Jove
Visualize
Contact Us

Related Concept Videos

Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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 relationship...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower Kd...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

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...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Postglacial genetic legacies and climate-driven demography inform conservation of silver fir.

Annals of botany·2026
Same author

BIGO: A web application to analyse gene enrichment analysis results.

Computational biology and chemistry·2018
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 15, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

A tissue-aware computational framework for confounding-controlled co-expression network analysis: Context-dependent

Marc Ríos-Cadenas1, Iván Segura-Carmona1, Aurelio López-Fernández1

  • 1SynergIA Laboratory (SIALAB), Universidad Pablo de Olavide, Ctra. Utrera km. 1, Seville, ES-41013, Spain.

Computational Biology and Chemistry
|July 13, 2026
PubMed
Summary

This study introduces a tissue-aware computational framework for pan-cancer pharmacogenomics, improving co-expression network analysis by reducing tissue identity bias. The framework demonstrated drug-context-dependent predictive benefits for targeted therapies.

Keywords:
Co-expression networksConfounder controlDrug sensitivityPan-cancerPharmacogenomicsWGCNA

Related Experiment Videos

Last Updated: Jul 15, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

  • Computational Biology
  • Pharmacogenomics
  • Cancer Research

Background:

  • Co-expression network analysis in pan-cancer pharmacogenomics is challenged by tissue identity confounding topological metrics.
  • Existing methods often inflate predictive performance and reduce biological interpretability due to dominant tissue-specific signals.

Purpose of the Study:

  • To develop and validate a tissue-aware computational framework for robust pan-cancer pharmacogenomic modeling.
  • To quantitatively control for tissue identity confounders in co-expression network analysis.
  • To assess the drug-context dependency of co-expression topology's predictive benefit.

Main Methods:

  • Integrated tissue-specific Weighted Gene Co-expression Network Analysis (WGCNA) construction.
  • Within-tissue edge disruption profiling and per-gene z-score standardization.
  • Tissue prediction accuracy as a quantitative criterion for confounder control, evaluated via stratified cross-validation with FDR correction.

Main Results:

  • Tissue-aware standardization reduced tissue identity encoding in topological features from 88.9% to 12.9%.
  • Within-tissue WGCNA modules demonstrated superior performance over a confounder-free Principal Component Analysis (PCA) baseline.
  • Osimertinib showed significant predictive improvement (Δρ=+0.063, padj<0.001), while crizotinib and KRAS G12C Inhibitor-12 did not, indicating drug-context dependency.

Conclusions:

  • The developed framework establishes a reproducible computational standard for confounder-controlled co-expression network analysis in pan-cancer pharmacogenomics.
  • The predictive utility of co-expression network topology is highly dependent on the specific drug and cancer context.
  • The approach has direct applicability to large-scale pharmacogenomic studies using datasets like CCLE and GDSC.