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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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Related Experiment Video

Updated: Sep 11, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

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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

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.