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Tang Z, Chen G, Yang H, Zhong W, Chen CYC. DSIL-DDI: A Domain-Invariant Substructure Interaction Learning for Generalizable Drug-Drug Interaction Prediction. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:10552-10560. [PMID: 37022856 DOI: 10.1109/tnnls.2023.3242656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Drug-drug interactions (DDIs) trigger unexpected pharmacological effects in vivo, often with unknown causal mechanisms. Deep learning methods have been developed to better understand DDI. However, learning domain-invariant representations for DDI remains a challenge. Generalizable DDI predictions are closer to reality than source domain predictions. For existing methods, it is difficult to achieve out-of-distribution (OOD) predictions. In this article, focusing on substructure interaction, we propose DSIL-DDI, a pluggable substructure interaction module that can learn domain-invariant representations of DDIs from source domain. We evaluate DSIL-DDI on three scenarios: the transductive setting (all drugs in test set appear in training set), the inductive setting (test set contains new drugs that were not present in training set), and OOD generalization setting (training set and test set belong to two different datasets). The results demonstrate that DSIL-DDI improve the generalization and interpretability of DDI prediction modeling and provides valuable insights for OOD DDI predictions. DSIL-DDI can help doctors ensuring the safety of drug administration and reducing the harm caused by drug abuse.
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Berckmans Y, Ene HM, Ben-Meir K, Martinez-Conde A, Wouters R, Van den Ende B, Van Mechelen S, Monin R, Frechtel-Gerzi R, Gabay H, Dor-On E, Haber A, Weinberg U, Vergote I, Giladi M, Coosemans A, Palti Y. Tumor Treating Fields (TTFields) induce homologous recombination deficiency in ovarian cancer cells, thus mitigating drug resistance. Front Oncol 2024; 14:1402851. [PMID: 38993641 PMCID: PMC11238040 DOI: 10.3389/fonc.2024.1402851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/10/2024] [Indexed: 07/13/2024] Open
Abstract
Background Ovarian cancer is the leading cause of mortality among gynecological malignancies. Carboplatin and poly (ADP-ribose) polymerase inhibitors (PARPi) are often implemented in the treatment of ovarian cancer. Homologous recombination deficient (HRD) tumors demonstrate increased sensitivity to these treatments; however, many ovarian cancer patients are homologous recombination proficient (HRP). TTFields are non-invasive electric fields that induce an HRD-like phenotype in various cancer types. The current study aimed to investigate the impact of TTFields applied together with carboplatin or PARPi (olaparib or niraparib) in preclinical ovarian cancer models. Methods A2780 (HRP), OVCAR3 (HRD), and A2780cis (platinum-resistant) human ovarian cancer cells were treated in vitro with TTFields (1 V/cm RMS, 200 kHz, 72 h), alone or with various drug concentrations. Treated cells were measured for cell count, colony formation, apoptosis, DNA damage, expression of DNA repair proteins, and cell cycle. In vivo, ID8-fLuc (HRP) ovarian cancer cells were inoculated intraperitoneally to C57BL/6 mice, which were then treated with either sham, TTFields (200 kHz), olaparib (50 mg/kg), or TTFields plus olaparib; over a period of four weeks. Tumor growth was analyzed using bioluminescent imaging at treatment cessation; and survival analysis was performed. Results The nature of TTFields-drug interaction was dependent on the drug's underlying mechanism of action and on the genetic background of the cells, with synergistic interactions between TTFields and carboplatin or PARPi seen in HRP and resistant cells. Treated cells demonstrated elevated levels of DNA damage, accompanied by G2/M arrest, and induction of an HRD-like phenotype. In the tumor-bearing mice, TTFields and olaparib co-treatment resulted in reduced tumor volume and a survival benefit relative to olaparib monotherapy and to control. Conclusion By inducing an HRD-like phenotype, TTFields sensitize HRP and resistant ovarian cancer cells to treatment with carboplatin or PARPi, potentially mitigating a-priori and de novo drug resistance, a major limitation in ovarian cancer treatment.
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Affiliation(s)
- Yani Berckmans
- Laboratory of Tumor Immunology and Immunotherapy, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium
| | | | | | | | - Roxanne Wouters
- Laboratory of Tumor Immunology and Immunotherapy, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium
- Oncoinvent AS, Oslo, Norway
| | - Bieke Van den Ende
- Laboratory of Tumor Immunology and Immunotherapy, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium
| | - Sara Van Mechelen
- Laboratory of Tumor Immunology and Immunotherapy, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium
| | | | | | | | | | | | | | - Ignace Vergote
- Department of Gynecology and Obstetrics, Gynecologic Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium
| | | | - An Coosemans
- Laboratory of Tumor Immunology and Immunotherapy, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium
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Zhao M, Shuai W, Su Z, Xu P, Wang A, Sun Q, Wang G. Protein tyrosine phosphatases: emerging role in cancer therapy resistance. Cancer Commun (Lond) 2024; 44:637-653. [PMID: 38741380 PMCID: PMC11194456 DOI: 10.1002/cac2.12548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 04/14/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Tyrosine phosphorylation of intracellular proteins is a post-translational modification that plays a regulatory role in signal transduction during cellular events. Dephosphorylation of signal transduction proteins caused by protein tyrosine phosphatases (PTPs) contributed their role as a convergent node to mediate cross-talk between signaling pathways. In the context of cancer, PTP-mediated pathways have been identified as signaling hubs that enabled cancer cells to mitigate stress induced by clinical therapy. This is achieved by the promotion of constitutive activation of growth-stimulatory signaling pathways or modulation of the immune-suppressive tumor microenvironment. Preclinical evidences suggested that anticancer drugs will release their greatest therapeutic potency when combined with PTP inhibitors, reversing drug resistance that was responsible for clinical failures during cancer therapy. AREAS COVERED This review aimed to elaborate recent insights that supported the involvement of PTP-mediated pathways in the development of resistance to targeted therapy and immune-checkpoint therapy. EXPERT OPINION This review proposed the notion of PTP inhibition in anticancer combination therapy as a potential strategy in clinic to achieve long-term tumor regression. Ongoing clinical trials are currently underway to assess the safety and efficacy of combination therapy in advanced-stage tumors.
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Affiliation(s)
- Min Zhao
- Innovation Center of Nursing ResearchNursing Key Laboratory of Sichuan ProvinceDepartment of BiotherapyCancer Center and State Key Laboratory of BiotherapyNational Clinical Research Center for GeriatricsWest China Hospital, West China School of Nursing, Sichuan UniversityChengduSichuanP. R. China
| | - Wen Shuai
- Innovation Center of Nursing ResearchNursing Key Laboratory of Sichuan ProvinceDepartment of BiotherapyCancer Center and State Key Laboratory of BiotherapyNational Clinical Research Center for GeriatricsWest China Hospital, West China School of Nursing, Sichuan UniversityChengduSichuanP. R. China
| | - Zehao Su
- Innovation Center of Nursing ResearchNursing Key Laboratory of Sichuan ProvinceDepartment of BiotherapyCancer Center and State Key Laboratory of BiotherapyNational Clinical Research Center for GeriatricsWest China Hospital, West China School of Nursing, Sichuan UniversityChengduSichuanP. R. China
- West China Biomedical Big Data CenterMed‐X Center for InformaticsSichuan UniversityChengduSichuanP. R. China
| | - Ping Xu
- Emergency DepartmentZigong Fourth People's HospitalChengduSichuanP. R. China
| | - Aoxue Wang
- Innovation Center of Nursing ResearchNursing Key Laboratory of Sichuan ProvinceDepartment of BiotherapyCancer Center and State Key Laboratory of BiotherapyNational Clinical Research Center for GeriatricsWest China Hospital, West China School of Nursing, Sichuan UniversityChengduSichuanP. R. China
| | - Qiu Sun
- Innovation Center of Nursing ResearchNursing Key Laboratory of Sichuan ProvinceDepartment of BiotherapyCancer Center and State Key Laboratory of BiotherapyNational Clinical Research Center for GeriatricsWest China Hospital, West China School of Nursing, Sichuan UniversityChengduSichuanP. R. China
| | - Guan Wang
- Innovation Center of Nursing ResearchNursing Key Laboratory of Sichuan ProvinceDepartment of BiotherapyCancer Center and State Key Laboratory of BiotherapyNational Clinical Research Center for GeriatricsWest China Hospital, West China School of Nursing, Sichuan UniversityChengduSichuanP. R. China
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Hu B, Yu Z, Li M. MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction. Molecules 2024; 29:2483. [PMID: 38893359 PMCID: PMC11173658 DOI: 10.3390/molecules29112483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 05/17/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
The combinatorial therapy with multiple drugs may lead to unexpected drug-drug interactions (DDIs) and result in adverse reactions to patients. Predicting DDI events can mitigate the potential risks of combinatorial therapy and enhance drug safety. In recent years, deep models based on heterogeneous graph representation learning have attracted widespread interest in DDI event prediction and have yielded satisfactory results, but there is still room for improvement in prediction performance. In this study, we proposed a meta-path-based heterogeneous graph contrastive learning model, MPHGCL-DDI, for DDI event prediction. The model constructs two contrastive views based on meta-paths: an average graph view and an augmented graph view. The former represents that there are connections between drugs, while the latter reveals how the drugs connect with each other. We defined three levels of data augmentation schemes in the augmented graph view and adopted a combination of three losses in the model training phase: multi-relation prediction loss, unsupervised contrastive loss and supervised contrastive loss. Furthermore, the model incorporates indirect drug information, protein-protein interactions (PPIs), to reveal latent relations of drugs. We evaluated MPHGCL-DDI on three different tasks of two datasets. Experimental results demonstrate that MPHGCL-DDI surpasses several state-of-the-art methods in performance.
