使用人工智能工具预测药物的药理动力学:系统性审查
Mahnaz Ahmadi1,2, Bahareh Alizadeh3, Seyed Mohammad Ayyoubzadeh4,5
1Student Research Committee, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
European journal of drug metabolism and pharmacokinetics
|March 8, 2024
概括
人工智能 (AI) 工具可以准确预测药物的药理动力学,减少对动物进行测试的需求. 这一系统性审查强调了人工智能.
科学领域:
- 药理动力学 药理动力学
- 药物开发 药物开发
- 计算生物学 计算生物学
背景情况:
- 药物动力学研究对于药物的有效性和安全性至关重要.
- 传统的实验室方法耗时且资源密集.
- 人工智能 (AI) 为药理动力学预测提供了一个有希望的替代方案.
研究的目的:
- 系统地审查应用人工智能工具来预测药物药理动力学的研究.
- 确定常见的AI模型及其在药理动力学评估中的表现.
主要方法:
- 在PubMed,Scopus和Web of Science进行了全面的文献搜索.
- 文章的选基于预定义的包含/排除标准.
- 包含的研究的质量使用评估工具进行了评估.
主要成果:
- 综述中包括了23篇相关文章.
- 清除和度-时间曲线下的面积 (AUC) 是经常研究的参数.
- 随机森林和极端梯度提升 (XGBoost) 是最常用的AI模型.
- 通用线性模型 (GLMnet) 和随机森林在预测清除方面表现出很高的表现.
结论:
- 人工智能工具提供了一个快速,精确和强大的方法来预测药理动力学参数.
- 人工智能可以利用患者或药物数据进行准确的药理动力学预测.
- 人工智能通过减少对传统方法的依赖,促进了更高效的药物开发.
相关概念视频
Analysis of Population Pharmacokinetic Data
254
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...
254
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
127
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
127
Pharmacokinetic Models: Overview
682
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...
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
682
Pharmacokinetic Models: Comparison and Selection Criterion
71
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.
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.
71
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
69
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...
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...
69
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
62
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...
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
62


