在AstraZeneca的ADME预测中使用机器学习的前景
Erik Gawehn1, Nigel Greene2, Filip Miljković3
1Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
Xenobiotica; the fate of foreign compounds in biological systems
|August 21, 2024
概括
阿斯特拉泽内卡使用机器学习和人工智能来预测药物药理动力学 (PK) 概况在发现早期. 这通过预测临床前和人类PK来提高效率,指导更好的药物设计.
科学领域:
- 药物的发现和开发.
- 药理动力学和药物代谢的药理动力学
- 计算化学和化学信息学
背景情况:
- 了解药物的药理动力学 (PK) 概况对于确定剂量,服用频率和潜在的不良反应至关重要.
- 在药物发现过程中早期预测PK特性对于提高效率和减少后期失败至关重要.
- 目前的方法通常需要实验数据,突出显示在分子合成之前需要预测模型.
研究的目的:
- 描述AstraZeneca的方法,以提高预测新分子的临床前和人类药物动力学概况.
- 利用机器学习 (ML) 和人工智能 (AI) 来改善 PK 预测.
- 将基于化学结构的方法与实验数据相结合,以便在体内更准确地进行药理动力学预测.
主要方法:
- 利用机器学习和人工智能算法来分析化学结构和实验数据.
- 结合in silico (基于结构的) 方法与体外实验性质.
- 开发临床前和人类药物动力学参数的预测模型.
主要成果:
- 通过整合化学结构和实验数据,证明了 in vivo 药理动学的改进预测.
- 扩展了对传统利宾斯基五项规则空间之外的分子的预测能力.
- 展示了结合体外和体内活体预测模型预测人类结果的潜力.
结论:
- 机器学习和人工智能显著提高了药物的药理动力学概况的预测.
- 整合不同的数据源 (化学结构,体外,体内) 导致更强大的PK预测.
- 这些预测能力可以优化化学设计,提高药物发现的效率.
相关概念视频
01:20Pharmacokinetic Models: Overview
2.7K
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...
2.7K
01:12Analysis of Population Pharmacokinetic Data
1.0K
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...
1.0K
01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
734
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...
734
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
335
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...
335
00:59Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
428
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...
428
01:26Pharmacokinetic Models: Comparison and Selection Criterion
492
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.
492