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Affiliation(s)
- Baofang Hu
- School of Data and Computer Science, Shandong Women’s University, Jinan 250030, China;
| | - Zhenmei Yu
- School of Data and Computer Science, Shandong Women’s University, Jinan 250030, China;
| | - Mingke Li
- School of Information Science and Engineering, University of Jinan, Jinan 250024, China;
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Flanary SM, Peak KE, Barocas VH. A Graphical Approach to Visualize and Interpret Biochemically Coupled Biomechanical Models. J Biomech Eng 2024; 146:054504. [PMID: 38421368 PMCID: PMC11005857 DOI: 10.1115/1.4064970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/02/2024]
Abstract
The last decade has seen the emergence of progressively more complex mechanobiological models, often coupling biochemical and biomechanical components. The complexity of these models makes interpretation difficult, and although computational tools can solve model equations, there is considerable potential value in a simple method to explore the interplay between different model components. Pump and system performance curves, long utilized in centrifugal pump selection and design, inspire the development of a graphical technique to depict visually the performance of biochemically-coupled mechanical models. Our approach is based on a biochemical performance curve (analogous to the classical pump curve) and a biomechanical performance curve (analogous to the system curve). Upon construction of the two curves, their intersection, or lack thereof, describes the coupled model's equilibrium state(s). One can also observe graphically how an applied perturbation shifts one or both curves, and thus how the other component will respond, without rerunning the full model. While the upfront cost of generating the performance curve graphic varies with the efficiency of the model components, the easily interpretable visual depiction of what would otherwise be nonintuitive model behavior is valuable. Herein, we outline how performance curves can be constructed and interpreted for biochemically-coupled biomechanical models and apply the technique to two independent models in the cardiovascular space. The performance curve approach can illustrate and help identify weaknesses in model construction, inform user-applied perturbations and fitting procedures to generate intended behaviors, and improve the efficiency of the model generation and application process.
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Affiliation(s)
- Shannon M. Flanary
- Department of Chemical Engineering & Materials Science, University of Minnesota, Nils Hasselmo Hall, Room 7-115, 312 Church St SE, Minneapolis, MN 55455
| | - Kara E. Peak
- Department of Biomedical Engineering, University of Minnesota, Nils Hasselmo Hall, Room 7-115, 312 Church St SE, Minneapolis, MN 55455
- University of Minnesota
| | - Victor H. Barocas
- Department of Biomedical Engineering, University of Minnesota, Nils Hasselmo Hall, Room 7-115, 312 Church St SE, Minneapolis, MN 55455
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Zhuo L, Stöckl JB, Fröhlich T, Moser S, Vollmar AM, Zahler S. A Novel Interaction of Slug (SNAI2) and Nuclear Actin. Cells 2024; 13:696. [PMID: 38667311 PMCID: PMC11049500 DOI: 10.3390/cells13080696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/10/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Actin is a protein of central importance to many cellular functions. Its localization and activity are regulated by interactions with a high number of actin-binding proteins. In a yeast two-hybrid (Y2H) screening system, snail family transcriptional repressor 2 (SNAI2 or slug) was identified as a yet unknown potential actin-binding protein. We validated this interaction using immunoprecipitation and analyzed the functional relation between slug and actin. Since both proteins have been reported to be involved in DNA double-strand break (DSB) repair, we focused on their interaction during this process after treatment with doxorubicin or UV irradiation. Confocal microscopy elicits that the overexpression of actin fused to an NLS stabilizes complexes of slug and γH2AX, an early marker of DNA damage repair.
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Affiliation(s)
- Ling Zhuo
- Center for Drug Research, Ludwig-Maximilians-University Munich, Butenandtstr, 5-13, 81377 Munich, Germany; (L.Z.); (A.M.V.)
| | - Jan B. Stöckl
- Laboratory for Functional Genome Analysis, Gene Center Munich, Ludwig-Maximilians-University Munich, Feodor-Lynen-Str. 25, 81377 Munich, Germany; (J.B.S.); (T.F.)
| | - Thomas Fröhlich
- Laboratory for Functional Genome Analysis, Gene Center Munich, Ludwig-Maximilians-University Munich, Feodor-Lynen-Str. 25, 81377 Munich, Germany; (J.B.S.); (T.F.)
| | - Simone Moser
- Department of Pharmacognosy, Institute of Pharmacy, University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria;
| | - Angelika M. Vollmar
- Center for Drug Research, Ludwig-Maximilians-University Munich, Butenandtstr, 5-13, 81377 Munich, Germany; (L.Z.); (A.M.V.)
| | - Stefan Zahler
- Center for Drug Research, Ludwig-Maximilians-University Munich, Butenandtstr, 5-13, 81377 Munich, Germany; (L.Z.); (A.M.V.)
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Liu JY, Sayes CM. Modeling mixtures interactions in environmental toxicology. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2024; 106:104380. [PMID: 38309542 DOI: 10.1016/j.etap.2024.104380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 02/05/2024]
Abstract
In the environment, organisms are exposed to mixtures of different toxicants, which may interact in ways that are difficult to predict when only considering each component individually. Adapting and expanding tools from pharmacology, the toxicology field uses analytical, graphical, and computational methods to identify and quantify interactions in multi-component mixtures. The two general frameworks are concentration addition, where components have similar modes of action and their effects sum together, or independent action, where components have dissimilar modes of action and do not interact. Other interaction behaviors include synergism and antagonism, where the combined effects are more or less than the additive sum of individual effects. This review covers foundational theory, methods, an in-depth survey of original research from the past 20 years, current trends, and future directions. As humans and ecosystems are exposed to increasingly complex mixtures of environmental contaminants, analyzing mixtures interactions will continue to become a more critical aspect of toxicological research.
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Affiliation(s)
- James Y Liu
- Department of Environmental Science, Baylor University, Waco, TX, USA
| | - Christie M Sayes
- Department of Environmental Science, Baylor University, Waco, TX, USA.
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Jin Q, Xie J, Huang D, Zhao C, He H. MSFF-MA-DDI: Multi-Source Feature Fusion with Multiple Attention blocks for predicting Drug-Drug Interaction events. Comput Biol Chem 2024; 108:108001. [PMID: 38154317 DOI: 10.1016/j.compbiolchem.2023.108001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/30/2023] [Accepted: 12/03/2023] [Indexed: 12/30/2023]
Abstract
The interaction of multiple drugs could lead to severe events, which cause medical injuries and expenses. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. However, there exist two issues worthy of further consideration. (i) The global features of drug molecules should be paid attention to, rather than just their local characteristics. (ii) The fusion of multi-source features should also be studied to capture the comprehensive features of the drug. This study designs a Multi-Source Feature Fusion framework with Multiple Attention blocks named MSFF-MA-DDI that utilizes multimodal data for DDI event prediction. MSFF-MA-DDI can (i) encode global correlations between long-distance atoms in drug molecular sequences by a self-attention layer based on a position embedding block and (ii) fuse drug sequence features and heterogeneous features (chemical substructure, target, and enzyme) through a multi-head attention block to better represent the features of drugs. Experiments on real-world datasets show that MSFF-MA-DDI can achieve performance that is close to or even better than state-of-the-art models. Especially in cold start scenarios, the model can achieve the best performance. The effectiveness of the model is also supported by the case study on nervous system drugs. The source codes and data are available at https://github.com/BioCenter-SHU/MSFF-MA-DDI.
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Affiliation(s)
- Qi Jin
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Jiang Xie
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
| | - Dingkai Huang
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Chang Zhao
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Hongjian He
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
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Tang C, Fu S, Jin X, Li W, Xing F, Duan B, Cheng X, Chen X, Wang S, Zhu C, Li G, Chuai G, He Y, Wang P, Liu Q. Personalized tumor combination therapy optimization using the single-cell transcriptome. Genome Med 2023; 15:105. [PMID: 38041202 PMCID: PMC10691165 DOI: 10.1186/s13073-023-01256-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 11/13/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment. METHODS Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization. RESULTS We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial data, perturbation of patient-derived cell line data, and finally in-vivo samples. CONCLUSIONS Overall, comboSC provides a feasible and one-stop computational prototype and a proof-of-concept study to predict potential drug combinations for further experimental validation and clinical usage using the single-cell transcriptome, which will facilitate and accelerate personalized tumor treatment by reducing screening time from a large drug combination space and saving valuable treatment time for individual patients. A user-friendly web server of comboSC for both clinical and research users is available at www.combosc.top . The source code is also available on GitHub at https://github.com/bm2-lab/comboSC .
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Affiliation(s)
- Chen Tang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Xuan Jin
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Wannian Li
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Bin Duan
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Xiaojie Cheng
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Xiaohan Chen
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Shuguang Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Chenyu Zhu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Gaoyang Li
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Guohui Chuai
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Yayi He
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, China.
| | - Ping Wang
- Tongji University Cancer Center, Shanghai Tenth People's Hospital of Tongji University, Tongji University, Shanghai, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, China.
- Tongji University Cancer Center, Shanghai Tenth People's Hospital of Tongji University, Tongji University, Shanghai, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
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Lin S, Mao X, Hong L, Lin S, Wei DQ, Xiong Y. MATT-DDI: Predicting multi-type drug-drug interactions via heterogeneous attention mechanisms. Methods 2023; 220:1-10. [PMID: 37858611 DOI: 10.1016/j.ymeth.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/13/2023] [Accepted: 10/17/2023] [Indexed: 10/21/2023] Open
Abstract
The joint use of multiple drugs can result in adverse drug-drug interactions (DDIs) and side effects that harm the body. Accurate identification of DDIs is crucial for avoiding accidental drug side effects and understanding potential mechanisms underlying DDIs. Several computational methods have been proposed for multi-type DDI prediction, but most rely on the similarity profiles of drugs as the drug feature vectors, which may result in information leakage and overoptimistic performance when predicting interactions between new drugs. To address this issue, we propose a novel method, MATT-DDI, for predicting multi-type DDIs based on the original feature vectors of drugs and multiple attention mechanisms. MATT-DDI consists of three main modules: the top k most similar drug pair selection module, heterogeneous attention mechanism module and multi‑type DDI prediction module. Firstly, based on the feature vector of the input drug pair (IDP), k drug pairs that are most similar to the input drug pair from the training dataset are selected according to cosine similarity between drug pairs. Then, the vectors of k selected drug pairs are averaged to obtain a new drug pair (NDP). Next, IDP and NDP are fed into heterogeneous attention modules, including scaled dot product attention and bilinear attention, to extract latent feature vectors. Finally, these latent feature vectors are taken as input of the classification module to predict DDI types. We evaluated MATT-DDI on three different tasks. The experimental results show that MATT-DDI provides better or comparable performance compared to several state-of-the-art methods, and its feasibility is supported by case studies. MATT-DDI is a robust model for predicting multi-type DDIs with excellent performance and no information leakage.
