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

Drug toxicity: Drug–Drug Interaction01:30

Drug toxicity: Drug–Drug Interaction

2
Drug–drug interactions can precipitate toxicity through multiple mechanisms. Absorption interactions alter how drugs enter the body, exemplified when ranitidine increases the absorption of basic drugs, while cholestyramine decreases the levels of propranolol. Protein binding interactions occur when drugs share the same binding sites on plasma proteins. Drugs like aspirin and warfarin, when bound in excess, can lead to increased free drug concentrations, enhancing the potential for...
2
Pharmacokinetics: Drug–Drug Interactions01:25

Pharmacokinetics: Drug–Drug Interactions

469
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...
469
Pharmacokinetics in Pediatric Patients: Drug Excretion01:26

Pharmacokinetics in Pediatric Patients: Drug Excretion

279
In pediatric medicine, understanding the renal function and drug elimination nuances is crucial for administering safe and effective treatments. Newborns, in particular, display markedly slower renal functions than adults, profoundly affecting how drugs are cleared from their bodies. This slower drug clearance requires clinicians to extend the dosing intervals for many medications to prevent drug accumulation and toxicity while ensuring therapeutic efficacy.One key area where these adjustments...
279
Pharmacokinetics in Pediatric Patients: Drug Distribution01:17

Pharmacokinetics in Pediatric Patients: Drug Distribution

334
Drug distribution in the pediatric population exhibits unique challenges and considerations due to the physiological differences between children, particularly neonates and infants, and adults. A crucial aspect of pediatric pharmacology is understanding how these differences impact the pharmacokinetics of various drugs, necessitating age-specific dosing strategies to ensure efficacy and safety.Neonates and infants have a higher total body water content, ~75%–90% of their body weight,...
334
Pharmacokinetics in Pediatric Patients: Drug Metabolism01:24

Pharmacokinetics in Pediatric Patients: Drug Metabolism

247
In pediatric care, understanding the nuances of hepatic drug metabolism is crucial, as it significantly differs from that of adults. This divergence is primarily due to the developmental stage of drug-metabolizing enzymes, which affects how medications are processed in the body. In neonates, for instance, the activity of Phase I enzymes—critical for the initial breakdown of drugs—is markedly reduced, functioning at just 20–40% of the levels seen in adults. This reduction poses...
247
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

7.6K
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....
7.6K

You might also read

Related Articles

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

Sort by
Same author

ILCOR Pediatric Life Support 2025: What is New and Why it Matters?

Indian journal of pediatrics·2026
Same author

Topologically informed echo state networks via poincaré return maps for chaotic time-series.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Development and characterization of NB-ARC gene-derived SSRs in wheat (Triticum aestivum L.).

Molecular biology reports·2026
Same author

Effect of remote ischemic conditioning on albuminuria in adults with diabetes mellitus (ricadime): a parallel group, double blind, sham controlled, randomized clinical trial.

Postgraduate medical journal·2026
Same author

Doubly stochastic inter-assembly coupling via entropic optimal transport in echo-state networks for chaotic flows.

Chaos (Woodbury, N.Y.)·2026
Same author

Intermammary pilonidal sinus in an adolescent female: a case report.

Journal of medical case reports·2026

Related Experiment Video

Updated: Feb 14, 2026

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

14.7K

ContraIndicator: A Natural Language Processing-Based Approach to Potential Drug-Drug Interaction Detection in

Pradeep Singh1, Akshaya Devadiga1, Ridam Pal1

  • 1Indraprastha Institute of Information Technology, New Delhi, India.

Clinical Drug Investigation
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

Potential drug-drug interactions are common in pediatric intensive care units, with more prescribed drugs significantly increasing the risk. This study used AI to identify and characterize these interactions, aiding future prevention efforts.

More Related Videos

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation
15:04

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Published on: January 19, 2019

12.8K
Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
05:56

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit

Published on: September 6, 2024

6.4K

Related Experiment Videos

Last Updated: Feb 14, 2026

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

14.7K
Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation
15:04

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Published on: January 19, 2019

12.8K
Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
05:56

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit

Published on: September 6, 2024

6.4K

Area of Science:

  • Pharmacology
  • Pediatric Intensive Care
  • Artificial Intelligence in Medicine

Background:

  • Hospitalized pediatric patients, especially in intensive care units, face high risks of drug-drug interactions (DDIs) due to complex physiology and polypharmacy.
  • Identifying and managing these DDIs is crucial for patient safety and effective treatment.

Purpose of the Study:

  • To examine and characterize clinically relevant drug-drug interactions in pediatric intensive care unit (PICU) patients.
  • To leverage artificial intelligence (AI) for enhanced identification and management of potential DDIs in this vulnerable population.

Main Methods:

  • Analysis of 8010 prescriptions from 899 PICU admissions using ContraIndicator, an AI-powered natural language processing framework.
  • Evaluation of medication information against the DDInter database for clinical relevance and severity (major, moderate, minor).
  • Logistic regression to assess associations between DDI severity, patient age, and the number of prescribed medications.

Main Results:

  • Identified 3884 potential drug-drug interactions among the analyzed prescriptions.
  • Nearly half of the patients (49.6%) experienced at least one DDI, with 38.8% having major interactions.
  • A higher number of prescribed drugs was significantly associated with an increased incidence of potential DDIs (OR: 12.79, p < 0.001).

Conclusions:

  • Potential drug-drug interactions are frequent in PICU settings, with moderate and major severity interactions being common.
  • The number of prescribed medications is a significant risk factor for DDIs in pediatric intensive care.
  • Findings support improved monitoring and prevention strategies for DDIs in critically ill children, facilitated by AI tools like ContraIndicator.