Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features
Cytotoxicity, usually represented by cell viability, is a crucial parameter for evaluating drug safety in vitro. Accurate prediction of cell viability/cytotoxicity could accelerate drug development in the early stage. In this study, by integrating cellular transcriptome and cell viability data using...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Acta Pharmaceutica Sinica B |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211383525000528 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849733150784094208 |
|---|---|
| author | You Wu Ke Tang Chunzheng Wang Hao Song Fanfan Zhou Ying Guo |
| author_facet | You Wu Ke Tang Chunzheng Wang Hao Song Fanfan Zhou Ying Guo |
| author_sort | You Wu |
| collection | DOAJ |
| description | Cytotoxicity, usually represented by cell viability, is a crucial parameter for evaluating drug safety in vitro. Accurate prediction of cell viability/cytotoxicity could accelerate drug development in the early stage. In this study, by integrating cellular transcriptome and cell viability data using four machine learning algorithms (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and two ensemble algorithms (voting and stacking), highly accurate prediction models of 50% and 80% cell viability were developed with area under the receiver operating characteristic curve (AUROC) of 0.90 and 0.84, respectively; these models also showed good performance when utilized for diverse cell lines. Concerning the characterization of the employed Feature Genes, the models were interpreted, and the mechanisms of bioactive compounds with a narrow therapeutic index (NTI) can also be analyzed. In summary, the models established in this research exhibit superior capacity to those of previous studies; these models enable accurate high-safety substance screening via cytotoxicity prediction across cell lines. Moreover, for the first time, Cytotoxicity Signature (CTS) genes were identified, which could provide additional clues for further study of mechanisms of action (MOA), especially for NTI compounds. |
| format | Article |
| id | doaj-art-ee3ec495497243d79ad943fe51c23b62 |
| institution | DOAJ |
| issn | 2211-3835 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Acta Pharmaceutica Sinica B |
| spelling | doaj-art-ee3ec495497243d79ad943fe51c23b622025-08-20T03:08:06ZengElsevierActa Pharmaceutica Sinica B2211-38352025-03-011531344135810.1016/j.apsb.2025.02.009Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome featuresYou Wu0Ke Tang1Chunzheng Wang2Hao Song3Fanfan Zhou4Ying Guo5Beijing Key Laboratory of New Drug Mechanisms and Pharmacological Evaluation Study, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, ChinaBeijing Key Laboratory of New Drug Mechanisms and Pharmacological Evaluation Study, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, ChinaBeijing Key Laboratory of New Drug Mechanisms and Pharmacological Evaluation Study, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, ChinaBeijing Key Laboratory of New Drug Mechanisms and Pharmacological Evaluation Study, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, ChinaBeijing Key Laboratory of New Drug Mechanisms and Pharmacological Evaluation Study, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, ChinaBeijing Key Laboratory of New Drug Mechanisms and Pharmacological Evaluation Study, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; Corresponding author.Cytotoxicity, usually represented by cell viability, is a crucial parameter for evaluating drug safety in vitro. Accurate prediction of cell viability/cytotoxicity could accelerate drug development in the early stage. In this study, by integrating cellular transcriptome and cell viability data using four machine learning algorithms (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and two ensemble algorithms (voting and stacking), highly accurate prediction models of 50% and 80% cell viability were developed with area under the receiver operating characteristic curve (AUROC) of 0.90 and 0.84, respectively; these models also showed good performance when utilized for diverse cell lines. Concerning the characterization of the employed Feature Genes, the models were interpreted, and the mechanisms of bioactive compounds with a narrow therapeutic index (NTI) can also be analyzed. In summary, the models established in this research exhibit superior capacity to those of previous studies; these models enable accurate high-safety substance screening via cytotoxicity prediction across cell lines. Moreover, for the first time, Cytotoxicity Signature (CTS) genes were identified, which could provide additional clues for further study of mechanisms of action (MOA), especially for NTI compounds.http://www.sciencedirect.com/science/article/pii/S2211383525000528Interpretable modelDrug safetyCell viabilityWeak cytotoxicityMachine learningTranscriptome |
| spellingShingle | You Wu Ke Tang Chunzheng Wang Hao Song Fanfan Zhou Ying Guo Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features Acta Pharmaceutica Sinica B Interpretable model Drug safety Cell viability Weak cytotoxicity Machine learning Transcriptome |
| title | Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features |
| title_full | Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features |
| title_fullStr | Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features |
| title_full_unstemmed | Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features |
| title_short | Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features |
| title_sort | establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features |
| topic | Interpretable model Drug safety Cell viability Weak cytotoxicity Machine learning Transcriptome |
| url | http://www.sciencedirect.com/science/article/pii/S2211383525000528 |
| work_keys_str_mv | AT youwu establishmentofinterpretablecytotoxicitypredictionmodelsusingmachinelearninganalysisoftranscriptomefeatures AT ketang establishmentofinterpretablecytotoxicitypredictionmodelsusingmachinelearninganalysisoftranscriptomefeatures AT chunzhengwang establishmentofinterpretablecytotoxicitypredictionmodelsusingmachinelearninganalysisoftranscriptomefeatures AT haosong establishmentofinterpretablecytotoxicitypredictionmodelsusingmachinelearninganalysisoftranscriptomefeatures AT fanfanzhou establishmentofinterpretablecytotoxicitypredictionmodelsusingmachinelearninganalysisoftranscriptomefeatures AT yingguo establishmentofinterpretablecytotoxicitypredictionmodelsusingmachinelearninganalysisoftranscriptomefeatures |