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Affiliation(s)
- Shenggeng Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xueying Mao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liang Hong
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China; School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shuangjun Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Zhongjing Research and Industrialization Institute of Chinese Medicine, Nanyang 473006, China; Peng Cheng National Laboratory, Shenzhen 518055, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China.
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Chen D, Wang X, Zhu H, Jiang Y, Li Y, Liu Q, Liu Q. Predicting anticancer synergistic drug combinations based on multi-task learning. BMC Bioinformatics 2023; 24:448. [PMID: 38012551 PMCID: PMC10680313 DOI: 10.1186/s12859-023-05524-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 10/09/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND The discovery of anticancer drug combinations is a crucial work of anticancer treatment. In recent years, pre-screening drug combinations with synergistic effects in a large-scale search space adopting computational methods, especially deep learning methods, is increasingly popular with researchers. Although achievements have been made to predict anticancer synergistic drug combinations based on deep learning, the application of multi-task learning in this field is relatively rare. The successful practice of multi-task learning in various fields shows that it can effectively learn multiple tasks jointly and improve the performance of all the tasks. METHODS In this paper, we propose MTLSynergy which is based on multi-task learning and deep neural networks to predict synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment, which are synergy prediction of drug combinations and sensitivity prediction of monotherapy. And MTLSynergy integrates the classification and regression of prediction tasks into the same model. Moreover, autoencoders are employed to reduce the dimensions of input features. RESULTS Compared with the previous methods listed in this paper, MTLSynergy achieves the lowest mean square error of 216.47 and the highest Pearson correlation coefficient of 0.76 on the drug synergy prediction task. On the corresponding classification task, the area under the receiver operator characteristics curve and the area under the precision-recall curve are 0.90 and 0.62, respectively, which are equivalent to the comparison methods. Through the ablation study, we verify that multi-task learning and autoencoder both have a positive effect on prediction performance. In addition, the prediction results of MTLSynergy in many cases are also consistent with previous studies. CONCLUSION Our study suggests that multi-task learning is significantly beneficial for both drug synergy prediction and monotherapy sensitivity prediction when combining these two tasks into one model. The ability of MTLSynergy to discover new anticancer synergistic drug combinations noteworthily outperforms other state-of-the-art methods. MTLSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.
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Affiliation(s)
- Danyi Chen
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Xiaowen Wang
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Hongming Zhu
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Yizhi Jiang
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Yulong Li
- School of Software Engineering, Tongji University, Shanghai, 201804, China
| | - Qi Liu
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Qin Liu
- School of Software Engineering, Tongji University, Shanghai, 201804, China.
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12
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Pan Y, Ren H, Lan L, Li Y, Huang T. Review of Predicting Synergistic Drug Combinations. Life (Basel) 2023; 13:1878. [PMID: 37763281 PMCID: PMC10533134 DOI: 10.3390/life13091878] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 08/31/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
The prediction of drug combinations is of great clinical significance. In many diseases, such as high blood pressure, diabetes, and stomach ulcers, the simultaneous use of two or more drugs has shown clear efficacy. It has greatly reduced the progression of drug resistance. This review presents the latest applications of methods for predicting the effects of drug combinations and the bioactivity databases commonly used in drug combination prediction. These studies have played a significant role in developing precision therapy. We first describe the concept of synergy. we study various publicly available databases for drug combination prediction tasks. Next, we introduce five algorithms applied to drug combinatorial prediction, which include traditional machine learning methods, deep learning methods, mathematical methods, systems biology methods and search algorithms. In the end, we sum up the difficulties encountered in prediction models.
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Affiliation(s)
- Yichen Pan
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
| | - Haotian Ren
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
| | - Liang Lan
- Department of Interactive Media, Hong Kong Baptist University, Hong Kong, China;
| | - Yixue Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- Guangzhou Laboratory, Guangzhou 510005, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
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13
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Wu S, Daston G, Rose J, Blackburn K, Fisher J, Reis A, Selman B, Naciff J. Identifying chemicals based on receptor binding/bioactivation/mechanistic explanation associated with potential to elicit hepatotoxicity and to support structure activity relationship-based read-across. Curr Res Toxicol 2023; 5:100108. [PMID: 37363741 PMCID: PMC10285556 DOI: 10.1016/j.crtox.2023.100108] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/02/2023] [Accepted: 06/06/2023] [Indexed: 06/28/2023] Open
Abstract
The liver is the most common target organ in toxicology studies. The development of chemical structural alerts for identifying hepatotoxicity will play an important role in in silico model prediction and help strengthen the identification of analogs used in structure activity relationship (SAR)- based read-across. The aim of the current study is development of an SAR-based expert-system decision tree for screening of hepatotoxicants across a wide range of chemistry space and proposed modes of action for clustering of chemicals using defined core chemical categories based on receptor-binding or bioactivation. The decision tree is based on ∼ 1180 different chemicals that were reviewed for hepatotoxicity information. Knowledge of chemical receptor binding, metabolism and mechanistic information were used to group these chemicals into 16 different categories and 102 subcategories: four categories describe binders to 9 different receptors, 11 categories are associated with possible reactive metabolites (RMs) and there is one miscellaneous category. Each chemical subcategory has been associated with possible modes of action (MOAs) or similar key structural features. This decision tree can help to screen potential liver toxicants associated with core structural alerts of receptor binding and/or RMs and be used as a component of weight of evidence decisions based on SAR read-across, and to fill data gaps.
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Hoch M, Smita S, Cesnulevicius K, Schultz M, Lescheid D, Wolkenhauer O, Gupta S. Network analyses reveal new insights into the effect of multicomponent Tr14 compared to single-component diclofenac in an acute inflammation model. J Inflamm (Lond) 2023; 20:12. [PMID: 36973809 PMCID: PMC10044762 DOI: 10.1186/s12950-023-00335-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 02/21/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Modifying the acute inflammatory response has wide clinical benefits. Current options include non-steroidal anti-inflammatory drugs (NSAIDs) and therapies that may resolve inflammation. Acute inflammation involves multiple cell types and various processes. We, therefore, investigated whether an immunomodulatory drug that acts simultaneously at multiple sites shows greater potential to resolve acute inflammation more effectively and with fewer side effects than a common anti-inflammatory drug developed as a small molecule for a single target. In this work, we used time-series gene expression profiles from a wound healing mouse model to compare the effects of Traumeel (Tr14), a multicomponent natural product, to diclofenac, a single component NSAID on inflammation resolution. RESULTS We advance previous studies by mapping the data onto the "Atlas of Inflammation Resolution", followed by in silico simulations and network analysis. We found that Tr14 acts primarily on the late phase of acute inflammation (during resolution) compared to diclofenac, which suppresses acute inflammation immediately after injury. CONCLUSIONS Our results provide new insights how network pharmacology of multicomponent drugs may support inflammation resolution in inflammatory conditions.
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Affiliation(s)
- Matti Hoch
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany
| | - Suchi Smita
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany
| | | | | | | | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, 85354, Germany
- Stellenbosch Institute of Advanced Study, Wallenberg Research Centre, Stellenbosch University, Stellenbosch, 7602, South Africa
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany.
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15
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Babaei M, Evers TMJ, Shokri F, Altucci L, de Lange ECM, Mashaghi A. Biochemical reaction network topology defines dose-dependent Drug-Drug interactions. Comput Biol Med 2023; 155:106584. [PMID: 36805215 DOI: 10.1016/j.compbiomed.2023.106584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/28/2022] [Accepted: 01/22/2023] [Indexed: 01/29/2023]
Abstract
Drug combination therapy is a promising strategy to enhance the desired therapeutic effect, while reducing side effects. High-throughput pairwise drug combination screening is a commonly used method for discovering favorable drug interactions, but is time-consuming and costly. Here, we investigate the use of reaction network topology-guided design of combination therapy as a predictive in silico drug-drug interaction screening approach. We focused on three-node enzymatic networks, with general Michaelis-Menten kinetics. The results revealed that drug-drug interactions critically depend on the choice of target arrangement in a given topology, the nature of the drug, and the desired level of change in the network output. The results showed a negative correlation between antagonistic interactions and the dosage of drugs. Overall, the negative feedback loops showed the highest synergistic interactions (the lowest average combination index) and, intriguingly, required the highest drug doses compared to other topologies under the same condition.
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Affiliation(s)
- Mehrad Babaei
- Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands; Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy.
| | - Tom M J Evers
- Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands.
| | - Fereshteh Shokri
- Leiden University Medical Center, Leiden, 2333ZA, the Netherlands.
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy; BIOGEM, Molecular Biology and Genetics Research Institute, Ariano Irpino, Italy.
| | - Elizabeth C M de Lange
- Predictive Pharmacology, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands.
| | - Alireza Mashaghi
- Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands.
