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Related Concept Videos

Time Course of Drug Effect01:14

Time Course of Drug Effect

The progression of a drug's impact can be analyzed by examining both the concentration-time course and the effect-time course. The concentration-time course is determined by the drug's half-life and is influenced by factors such as its pharmacokinetics, including absorption, distribution, metabolism, and elimination. The effect of the drug is often related to its concentration in the plasma and is calculated using the maximum drug effect and the plasma concentration that generates 50 percent of...
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the lowest drug...
Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
Nonlinear Pharmacokinetics: Dependence of Elimination Half-Life and Dose Clearance01:23

Nonlinear Pharmacokinetics: Dependence of Elimination Half-Life and Dose Clearance

The elimination half-life and drug clearance of drugs following nonlinear kinetics can vary with dosage. The Michaelis-Menten parameters and drug concentration influence these factors. As the dose increases, the elimination half-life tends to lengthen, resulting in a reduction in clearance and a disproportionately larger area under the curve. The total clearance can be derived from the Michaelis-Menten equation for drugs following a one-compartment model.
A study on guinea pigs examined the...
Antidepressant Drugs: MAOIs and Other Agents01:23

Antidepressant Drugs: MAOIs and Other Agents

Atypical antidepressants, including bupropion (Wellbutrin), mirtazapine (Remeron), nefazodone (Serzone), trazodone (Desyrel), and vilazodone (Viibryd), offer unique mechanisms of action. Bupropion weakly inhibits dopamine and norepinephrine reuptake, aiding depression treatment and smoking cessation, with a low risk of sexual dysfunction. Mirtazapine enhances serotonin and norepinephrine neurotransmission, leading to sedation, increased appetite, and weight gain. As a result, it helps treat...
Pharmacokinetic–Pharmacodynamic Relationship: Duration of Dose-Effect Relationship01:14

Pharmacokinetic–Pharmacodynamic Relationship: Duration of Dose-Effect Relationship

For drugs producing a quantal response, onset occurs when plasma concentration reaches a minimum effective level (Cmin). The drug's action duration depends on how long the plasma concentration remains above Cmin.Two primary factors influence this duration: dose size and the rate of drug removal from the action site. Both depend on the drug's redistribution to poorly perfused tissues and elimination processes. A larger dose promotes rapid onset and prolongs the effect's duration.Consider a...

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Related Experiment Video

Updated: Jul 16, 2026

A Computerized Test Battery to Study Pharmacodynamic Effects on the Central Nervous System of Cholinergic Drugs in Early Phase Drug Development
07:02

A Computerized Test Battery to Study Pharmacodynamic Effects on the Central Nervous System of Cholinergic Drugs in Early Phase Drug Development

Published on: February 11, 2019

Systemic Drug Effects in Vortioxetine-Induced Time-Series Datasets.

Shinuk Kim1

  • 1College of Gyedang General Education, Sangmyung University, Cheonan 31066, Chung-Nam, Republic of Korea.

International Journal of Molecular Sciences
|July 15, 2026
PubMed
Summary

Vortioxetine, an antidepressant, impacts gene networks in glioblastoma. It inhibits proto-oncogenes JUN and CCND1, suggesting potential therapeutic effects in cancer treatment.

Keywords:
ERBB signaling pathwayGLIOMA pathwaydrug effectgene–gene interactionsystemic modeltime-series data

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The Use of Pharmacological-challenge fMRI in Pre-clinical Research: Application to the 5-HT System
11:27

The Use of Pharmacological-challenge fMRI in Pre-clinical Research: Application to the 5-HT System

Published on: April 25, 2012

Related Experiment Videos

Last Updated: Jul 16, 2026

A Computerized Test Battery to Study Pharmacodynamic Effects on the Central Nervous System of Cholinergic Drugs in Early Phase Drug Development
07:02

A Computerized Test Battery to Study Pharmacodynamic Effects on the Central Nervous System of Cholinergic Drugs in Early Phase Drug Development

Published on: February 11, 2019

The Use of Pharmacological-challenge fMRI in Pre-clinical Research: Application to the 5-HT System
11:27

The Use of Pharmacological-challenge fMRI in Pre-clinical Research: Application to the 5-HT System

Published on: April 25, 2012

Area of Science:

  • Oncology
  • Systems Biology
  • Pharmacology

Background:

  • Glioblastoma is a complex cancer with intertwined signaling pathways.
  • Understanding drug-induced gene regulatory networks is crucial for cancer therapy.
  • Vortioxetine's effects on glioblastoma cellular mechanisms require detailed investigation.

Purpose of the Study:

  • To infer gene regulatory networks in vortioxetine-treated glioblastoma cells.
  • To investigate vortioxetine's systemic effects on cancer-related pathways.
  • To identify key genes and pathways modulated by vortioxetine.

Main Methods:

  • Utilized an ordinary differential equation (ODE)-based inverse problem approach.
  • Analyzed time-series gene expression datasets generated at multiple time points (0-24 h).
  • Focused on the GLIOMA and ERBB signaling pathways.

Main Results:

  • Vortioxetine activated PIK3R5 and CALML6 while inhibiting proto-oncogenes JUN, CDK4, and CCND1.
  • PIK3R5 activation was consistently linked to PAK6.
  • Distinct gene expression changes were observed between 6 and 22 hours post-treatment, with notable genes including CRK, JUN, BRAF, and MAP2K2.

Conclusions:

  • Vortioxetine demonstrates a time-dependent inhibitory effect on key proto-oncogenes (JUN, CCND1) in glioblastoma.
  • The findings suggest vortioxetine's potential role in modulating cancer progression through gene network regulation.
  • Further research into vortioxetine's anti-cancer mechanisms is warranted.