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He X, Abulizi X, Li X, Ma G, Sun Z, Wei H, Xu X, Shi L, Zhang J. Daphnane-Type Diterpenes from Stelleropsis tianschanica and Their Antitumor Activity. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27175701. [PMID: 36080468 PMCID: PMC9458044 DOI: 10.3390/molecules27175701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 08/29/2022] [Accepted: 09/02/2022] [Indexed: 11/16/2022]
Abstract
Four new daphnane-type diterpenes named tianchaterpenes C-F (1–4) and six known ones were isolated from Stelleropsis tianschanica. Their structures were elucidated based on chemical and spectral analyses. The comparisons of calculated and experimental electronic circular dichroism (ECD) methods were used to determine the absolute configurations of new compounds. Additionally, compounds 1–10 were evaluated for their cytotoxic activities against HGC-27 cell lines; the results demonstrate that compound 2 had strong cytotoxic activities with IC50 values of 8.8 µM, for which activity was better than that of cisplatin (13.2 ± 0.67 µM).
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Affiliation(s)
- Xiaoyan He
- College of Chinese Medicine Material, Jilin Agricultural University, Changchun 130118, China
| | - Xiatiguli Abulizi
- Xinjiang Institute of Chinese and Ethnic Medicine, Urumqi 830002, China
| | - Xiaowan Li
- Xinjiang Agricultural Vocational and Technical College Biological Technology Branch, Changji 831100, China
| | - Guoxu Ma
- Xinjiang Institute of Chinese and Ethnic Medicine, Urumqi 830002, China
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China
| | - Zhaocui Sun
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China
| | - Hongyan Wei
- Xinjiang Institute of Chinese and Ethnic Medicine, Urumqi 830002, China
| | - Xudong Xu
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China
| | - Leiling Shi
- Xinjiang Institute of Chinese and Ethnic Medicine, Urumqi 830002, China
- Correspondence: (L.S.); (J.Z.)
| | - Jing Zhang
- College of Chinese Medicine Material, Jilin Agricultural University, Changchun 130118, China
- Correspondence: (L.S.); (J.Z.)
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2
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Wang H, Wang R, Huang D, Li S, Gao B, Kang Z, Tang B, Xie J, Yan F, Liang R, Li H, Yan J. Homoharringtonine Exerts Anti-tumor Effects in Hepatocellular Carcinoma Through Activation of the Hippo Pathway. Front Pharmacol 2021; 12:592071. [PMID: 33716735 PMCID: PMC7943857 DOI: 10.3389/fphar.2021.592071] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 01/07/2021] [Indexed: 12/17/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most prevalent subtype of liver cancer with a mortality rate of approximately 3–6/100,000 and is the third leading cause of cancer-related death worldwide. Although several small-molecule drugs have been developed for the treatment of HCC, the choice of an agent for patients who require systemic chemotherapy at an advanced stage is still limited. The Hippo pathway is an evolutionarily conserved tumor suppressive pathway commonly dysregulated in HCC, which makes it a promising target for anti-HCC therapies. Homoharringtonine (HHT) is an FDA-approved anti-leukemia drug with proven strong anti-tumor activity in solid tumors. In this study, we found that HHT could significantly inhibit HCC cell growth by suppressing cell proliferation and colony formation. Moreover, HHT repressed cell invasion and migration remarkably. Additionally, HHT induced cell cycle arrest at S phase and promoted apoptosis. Most importantly, we showed that HHT-induced apoptosis was a consequence of the Hippo pathway activation. Consistently, the MST1/2 inhibitor, XMU-MP-1, could restore cell viability and reverse HHT-induced cell apoptosis. Furthermore, in vivo results confirmed the tumor inhibitory effect of HHT. Taken together, our findings suggest that HHT is a potential alternative therapeutic agent for the treatment of HCC.
