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Related Concept Videos

Teratogenicity01:07

Teratogenicity

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The ability of a drug to produce structural deformations and functional abnormalities in the developing embryo or the fetus is called teratogenicity, and the drug producing this effect is known as a teratogen. Teratogenic effects include stillbirth, miscarriage, intrauterine growth restriction, and neurocognitive delay. A teratogen may affect the embryo at different stages of development, which is important in determining the type and extent of the damage. During blastocyst formation, the early...
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Mutagenicity and Carcinogenicity01:25

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Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
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Machine learning on drug-specific data to predict small molecule teratogenicity.

Anup P Challa1, Andrew L Beam2, Min Shen3

  • 1Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville 37203, TN, United States; Department of Biomedical Informatics, Harvard Medical School, Boston 02115, MA, United States; National Center for Advancing Translational Sciences, National Institutes of Health, Rockville 20850, MD, United States; Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville 37212, TN, United States.

Reproductive Toxicology (Elmsford, N.Y.)
|May 20, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence can now predict drug teratogenicity in pregnant women. This AI model identifies harmful chemical structures, improving safety for expecting mothers and developing fetuses.

Keywords:
Chemical structureDrug developmentDrug exposureHigh-throughput screeningInformaticsMachine learningTeratogenicityTranslational medicine

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Area of Science:

  • Pharmacology
  • Toxicology
  • Computational Chemistry

Background:

  • Pregnant populations are excluded from clinical trials, leading to limited data on drug safety during pregnancy.
  • This data gap increases risks for adverse drug outcomes and compromises care for pregnant individuals.
  • Prescribing decisions for pregnant patients often rely on insufficient evidence and conflicting case reports.

Purpose of the Study:

  • To develop and apply artificial intelligence (AI) for predicting the teratogenicity of small molecules.
  • To identify structural and bioactivity patterns associated with drug-induced birth defects.
  • To create a computational platform for assessing drug safety in pregnancy.

Main Methods:

  • Utilized unsupervised and supervised machine learning algorithms.
  • Analyzed small molecules with known structure and teratogenicity data from research-amenable sources.
  • Identified patterns in structural, meta-structural, and in vitro bioactivity data to predict teratogenicity scores.

Main Results:

  • Discovered three chemical functionalities linked to increased teratogenicity and two moieties with protective effects.
  • Developed AI models predicting three clinically relevant classes of teratogenicity with an AUC of 0.8.
  • Achieved nearly double the predictive accuracy compared to a blind control model.

Conclusions:

  • The developed AI platform offers a novel approach to systematically predict drug teratogenicity.
  • Identified key chemical features influencing drug safety during fetal development.
  • Highlights significant barriers to translational research in pregnant populations, emphasizing the need for data-driven solutions.