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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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...
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...
Pharmacodynamic Models: Logarithmic Concentration–Effect Model01:15

Pharmacodynamic Models: Logarithmic Concentration–Effect Model

The log-linear model is a pharmacological framework used to describe the relationship between drug concentration and its effect. This model is particularly relevant when the observed effects range between 20% and 80% of the drug’s maximum effect (Emax), where a near-linear relationship is observed between the log of drug concentration and the measured effect. However, the log-linear model does not predict the maximum possible effect (Emax) or the effect at zero drug concentration, limiting its...
Pharmacodynamic Models: Emax Drug–Concentration Effect Model01:18

Pharmacodynamic Models: Emax Drug–Concentration Effect Model

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...
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...

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Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response
09:45

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Published on: August 10, 2017

Simultaneous modeling of concentration-effect and time-course patterns in gene expression data from microarrays.

Yseult F Brun1, Ram Varma, Suzanne M Hector

  • 1Cancer Prevention and Population Sciences, Roswell Park Cancer Institute, Buffalo, NY 14263, USA.

Cancer Genomics & Proteomics
|March 25, 2008
PubMed
Summary

This study introduces a new method for analyzing complex gene expression data from time-course and drug concentration experiments. The approach helps distinguish gene expression patterns, aiding in understanding drug mechanisms and action timing.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Pharmacology

Background:

  • Microarray studies often use limited experimental designs (treated vs. control).
  • Complex time-course and concentration-effect experiments yield richer data but pose analytical challenges.

Purpose of the Study:

  • To develop a semi-automated method for simultaneously fitting time profiles and concentration-effect patterns in gene expression data.
  • To enable more comprehensive analysis of complex experimental designs.

Main Methods:

  • Implemented a semi-automated method integrating exponential models (for time-course) and a 4-parameter Hill model (for concentration-effect).
  • Applied the method to Affymetrix HG-U95Av2 data from platinum drug (cisplatin, oxaliplatin) treatment of ovarian carcinoma cells.
  • Simultaneously modeled time-course and concentration-effect for 18 selected genes.

Main Results:

  • Successfully applied the method to a dataset of 51 arrays.
  • The analysis distinguished genes with different expression patterns between cisplatin and oxaliplatin treatments.
  • Model parameters provided insights into gene behavior across time and drug concentrations.

Conclusions:

  • The developed method effectively analyzes complex gene expression data.
  • This approach aids in understanding molecular mechanisms and the temporal dynamics of drug actions.
  • Facilitates deeper insights into drug responses compared to simpler experimental designs.