134) 这是一种乳糖.机器学习 机器学习类醇 (PubChem CID: 1983) 是一种药物.制药制造业 制药制造业 制药制造业过程建模的过程建模.糖 (PubChem CID: 5988) 的使用情况

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科学领域:

  • 制药制造业 制药制造业 制药制造业
  • 工业化学 工业化学 工业化学
  • 数据科学数据科学数据科学

背景情况:

  • 制药行业正在转向先进制造业,这是由监管举措和工业4.0推动的.
  • 这种转变需要预测建模,实时优化和质量控制.

研究的目的:

  • 审查机器学习 (ML) 在口服固体剂型制造中的应用.
  • 专注于单元操作和过程分析技术 (PAT).

主要方法:

  • 在制药生产中对ML应用的审查.
  • 专注于湿颗粒,挤出和PAT框架.
  • 对ML进行分析,以预测关键质量属性和优化过程参数.

主要成果:

  • ML的应用包括预测颗粒大小分布和水分含量.
  • ML优化了挤出工艺参数,以达到所需的产品品质.
  • 使用 ML 开发可适应的实时 PAT 框架.

结论:

  • 对于先进的制药制造要求,ML显示出显著的前景.
  • 挑战包括数据可用性,模型可解释性和整合性.
  • 未来的工作涉及数字双胞胎,可扩展性和不确定性量化.