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Related Concept Videos

Drug toxicity: Drug–Drug Interaction01:30

Drug toxicity: Drug–Drug Interaction

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...
Pharmacokinetics: Drug–Drug Interactions01:25

Pharmacokinetics: Drug–Drug Interactions

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...
Drug Toxicity: Risk factors01:24

Drug Toxicity: Risk factors

Adverse Drug Reactions (ADRs) are potential complications that arise during pharmacotherapy, influenced by multiple risk factors. Age plays a significant role; both neonates and the elderly are at heightened risk due to their respective immature and diminished metabolic and elimination processes. Gender also impacts ADRs, with females experiencing a 1.5 to 1.7-fold greater risk than males, which may be linked to pharmacokinetic, pharmacodynamic, and hormonal differences. Notably, neonates, the...
Drug Product Performance: In Vitro–In Vivo Correlation01:20

Drug Product Performance: In Vitro–In Vivo Correlation

In pharmaceutical development, it's crucial to establish a predictive in vitro–in vivo correlation (IVIVC) for two or more formulations to gain a comprehensive understanding of release properties. IVIVC reduces the need for costly in vivo studies and facilitates the establishment of meaningful dissolution specifications with significant cost savings and decreased regulatory burden. Furthermore, a meaningful IVIVC should predict Cmax and AUC within 20%, aligning with FDA guidance while adhering...
Pharmacokinetics: Drug–Food and Drug–Viral Interactions01:26

Pharmacokinetics: Drug–Food and Drug–Viral Interactions

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 many...
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...

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Related Experiment Video

Updated: Jun 15, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Drug-drug interaction prediction assessment.

Jihao Zhou1, Zhaohui Qin, Quinney K Sara

  • 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.

Journal of Biopharmaceutical Statistics
|February 26, 2010
PubMed
Summary

Model-based drug-drug interaction (DDI) simulations require statistical validation. This study introduces new methods to assess DDI prediction performance, finding current models underpredict key interaction metrics.

Related Experiment Videos

Last Updated: Jun 15, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Area of Science:

  • Pharmacokinetics and Pharmacodynamics
  • Computational Toxicology
  • Drug Metabolism and Interactions

Background:

  • Model-based drug-drug interaction (DDI) assessment is crucial for predicting in vivo consequences from in vitro data.
  • Establishing robust DDI models requires validation against known interaction data before application to new compounds.

Purpose of the Study:

  • To introduce novel statistical tests for comparing reported and model-based simulated DDI (log AUCR) in mean and variance.
  • To develop methods for assessing DDI prediction performance using bias and confidence interval coverage.
  • To establish sample size and power guidelines for DDI model simulations.

Main Methods:

  • Implementation of difference and equivalence tests for comparing reported and simulated DDI log AUCR.
  • Introduction of bias and predictive confidence interval coverage probabilities for performance assessment.
  • Demonstration using a ketoconazole (KETO)/midazolam (MDZ) interaction example with published pharmacokinetic and in vitro data.

Main Results:

  • Current model-based DDI prediction underpredicts the area under concentration curve ratio (AUCR) compared to reported studies.
  • The between-subject variance of AUCR is also underpredicted by existing DDI models.
  • Statistical methods for assessing DDI prediction performance and developing simulation guidelines were demonstrated.

Conclusions:

  • There is a need for improved statistical validation of model-based DDI prediction tools.
  • Current DDI models may not fully capture the variability observed in clinical drug interactions.
  • The developed statistical approaches provide a framework for enhancing the reliability of in silico DDI assessments.