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Related Concept Videos

Chemical Shift: Internal References and Solvent Effects01:17

Chemical Shift: Internal References and Solvent Effects

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In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
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Inductive Effects on Chemical Shift: Overview01:27

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The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
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Updated: Sep 17, 2025

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
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Substantiating chemical groups for read-across using molecular response profiles.

Rosemary E Barnett1, Thomas N Lawson1, Claudia Rivetti2

  • 1Michabo Health Science Limited, Union House, 111 New Union Street, Coventry, CV1 2NT, U.K.

Regulatory Toxicology and Pharmacology : RTP
|June 28, 2025
PubMed
Summary

Multi-omics bioactivity data enhances chemical grouping for risk assessment. This approach improves confidence in classifying chemicals by their mode of action, reducing reliance on animal testing.

Keywords:
Bioactivity similarityDaphniaGroupingHierarchical clusteringMetabolomicsPlausible toxicological interpretationRead-acrossReplicability confidenceTranscriptomics

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Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
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Area of Science:

  • Environmental toxicology
  • Computational toxicology
  • Omics sciences

Background:

  • Chemical grouping uses structural similarity to predict toxicity, aiding risk assessment and reducing animal testing.
  • Structural similarity alone is often insufficient; additional data strengthens grouping justifications.
  • Multi-omics data offers a way to assess chemical bioactivity and infer mode of action.

Purpose of the Study:

  • To demonstrate how multi-omics bioactivity data can increase confidence in chemical grouping hypotheses.
  • To investigate the utility of bioactivity profiles in reflecting chemical modes of action.
  • To assess the performance of structure-based versus bioactivity-based grouping.

Main Methods:

  • Investigated three phthalates and three uncouplers of oxidative phosphorylation.
  • Applied structure-based grouping and short-term exposures of Daphnia magna to generate multi-omics data.
  • Assessed bioactivity similarities using t-statistics and hierarchical cluster analysis.

Main Results:

  • Conventional structure-based grouping failed to separate phthalates and uncouplers into distinct categories.
  • Bioactivity profile-based grouping, after thresholding, correctly separated the remaining five substances into two chemical classes.
  • High replicability confidence was achieved with the bioactivity profile-based grouping.

Conclusions:

  • Multi-omics bioactivity profiles significantly increase confidence in chemical grouping for risk assessment.
  • Bioactivity profile-based grouping demonstrates a viable strategy for classifying chemicals based on their molecular responses.
  • Interpreting the specific molecular features driving 'omics-based grouping remains a challenge.