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Affiliation(s)
- Haina Wang
- Department of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, Dalian Key Laboratory of Hematology, Second Hospital of Dalian Medical University, Dalian, China.,Diamond Bay Institute of Hematology, Second Hospital of Dalian Medical University, Dalian, China
| | - Rui Wang
- Department of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, Dalian Key Laboratory of Hematology, Second Hospital of Dalian Medical University, Dalian, China
| | - Dan Huang
- Department of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, Dalian Key Laboratory of Hematology, Second Hospital of Dalian Medical University, Dalian, China
| | - Sihan Li
- College of Pharmacy, Dalian Medical University, Dalian, China
| | - Beibei Gao
- Department of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, Dalian Key Laboratory of Hematology, Second Hospital of Dalian Medical University, Dalian, China
| | - Zhijie Kang
- Department of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, Dalian Key Laboratory of Hematology, Second Hospital of Dalian Medical University, Dalian, China
| | - Bo Tang
- Department of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, Dalian Key Laboratory of Hematology, Second Hospital of Dalian Medical University, Dalian, China
| | - Jiajun Xie
- Department of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, Dalian Key Laboratory of Hematology, Second Hospital of Dalian Medical University, Dalian, China
| | - Fanzhi Yan
- Department of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, Dalian Key Laboratory of Hematology, Second Hospital of Dalian Medical University, Dalian, China
| | - Rui Liang
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, Second Hospital of Dalian Medical University, Dalian, China
| | - Hua Li
- College of Pharmacy, Dalian Medical University, Dalian, China
| | - Jinsong Yan
- Department of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, Dalian Key Laboratory of Hematology, Second Hospital of Dalian Medical University, Dalian, China.,Diamond Bay Institute of Hematology, Second Hospital of Dalian Medical University, Dalian, China
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3
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Cabrera-Andrade A, López-Cortés A, Munteanu CR, Pazos A, Pérez-Castillo Y, Tejera E, Arrasate S, González-Díaz H. Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds. ACS OMEGA 2020; 5:27211-27220. [PMID: 33134682 PMCID: PMC7594149 DOI: 10.1021/acsomega.0c03356] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 10/06/2020] [Indexed: 06/11/2023]
Abstract
Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19-95.25% for training and 89.22-95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.
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Affiliation(s)
- Alejandro Cabrera-Andrade
- Grupo
de Bio-Quimioinformática, Universidad
de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- Carrera
de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
| | - Andrés López-Cortés
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
- Centro
de Investigación Genética y Genómica, Facultad
de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, Quito 170129, Ecuador
| | - Cristian R. Munteanu
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
- Biomedical
Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña 15006, Spain
- Centro de
Investigación en Tecnologías de la Información
y las Comunicaciones (CITIC), Campus de
Elviña s/n, A Coruña 15071, Spain
| | - Alejandro Pazos
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
- Biomedical
Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña 15006, Spain
| | - Yunierkis Pérez-Castillo
- Grupo
de Bio-Quimioinformática, Universidad
de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- Escuela
de Ciencias Físicas y Matemáticas, Universidad de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
| | - Eduardo Tejera
- Grupo
de Bio-Quimioinformática, Universidad
de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- Facultad
de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
| | - Sonia Arrasate
- Department
of Organic Chemistry II and Basque Center for Biophysics, University of Basque Country UPV/EHU, Leioa 48940, Biscay, Spain
| | - Humbert González-Díaz
- Department
of Organic Chemistry II and Basque Center for Biophysics, University of Basque Country UPV/EHU, Leioa 48940, Biscay, Spain
- Ikerbasque,
Basque Foundation for Science, Bilbao 48011, Biscay, Spain
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4
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Chen ZH, You ZH, Guo ZH, Yi HC, Luo GX, Wang YB. Prediction of Drug-Target Interactions From Multi-Molecular Network Based on Deep Walk Embedding Model. Front Bioeng Biotechnol 2020; 8:338. [PMID: 32582646 PMCID: PMC7283956 DOI: 10.3389/fbioe.2020.00338] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 03/26/2020] [Indexed: 12/16/2022] Open
Abstract
Predicting drug-target interactions (DTIs) is crucial in innovative drug discovery, drug repositioning and other fields. However, there are many shortcomings for predicting DTIs using traditional biological experimental methods, such as the high-cost, time-consumption, low efficiency, and so on, which make these methods difficult to widely apply. As a supplement, the in silico method can provide helpful information for predictions of DTIs in a timely manner. In this work, a deep walk embedding method is developed for predicting DTIs from a multi-molecular network. More specifically, a multi-molecular network, also called molecular associations network, is constructed by integrating the associations among drug, protein, disease, lncRNA, and miRNA. Then, each node can be represented as a behavior feature vector by using a deep walk embedding method. Finally, we compared behavior features with traditional attribute features on an integrated dataset by using various classifiers. The experimental results revealed that the behavior feature could be performed better on different classifiers, especially on the random forest classifier. It is also demonstrated that the use of behavior information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work is not only extremely suitable for predicting DTIs, but also provides a new perspective for the prediction of other biomolecules' associations.
