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Related Concept Videos

Toxicity Testing in Animals01:23

Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...
Types of Toxins01:36

Types of Toxins

Humans continually engage with an environment rich in potentially harmful chemicals. These are introduced to our bodies through inhalation, ingestion, or skin contact. These chemicals exist in various forms, such as air and environmental pollutants, agricultural chemicals, organic solvents, and heavy metals.
Air pollutants, primarily gases, pose significant threats to respiratory health, leading to conditions like hypoxia, lung cancer, and in extreme cases, death.
Environmental pollutants like...
Toxic Reactions: Overview01:26

Toxic Reactions: Overview

When toxic substances penetrate the human body, they disseminate to various tissues, undergoing metabolic changes. This process yields reactive metabolites that may covalently bind with specific target molecules, resulting in toxicity.
Toxicity falls into two primary categories: local and systemic.
Local toxicity appears at the exposure site, such as protein denaturation caused by caustic substances.
In contrast, systemic toxicity requires the toxic agent's absorption and distribution,...
Toxicokinetics: Overview01:21

Toxicokinetics: Overview

Studies that assess how a drug is absorbed, distributed, metabolized, and excreted (ADME) at toxic doses are termed toxicokinetics. Understanding toxicokinetics helps predict adverse drug reactions (ADRs) and manage toxicity in humans.Toxicokinetics differs from pharmacokinetics mainly in the dose levels studied, with toxicokinetics focusing on higher toxic doses. The kinetics at these levels can be non-linear due to altered physiological processes. Toxicodynamics examines the relationship...
Effects of Chemicals: Overview01:27

Effects of Chemicals: Overview

Drugs, encompassing various chemical compounds from natural sources, lab synthesis, or genetic engineering, elicit different biological responses in living organisms. Some of these responses are desirable or therapeutic, while others are undesirable. The primary goal of administering a drug is to achieve a therapeutic effect, that is, to address a specific disease or health condition. Any concurrent effects outside of this therapeutic outcome are considered undesirable. These undesirable...
Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...

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Related Experiment Video

Updated: Jun 9, 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

Green toxicology only becomes beautiful through AI.

Alexandra Maertens1, Thomas Hartung1,2,3

  • 1Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering, Baltimore, MD, United States.

Frontiers in Chemistry
|June 8, 2026
PubMed
Summary

Green Toxicology integrates toxicological foresight into chemical design to prevent hazards. Artificial intelligence (AI) combined with new approach methodologies (NAMs) offers a scalable, predictive framework for sustainable chemistry and public health protection.

Keywords:
AI-driven chemical discoverydecision supporthazard-informed designmechanistic toxicologymolecular designread-acrosssustainable chemistrytoxicity prediction

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A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
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A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

Related Experiment Videos

Last Updated: Jun 9, 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

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
09:01

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

Area of Science:

  • Environmental Science
  • Toxicology
  • Computational Chemistry

Background:

  • Green Toxicology applies Green Chemistry principles to anticipate and prevent chemical hazards.
  • Key pillars include prevention, precaution, life-cycle thinking, and avoiding regrettable substitutions.
  • Current limitations involve fragmented data, slow regulatory acceptance, and validation challenges for New Approach Methodologies (NAMs).

Purpose of the Study:

  • To explore the transformative potential of Artificial Intelligence (AI) in advancing Green Toxicology.
  • To outline a practical framework integrating AI and NAMs for sustainable chemical innovation.
  • To address challenges in data integration, predictive accuracy, and risk assessment within Green Toxicology.

Main Methods:

  • Leveraging deep learning, natural language processing, and explainable AI to analyze legacy toxicological data.
  • Integrating AI with microphysiological systems and omics for predictive, human-relevant assessments.
  • Developing probabilistic risk assessment models enabled by AI.

Main Results:

  • AI can effectively integrate heterogeneous datasets for enhanced predictive toxicology.
  • AI facilitates the mining of existing studies and linking of adverse outcome pathways.
  • AI enables the proactive design of safer chemicals and scalable risk assessments.

Conclusions:

  • AI offers a transformative solution to overcome current limitations in Green Toxicology.
  • The integration of AI, NAMs, and omics creates a predictive and scalable framework for sustainable chemistry.
  • This approach reconciles industrial needs with ecological integrity and public health protection.