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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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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.
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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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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.
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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...
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Finding the dark matter: Large language model-based enzyme kinetic data extractor and its validation.

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
Summary
This summary is machine-generated.

EnzyExtract, a large language model pipeline, automatically extracts enzyme kinetics data from scientific literature. This unlocks vast "dark matter" data, significantly expanding datasets for improved enzyme engineering models.

Keywords:
deep learningenzymology datakinetics parameterlarge language model

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Area of Science:

  • Biochemistry and Bioinformatics
  • Computational Biology
  • Enzyme Engineering

Background:

  • Vast amounts of enzyme kinetic data are unstructured and inaccessible in scientific literature.
  • This limits the development of accurate predictive models for enzyme engineering.
  • Relational data linking enzyme sequence, substrate, kinetics, and conditions are largely uncollected.

Purpose of the Study:

  • To develop a pipeline (EnzyExtract) for automated extraction, verification, and structuring of enzyme kinetics data from scientific literature.
  • To create a comprehensive, structured database (EnzyExtractDB) of enzyme kinetic information.
  • To enhance the size and diversity of available enzyme datasets for predictive modeling.

Main Methods:

  • Utilized a large language model-powered pipeline (EnzyExtract) to process 137,892 full-text publications.
  • Automated extraction of enzyme-substrate-kinetics entries, including kcat and Km values.
  • Mapped extracted data to enzyme sequences (UniProt) and substrate information (PubChem).

Main Results:

  • Collected over 218,095 enzyme-substrate-kinetics entries, significantly expanding the known enzymology dataset.
  • Identified 89,544 unique kinetic entries not present in existing databases like BRENDA.
  • Generated 92,286 high-confidence, sequence-mapped kinetic entries.
  • Retrained predictive models using EnzyExtractDB, showing improved performance (RMSE, MAE, R²).

Conclusions:

  • EnzyExtract successfully automates the curation of enzyme kinetics data from literature.
  • EnzyExtractDB provides a valuable, large-scale, literature-derived dataset for enhancing enzyme kinetic prediction.
  • The open availability of EnzyExtract and EnzyExtractDB facilitates further research and development in enzyme engineering.