Dongying Li
Researcher
Also affiliated: Qingdao University (2020); Tulane University (2016–2018); United States Food and Drug Administration (2019–2025); Harvard University (2024); Nanchang University (2024–2026); Minzu University of China (2018–2026); Hainan Normal University (2022–2023); Chongqing University (2014–2015); Shanghai Jiao Tong University (2022); Chinese Academy of Medical Sciences & Peking Union Medical College (2013–2024); Shaoyang University (2014–2024); National Medical Products Administration (2025–2026); Tianjin Medical University Cancer Institute and Hospital (2017–2025); The Medical Center of Aurora (2014); Southern University of Science and Technology (2024); People 's Hospital of Jilin Province (2017); Shanghai Ninth People's Hospital (2022); Second Affiliated Hospital of Nanchang University (2024–2026); Zhoushan Hospital (2016); Weihai Maternal and Child Health Hospital (2019); Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine (2019); Stomatology Hospital (2025); Louisiana Cancer Research Center (2016); Food and Drug Administration (2025); First Affiliated Hospital of Zhengzhou University (2015–2022); Beijing Fengtai Hospital (2010); Indiana University School of Medicine (2014); University of Colorado Anschutz Medical Campus (2014–2016); University of Coimbra (2019); South China University of Technology (2024); University of Colorado Denver (2014–2016)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Dongying Li's research focuses on understanding and mitigating chemical and drug-induced toxicity, particularly in the liver and cardiovascular system. Li has investigated the characterization of cytochrome P450 (CYP)-overexpressing HepG2 cells as a model for assessing drug and chemical-induced liver toxicity, contributing to improved methods for preclinical safety evaluations. Another area of Li's work involves the analysis of biochemical features and mutations in SARS-CoV-2, with implications for the development of RNA therapeutics. Li has also developed computational frameworks for risk assessment, such as DICTrank, which ranks human drugs by their risk of drug-induced cardiotoxicity using FDA labeling data. This work supports regulatory toxicology and aids in identifying potential safety concerns for pharmaceuticals.
Metrics
- h-index: 20
- Publications: 107
- Citations: 1,243
Selected Publications
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SARS-CoV-2 Spike Protein’s Structural Dynamics Affect the Activity of the Bebtelovimab Antibody (2026)
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Microphysiological systems as an emerging in vitro approach for the evaluation of drug absorption, distribution, metabolism, and excretion and toxicity (2025)
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Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction (2025)
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DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review (2025)
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New approach methodologies (NAMs) for drug-induced liver injury (DILI): Where are we now? (2025)
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DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods (2025)
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A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document (2024)
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DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling (2023)
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DILIrank dataset for QSAR modeling of drug-induced liver injury (2023)
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DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling (2023)
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Applying genomics in regulatory toxicology: a report of the ECETOC workshop on omics threshold on non-adversity (2023)
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Pharmacological Effects of Ketoconazole in the Treatment of Steroidogenesis Suppression via CYP17A1 Inhibition May Involve MicroRNA Regulation (2023)
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Epigenetics in drug disposition & drug therapy: symposium report of the 24<sup>th</sup> North American meeting of the International Society for the Study of Xenobiotics (ISSX) (2022)
Collaboration Network
Top Collaborators
- Biochemical features and mutations of key proteins in SARS-CoV-2 and their impacts on RNA therapeutics
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Integrative approaches for studying the role of noncoding RNAs in influencing drug efficacy and toxicity
- New approach methodologies (NAMs) for drug-induced liver injury (DILI): Where are we now?
Showing 5 of 9 shared publications
- Characterization of cytochrome P450s (CYP)-overexpressing HepG2 cells for assessing drug and chemical-induced liver toxicity
- Biochemical features and mutations of key proteins in SARS-CoV-2 and their impacts on RNA therapeutics
- Integrative approaches for studying the role of noncoding RNAs in influencing drug efficacy and toxicity
- Epigenetics in drug disposition & drug therapy: symposium report of the 24<sup>th</sup> North American meeting of the International Society for the Study of Xenobiotics (ISSX)
- Pharmacological Effects of Ketoconazole in the Treatment of Steroidogenesis Suppression via CYP17A1 Inhibition May Involve MicroRNA Regulation
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
- Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction
- Integrative approaches for studying the role of noncoding RNAs in influencing drug efficacy and toxicity
- New approach methodologies (NAMs) for drug-induced liver injury (DILI): Where are we now?
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction
- Biochemical features and mutations of key proteins in SARS-CoV-2 and their impacts on RNA therapeutics
- Biochemical features and mutations of key proteins in SARS-CoV-2 and their impacts on RNA therapeutics
- Characterization of cytochrome P450s (CYP)-overexpressing HepG2 cells for assessing drug and chemical-induced liver toxicity
- Characterization of cytochrome P450s (CYP)-overexpressing HepG2 cells for assessing drug and chemical-induced liver toxicity
- Characterization of cytochrome P450s (CYP)-overexpressing HepG2 cells for assessing drug and chemical-induced liver toxicity
- Characterization of cytochrome P450s (CYP)-overexpressing HepG2 cells for assessing drug and chemical-induced liver toxicity
- Characterization of cytochrome P450s (CYP)-overexpressing HepG2 cells for assessing drug and chemical-induced liver toxicity
- Characterization of cytochrome P450s (CYP)-overexpressing HepG2 cells for assessing drug and chemical-induced liver toxicity
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