人工智能和机器学习应用于抗生素的药理学建模和剂量预测:一个范围审查
Iria Varela-Rey1,2,3, Enrique Bandín-Vilar1,2,3, Francisco José Toja-Camba1,2,3
1Clinical Pharmacology Group, Health Research Institute of Santiago de Compostela (IDIS), 15706 Santiago de Compostela, Spain.
Antibiotics (Basel, Switzerland)
|January 8, 2025
概括
人工智能 (AI) 和机器学习 (ML) 在药理动力学中显示出对抗生素剂量预测的前景. 虽然有效,但人工智能方法目前应该补充,而不是取代临床实践中的传统人口药理动力学模型.
科学领域:
- 药理动力学和药理动力学
- 人工智能在医学中的应用
- 计算生物学 计算生物学
背景情况:
- 人工智能 (AI) 的应用,特别是机器学习 (ML) 在医疗保健领域正在扩大.
- AI/ML为分析复杂的药理动力学数据和开发高效的预测剂量模型提供了潜力.
- 抗生素的药理动力学监测从AI/ML中受益,因为它能够简化流程,减少时间,并纳入比经典方法更多的因素.
研究的目的:
- 审查使用AI/ML技术进行抗生素剂量预测的研究.
- 为了比较AI/ML方法的性能与经典的药理动力学方法.
- 分析使用的技术和精确度指标,以便更好地解释结果.
主要方法:
- 在EMBASE,OVID和PubMed数据库中进行系统的文献搜索.
- 对精选的文章进行详细分析,重点关注AI/ML在药物动力学剂量预测中的应用.
- 评估AI/ML与古典人口药理动力学模型之间的比较指标.
主要成果:
- 在13篇选出的文章中,有10篇是在过去三年内发表的,这表明了最近的进展.
- 范科米辛是最常被监测的抗生素;没有研究专注于新型抗生素.
- XGBoost和神经网络是主要使用的AI/ML技术,通常与人口药理动力学模型进行比较.
结论:
- 人工智能技术在药物动力学剂量预测方面显示出有前途的潜力.
- 统计指标和研究功率的变化使综合评估变得复杂.
- 当前的临床实践应该整合基于AI的ML技术,作为已建立的人口药理动力学模型的辅助.
相关概念视频
01:26Pharmacokinetic Models: Comparison and Selection Criterion
38
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.
38
01:06Model 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...
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...
56
01:20Pharmacokinetic Models: Overview
577
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...
577
01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
87
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...
87
01:14Model Approaches for Pharmacokinetic Data: Compartment Models
75
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...
Two primary types of compartment models are recognized: mammillary and catenary. The more...
75
01:21Mechanistic 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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