Teratogenic effect of decitabine in a pregnant patient with acute myeloid leukemia: a case report

Lee, Bee Sun; Sathar, Jameela; Ong, Tze San

doi:10.1016/j.htct.2020.11.004

“…There is no adequate information on the adverse effects of HMAs on human fetuses and pregnancies. However, there is a recently reported case of suspected teratogenicity of decitabine in a human fetus [30]. As with the prediction of the model, this does not completely rule out the possibility of toxicity of the drug to human fetuses.…”

Section: Resultsmentioning

confidence: 94%

“…It was labeled as ‘non-fetotoxic’ according to the ‘2nd class’ in the KIDS dataset. Decitabine is a hypomethylating agent (HMA) and is approved for the treatment of patients with myelodysplastic syndromes, chronic myelomonocytic leukemia, and acute myeloid leukemia [30]. HMAs have been shown to be teratogenic, fetotoxic, and embryotoxic in preclinical studies using mice and rats [31].…”

Section: Resultsmentioning

confidence: 99%

FetoML: Interpretable predictions of the fetotoxicity of drugs based on machine learning approaches

Jeong,

Yoo

2023

Preprint

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Pregnant females may use medications to manage health problems that develop during pregnancy or that they had prior to pregnancy. However, using medications during pregnancy has a potential risk to the fetus. Assessing the fetotoxicity of drugs is essential to ensure safe treatments, but the current process is challenged by ethical issues, time, and cost. Therefore, the need forin silicomodels to efficiently assess the fetotoxicity of drugs has recently emerged. Previous studies have proposed successful machine learning models for fetotoxicity prediction and even suggest molecular substructures that are possibly associated with fetotoxicity risks or protective effects. However, the interpretation of the decisions of the models on fetotoxicity prediction for each drug is still insufficient. This study constructed machine learning-based models that can predict the fetotoxicity of drugs while providing explanations for the decisions. For this, permutation feature importance was used to identify the general features that the model made significant in predicting the fetotoxicity of drugs. In addition, features associated with fetotoxicity for each drug were analyzed using the attention mechanism. The predictive performance of all the constructed models was significantly high (AUROC: 0.854–0.974, AUPR: 0.890–0.975). Furthermore, we conducted literature reviews on the predicted important features and found that they were highly associated with fetotoxicity. We expect that our model will benefit fetotoxicity research by providing an evaluation of fetotoxicity risk for drugs or drug candidates, along with an interpretation of that prediction.Author summaryDrugs are often necessary for the treatment of diseases in pregnant females. However, some drugs can potentially cause fetotoxicities, such as teratogenicity and abortion. Therefore, it is essential to study fetotoxicity, but traditional toxicity testing demands time, money, and labor. To modernize these testing methods,in silicoapproaches for predicting the fetotoxicity of drugs are emerging. The proposed models so far have successfully predicted the fetotoxicity of drugs and proposed some fetotoxicity-related substructures, but the interpretation of the model’s determination is still insufficient. In this study, we proposed FetoML to predict the fetotoxicity of drugs based on machine learning and provide the substructures that the model focused on in predicting fetotoxicity for each drug. We confirmed the significant predictive performance and interpretability of the model through a quantitative performance evaluation and literature review. We expect FetoML to benefit fetotoxicity studies of drugs by modernizing the paradigm of fetotoxicity testing and providing insights to researchers.

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“…There is no adequate information on the adverse effects of HMAs on human fetuses and pregnancies. However, there is a recently reported case of suspected teratogenicity of decitabine in a human fetus [61]. As with the prediction of the model, this does not completely rule out the possibility of toxicity of the drug to human fetuses.…”

Section: Analysis Of Model Predictionsmentioning

confidence: 88%

“…It was labeled as 'non-fetotoxic' according to the '2nd class' in the KIDS dataset. Decitabine is a hypomethylating agent (HMA) and is approved for the treatment of patients with myelodysplastic syndromes, chronic myelomonocytic leukemia, and acute myeloid leukemia [61]. HMAs have been shown to be teratogenic, fetotoxic, and embryotoxic in preclinical studies using mice and rats [62].…”

Section: Analysis Of Model Predictionsmentioning

confidence: 99%

FetoML: Interpretable predictions of the fetotoxicity of drugs based on machine learning approaches

Jeong,

Yoo

2024

Molecular Informatics

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Pregnant females may use medications to manage health problems that develop during pregnancy or that they had prior to pregnancy. However, using medications during pregnancy has a potential risk to the fetus. Assessing the fetotoxicity of drugs is essential to ensure safe treatments, but the current process is challenged by ethical issues, time, and cost. Therefore, the need for in silico models to efficiently assess the fetotoxicity of drugs has recently emerged. Previous studies have proposed successful machine learning models for fetotoxicity prediction and even suggest molecular substructures that are possibly associated with fetotoxicity risks or protective effects. However, the interpretation of the decisions of the models on fetotoxicity prediction for each drug is still insufficient. This study constructed machine learning‐based models that can predict the fetotoxicity of drugs while providing explanations for the decisions. For this, permutation feature importance was used to identify the general features that the model made significant in predicting the fetotoxicity of drugs. In addition, features associated with fetotoxicity for each drug were analyzed using the attention mechanism. The predictive performance of all the constructed models was significantly high (AUROC: 0.854‐0.974, AUPR: 0.890‐0.975). Furthermore, we conducted literature reviews on the predicted important features and found that they were highly associated with fetotoxicity. We expect that our model will benefit fetotoxicity research by providing an evaluation of fetotoxicity risks for drugs or drug candidates, along with an interpretation of that prediction.

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Cytarabine/daunorubicin/decitabine

2022

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Teratogenic effect of decitabine in a pregnant patient with acute myeloid leukemia: a case report

Cited by 3 publications

References 7 publications

FetoML: Interpretable predictions of the fetotoxicity of drugs based on machine learning approaches

FetoML: Interpretable predictions of the fetotoxicity of drugs based on machine learning approaches

FetoML: Interpretable predictions of the fetotoxicity of drugs based on machine learning approaches

Cytarabine/daunorubicin/decitabine

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