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16
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Comparing the Effects of Encapsulated and Non-Encapsulated Propolis Extracts on Model Lipid Membranes and Lactic Bacteria, with Emphasis on the Synergistic Effects of Its Various Compounds. Molecules 2023; 28:molecules28020712. [PMID: 36677770 PMCID: PMC9865961 DOI: 10.3390/molecules28020712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/30/2022] [Accepted: 01/07/2023] [Indexed: 01/13/2023] Open
Abstract
Propolis is a resinous compound made by bees with well-known biological activity. However, comparisons between encapsulated and non-encapsulated propolis are lacking. Therefore, the antibacterial activity, effect on the phase transition of lipids, and inhibition of UV-induced lipid oxidation of the two forms of propolis were compared. The results showed that non-encapsulated propolis produces quicker effects, thus being better suited when more immediate effects are required (e.g., antibacterial activity). In order to gain an in-depth introspective on these effects, we further studied the synergistic effect of propolis compounds on the integrity of lipid membranes. The knowledge of component synergism is important for the understanding of effective propolis pathways and for the perspective of modes of action of synergism between different polyphenols in various extracts. Thus, five representative molecules, all previously isolated from propolis (chrysin, quercetin, trans-ferulic acid, caffeic acid, (-)-epigallocatechin-3-gallate) were mixed, and their synergistic effects on lipid bilayers were investigated, mainly using DSC. The results showed that some compounds (quercetin, chrysin) exhibit synergism, whereas others (caffeic acid, t-ferulic acid) do not show any such effects. The results also showed that the synergistic effects of mixtures composed from several different compounds are extremely complex to study, and that their prediction requires further modeling approaches.
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17
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Chagas MDS, Medeiros F, dos Santos MT, de Menezes MA, Carvalho-Assef APD, da Silva FAB. An updated gene regulatory network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851. Mem Inst Oswaldo Cruz 2022; 117:e220111. [PMID: 36259790 PMCID: PMC9565603 DOI: 10.1590/0074-02760220111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 09/09/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Healthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behaviour and provide novel ways to identify potential therapeutic targets and develop new drugs. Gene regulatory networks (GRN) are examples of in silico representation of interaction between regulatory genes and their targets. OBJECTIVES In this work, we update the MDR P. aeruginosa CCBH4851 GRN reconstruction and analyse and discuss its structural properties. METHODS We based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process. FINDINGS Our result is a GRN with a greater number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa. MAIN CONCLUSIONS Here, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.
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Affiliation(s)
- Márcia da Silva Chagas
- Fundação Oswaldo Cruz-Fiocruz, Programa de Computação Científica, Rio de Janeiro, RJ, Brasil,+ Corresponding authors: /
| | - Fernando Medeiros
- Fundação Oswaldo Cruz-Fiocruz, Instituto Nacional de Infectologia, Laboratório de Pesquisa Clínica em Doenças Febris Agudas, Rio de Janeiro, RJ, Brasil
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18
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Hintzen G, Dulat HJ, Rajkovic E. Engaging innate immunity for targeting the epidermal growth factor receptor: Therapeutic options leveraging innate immunity versus adaptive immunity versus inhibition of signaling. Front Oncol 2022; 12:892212. [PMID: 36185288 PMCID: PMC9518002 DOI: 10.3389/fonc.2022.892212] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 07/28/2022] [Indexed: 12/15/2022] Open
Abstract
The epidermal growth factor receptor (EGFR) is a key player in the normal tissue physiology and the pathology of cancer. Therapeutic approaches have now been developed to target oncogenic genetic aberrations of EGFR, found in a subset of tumors, and to take advantage of overexpression of EGFR in tumors. The development of small-molecule inhibitors and anti-EGFR antibodies targeting EGFR activation have resulted in effective but limited treatment options for patients with mutated or wild-type EGFR-expressing cancers, while therapeutic approaches that deploy effectors of the adaptive or innate immune system are still undergoing development. This review discusses EGFR-targeting therapies acting through distinct molecular mechanisms to destroy EGFR-expressing cancer cells. The focus is on the successes and limitations of therapies targeting the activation of EGFR versus those that exploit the cytotoxic T cells and innate immune cells to target EGFR-expressing cancer cells. Moreover, we discuss alternative approaches that may have the potential to overcome limitations of current therapies; in particular the innate cell engagers are discussed. Furthermore, this review highlights the potential to combine innate cell engagers with immunotherapies, to maximize their effectiveness, or with unspecific cell therapies, to convert them into tumor-specific agents.
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19
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Choi M, Park SM, Cho KH. Evaluating a therapeutic window for precision medicine by integrating genomic profiles and p53 network dynamics. Commun Biol 2022; 5:924. [PMID: 36071176 PMCID: PMC9452682 DOI: 10.1038/s42003-022-03872-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 08/23/2022] [Indexed: 11/08/2022] Open
Abstract
The response variation to anti-cancer drugs originates from complex intracellular network dynamics of cancer. Such dynamic networks present challenges to determining optimal drug targets and stratifying cancer patients for precision medicine, although several cancer genome studies provided insights into the molecular characteristics of cancer. Here, we introduce a network dynamics-based approach based on attractor landscape analysis to evaluate the therapeutic window of a drug from cancer signaling networks combined with genomic profiles. This approach allows for effective screening of drug targets to explore potential target combinations for enhancing the therapeutic window of drug responses. We also effectively stratify patients into desired/undesired response groups using critical genomic determinants, which are network-specific origins of variability to drug response, and their dominance relationship. Our methods provide a viable and quantitative framework to connect genotype information to the phenotypes of drug response with regard to network dynamics determining the therapeutic window.
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Affiliation(s)
- Minsoo Choi
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Sang-Min Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- College of Pharmacy, Chungnam National University, Daejeon, 34134, Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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20
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Lombardi F, Augello FR, Artone S, Ayroldi E, Giusti I, Dolo V, Cifone MG, Cinque B, Palumbo P. Cyclooxygenase-2 Upregulated by Temozolomide in Glioblastoma Cells Is Shuttled In Extracellular Vesicles Modifying Recipient Cell Phenotype. Front Oncol 2022; 12:933746. [PMID: 35936755 PMCID: PMC9355724 DOI: 10.3389/fonc.2022.933746] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 06/24/2022] [Indexed: 11/13/2022] Open
Abstract
Temozolomide (TMZ) resistance is frequent in patients with glioblastoma (GBM), a tumor characterized by a marked inflammatory microenvironment. Recently, we reported that cyclooxygenase-2 (COX-2) is upregulated in TMZ-resistant GBM cells treated with high TMZ concentrations. Moreover, COX-2 activity inhibition significantly counteracted TMZ-resistance of GBM cells. Extracellular vesicles (EV) are considered crucial mediators in orchestrating GBM drug resistance by modulating the tumor microenvironment (TME) and affecting the surrounding recipient cell phenotype and behavior. This work aimed to verify whether TMZ, at low and clinically relevant doses (5-20 µM), could induce COX-2 overexpression in GBM cells (T98G and U87MG) and explore if secreted EV shuttled COX-2 to recipient cells. The effect of COX-2 inhibitors (COXIB), Celecoxib (CXB), or NS398, alone or TMZ-combined, was also investigated. Our results indicated that TMZ at clinically relevant doses upregulated COX-2 in GBM cells. COXIB treatment significantly counteracted TMZ-induced COX-2 expression, confirming the crucial role of the COX-2/PGE2 system in TMZ-resistance. The COXIB specificity was verified on U251MG, COX-2 null GBM cells. Western blotting of GBM-EV cells showed the COX-2 presence, with the same intracellular trend, increasing in EV derived from TMZ-treated cells and decreasing in those derived from COXIB+TMZ-treated cells. We then evaluated the effect of EV secreted by TMZ-treated cells on U937 and U251MG, used as recipient cells. In human macrophage cell line U937, the internalization of EV derived by TMZ-T98G cells led to a shift versus a pro-tumor M2-like phenotype. On the other hand, EV from TMZ-T98G induced a significant decrease in TMZ sensitivity in U251MG cells. Overall, our results, in confirming the crucial role played by COX-2 in TMZ-resistance, provide the first evidence of the presence and effective functional transfer of this enzyme through EV derived from GBM cells, with multiple potential consequences at the level of TME.
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Affiliation(s)
- Francesca Lombardi
- Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy
| | | | - Serena Artone
- Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy
| | - Emira Ayroldi
- Department of Medicine and Surgery, Section of Pharmacology, University of Perugia, Perugia, Italy
| | - Ilaria Giusti
- Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy
| | - Vincenza Dolo
- Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy
| | - Maria Grazia Cifone
- Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy
| | - Benedetta Cinque
- Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy
- *Correspondence: Benedetta Cinque, ; Paola Palumbo,
| | - Paola Palumbo
- Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy
- *Correspondence: Benedetta Cinque, ; Paola Palumbo,
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Liu Q, Yao E, Liu C, Zhou X, Li Y, Xu M. M2GCN: multi-modal graph convolutional network for modeling polypharmacy side effects. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03839-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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22
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Sun M, She S, Chen H, Cheng J, Ji W, Wang D, Feng C. Prediction Model for Synergistic Anti-tumor Multi-compound Combinations from Traditional Chinese Medicine based on Extreme Gradient Boosting, Targets and Gene Expression Data. J Bioinform Comput Biol 2022; 20:2250016. [DOI: 10.1142/s0219720022500160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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23
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Rani P, Dutta K, Kumar V. Artificial intelligence techniques for prediction of drug synergy in malignant diseases: Past, present, and future. Comput Biol Med 2022; 144:105334. [DOI: 10.1016/j.compbiomed.2022.105334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/13/2022] [Accepted: 02/13/2022] [Indexed: 12/22/2022]
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24
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Lv J, Liu G, Dong W, Ju Y, Sun Y. ACDB: An Antibiotic Combination DataBase. Front Pharmacol 2022; 13:869983. [PMID: 35370670 PMCID: PMC8971807 DOI: 10.3389/fphar.2022.869983] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 02/28/2022] [Indexed: 01/22/2023] Open
Affiliation(s)
- Ji Lv
- College of Computer Science and Technology, Jilin University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Guixia Liu
- College of Computer Science and Technology, Jilin University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
- *Correspondence: Guixia Liu,
| | - Wenxuan Dong
- College of Computer Science, Sichuan University, Chengdu, China
| | - Yuan Ju
- Sichuan University Library, Sichuan University, Chengdu, China
| | - Ying Sun
- Department of Respiratory Medicine, The First Hospital of Jilin University, Changchun, China
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Lakizadeh A, Babaei M. Detection of polypharmacy side effects by integrating multiple data sources and convolutional neural networks. Mol Divers 2022; 26:3193-3203. [PMID: 35072838 DOI: 10.1007/s11030-022-10382-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 01/11/2022] [Indexed: 11/30/2022]
Abstract
The consumption of drug combinations, named polypharmacy, is commonly used for treating patients with several diseases or those with complex conditions. However, the main drawback of polypharmacy is the increased probability of harmful side effects. The polypharmacy side effects are caused by an interaction between two medications. It means that the drug-drug interaction causes changes in their activities due to interfering in each other's performance. Therefore, discovering these side effects is one of the most challenging and important aspects of drug production and consumption as it is associated with human health. In this paper, a method has been introduced for predicting the polypharmacy side effects, called PSECNN. It is a multi-label multi-class deep learning method that combines various basic features of drugs to predict the polypharmacy side effects. Firstly, PSECNN collects five basic features of drugs, such as individual drug's side effects, drug-protein interactions, chemical substructures, targets, and enzymes in order to create a novel combination of drug features. A feature extraction module creates five feature vectors with the same dimension for each drug based on the Jaccard similarity index. Based on the feature vectors, a unique representative is then created for each drug. These representative vectors are given in pairs as input to the deep neural network to predict the occurrence probability of side effects. According to the experimental evaluations, PSECNN could outperform the state-of-the-art polypharmacy side effects prediction methods up to 74%. It has been found that PSECNN has better performance with polypharmacy side effects with a cause of molecular basis due to the novel combination of basic drug features.
