IMPACT OF REAL-LIFE ENVIRONMENTAL EXPOSURES ON REPRODUCTION: A contemporary review of machine learning to predict adverse pregnancy outcomes from pharmaceuticals, including DDIs.

Journal: Reproduction (Cambridge, England)

PMID: 39374154

Abstract

IN BRIEF: Clinical drug trials often do not include pregnant people due to health risks; therefore, many medications have an unknown effect on the developing fetus. Machine learning QSAR models have been used successfully to predict the fetal risk of pharmaceutical use during pregnancy.

Authors

Julie Gardella

NYU Langone Health, Department of Medicine, Division of Environmental Medicine, New York, USA.
Dimitri Abrahamsson

Program on Reproductive Health and the Environment, Department of Obstetrics and Gynecology, University of California, San Francisco, California 94158, United States.
Judith Zelikoff

NYU Langone Health, Department of Medicine, Division of Environmental Medicine, New York, USA.

Keywords

Drug Interactions Drug-Related Side Effects and Adverse Reactions Environmental Exposure Female Humans Machine Learning Pregnancy Pregnancy Outcome Quantitative Structure-Activity Relationship Reproduction

External Resources

View on PubMed Access via DOI PubMed (39374154)

IMPACT OF REAL-LIFE ENVIRONMENTAL EXPOSURES ON REPRODUCTION: A contemporary review of machine learning to predict adverse pregnancy outcomes from pharmaceuticals, including DDIs.

Abstract

Authors

Keywords

External Resources

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