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Related Concept Videos

Dose-Response Relationship: Overview01:03

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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

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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.
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Analysis of Population Pharmacokinetic Data01:12

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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...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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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Pharmacokinetic Models: Overview01:20

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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.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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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Related Experiment Video

Updated: May 11, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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Modeling omics dose-response at the pathway level with DoseRider.

Pablo Monfort-Lanzas1,2, Johanna M Gostner1, Hubert Hackl2

  • 1Institute of Medical Biochemistry, Biocenter, Medical University Innsbruck, 6020 Innsbruck, Austria.

Computational and Structural Biotechnology Journal
|April 17, 2025
PubMed
Summary
This summary is machine-generated.

DoseRider is a new web application and R package for analyzing omics data to model dose-response relationships and identify biological exposure limits. It introduces trend change doses (TCDs) for complex curves, aiding toxicological and pharmacological research.

Keywords:
Benchmark doseDose-response modelingMixed modelsMulti-omicsSystem biologyToxicologyTrend change dose

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Area of Science:

  • Pharmacogenomics and Toxicogenomics
  • Computational Biology and Bioinformatics
  • Molecular Toxicology

Background:

  • Omics data generation is crucial for mechanistic insights in pharmacology and toxicology.
  • Non-linear dose-response relationships necessitate robust modeling for inferring biological exposure limits.
  • Existing tools for dose-response modeling often lack comprehensive pathway-level analysis.

Purpose of the Study:

  • To introduce DoseRider, a web application and R package for dose-response modeling of omics data.
  • To enable assessment of benchmark doses (BMD) at biological pathway or signature levels.
  • To present a novel concept of trend change doses (TCDs) for characterizing complex dose-response effects.

Main Methods:

  • Development of DoseRider, a user-friendly web application and R package.
  • Utilized generalized mixed effect models for linear and non-linear dose-response modeling.
  • Integrated multi-omics data (RNA sequencing, metabolomics) with pathway and gene set annotations.
  • Applied DoseRider to RNA sequencing data from bisphenol AF (BPAF) treated MCF-7 cells.

Main Results:

  • DoseRider successfully analyzed custom and provided multi-omics data across species.
  • Demonstrated usability with BPAF treatment data, identifying a BMD of 0.2 µM for estrogen-responsive genes.
  • Identified the lowest trend change dose (TCD1) at 0.003 µM for BPAF, indicating early biological effects.

Conclusions:

  • DoseRider provides a comprehensive platform for dose-response modeling and BMD assessment using omics data.
  • The introduction of TCDs offers a new metric for understanding complex dose-response phenomena.
  • DoseRider is suitable for pharmacogenomics, toxicogenomics, and broader biological research applications.