ArticleArchives of toxicology2021
Prediction of oral hepatotoxic dose of natural products derived from traditional Chinese medicines based on SVM classifier and PBPK modeling.
Article in Archives of toxicology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- Development and application of artificial intelligence in traditional Chinese medicine research and development.Chinese medicine · 2026Review
- In Silico ADME Methods Used in the Evaluation of Natural Products.Pharmaceutics · 2025Review
- A review of recent artificial intelligence for traditional medicine.Journal of traditional and complementary medicine · 2025Review
- PBPK Modeling: Empowering Drug Development and Precision Dosing in China.CPT: pharmacometrics & systems pharmacology · 2025Review
- Computational approaches to predict the toxicity of bioactive natural products: a mini review of methodologies.Food science and biotechnology · 2025Review
- Therapeutic target mapping from the genome of Kingella negevensis and biophysical inhibition assessment through PNP synthase binding with traditional medicinal compounds.Molecular diversity · 2024Article
- Hypoxia signaling in hepatocellular carcinoma: Challenges and therapeutic opportunities.Cancer metastasis reviews · 2023Review
- Predicting Food Effects on Oral Extended-Release Drug Products: A Retrospective Evaluation.The AAPS journal · 2023Article
- Pharmacokinetics of Novel Furoxan/Coumarin Hybrids in Rats Using LC-MS/MS Method and Physiologically Based Pharmacokinetic Model.Molecules (Basel, Switzerland) · 2023Article
- Regulatory mechanism and promising clinical application of exosomal circular RNA in gastric cancer.Frontiers in oncology · 2023Review
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- Utilization of Physiologically Based Pharmacokinetic Modeling in Pharmacokinetic Study of Natural Medicine: An Overview.Molecules (Basel, Switzerland) · 2022Review
- Biosynthesis of silver nanoparticles using Lawsonia inermis and their biomedical application.IET nanobiotechnology · 2022Article
- Aminoacylase-1 plays a key role in myocardial fibrosis and the therapeutic effects of 20(S)-ginsenoside Rg3 in mouse heart failure.Acta pharmacologica Sinica · 2022Article
- Dried Blood Spots as Matrix for Evaluation of Valproate Levels and the Immediate and Delayed Metabolomic Changes Induced by Single Valproate Dose Treatment.International journal of molecular sciences · 2022Article
- Molecular dynamics simulation approach for discovering potential inhibitors against SARS-CoV-2: A structural review.Journal of molecular liquids · 2022Review
- Examination of the Impact of CYP3A4/5 on Drug-Drug Interaction between Schizandrol A/Schizandrol B and Tacrolimus (FK-506): A Physiologically Based Pharmacokinetic Modeling Approach.International journal of molecular sciences · 2022Article
- A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide.Scientific reports · 2022Article
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Authors and funding
11 authors.
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Abstract
The risk of drug-induced liver injury (DILI) poses a major challenge for development of natural products derived from traditional Chinese medicines (NP-TCMs). It is urgent to find a new method for the safety assessment of the NP-TCMs. Recent study has reported an in vitro/in silico method to estimate the acceptable daily intake of hepatotoxic compounds using support vector machine (SVM) classifier and physiologically based pharmacokinetic (PBPK) modeling. However, this method is not suitable for estimating the dosing schedule of compounds which are administered in multiple daily doses. Thus, in this study, the method mentioned above was in particular optimized, and used to estimate the hepatotoxic plasma concentrations of 17 NP-TCMs. Additionally, the oral dosing schedules of the triptolide, emodin, matrine and oxymatrine were also predicted by the SVM classifier and PBPK modeling. The optimization included that: (1) in vitro cytotoxicity data of 28 training set compounds was optimized using benchmark concentrations (BMC) modeling; (2) AUC of the training set compound was used as the in vivo metric instead of C
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