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Affiliation(s)
- Amir Lakizadeh
- Computer Engineering Department, University of Qom, Qom, Iran.
| | - Mahdi Babaei
- Computer Engineering Department, University of Qom, Qom, Iran
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26
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Song F, Tan S, Dou Z, Liu X, Ma X. Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks. BMC Bioinformatics 2022; 23:34. [PMID: 35016602 PMCID: PMC8753820 DOI: 10.1186/s12859-022-04567-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 01/03/2022] [Indexed: 02/02/2023] Open
Abstract
Background Drug combination, offering an insight into the increased therapeutic efficacy and reduced toxicity, plays an essential role in the therapy of many complex diseases. Although significant efforts have been devoted to the identification of drugs, the identification of drug combination is still a challenge. The current algorithms assume that the independence of feature selection and drug prediction procedures, which may result in an undesirable performance. Results To address this issue, we develop a novel Semi-supervised Heterogeneous Network Embedding algorithm (called SeHNE) to predict the combination patterns of drugs by exploiting the graph embedding. Specifically, the ATC similarity of drugs, drug–target, and protein–protein interaction networks are integrated to construct the heterogeneous networks. Then, SeHNE jointly learns drug features by exploiting the topological structure of heterogeneous networks and predicting drug combination. One distinct advantage of SeHNE is that features of drugs are extracted under the guidance of classification, which improves the quality of features, thereby enhancing the performance of prediction of drugs. Experimental results demonstrate that the proposed algorithm is more accurate than state-of-the-art methods on various data, implying that the joint learning is promising for the identification of drug combination. Conclusions The proposed model and algorithm provide an effective strategy for the prediction of combinatorial patterns of drugs, implying that the graph-based drug prediction is promising for the discovery of drugs.
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Radiosensitizing effect of galunisertib, a TGF-ß receptor I inhibitor, on head and neck squamous cell carcinoma in vitro. Invest New Drugs 2022; 40:478-486. [PMID: 34985593 PMCID: PMC9098568 DOI: 10.1007/s10637-021-01207-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 12/24/2021] [Indexed: 12/24/2022]
Abstract
Background. Resistance to radiation therapy poses a major clinical problem for patients suffering from head and neck squamous cell carcinoma (HNSCC). Transforming growth factor ß (TGF-ß) has emerged as a potential target. This study aimed to investigate the radiosensitizing effect of galunisertib, a small molecule TGF-ß receptor kinase I inhibitor, on HNSCC cells in vitro. Methods. Three HNSCC cell lines were treated with galunisertib alone, or in combination with radiation. Of those three cell lines, one has a known inactivating mutation of the TGF-ß pathway (Cal27), one has a TGF-ß pathway deficiency (FaDu) and one has no known alteration (SCC-25). The effect on metabolic activity was evaluated by a resazurin-based reduction assay. Cell migration was evaluated by wound-healing assay, clonogenic survival by colony formation assay and cell cycle by FACS analysis. Results. Galunisertib reduced metabolic activity in FaDu, increased in SCC-25 and had no effect on CAL27. Migration was significantly reduced by galunisertib in all three cell lines and showed additive effects in combination with radiation in CAL27 and SCC-25. Colony-forming capabilities were reduced in SCC-25 by galunisertib and also showed an additive effect with adjuvant radiation treatment. Cell cycle analysis showed a reduction of cells in G1 phase in response to galunisertib treatment. Conclusion. Our results indicate a potential antineoplastic effect of galunisertib in HNSCC with intact TGF-ß signaling in combination with radiation.
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Repositioning of Fungal-based Peptides as Modulators of Angiotensin-converting Enzyme-related Carboxypeptidase, SARS-coronavirus HR2 Domain, and Coronavirus Disease 2019 Main Protease. J Transl Int Med 2021; 9:190-199. [PMID: 34900630 PMCID: PMC8629419 DOI: 10.2478/jtim-2021-0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background and Objectives Angiotensin-converting enzyme-related carboxypeptidase, SARS-Coronavirus HR2 Domain, and COVID-19 main protease are essential for the cellular entry and replication of coronavirus in the host. This study investigated the putative inhibitory action of peptides form medicinal mushrooms, namely Pseudoplectania nigrella, Russula paludosa, and Clitocybe sinopica, towards selected proteins through computational studies. Materials and Methods The respective physicochemical properties of selected peptides were predicted using ProtParam tool, while the binding modes and binding free energy of selected peptides toward proteins were computed through HawkDock server. The structural flexibility and stability of docked protein-peptide complexes were assessed through iMODS server. Results The peptides showed an optimum binding afinity with the molecular targets; plectasin from P. nigrella showed the highest binding free energy compared to peptides from R. paludosa and C. sinopica. Besides, molecular dynamic simulations showed all fungal-based peptides could influence the flexibility and stability of selected proteins. Conclusion The study revealed fungal-based peptides could be explored as functional modulators of essential proteins that are involved in the cellular entry of coronavirus.
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Khorsandi SE, Dokal AD, Rajeeve V, Britton DJ, Illingworth MS, Heaton N, Cutillas PR. Computational Analysis of Cholangiocarcinoma Phosphoproteomes Identifies Patient-Specific Drug Targets. Cancer Res 2021; 81:5765-5776. [PMID: 34551960 PMCID: PMC9397618 DOI: 10.1158/0008-5472.can-21-0955] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 08/11/2021] [Accepted: 09/20/2021] [Indexed: 01/07/2023]
Abstract
Cholangiocarcinoma is a form of hepatobiliary cancer with an abysmal prognosis. Despite advances in our understanding of cholangiocarcinoma pathophysiology and its genomic landscape, targeted therapies have not yet made a significant impact on its clinical management. The low response rates of targeted therapies in cholangiocarcinoma suggest that patient heterogeneity contributes to poor clinical outcome. Here we used mass spectrometry-based phosphoproteomics and computational methods to identify patient-specific drug targets in patient tumors and cholangiocarcinoma-derived cell lines. We analyzed 13 primary tumors of patients with cholangiocarcinoma with matched nonmalignant tissue and 7 different cholangiocarcinoma cell lines, leading to the identification and quantification of more than 13,000 phosphorylation sites. The phosphoproteomes of cholangiocarcinoma cell lines and patient tumors were significantly correlated. MEK1, KIT, ERK1/2, and several cyclin-dependent kinases were among the protein kinases most frequently showing increased activity in cholangiocarcinoma relative to nonmalignant tissue. Application of the Drug Ranking Using Machine Learning (DRUML) algorithm selected inhibitors of histone deacetylase (HDAC; belinostat and CAY10603) and PI3K pathway members as high-ranking therapies to use in primary cholangiocarcinoma. The accuracy of the computational drug rankings based on predicted responses was confirmed in cell-line models of cholangiocarcinoma. Together, this study uncovers frequently activated biochemical pathways in cholangiocarcinoma and provides a proof of concept for the application of computational methodology to rank drugs based on efficacy in individual patients. SIGNIFICANCE: Phosphoproteomic and computational analyses identify patient-specific drug targets in cholangiocarcinoma, supporting the potential of a machine learning method to predict personalized therapies.
