Jove
Visualize
Contact Us
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 Concept Videos

Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

47
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...
47
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

60
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...
60
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

41
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...
41
Pharmacokinetic–Pharmacodynamic Relationship: Model Components01:14

Pharmacokinetic–Pharmacodynamic Relationship: Model Components

73
Pharmacokinetic-pharmacodynamic (PK–PD) modeling is essential in drug development and clinical pharmacology. It provides a quantitative framework to predict drug behavior and response over time. This approach integrates pharmacokinetics (PK), which describes the drug's absorption, distribution, metabolism, and excretion, with pharmacodynamics (PD), which characterizes the drug’s biological effects and mechanisms of action.The disposition kinetics of a drug determine its plasma...
73
Pharmacodynamic Models: Emax Drug–Concentration Effect Model01:18

Pharmacodynamic Models: Emax Drug–Concentration Effect Model

81
The Emax drug-concentration effect model is central to pharmacodynamics in drug discovery and development. This model is predicated on the receptor occupancy theory, which posits that the effect of a drug is directly related to the number of receptors occupied by the drug and the resultant complex formation.The model describes the reversible interaction between a drug (C) and a receptor (R) to form a drug-receptor complex (RC). The kinetics of this interaction are quantified by an equation that...
81
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

322
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
322

You might also read

Related Articles

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

Sort by
Same author

Segmentation and classification of hippocampal subregions using multi-task generative adversarial networks.

Scientific reports·2026
Same author

Deep learning multi-omics integration identifies new molecular subtypes of lung cancer.

BioData mining·2026
Same author

ExPO: an exposure-conditioned neural operator for L1000 signature prediction.

Journal of cheminformatics·2026
Same author

Population Analysis and Immunologic Landscape of Melanoma in People Living with HIV.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Early Sepsis Prediction Using Publicly Available Data: High-Performance AI/ML Models with First-Hour Clinical Information.

Diagnostics (Basel, Switzerland)·2025
Same author

An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.

NAR genomics and bioinformatics·2025

Related Experiment Video

Updated: Feb 28, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

10.7K

GenReP: An Ensemble Model for Predicting TP53 in Response to Pharmaceutical Compounds.

Austin Spadaro1, Alok Sharma2,3,4, Iman Dehzangi1,5,6

  • 1Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.

Molecules (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel machine-learning model to predict how drugs affect the TP53 tumor-suppressor gene. This tool aids in developing new cancer therapies by understanding gene expression changes.

Keywords:
Connectivity MapTP53ensemble classifierfeature extractiongene expression

More Related Videos

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

1.0K
Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.6K

Related Experiment Videos

Last Updated: Feb 28, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

10.7K
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

1.0K
Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.6K

Area of Science:

  • Genomics
  • Computational Biology
  • Pharmacology

Background:

  • TP53 is a critical tumor-suppressor gene regulating apoptosis, DNA repair, and genomic stability.
  • TP53 mutations are found in about half of all cancers, making it a key therapeutic target.
  • Predictive tools are needed to assess drug effects on TP53 gene expression.

Purpose of the Study:

  • To develop an ensemble machine-learning model for predicting TP53 relative gene expression changes in response to pharmaceutical compounds.
  • To create a novel predictor for TP53 gene regulation by drugs.

Main Methods:

  • Utilized molecular fingerprints, descriptors, and scaffold-based features from SMILES representations.
  • Concatenated features into a single vector for model input.
  • Trained the model on a new benchmark dataset from the Connectivity Map (CMap) database.
  • Addressed class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE).

Main Results:

  • The model achieved 62.9% accuracy, 93.9% sensitivity, 40.3% specificity, and a 0.39 Matthews Correlation Coefficient (MCC).
  • Demonstrated proof-of-concept for predicting TP53 gene regulation.
  • The predictor, source code, and dataset are publicly available.

Conclusions:

  • The developed machine-learning model is a novel tool for predicting TP53 gene expression changes induced by drugs.
  • This work provides a foundation for future research in personalized cancer therapy and drug development.
  • Public availability of the predictor and dataset facilitates further scientific investigation.