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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
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...
126
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

224
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...
224
Determination of Michaelis Constant and Maximum Elimination Rate01:20

Determination of Michaelis Constant and Maximum Elimination Rate

171
The Michaelis constant (KM) and the theoretical maximum process rate (Vmax) are vital parameters in the Michaelis-Menten equation, central to many biochemical reactions. They provide essential insights into enzyme kinetics and drug metabolism.
These parameters can be estimated by analyzing plasma concentration data post-drug administration. A notable example of this application is phenytoin, a drug with capacity-limited kinetics. It's recommended that phenytoin should be administered at two...
171
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
86
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

712
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
712

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Updated: Sep 11, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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发现暗物质:基于大型语言模型的酶动态数据提取器及其验证.

Galen Wei1, Xinchun Ran1, Runeem Ai-Abssi1

  • 1Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA.

Protein science : a publication of the Protein Society
|August 15, 2025
PubMed
概括

EnzyExtract是一个大型语言模型管道,自动从科学文献中提取酶动力学数据. 这解锁了大量的"暗物质"数据,显著扩大了数据集,用于改进的酶工程模型.

关键词:
深度学习是一种深度学习.酶学数据 酶学数据动力学参数参数的动力学.大型语言模型

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科学领域:

  • 生物化学和生物信息学
  • 计算生物学 计算生物学
  • 酶工程是什么? 酶工程是什么?

背景情况:

  • 大量的酶动力学数据是无结构的,在科学文献中无法获得的.
  • 这限制了对酶工程的准确预测模型的开发.
  • 链接酶序列,基质,动力学和条件的关系数据基本上没有收集到.

研究的目的:

  • 开发一个管道 (EnzyExtract) 来自动提取,验证和结构化来自科学文献的酶动力学数据.
  • 创建一个全面的,结构化的酶动态信息数据库 (EnzyExtractDB).
  • 增强可用酶数据集的规模和多样性,用于预测建模.

主要方法:

  • 利用一个大型语言模型驱动的管道 (EnzyExtract) 来处理137,892个全文出版物.
  • 自动提取酶-基质-动力学条目,包括kcat和Km值.
  • 将提取的数据映射到酶序列 (UniProt) 和基质信息 (PubChem).

主要成果:

  • 收集了超过218,095个酶基质动力学条目,显著扩大了已知的酶学数据集.
  • 识别了89544个独特的动态条目,这些条目不在现有数据库中,比如BRENDA.
  • 产生了92,286个高可靠性,序列映射的动态条目.
  • 使用EnzyExtractDB重新训练预测模型,显示性能改善 (RMSE,MAE,R2).

结论:

  • EnzyExtract成功地自动化了从文献中获取的酶动力学数据的修复.
  • EnzyExtractDB提供了一个有价值的,大规模的,文学衍生数据集,用于增强酶动力学预测.
  • EnzyExtract和EnzyExtractDB的开放性可用性促进了酶工程的进一步研究和开发.