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Yu W, Fang S, Xie X, Liu W, Liu X, Du Y, Zheng P, Liu G. Deuterium Editing of Small Molecules: A Case Study on Antitumor Activity of 1,4-Benzodiazepine-2,5-dione Derivatives. J Med Chem 2024. [PMID: 39026395 DOI: 10.1021/acs.jmedchem.4c00796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Substituting hydrogen with deuterium in drug molecules is an appealing bioisosteric strategy for the generation of novel chemical entities in drug development. Optimizing lead compounds through deuteration has proven to be challenging and unpredictable, particularly for compounds with multiple metabolic sites. This study presents the pioneering achievement of substituting up to 19 hydrogen atoms with deuterium on 1,4-benzodiazepine-2,5-dione derivatives, shedding light on the structure-metabolism relationship and the impact of multiple deuterations on drug-like properties. Notably, the deuterated compound 3f exhibited remarkable antitumor activity in vivo and demonstrated favorable drug-like properties as a drug candidate.
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Affiliation(s)
- Wenjun Yu
- Ningbo Combireg Pharmaceutical Technology Co., Ltd., Ningbo 315336, P. R. China
| | - Shiping Fang
- School of Pharmaceutical Sciences, Tsinghua University, Haidian Dist, Beijing 100084, P. R. China
| | - Xilei Xie
- School of Pharmaceutical Sciences, Tsinghua University, Haidian Dist, Beijing 100084, P. R. China
| | - Wenwu Liu
- School of Pharmaceutical Sciences, Tsinghua University, Haidian Dist, Beijing 100084, P. R. China
| | - Xinhua Liu
- School of Pharmaceutical Sciences, Tsinghua University, Haidian Dist, Beijing 100084, P. R. China
| | - Yanan Du
- School of Biomedical Engineering, Tsinghua-Peking Center for Life Sciences, Tsinghua University, Haidian Dist, Beijing 100084, P. R. China
| | - Purong Zheng
- Ningbo Combireg Pharmaceutical Technology Co., Ltd., Ningbo 315336, P. R. China
| | - Gang Liu
- School of Pharmaceutical Sciences, Tsinghua University, Haidian Dist, Beijing 100084, P. R. China
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He S, Ye T, Wang R, Zhang C, Zhang X, Sun G, Sun X. An In Silico Model for Predicting Drug-Induced Hepatotoxicity. Int J Mol Sci 2019; 20:E1897. [PMID: 30999595 PMCID: PMC6515336 DOI: 10.3390/ijms20081897] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 04/09/2019] [Accepted: 04/15/2019] [Indexed: 01/10/2023] Open
Abstract
As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure-activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.
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Affiliation(s)
- Shuaibing He
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Tianyuan Ye
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Ruiying Wang
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Chenyang Zhang
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Xuelian Zhang
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Guibo Sun
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
| | - Xiaobo Sun
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
- Key Laboratory of Efficacy Evaluation of Chinese Medicine against Glycolipid Metabolic Disorders, State Administration of Traditional Chinese Medicine, Beijing 100193, China.
- Key Laboratory of New Drug Discovery Based on Classic Chinese Medicine Prescription, Chinese Academy of Medical Sciences, Beijing 100193, China.
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3
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Fu X, He S, Du L, Lv Z, Zhang Y, Zhang Q, Wang Y. Using chemical bond-based method to predict site of metabolism for five biotransformations mediated by CYP 3A4, 2D6, and 2C9. Biochem Pharmacol 2018; 152:302-314. [PMID: 29588194 DOI: 10.1016/j.bcp.2018.03.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 03/22/2018] [Indexed: 11/29/2022]
Abstract
Although it has been proposed for decades to predict site of metabolism (SOM) by in silico methods, identifying SOM correctly remains an unsolved fundamental problem and is an active area of research. In our prior works, we proposed a chemical bond-based approach to construction of SOM prediction models by integrating chemical bond descriptors and drug-metabolizing enzymes data. Although it has been evaluated with both 10-fold cross-validation and independent validation, we believe comparisons between this method and prior methods using publicly accessible external datasets are indispensable and more desirable. In the current study, based on chemical bond-based method, metabolism data released by Sheridan et al. and Zaretzki et al. was utilized to establish metabolite prediction models for CYP450 3A4, 2D6, and 2C9. Five major reaction types were involved, including Aliphatic C-hydroxylation, Aromatic C-hydroxylation, N-dealkylation, O-dealkylation, and S-Oxidation. Consequently, all our five models showed impressive performance on predicting SOMs, with accuracy and area under curve exceeded 0.940 and 0.953, respectively. Compared to prior works, our models were better than SOMP both in "SOM-scale" and "molecule-scale". In conclusion, comparisons between chemical-bond based method and prior works were conducted for the first time, which demonstrated that chemical-bond based method is better than or at least comparable to prior works.
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Affiliation(s)
- XuYan Fu
- Key Laboratory of Traditional Chinese Medicine Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China
| | - ShuaiBing He
- Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine from Ministry of Education, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China
| | - Li Du
- Key Laboratory of Traditional Chinese Medicine Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China
| | - ZhaoLei Lv
- Key Laboratory of Traditional Chinese Medicine Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China
| | - Yi Zhang
- Key Laboratory of Traditional Chinese Medicine Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China
| | - Qian Zhang
- Key Laboratory of Traditional Chinese Medicine Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China
| | - Yun Wang
- Key Laboratory of Traditional Chinese Medicine Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China.
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5
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Šícho M, de Bruyn Kops C, Stork C, Svozil D, Kirchmair J. FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity. J Chem Inf Model 2017; 57:1832-1846. [DOI: 10.1021/acs.jcim.7b00250] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Martin Šícho
- Faculty
of Mathematics, Informatics and Natural Sciences, Department of Computer
Science, Center for Bioinformatics, Universität Hamburg, Hamburg, 20146, Germany
- CZ-OPENSCREEN:
National Infrastructure for Chemical Biology, Laboratory of Informatics
and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, 166 28 Prague 6, Czech Republic
| | - Christina de Bruyn Kops
- Faculty
of Mathematics, Informatics and Natural Sciences, Department of Computer
Science, Center for Bioinformatics, Universität Hamburg, Hamburg, 20146, Germany
| | - Conrad Stork
- Faculty
of Mathematics, Informatics and Natural Sciences, Department of Computer
Science, Center for Bioinformatics, Universität Hamburg, Hamburg, 20146, Germany
| | - Daniel Svozil
- CZ-OPENSCREEN:
National Infrastructure for Chemical Biology, Laboratory of Informatics
and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, 166 28 Prague 6, Czech Republic
| | - Johannes Kirchmair
- Faculty
of Mathematics, Informatics and Natural Sciences, Department of Computer
Science, Center for Bioinformatics, Universität Hamburg, Hamburg, 20146, Germany
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