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相关概念视频

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

48
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.
48
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

559
The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
559
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

63
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...
63
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

237
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...
237
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

82
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
82

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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稳定性建模方法,使患者能够更早地访问.

Andrew Lennard1, Boris Zimmermann2, Didier Clenet3

  • 1Amgen Limited, 4 Uxbridge Business Park, Sanderson Road, Uxbridge UB8 1DH, UK.

Journal of pharmaceutical sciences
|September 29, 2024
PubMed
概括
此摘要是机器生成的。

使用人工智能和统计数据的预测稳定性建模提供了对药物的可靠保质期预测. 这种方法通过补充实时数据来加快新药的可用性,确保质量和安全.

关键词:
生物技术是生物技术.化学稳定性 化学稳定性计算生物学是一种计算生物学.脱化除化运动学 运动学机械模型的建模.单克隆抗体 (monoclonal antibody) 是一种单克隆的抗体.物理稳定性 物理稳定性物理化学特性 物理化学特性稳定的稳定性 稳定的稳定性接种疫苗的方法

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科学领域:

  • 制药科学 制药科学
  • 计算化学计算化学
  • 监管科学 监管科学

背景情况:

  • 预测稳定性建模越来越被接受,用于确定药品的保质期.
  • 科学和基于风险的方法可以克服监管提交不完整实时稳定性数据的局限性.
  • 加快药物可用性对于患者获得新疗法至关重要.

研究的目的:

  • 突出预测稳定性建模在保质期确定中的能力.
  • 强调这些模型在加快监管提交和药物可用性方面的作用.
  • 讨论各种统计和人工智能工具在稳定性评估中的整合.

主要方法:

  • 利用统计工具,先验知识和行业经验进行预测建模.
  • 使用诸如加速稳定性评估程序 (ASAP) 和高级运动建模 (AKM) 等方法.
  • 纳入贝叶斯统计和人工智能 (AI) 应用程序,如机器学习 (ML),用于合成和生物分子.

主要成果:

  • 预测模型提供了强大而可靠的保质期/过期或重新测试期预测.
  • 这些模型有助于在没有完整的实时稳定性数据的情况下进行监管提交.
  • 对于解决生物稳定性建模中的局限性而言,先前的知识特别有价值.

结论:

  • 预测稳定性建模,包括AI/ML,为保质期预测提供了可靠的替代方案.
  • 通过实时数据进行持续的验证,使监管机构对这些方法产生了信心.
  • 稳定性建模的监管接受可以加快患者获得必需药物的速度,而不会影响安全性或疗效.