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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

56
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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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...
558
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.
38
PD Controller: Design01:26

PD Controller: Design

171
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
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Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

218
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
218
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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数据驱动型预测控制模型用于连续制药制造.

Consuelo Vega-Zambrano1, Nikolaos A Diangelakis2, Vassilis M Charitopoulos1

  • 1Department of Chemical Engineering, The Sargent Centre for Process Systems Engineering, University College London, Torrington Place, London, WC1E 7JE, UK.

International journal of pharmaceutics
|February 8, 2025
PubMed
概括
此摘要是机器生成的。

使用动态模式分解与控制 (DMDc) 的可解释机器学习模型对于制药连续制造是可行的. 这使实时监控和先进的过程控制成为可能,以提高运营效率和颗粒大小的一致性.

关键词:
连续制药制造业 连续制药制造业数据驱动的控制是数据驱动的控制.动态模式分解分解可以解释性 解释性模型预测控制模型预测控制通过控制来控制质量.双螺丝颗粒机 双螺丝颗粒机

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科学领域:

  • 制药制造业 制药制造业 制药制造业
  • 化学工程是化学工程的重要组成部分.
  • 机器学习 机器学习

背景情况:

  • 制药连续制造需要运营效率,以实现利能力和可持续性.
  • 采用先进的建模技术对于良好制造实践 (GMP) 规范的环境至关重要.
  • 现有的数据驱动方法可能缺乏可解释性或与复杂的非线性动态作斗争.

研究的目的:

  • 通过使用动态模式分解与控制 (DMDc) 来证明可解释的,基于数据的制药连续制造模型的可行性.
  • 为设计和调整模型预测控制 (MPC) 系统提供实时监控战略框架.
  • 在双螺丝颗粒加工过程中实现精确的颗粒尺寸控制.

主要方法:

  • 动态模式分解与控制 (DMDc) 的应用,一种机器学习技术,用于系统识别.
  • 开发一个可解释的DMDc动态模型,捕捉多输入多输出 (MIMO) 系统的非线性动态.
  • 集成DMDc模型与模型预测控制 (MPC) 系统进行先进的过程控制.

主要成果:

  • 在重建未见过的测试数据方面,DMDc模型实现了高性能 (R2>0.93对于D50预测).
  • 该模型表现出较低的计算复杂性,并且不需要第一原则知识.
  • DMDc-MPC框架已成功实施并测试了设定点跟踪和干扰拒绝.

结论:

  • 使用DMDc的可解释,数据驱动的模型可用于制药连续制造.
  • 开发的DMDc-MPC框架为实时监控和先进的过程控制提供了强大的解决方案.
  • 这种方法提高了运营效率,并确保了制药生产中的颗粒大小的一致性.