一个框架,用于对MPC在CDC线路中的稳定性进行in silico评估,以过程可变性为函数
Ruben Waeytens1, Daan Van Hauwermeiren2, Wouter Grymonpré3
1Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium; BIOMATH, Department of Mathematical Modelling, Statistics and Bio-informatics, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium.
International journal of pharmaceutics
|April 26, 2024
概括
口服固体剂型的连续制造需要强大的工艺控制. 本研究引入了一个随机建模框架,以考虑固有的过程变性,改进控制策略分析和制药生产的可转移性.
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.
- 过程控制 过程控制
背景情况:
- 制药行业正在从批量制造转向口服固体剂量的连续制造.
- 现有的过程模型和控制策略往往依赖于确定性方法,忽视了固有的系统变性.
- 确定性模型可能导致不准确的控制操作和在现实应用中性能转移性差.
研究的目的:
- 开发和验证连续制药制造工艺的随机建模框架.
- 为了评估工艺变异对控制策略性能的影响,使用连续料混合平板压缩系统.
- 通过纳入过程变化和预测错误来增强控制策略的in silico分析和可转移性.
主要方法:
- 对模型参数进行概率分布的随机模型的开发.
- 在连续料混合装置 (FE CPS) 和平板压力机 (FE 55) 上测试框架.
- 使用蒙特卡洛 (MC) 分析评估过程变化影响的模型预测控制 (MPC) 策略的评估.
- 包括化学测量模型预测错误和现实的料速率干扰.
主要成果:
- 工艺变化是粉末处理和连续制造的重要因素.
- 确定性建模可能导致关于控制策略性能的错误结论.
- 随机建模提供了对控制性能更现实的评估,并提高了可转移性.
结论:
- 过程变化必须明确地包含在对连续制药制造的控制策略的in silico分析中.
- 随机模型为制药生产中开发和验证过程控制提供了更准确和可靠的方法.
- 拟议的框架提高了连续制造工艺的稳定性和可预测性.
相关概念视频
01:16In vitro Mutagenesis
To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
01:11Variability: Analysis
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
00:59Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...