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Affiliation(s)
- Shirin Elizabeth Khorsandi
- Institute of Liver Studies, Kings College Hospital, London, United Kingdom.,The Roger Williams Institute of Hepatology, Foundation for Liver Research, London, United Kingdom.,Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom.,Corresponding Authors: Pedro R. Cutillas, Cell Signaling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, United Kingdom. Phone: 207-882-5555; E-mail: ; and Shirin Elizabeth Khorsandi, The Roger Williams Institute of Hepatology, 111 Coldharbor Lane, London SE5 9NT, United Kingdom. E-mail:
| | - Arran D. Dokal
- Cell Signaling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.,Mass Spectrometry Laboratory, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.,Kinomica Ltd, Cheshire, United Kingdom
| | - Vinothini Rajeeve
- Cell Signaling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.,Mass Spectrometry Laboratory, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - David J. Britton
- Cell Signaling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.,Mass Spectrometry Laboratory, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.,Kinomica Ltd, Cheshire, United Kingdom
| | - Megan S. Illingworth
- The Roger Williams Institute of Hepatology, Foundation for Liver Research, London, United Kingdom.,Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom
| | - Nigel Heaton
- Institute of Liver Studies, Kings College Hospital, London, United Kingdom
| | - Pedro R. Cutillas
- Cell Signaling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.,Mass Spectrometry Laboratory, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.,Kinomica Ltd, Cheshire, United Kingdom.,The Alan Turing Institute, The British Library, London, United Kingdom.,Corresponding Authors: Pedro R. Cutillas, Cell Signaling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, United Kingdom. Phone: 207-882-5555; E-mail: ; and Shirin Elizabeth Khorsandi, The Roger Williams Institute of Hepatology, 111 Coldharbor Lane, London SE5 9NT, United Kingdom. E-mail:
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Kpanou R, Osseni MA, Tossou P, Laviolette F, Corbeil J. On the robustness of generalization of drug-drug interaction models. BMC Bioinformatics 2021; 22:477. [PMID: 34607569 PMCID: PMC8489092 DOI: 10.1186/s12859-021-04398-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Deep learning methods are a proven commodity in many fields and endeavors. One of these endeavors is predicting the presence of adverse drug-drug interactions (DDIs). The models generated can predict, with reasonable accuracy, the phenotypes arising from the drug interactions using their molecular structures. Nevertheless, this task requires improvement to be truly useful. Given the complexity of the predictive task, an extensive benchmarking on structure-based models for DDIs prediction was performed to evaluate their drawbacks and advantages. RESULTS We rigorously tested various structure-based models that predict drug interactions using different splitting strategies to simulate different real-world scenarios. In addition to the effects of different training and testing setups on the robustness and generalizability of the models, we then explore the contribution of traditional approaches such as multitask learning and data augmentation. CONCLUSION Structure-based models tend to generalize poorly to unseen drugs despite their ability to identify new DDIs among drugs seen during training accurately. Indeed, they efficiently propagate information between known drugs and could be valuable for discovering new DDIs in a database. However, these models will most probably fail when exposed to unknown drugs. While multitask learning does not help in our case to solve the problem, the use of data augmentation does at least mitigate it. Therefore, researchers must be cautious of the bias of the random evaluation scheme, especially if their goal is to discover new DDIs.
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Affiliation(s)
- Rogia Kpanou
- Computer Science and Software Engineering, Université Laval, 1065, av. de la Médecine, Quebec, CA Canada
- InVivo AI, Mila - 180 Corporate Lab L, 6650, 01 Rue Saint-Urbain, Montreal, CA H2S 3G9 Canada
| | - Mazid Abiodoun Osseni
- Computer Science and Software Engineering, Université Laval, 1065, av. de la Médecine, Quebec, CA Canada
| | - Prudencio Tossou
- Computer Science and Software Engineering, Université Laval, 1065, av. de la Médecine, Quebec, CA Canada
- InVivo AI, Mila - 180 Corporate Lab L, 6650, 01 Rue Saint-Urbain, Montreal, CA H2S 3G9 Canada
| | - Francois Laviolette
- Computer Science and Software Engineering, Université Laval, 1065, av. de la Médecine, Quebec, CA Canada
| | - Jacques Corbeil
- Department of Molecular Medicine, Université Laval, 1065, av. de la Médecine, Quebec, CA Canada
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Uehling DE, Joseph B, Chung KC, Zhang AX, Ler S, Prakesch MA, Poda G, Grouleff J, Aman A, Kiyota T, Leung-Hagesteijn C, Konda JD, Marcellus R, Griffin C, Subramaniam R, Abibi A, Strathdee CA, Isaac MB, Al-Awar R, Tiedemann RE. Design, Synthesis, and Characterization of 4-Aminoquinazolines as Potent Inhibitors of the G Protein-Coupled Receptor Kinase 6 (GRK6) for the Treatment of Multiple Myeloma. J Med Chem 2021; 64:11129-11147. [PMID: 34291633 DOI: 10.1021/acs.jmedchem.1c00506] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Both previous and additional genetic knockdown studies reported herein implicate G protein-coupled receptor kinase 6 (GRK6) as a critical kinase required for the survival of multiple myeloma (MM) cells. Therefore, we sought to develop a small molecule GRK6 inhibitor as an MM therapeutic. From a focused library of known kinase inhibitors, we identified two hits with moderate biochemical potencies against GRK6. From these hits, we developed potent (IC50 < 10 nM) analogues with selectivity against off-target kinases. Further optimization led to the discovery of an analogue (18) with an IC50 value of 6 nM against GRK6 and selectivity against a panel of 85 kinases. Compound 18 has potent cellular target engagement and antiproliferative activity against MM cells and is synergistic with bortezomib. In summary, we demonstrate that targeting GRK6 with small molecule inhibitors represents a promising approach for MM and identify 18 as a novel, potent, and selective GRK6 inhibitor.
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Affiliation(s)
- David E Uehling
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Babu Joseph
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Kim Chan Chung
- Princess Margaret Cancer Centre, Toronto Medical Discovery Tower, 101 College Street, Room 12-306, Toronto, Ontario M5G 1L7, Canada
| | - Andrew X Zhang
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Spencer Ler
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Michael A Prakesch
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Gennady Poda
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada.,Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College Street, Toronto, Ontario M5S 3M2, Canada
| | - Julie Grouleff
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Ahmed Aman
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada.,Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College Street, Toronto, Ontario M5S 3M2, Canada
| | - Taira Kiyota
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Chungyee Leung-Hagesteijn
- Princess Margaret Cancer Centre, Toronto Medical Discovery Tower, 101 College Street, Room 12-306, Toronto, Ontario M5G 1L7, Canada
| | - John David Konda
- Princess Margaret Cancer Centre, Toronto Medical Discovery Tower, 101 College Street, Room 12-306, Toronto, Ontario M5G 1L7, Canada
| | - Richard Marcellus
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Carly Griffin
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Ratheesh Subramaniam
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Ayome Abibi
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Craig A Strathdee
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Methvin B Isaac
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Rima Al-Awar
- Drug Discovery Program, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario M5G 0A3, Canada.,Department of Pharmacology and Toxicology, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, Ontario M5S 1A8, Canada
| | - Rodger E Tiedemann
- Princess Margaret Cancer Centre, Toronto Medical Discovery Tower, 101 College Street, Room 12-306, Toronto, Ontario M5G 1L7, Canada
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Datta A, Flynn NR, Barnette DA, Woeltje KF, Miller GP, Swamidass SJ. Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort. PLoS Comput Biol 2021; 17:e1009053. [PMID: 34228716 PMCID: PMC8284671 DOI: 10.1371/journal.pcbi.1009053] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 07/16/2021] [Accepted: 05/08/2021] [Indexed: 01/14/2023] Open
Abstract
Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations' data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).
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Affiliation(s)
- Arghya Datta
- Department of Computer Science and Engineering, Washington University in Saint Louis, Saint Louis, Missouri, United States of America
| | - Noah R. Flynn
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Dustyn A. Barnette
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Keith F. Woeltje
- Department of Internal Medicine, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Center for Clinical Excellence at BJC HealthCare, Saint Louis, Missouri, United States of America
| | - Grover P. Miller
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - S. Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- * E-mail:
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Behl T, Kaur I, Sehgal A, Singh S, Bhatia S, Al-Harrasi A, Zengin G, Babes EE, Brisc C, Stoicescu M, Toma MM, Sava C, Bungau SG. Bioinformatics Accelerates the Major Tetrad: A Real Boost for the Pharmaceutical Industry. Int J Mol Sci 2021; 22:6184. [PMID: 34201152 PMCID: PMC8227524 DOI: 10.3390/ijms22126184] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 06/03/2021] [Accepted: 06/05/2021] [Indexed: 02/01/2023] Open
Abstract
With advanced technology and its development, bioinformatics is one of the avant-garde fields that has managed to make amazing progress in the pharmaceutical-medical field by modeling the infrastructural dimensions of healthcare and integrating computing tools in drug innovation, facilitating prevention, detection/more accurate diagnosis, and treatment of disorders, while saving time and money. By association, bioinformatics and pharmacovigilance promoted both sample analyzes and interpretation of drug side effects, also focusing on drug discovery and development (DDD), in which systems biology, a personalized approach, and drug repositioning were considered together with translational medicine. The role of bioinformatics has been highlighted in DDD, proteomics, genetics, modeling, miRNA discovery and assessment, and clinical genome sequencing. The authors have collated significant data from the most known online databases and publishers, also narrowing the diversified applications, in order to target four major areas (tetrad): DDD, anti-microbial research, genomic sequencing, and miRNA research and its significance in the management of current pandemic context. Our analysis aims to provide optimal data in the field by stratification of the information related to the published data in key sectors and to capture the attention of researchers interested in bioinformatics, a field that has succeeded in advancing the healthcare paradigm by introducing developing techniques and multiple database platforms, addressed in the manuscript.
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Affiliation(s)
- Tapan Behl
- Department of Pharmacology, Chitkara College of Pharmacy, Chitkara University, Punjab 140401, India; (I.K.); (A.S.); (S.S.)
| | - Ishnoor Kaur
- Department of Pharmacology, Chitkara College of Pharmacy, Chitkara University, Punjab 140401, India; (I.K.); (A.S.); (S.S.)
| | - Aayush Sehgal
- Department of Pharmacology, Chitkara College of Pharmacy, Chitkara University, Punjab 140401, India; (I.K.); (A.S.); (S.S.)
| | - Sukhbir Singh
- Department of Pharmacology, Chitkara College of Pharmacy, Chitkara University, Punjab 140401, India; (I.K.); (A.S.); (S.S.)
| | - Saurabh Bhatia
- Amity Institute of Pharmacy, Amity University, Gurugram 122413, India;
- Natural & Medical Sciences Research Centre, University of Nizwa, Birkat Al Mauz, Nizwa 616, Oman;
| | - Ahmed Al-Harrasi
- Natural & Medical Sciences Research Centre, University of Nizwa, Birkat Al Mauz, Nizwa 616, Oman;
| | - Gokhan Zengin
- Department of Biology, Faculty of Science, Selcuk University Campus, 42130 Konya, Turkey;
| | - Elena Emilia Babes
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410073 Oradea, Romania; (E.E.B.); (C.B.); (M.S.); (C.S.)
| | - Ciprian Brisc
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410073 Oradea, Romania; (E.E.B.); (C.B.); (M.S.); (C.S.)
