DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914717103030272 |
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| author | Wu, Yushuai Zhang, Ting Zhou, Hao Wu, Hainan Sunchu, Hanwen Hu, Lei Chen, Xiaofang Zhao, Suyuan Liu, Gaochao Sun, Chao Zhang, Jiahuan Luo, Yizhen Liu, Peng Nie, Zaiqing Wu, Yushuai |
| author_facet | Wu, Yushuai Zhang, Ting Zhou, Hao Wu, Hainan Sunchu, Hanwen Hu, Lei Chen, Xiaofang Zhao, Suyuan Liu, Gaochao Sun, Chao Zhang, Jiahuan Luo, Yizhen Liu, Peng Nie, Zaiqing Wu, Yushuai |
| contents | The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i.e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials. One of the reasons is the inadequacy of Cross-drug Response Evaluation (CRE) during the late stages of drug R&D. Although in-silico CRE models bring a promising solution, existing methodologies are restricted to early stages of drug R&D, such as target and cell-line levels, offering limited improvement to clinical success rates. Herein, we introduce DeepCRE, a pioneering AI model designed to predict CRE effectively in the late stages of drug R&D. DeepCRE outperforms the existing best models by achieving an average performance improvement of 17.7% in patient-level CRE, and a 5-fold increase in indication-level CRE, facilitating more accurate personalized treatment predictions and better pharmaceutical value assessment for indications, respectively. Furthermore, DeepCRE has identified a set of six drug candidates that show significantly greater effectiveness than a comparator set of two approved drugs in 5/8 colorectal cancer organoids. This demonstrates the capability of DeepCRE to systematically uncover a spectrum of drug candidates with enhanced therapeutic effects, highlighting its potential to transform drug R&D. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03768 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation Wu, Yushuai Zhang, Ting Zhou, Hao Wu, Hainan Sunchu, Hanwen Hu, Lei Chen, Xiaofang Zhao, Suyuan Liu, Gaochao Sun, Chao Zhang, Jiahuan Luo, Yizhen Liu, Peng Nie, Zaiqing Wu, Yushuai Artificial Intelligence Machine Learning Quantitative Methods The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i.e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials. One of the reasons is the inadequacy of Cross-drug Response Evaluation (CRE) during the late stages of drug R&D. Although in-silico CRE models bring a promising solution, existing methodologies are restricted to early stages of drug R&D, such as target and cell-line levels, offering limited improvement to clinical success rates. Herein, we introduce DeepCRE, a pioneering AI model designed to predict CRE effectively in the late stages of drug R&D. DeepCRE outperforms the existing best models by achieving an average performance improvement of 17.7% in patient-level CRE, and a 5-fold increase in indication-level CRE, facilitating more accurate personalized treatment predictions and better pharmaceutical value assessment for indications, respectively. Furthermore, DeepCRE has identified a set of six drug candidates that show significantly greater effectiveness than a comparator set of two approved drugs in 5/8 colorectal cancer organoids. This demonstrates the capability of DeepCRE to systematically uncover a spectrum of drug candidates with enhanced therapeutic effects, highlighting its potential to transform drug R&D. |
| title | DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation |
| topic | Artificial Intelligence Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2403.03768 |