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Affiliation(s)
- Zhan-Heng Chen
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhu-Hong You
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhen-Hao Guo
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hai-Cheng Yi
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Gong-Xu Luo
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yan-Bin Wang
- School of Cyber Science and Technology, Zhejiang University, Hangzhou, China
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Yao J, Hurle MR, Nelson MR, Agarwal P. Predicting clinically promising therapeutic hypotheses using tensor factorization. BMC Bioinformatics 2019; 20:69. [PMID: 30736745 PMCID: PMC6368709 DOI: 10.1186/s12859-019-2664-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 01/30/2019] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. With current informatics technology and machine learning algorithms, it is now possible to computationally discover therapeutic hypotheses by predicting clinically promising drug targets based on the evidence associating drug targets with disease indications. We have collected this evidence from Open Targets and additional databases that covers 17 sources of evidence for target-indication association and represented the data as a tensor of 21,437 × 2211 × 17. RESULTS As a proof-of-concept, we identified examples of successes and failures of target-indication pairs in clinical trials across 875 targets and 574 disease indications to build a gold-standard data set of 6140 known clinical outcomes. We designed and executed three benchmarking strategies to examine the performance of multiple machine learning models: Logistic Regression, LASSO, Random Forest, Tensor Factorization and Gradient Boosting Machine. With 10-fold cross-validation, tensor factorization achieved AUROC = 0.82 ± 0.02 and AUPRC = 0.71 ± 0.03. Across multiple validation schemes, this was comparable or better than other methods. CONCLUSION In this work, we benchmarked a machine learning technique called tensor factorization for the problem of predicting clinical outcomes of therapeutic hypotheses. Results have shown that this method can achieve equal or better prediction performance compared with a variety of baseline models. We demonstrate one application of the method to predict outcomes of trials on novel indications of approved drug targets. This work can be expanded to targets and indications that have never been clinically tested and proposing novel target-indication hypotheses. Our proposed biologically-motivated cross-validation schemes provide insight into the robustness of the prediction performance. This has significant implications for all future methods that try to address this seminal problem in drug discovery.
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Affiliation(s)
- Jin Yao
- Computational Biology, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA USA
| | - Mark R. Hurle
- Computational Biology, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA USA
| | - Matthew R. Nelson
- Genetics, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA USA
| | - Pankaj Agarwal
- Computational Biology, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA USA
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Colbaugh R, Glass K. Early Identification of Patentable Medical Innovations. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:4924-4926. [PMID: 30441447 DOI: 10.1109/embc.2018.8513226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
There is considerable interest in developing methods for early detection of medical advances which are likely to lead to effective treatments. Because the number of potentially significant discoveries published each year is very large, practically-useful detection must be accomplished using computational models. This paper considers a key component of this detection process - early identification of patentable innovations - and presents a novel machine learning-based prediction model to perform the identification. Experiments demonstrate the efficacy of the proposed approach.
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7
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Vlietstra WJ, Vos R, Sijbers AM, van Mulligen EM, Kors JA. Using predicate and provenance information from a knowledge graph for drug efficacy screening. J Biomed Semantics 2018; 9:23. [PMID: 30189889 PMCID: PMC6127943 DOI: 10.1186/s13326-018-0189-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 08/01/2018] [Indexed: 12/11/2022] Open
Abstract
Background Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relationship between subject and object. A triple can also contain provenance information, which consists of references to the sources of the triple (e.g. scientific publications or database entries). Knowledge graphs have been used to classify drug-disease pairs for drug efficacy screening, but existing computational methods have often ignored predicate and provenance information. Using this information, we aimed to develop a supervised machine learning classifier and determine the added value of predicate and provenance information for drug efficacy screening. To ensure the biological plausibility of our method we performed our research on the protein level, where drugs are represented by their drug target proteins, and diseases by their disease proteins. Results Using random forests with repeated 10-fold cross-validation, our method achieved an area under the ROC curve (AUC) of 78.1% and 74.3% for two reference sets. We benchmarked against a state-of-the-art knowledge-graph technique that does not use predicate and provenance information, obtaining AUCs of 65.6% and 64.6%, respectively. Classifiers that only used predicate information performed superior to classifiers that only used provenance information, but using both performed best. Conclusion We conclude that both predicate and provenance information provide added value for drug efficacy screening. Electronic supplementary material The online version of this article (10.1186/s13326-018-0189-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wytze J Vlietstra
- Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, 3015, GE, the Netherlands.
| | - Rein Vos
- Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, 3015, GE, the Netherlands.,Department of Methodology and Statistics, Maastricht University, Maastricht, 6200, MD, the Netherlands
| | - Anneke M Sijbers
- Centre for Molecular and Biomolecular Informatics, Radboudumc, Nijmegen, 6525, GA, the Netherlands
| | - Erik M van Mulligen
- Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, 3015, GE, the Netherlands
| | - Jan A Kors
- Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, 3015, GE, the Netherlands
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8
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Rouillard AD, Hurle MR, Agarwal P. Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets. PLoS Comput Biol 2018; 14:e1006142. [PMID: 29782487 PMCID: PMC5983857 DOI: 10.1371/journal.pcbi.1006142] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 06/01/2018] [Accepted: 04/13/2018] [Indexed: 11/19/2022] Open
Abstract
Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC = 0.57 and AUPR = 0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub at https://github.com/arouillard/omic-features-successful-targets.
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Affiliation(s)
| | - Mark R. Hurle
- Computational Biology, GSK, Collegeville, PA, United States of America
| | - Pankaj Agarwal
- Computational Biology, GSK, Collegeville, PA, United States of America
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