| | - Manuela Stoicescu
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410073 Oradea, Romania; (E.E.B.); (C.B.); (M.S.); (C.S.)
| | - Mirela Marioara Toma
- Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, 410028 Oradea, Romania;
- Doctoral School of Biomedical Sciences, University of Oradea, 410087 Oradea, Romania
| | - Cristian Sava
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410073 Oradea, Romania; (E.E.B.); (C.B.); (M.S.); (C.S.)
| | - Simona Gabriela Bungau
- Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, 410028 Oradea, Romania;
- Doctoral School of Biomedical Sciences, University of Oradea, 410087 Oradea, Romania
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Cold-Start Problems in Data-Driven Prediction of Drug-Drug Interaction Effects. Pharmaceuticals (Basel) 2021; 14:ph14050429. [PMID: 34063324 PMCID: PMC8147651 DOI: 10.3390/ph14050429] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 02/02/2023] Open
Abstract
Combining drugs, a phenomenon often referred to as polypharmacy, can induce additional adverse effects. The identification of adverse combinations is a key task in pharmacovigilance. In this context, in silico approaches based on machine learning are promising as they can learn from a limited number of combinations to predict for all. In this work, we identify various subtasks in predicting effects caused by drug–drug interaction. Predicting drug–drug interaction effects for drugs that already exist is very different from predicting outcomes for newly developed drugs, commonly called a cold-start problem. We propose suitable validation schemes for the different subtasks that emerge. These validation schemes are critical to correctly assess the performance. We develop a new model that obtains AUC-ROC =0.843 for the hardest cold-start task up to AUC-ROC =0.957 for the easiest one on the benchmark dataset of Zitnik et al. Finally, we illustrate how our predictions can be used to improve post-market surveillance systems or detect drug–drug interaction effects earlier during drug development.
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35
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Huang H, Chen X. Compromise design for combination experiment of two drugs. Comput Stat Data Anal 2021. [DOI: 10.1016/j.csda.2020.107150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Li S, Zhang F, Xiao X, Guo Y, Wen Z, Li M, Pu X. Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics. Front Pharmacol 2021; 12:634097. [PMID: 33986671 PMCID: PMC8112211 DOI: 10.3389/fphar.2021.634097] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 02/04/2021] [Indexed: 12/26/2022] Open
Abstract
Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.
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Affiliation(s)
- Shiqi Li
- College of Chemistry, Sichuan University, Chengdu, China
| | - Fuhui Zhang
- College of Chemistry, Sichuan University, Chengdu, China
| | - Xiuchan Xiao
- School of Material Science and Environmental Engineering, Chengdu Technological University, Chengdu, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, China
| | - Zhining Wen
- College of Chemistry, Sichuan University, Chengdu, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu, China
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Yuan B, Shen C, Luna A, Korkut A, Marks DS, Ingraham J, Sander C. CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy. Cell Syst 2020; 12:128-140.e4. [PMID: 33373583 DOI: 10.1016/j.cels.2020.11.013] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/13/2020] [Accepted: 11/25/2020] [Indexed: 01/13/2023]
Abstract
Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides informative data resources for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in a complex multidimensional space and mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of cell dynamics with a machine-learning framework, implemented in TensorFlow. We tested the modeling framework on a perturbation-response dataset of a melanoma cell line after drug treatments. The models can be efficiently trained to describe cellular behavior accurately. Even though completely data driven and independent of prior knowledge, the resulting de novo network models recapitulate some known interactions. The approach is readily applicable to various kinetic models of cell biology. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.
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Affiliation(s)
- Bo Yuan
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA; cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA.
| | - Ciyue Shen
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA; cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA.
| | - Augustin Luna
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA; cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA
| | - Anil Korkut
- Department of Bioinformatics & Computational Biology, the University of Texas M D Anderson Cancer Center, Houston, TX, USA
| | - Debora S Marks
- Broad Institute, Cambridge, MA, USA; Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - John Ingraham
- MIT Computer Science & Artificial Intelligence Laboratory, Boston, MA, USA
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA; cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA.
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Cappuccio A, Jensen ST, Hartmann BM, Sealfon SC, Soumelis V, Zaslavsky E. Deciphering the combinatorial landscape of immunity. eLife 2020; 9:e62148. [PMID: 33225996 PMCID: PMC7748411 DOI: 10.7554/elife.62148] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 11/05/2020] [Indexed: 12/23/2022] Open
Abstract
From cellular activation to drug combinations, immunological responses are shaped by the action of multiple stimuli. Synergistic and antagonistic interactions between stimuli play major roles in shaping immune processes. To understand combinatorial regulation, we present the immune Synergistic/Antagonistic Interaction Learner (iSAIL). iSAIL includes a machine learning classifier to map and interpret interactions, a curated compendium of immunological combination treatment datasets, and their global integration into a landscape of ~30,000 interactions. The landscape is mined to reveal combinatorial control of interleukins, checkpoints, and other immune modulators. The resource helps elucidate the modulation of a stimulus by interactions with other cofactors, showing that TNF has strikingly different effects depending on co-stimulators. We discover new functional synergies between TNF and IFNβ controlling dendritic cell-T cell crosstalk. Analysis of laboratory or public combination treatment studies with this user-friendly web-based resource will help resolve the complex role of interaction effects on immune processes.
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Affiliation(s)
- Antonio Cappuccio
- Institut Curie, Integrative Biology of Human Dendritic Cells and T Cells Laboratory, PSL Research University, Inserm, U932ParisFrance
- Department of Neurology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Shane T Jensen
- Department of Statistics, Wharton School, University of PennsylvaniaPhiladelphiaUnited States
| | - Boris M Hartmann
- Department of Neurology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Vassili Soumelis
- Institut Curie, Integrative Biology of Human Dendritic Cells and T Cells Laboratory, PSL Research University, Inserm, U932ParisFrance
- Laboratoire d'immunologie, biologie et histocompatibilité, AP-HP, Hôpital Saint-LouisParisFrance
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
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Gregory JV, Vogus DR, Barajas A, Cadena MA, Mitragotri S, Lahann J. Programmable Delivery of Synergistic Cancer Drug Combinations Using Bicompartmental Nanoparticles. Adv Healthc Mater 2020; 9:e2000564. [PMID: 32959525 DOI: 10.1002/adhm.202000564] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/23/2020] [Indexed: 12/27/2022]
Abstract
Delivery of multiple therapeutics has become a preferred method of treating cancer, albeit differences in the biodistribution and pharmacokinetic profiles of individual drugs pose challenges in effectively delivering synergistic drug combinations to and at the tumor site. Here, bicompartmental Janus nanoparticles comprised of domains are reported with distinct bulk properties that allow for independent drug loading and release. Programmable drug release can be triggered by a change in the pH value and depends upon the bulk properties of the polymers used in the respective compartments, rather than the molecular structures of the active agents. Bicompartmental nanoparticles delivering a synergistic combination of lapatinib and paclitaxel result in increased activity against HER2+ breast cancer cells. Surprisingly, the dual drug loaded particles also show significant efficacy toward triple negative breast cancer, even though this cancer model is unresponsive to lapatinib alone. The broad versatility of the nanoparticle platform allows for rapid exploration of a wide range of drug combinations where both their relative drug ratios and temporal release profiles can be optimized.
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Affiliation(s)
- Jason V. Gregory
- Biointerfaces Institute and Department of Chemical Engineering University of Michigan Ann Arbor MI 48109 USA
| | - Douglas R. Vogus
- John A Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA
| | - Alexandra Barajas
- Department of Chemical Engineering University of California, Santa Barbara Santa Barbara CA 93106 USA
| | - Melissa A. Cadena
- Department of Biomedical Engineering University of Michigan Ann Arbor MI 48109 USA
| | - Samir Mitragotri
- John A Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA
| | - Joerg Lahann
- Biointerfaces Institute and Department of Chemical Engineering University of Michigan Ann Arbor MI 48109 USA
- Department of Biomedical Engineering University of Michigan Ann Arbor MI 48109 USA
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Mansano AS, Moreira RA, Dornfeld HC, Freitas EC, Vieira EM, Daam MA, Rocha O, Seleghim MHR. Individual and mixture toxicity of carbofuran and diuron to the protozoan Paramecium caudatum and the cladoceran Ceriodaphnia silvestrii. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2020; 201:110829. [PMID: 32531577 DOI: 10.1016/j.ecoenv.2020.110829] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 05/23/2020] [Accepted: 05/27/2020] [Indexed: 06/11/2023]
Abstract
The toxicity of the insecticide carbofuran and herbicide diuron (individually and in mixture) to the invertebrates Paramecium caudatum and Ceriodaphnia silvestrii was evaluated. Acute and chronic toxicity tests were carried out with the diuron and carbofuran active ingredients and their commercial products, Diuron Nortox® 500 SC and Furadan® 350 SC, respectively. Individual toxicity tests showed that C. silvestrii was more sensitive to both carbofuran and diuron than P. caudatum. In single exposures, both pesticides caused adverse effects to C. silvestrii in environmentally relevant concentrations (48 h EC50 = 0.001 mg L-1 and 8 d LOEC = 0.00038 mg L-1 for formulated carbofuran; 8 d LOEC < 0.05 mg L-1 for formulated diuron). For P. caudatum, carbofuran and diuron in single exposures were only slightly toxic (24 h IC50 = 5.1 mg L-1 and 6.9 mg L-1 for formulated carbofuran and diuron, respectively). Acute and chronic exposures to diuron and carbofuran mixtures caused significant deviations of the toxicity predicted by the Concentration Addition and Independent Action reference models for both test species. For the protozoan P. caudatum, a dose-dependent deviation was verified for mortality, with synergism caused mainly by carbofuran and antagonism caused mainly by diuron. For protozoan population growth, however, an antagonistic deviation was observed when the active ingredient mixtures were tested. In the case of C. silvestrii, antagonism at low concentrations and synergism at high concentrations were revealed after acute exposure to active ingredient mixtures, whereas for reproduction an antagonistic deviation was found. Commercial formulation mixtures presented significantly higher toxicity than the active ingredient mixtures. Our results showed that carbofuran and diuron interact and cause different toxic responses than those predicted by the individually tested compounds. Their mixture toxicity should therefore be considered in risk assessments as these pesticides are likely to be present simultaneously in edge-of-field waterbodies.
