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Related Concept Videos

Transducer Mechanism: Nuclear Receptors01:31

Transducer Mechanism: Nuclear Receptors

Nuclear receptors, or NRs, are unique transcription factors that regulate gene transcription and affect the cellular pathways involved in reproduction, development, or metabolism. Their ability to be stimulated by small lipophilic ligands and control vital cellular processes makes them ideal drug targets. Nearly 10-15% of currently prescribed drugs target these receptors.
About 48 different soluble family members of nuclear receptors are identified that can be divided into two main classes:
Chemical Signaling in the Endocrine System01:08

Chemical Signaling in the Endocrine System

A signaling cascade is a series of events that facilitates the transmission of information within or between cells, culminating in a targeted response in the recipient cell. As chemical messengers, hormones are pivotal in initiating and modulating these intricate signaling cascades based on their solubility.
Lipid-soluble hormones, such as steroid hormones, demonstrate an intracellular action. These hormones traverse cell membranes due to their lipid nature. Once inside the target cell, they...
Endocrine Signaling01:45

Endocrine Signaling

Endocrine cells produce hormones to communicate with remote target cells found in other organs. The hormone reaches these distant areas using the circulatory system. This exposes the whole organism to the hormone but only those cells expressing hormone receptors or target cells are affected. Thus, endocrine signaling induces slow responses from its target cells but these effects also last longer.
Endocrine Signaling01:45

Endocrine Signaling

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Target Cell Response to Hormones01:22

Target Cell Response to Hormones

Hormones intricately bind to receptors on the surface or within target cells, initiating a cascade of cellular responses.
Notably, the cellular response can be regulated by altering the number of receptors expressed in the cell. For example, prolonged exposure to elevated hormone levels results in a gradual decline or down-regulation in the number of receptors for that specific hormone on the cell surface. Conversely, in response to low hormone levels, cells may use up-regulation, producing an...
Types of Hormones02:13

Types of Hormones

Hormones can be classified into three main types based on their chemical structures: steroids, peptides, and amines. Their actions are mediated by the specific receptors they bind to on target cells.

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Related Experiment Video

Updated: Jun 27, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

Binary classification models for endocrine disrupter effects mediated through the estrogen receptor.

A Roncaglioni1, N Piclin, M Pintore

  • 1Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy. aroncaglioni@marionegri.it

SAR and QSAR in Environmental Research
|December 9, 2008
PubMed
Summary

This study developed accurate Quantitative Structure-Activity Relationship (QSAR) models to predict endocrine disruptor (ED) effects on the estrogen receptor (ER). These models offer a valuable tool for assessing chemical safety under REACH regulations.

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Area of Science:

  • Environmental chemistry
  • Toxicology
  • Computational chemistry

Background:

  • Endocrine disruptors (EDs) are exogenous substances interfering with the endocrine system and developmental processes.
  • EDs are a focus of REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) legislation, requiring robust assessment methods.
  • Quantitative Structure-Activity Relationship (QSAR) modeling is a key approach to address data gaps for chemical safety assessments.

Purpose of the Study:

  • To develop and validate QSAR models for predicting estrogenicity of chemical compounds.
  • To assess endocrine-disrupting effects mediated through the estrogen receptor (ER).
  • To evaluate both binding activity (RBA) and transcriptional activity (RA) of potential EDs.

Main Methods:

  • Development of QSAR binary classifiers using diverse data mining techniques: classification trees, decision forest, fuzzy logic, neural networks, and support vector machines.
  • Utilized a large and heterogeneous dataset of chemical compounds.
  • Explored model combinations to enhance predictive power.

Main Results:

  • Achieved high accuracy (over 80%) for both transcriptional activity (RA) and relative binding affinity (RBA) predictive models.
  • Demonstrated the effectiveness of various data mining techniques in QSAR modeling for EDs.
  • Validated the utility of QSAR for assessing ER-mediated effects.

Conclusions:

  • QSAR models provide a reliable method for predicting estrogenicity and assessing endocrine-disrupting potential.
  • The developed models can support regulatory efforts like REACH by addressing data gaps.
  • This approach aids in characterizing the complex effects of EDs on the estrogen receptor.