Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation
Drug combination therapy is a highly effective approach for enhancing the therapeutic efficacy of anti-cancer drugs and overcoming drug resistance. However, the innumerable possible drug combinations make it impractical to screen all synergistic drug pairs. Moreover, biological insights into synergi...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Pharmacological Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S104366182300186X |
| _version_ | 1797741072481255424 |
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| author | Jing-Bo Zhou Dongyang Tang Lin He Shiqi Lin Josh Haipeng Lei Heng Sun Xiaoling Xu Chu-Xia Deng |
| author_facet | Jing-Bo Zhou Dongyang Tang Lin He Shiqi Lin Josh Haipeng Lei Heng Sun Xiaoling Xu Chu-Xia Deng |
| author_sort | Jing-Bo Zhou |
| collection | DOAJ |
| description | Drug combination therapy is a highly effective approach for enhancing the therapeutic efficacy of anti-cancer drugs and overcoming drug resistance. However, the innumerable possible drug combinations make it impractical to screen all synergistic drug pairs. Moreover, biological insights into synergistic drug pairs are still lacking. To address this challenge, we systematically analyzed drug combination datasets curated from multiple databases to identify drug pairs more likely to show synergy. We classified drug pairs based on their MoA and discovered that 110 MoA pairs were significantly enriched in synergy in at least one type of cancer. To improve the accuracy of predicting synergistic effects of drug pairs, we developed a suite of machine learning models that achieve better predictive performance. Unlike most previous methods that were rarely validated by wet-lab experiments, our models were validated using two-dimensional cell lines and three-dimensional tumor slice culture (3D-TSC) models, implying their practical utility. Our prediction and validation results indicated that the combination of the RTK inhibitors Lapatinib and Pazopanib exhibited a strong therapeutic effect in breast cancer by blocking the downstream PI3K/AKT/mTOR signaling pathway. Furthermore, we incorporated molecular features to identify potential biomarkers for synergistic drug pairs, and almost all potential biomarkers found connections between drug targets and corresponding molecular features using protein-protein interaction network. Overall, this study provides valuable insights to complement and guide rational efforts to develop drug combination treatments. |
| first_indexed | 2024-03-12T14:21:37Z |
| format | Article |
| id | doaj.art-96b73912b9154d6a84cfac543d5096ee |
| institution | Directory Open Access Journal |
| issn | 1096-1186 |
| language | English |
| last_indexed | 2024-03-12T14:21:37Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Pharmacological Research |
| spelling | doaj.art-96b73912b9154d6a84cfac543d5096ee2023-08-19T04:31:26ZengElsevierPharmacological Research1096-11862023-08-01194106830Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validationJing-Bo Zhou0Dongyang Tang1Lin He2Shiqi Lin3Josh Haipeng Lei4Heng Sun5Xiaoling Xu6Chu-Xia Deng7Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, ChinaCancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, ChinaCancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, ChinaCancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, ChinaCancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, ChinaCancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, ChinaCancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China; MOE Frontier Science Center for Precision Oncology, University of Macau, Macau SAR, ChinaCancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China; MOE Frontier Science Center for Precision Oncology, University of Macau, Macau SAR, China; Correspondence to: Faculty of Health Sciences, University of Macau, E12, Room 4041, Macau SAR, China.Drug combination therapy is a highly effective approach for enhancing the therapeutic efficacy of anti-cancer drugs and overcoming drug resistance. However, the innumerable possible drug combinations make it impractical to screen all synergistic drug pairs. Moreover, biological insights into synergistic drug pairs are still lacking. To address this challenge, we systematically analyzed drug combination datasets curated from multiple databases to identify drug pairs more likely to show synergy. We classified drug pairs based on their MoA and discovered that 110 MoA pairs were significantly enriched in synergy in at least one type of cancer. To improve the accuracy of predicting synergistic effects of drug pairs, we developed a suite of machine learning models that achieve better predictive performance. Unlike most previous methods that were rarely validated by wet-lab experiments, our models were validated using two-dimensional cell lines and three-dimensional tumor slice culture (3D-TSC) models, implying their practical utility. Our prediction and validation results indicated that the combination of the RTK inhibitors Lapatinib and Pazopanib exhibited a strong therapeutic effect in breast cancer by blocking the downstream PI3K/AKT/mTOR signaling pathway. Furthermore, we incorporated molecular features to identify potential biomarkers for synergistic drug pairs, and almost all potential biomarkers found connections between drug targets and corresponding molecular features using protein-protein interaction network. Overall, this study provides valuable insights to complement and guide rational efforts to develop drug combination treatments.http://www.sciencedirect.com/science/article/pii/S104366182300186XDrug combination therapyMachine learningThree-dimensional tumor slice culturePotential combination biomarkers |
| spellingShingle | Jing-Bo Zhou Dongyang Tang Lin He Shiqi Lin Josh Haipeng Lei Heng Sun Xiaoling Xu Chu-Xia Deng Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation Pharmacological Research Drug combination therapy Machine learning Three-dimensional tumor slice culture Potential combination biomarkers |
| title | Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation |
| title_full | Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation |
| title_fullStr | Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation |
| title_full_unstemmed | Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation |
| title_short | Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation |
| title_sort | machine learning model for anti cancer drug combinations analysis prediction and validation |
| topic | Drug combination therapy Machine learning Three-dimensional tumor slice culture Potential combination biomarkers |
| url | http://www.sciencedirect.com/science/article/pii/S104366182300186X |
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