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

Pharmacokinetics: Drug–Drug Interactions01:25

Pharmacokinetics: Drug–Drug Interactions

370
Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
370
Pharmacokinetics: Drug–Food and Drug–Viral Interactions01:26

Pharmacokinetics: Drug–Food and Drug–Viral Interactions

221
A drug interaction occurs when the concurrent use of another drug, food, or an external substance alters the pharmacological activity of a drug. This interaction can modify the action of the original drug, affecting its effectiveness and safety.Drug–food interactions are significant as they impact drug absorption, metabolism, and excretion. For example, grapefruit juice is a well-known disruptor of drug metabolism. It inhibits the cytochrome P450 3A4 enzyme, crucial for the metabolism of...
221
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

7.3K
Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
7.3K
Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

564
Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
Displacement interactions can have varying outcomes, ranging from toxicity to virtually...
564
Factors Affecting Renal Clearance: Drug Distribution and Drug Interactions01:09

Factors Affecting Renal Clearance: Drug Distribution and Drug Interactions

513
Renal clearance plays a pivotal role in drug elimination from the body and can be influenced by drug distribution and interactions. Understanding these factors is crucial in pharmacology as they impact the effectiveness and duration of drug therapy.
One important factor is the relationship between renal clearance and the apparent volume of distribution. Renal clearance tends to be inversely proportional to the apparent volume of distribution. Drugs with an extensive distribution volume or those...
513
Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

4.9K
An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
Antagonists can be classified as competitive or noncompetitive based on their...
4.9K

You might also read

Related Articles

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

Sort by
Same author

CLC-Pred Synergy: Web Application for Predicting Pairwise Drug Combinations with Synergistic Activity Against NCI60 Cancer Cell Lines.

International journal of molecular sciences·2026
Same author

An Integrated Text Mining Approach for Discovering Pharmacological Effects, Drug Combinations, and Repurposing Opportunities of ACE Inhibitors.

International journal of molecular sciences·2026
Same author

A computational approach for classification of HIV drug resistance based on the self-consistent extreme classifier.

Computer methods and programs in biomedicine·2026
Same author

MetaStab-Analyzer: Classification and Regression Models for Metabolic Stability Prediction.

Molecular informatics·2026
Same author

SAR Modeling to Predict Ames Mutagenicity Across Different <i>Salmonella typhimurium</i> Strains.

Pharmaceuticals (Basel, Switzerland)·2025
Same author

QSAR Modeling for Predicting IC<sub>50</sub> and GI<sub>50</sub> Values for Human Cell Lines Used in Toxicological Studies.

International journal of molecular sciences·2025

Related Experiment Video

Updated: Jan 20, 2026

Pharmacokinetics: Drug–Drug Interactions
01:25

Pharmacokinetics: Drug–Drug Interactions

Published on: October 3, 2025

370

Drug-drug interaction prediction using PASS.

A V Dmitriev1, D A Filimonov1, A V Rudik1

  • 1Department for Bioinformatics, Institute of Biomedical Chemistry (IBMC), Moscow, Russia.

SAR and QSAR in Environmental Research
|September 5, 2019
PubMed
Summary

Predicting drug-drug interactions (DDIs) is crucial for patient safety. This study developed a new method using chemical descriptors to classify and predict DDIs with high accuracy, offering a valuable tool for healthcare professionals.

Keywords:
DDIDrug interactionPASSPoSMNApharmacokineticpolypharmacypredictionxenobiotics metabolism

More Related Videos

An In Vitro Caseum Binding Assay that Predicts Drug Penetration in Tuberculosis Lesions
12:17

An In Vitro Caseum Binding Assay that Predicts Drug Penetration in Tuberculosis Lesions

Published on: May 8, 2017

12.4K
Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
11:56

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

Published on: October 25, 2013

14.6K

Related Experiment Videos

Last Updated: Jan 20, 2026

Pharmacokinetics: Drug–Drug Interactions
01:25

Pharmacokinetics: Drug–Drug Interactions

Published on: October 3, 2025

370
An In Vitro Caseum Binding Assay that Predicts Drug Penetration in Tuberculosis Lesions
12:17

An In Vitro Caseum Binding Assay that Predicts Drug Penetration in Tuberculosis Lesions

Published on: May 8, 2017

12.4K
Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
11:56

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

Published on: October 25, 2013

14.6K

Area of Science:

  • Pharmacology
  • Computational Chemistry
  • Bioinformatics

Background:

  • Drug-Drug Interactions (DDIs) can alter drug metabolism by affecting Drug-Metabolizing Enzymes (DMEs), leading to altered pharmacological effects.
  • Understanding and predicting DDIs is essential for safe and effective pharmacotherapy.
  • The OpeRational ClassificAtion (ORCA) system categorizes DDIs into five classes based on interaction severity.

Purpose of the Study:

  • To develop a computational method for predicting and classifying Drug-Drug Interactions (DDIs).
  • To improve the accuracy of DDI prediction by considering drug pairs rather than individual drugs.

Main Methods:

  • Utilized the OpeRational ClassificAtion (ORCA) system for DDI categorization.
  • Developed novel chemical descriptors, Pairs of Substances Multilevel Neighbourhoods of Atoms (PoSMNA), for machine-readable representation of drug pairs.
  • Implemented the PoSMNA descriptors within the PASS software for DDI prediction, focusing on higher-risk interaction classes.

Main Results:

  • Achieved an average prediction accuracy of 0.84 for DDI classes.
  • Successfully developed and implemented a system for predicting DDIs based on chemical structure pairs.
  • Created a publicly accessible web resource for DDI prediction.

Conclusions:

  • The developed PoSMNA descriptors and PASS algorithm offer a robust approach to DDI prediction.
  • The findings contribute to enhanced drug safety by providing a tool to anticipate potential interactions.
  • The freely available web resource facilitates broader application in clinical and research settings.