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Affiliation(s)
- Adrislaine S Mansano
- Department of Ecology and Evolutionary Biology, Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil; Post-Graduate Program in Ecology and Natural Resources (PPGERN), Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil.
| | - Raquel A Moreira
- Department of Ecology and Evolutionary Biology, Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil
| | - Hugo C Dornfeld
- Department of Ecology and Evolutionary Biology, Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil
| | - Emanuela C Freitas
- Department of Ecology and Evolutionary Biology, Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil
| | - Eny M Vieira
- São Carlos Institute of Chemistry, University of São Paulo, Av. Trabalhador São Carlense, 400, 13560-970, São Carlos, SP, Brazil
| | - Michiel A Daam
- CENSE, Department of Environmental Sciences and Engineering, Faculty of Sciences and Technology, New University of Lisbon, Quinta da Torre, 2829-516, Caparica, Portugal
| | - Odete Rocha
- Department of Ecology and Evolutionary Biology, Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil; Post-Graduate Program in Ecology and Natural Resources (PPGERN), Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil
| | - Mirna H R Seleghim
- Department of Ecology and Evolutionary Biology, Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil; Post-Graduate Program in Ecology and Natural Resources (PPGERN), Federal University of São Carlos, Rod. Washington Luis, Km 235, 13565-905, São Carlos, SP, Brazil
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Chaudhari R, Fong LW, Tan Z, Huang B, Zhang S. An up-to-date overview of computational polypharmacology in modern drug discovery. Expert Opin Drug Discov 2020; 15:1025-1044. [PMID: 32452701 PMCID: PMC7415563 DOI: 10.1080/17460441.2020.1767063] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 05/06/2020] [Indexed: 12/30/2022]
Abstract
INTRODUCTION In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success. AREAS COVERED In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies. EXPERT OPINION Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.
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Affiliation(s)
- Rajan Chaudhari
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Long Wolf Fong
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
- MD Anderson UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Avenue, Houston, Texas 77030, United States
| | - Zhi Tan
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Beibei Huang
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Shuxing Zhang
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
- MD Anderson UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Avenue, Houston, Texas 77030, United States
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Cuvitoglu A, Zhou JX, Huang S, Isik Z. Predicting drug synergy for precision medicine using network biology and machine learning. J Bioinform Comput Biol 2020; 17:1950012. [PMID: 31057072 DOI: 10.1142/s0219720019500124] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for in vitro experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effective anti-cancer drug pairs using in silico network biology approach. Based on the hypotheses that the drug synergy comes from the collective effects on the biological network, therefore, we developed six network biology features, including overlap and distance of drug perturbation network, that were derived by using individual drug-perturbed transcriptome profiles and the relevant biological network analysis. Using publicly available drug synergy databases and three machine-learning (ML) methods, the model was trained to discriminate the positive (synergistic) and negative (nonsynergistic) drug combinations. The proposed models were evaluated on the test cases to predict the most promising network biology feature, which is the network degree activity, i.e. the synergistic effect between drug pairs is mainly accounted by the complementary signaling pathways or molecular networks from two drugs.
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Affiliation(s)
- Ali Cuvitoglu
- 1 Computer Engineering Department, Dokuz Eylul University, Tinaztepe Kampusu, Izmir 35160, Turkey
| | - Joseph X Zhou
- 2 Institute for Systems Biology, 401 Terry Ave. N. Seattle, WA 98109, USA
| | - Sui Huang
- 2 Institute for Systems Biology, 401 Terry Ave. N. Seattle, WA 98109, USA
| | - Zerrin Isik
- 1 Computer Engineering Department, Dokuz Eylul University, Tinaztepe Kampusu, Izmir 35160, Turkey
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43
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Wang A, Xu H, Ding X. Simultaneous Optimization of Drug Combination Dose‐Ratio Sequence with Innovative Design and Active Learning. ADVANCED THERAPEUTICS 2020. [DOI: 10.1002/adtp.201900135] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Aiting Wang
- School of Biomedical Engineering, Institute for Personalized MedicineShanghai Jiao Tong University Shanghai 200030 China
| | - Hongquan Xu
- Department of StatisticsUniversity of California Los Angeles CA 90095 USA
| | - Xianting Ding
- School of Biomedical Engineering, Institute for Personalized MedicineShanghai Jiao Tong University Shanghai 200030 China
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Liu H, Zhang W, Nie L, Ding X, Luo J, Zou L. Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network. BMC Bioinformatics 2019; 20:645. [PMID: 31818267 PMCID: PMC6902475 DOI: 10.1186/s12859-019-3288-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 11/21/2019] [Indexed: 01/30/2023] Open
Abstract
Background Although targeted drugs have contributed to impressive advances in the treatment of cancer patients, their clinical benefits on tumor therapies are greatly limited due to intrinsic and acquired resistance of cancer cells against such drugs. Drug combinations synergistically interfere with protein networks to inhibit the activity level of carcinogenic genes more effectively, and therefore play an increasingly important role in the treatment of complex disease. Results In this paper, we combined the drug similarity network, protein similarity network and known drug-protein associations into a drug-protein heterogenous network. Next, we ran random walk with restart (RWR) on the heterogenous network using the combinatorial drug targets as the initial probability, and obtained the converged probability distribution as the feature vector of each drug combination. Taking these feature vectors as input, we trained a gradient tree boosting (GTB) classifier to predict new drug combinations. We conducted performance evaluation on the widely used drug combination data set derived from the DCDB database. The experimental results show that our method outperforms seven typical classifiers and traditional boosting algorithms. Conclusions The heterogeneous network-derived features introduced in our method are more informative and enriching compared to the primary ontology features, which results in better performance. In addition, from the perspective of network pharmacology, our method effectively exploits the topological attributes and interactions of drug targets in the overall biological network, which proves to be a systematic and reliable approach for drug discovery.
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Affiliation(s)
- Hui Liu
- Lab of Information Management, Changzhou University, Jiangsu, China
| | - Wenhao Zhang
- Lab of Information Management, Changzhou University, Jiangsu, China
| | - Lixia Nie
- School of Information Science and Engineering, Changzhou University, Jiangsu, China
| | - Xiancheng Ding
- Information Center, Changzhou University, Jiangsu, 213164, China
| | - Judong Luo
- Department of Radiation Oncology, the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China.
| | - Ling Zou
- School of Information Science and Engineering, Changzhou University, Jiangsu, China.
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Zitnik M, Nguyen F, Wang B, Leskovec J, Goldenberg A, Hoffman MM. Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2019; 50:71-91. [PMID: 30467459 PMCID: PMC6242341 DOI: 10.1016/j.inffus.2018.09.012] [Citation(s) in RCA: 222] [Impact Index Per Article: 44.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.
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Affiliation(s)
- Marinka Zitnik
- Department of Computer Science, Stanford University,
Stanford, CA, USA
| | - Francis Nguyen
- Department of Medical Biophysics, University of Toronto,
Toronto, ON, Canada
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Bo Wang
- Hikvision Research Institute, Santa Clara, CA, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University,
Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Anna Goldenberg
- Genetics & Genome Biology, SickKids Research Institute,
Toronto, ON, Canada
- Department of Computer Science, University of Toronto,
Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Michael M. Hoffman
- Department of Medical Biophysics, University of Toronto,
Toronto, ON, Canada
- Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Computer Science, University of Toronto,
Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
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Zitnik M, Agrawal M, Leskovec J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 2019; 34:i457-i466. [PMID: 29949996 PMCID: PMC6022705 DOI: 10.1093/bioinformatics/bty294] [Citation(s) in RCA: 408] [Impact Index Per Article: 81.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Motivation The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity. Results Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies. Availability and implementation Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon.
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Affiliation(s)
- Marinka Zitnik
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Monica Agrawal
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
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47
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Sheng Z, Sun Y, Yin Z, Tang K, Cao Z. Advances in computational approaches in identifying synergistic drug combinations. Brief Bioinform 2019; 19:1172-1182. [PMID: 28475767 DOI: 10.1093/bib/bbx047] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Indexed: 12/21/2022] Open
Abstract
Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.
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Affiliation(s)
- Zhen Sheng
- School of Life Sciences and Technology, Tongji University
| | - Yi Sun
- School of Life Sciences and Technology, Tongji University
| | - Zuojing Yin
- School of Life Sciences and Technology, Tongji University
| | - Kailin Tang
- Advanced Institute of Translational Medicine, Tongji University
| | - Zhiwei Cao
- School of Life Sciences and Technology, Tongji University
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48
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Esteban-Medina M, Peña-Chilet M, Loucera C, Dopazo J. Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models. BMC Bioinformatics 2019; 20:370. [PMID: 31266445 PMCID: PMC6604281 DOI: 10.1186/s12859-019-2969-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 06/25/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases. RESULTS The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets. CONCLUSIONS The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.
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Affiliation(s)
- Marina Esteban-Medina
- Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocio, 41013 Sevilla, Spain
| | - María Peña-Chilet
- Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocio, 41013 Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER). Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013 Sevilla, Spain
| | - Carlos Loucera
- Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocio, 41013 Sevilla, Spain
| | - Joaquín Dopazo
- Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocio, 41013 Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER). Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013 Sevilla, Spain
- INB-ELIXIR-es, FPS, Hospital Virgen del Rocío, 42013 Sevilla, Spain
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Clarke R, Tyson JJ, Tan M, Baumann WT, Jin L, Xuan J, Wang Y. Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers. Endocr Relat Cancer 2019; 26:R345-R368. [PMID: 30965282 PMCID: PMC7045974 DOI: 10.1530/erc-18-0309] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 04/08/2019] [Indexed: 12/12/2022]
Abstract
Drawing on concepts from experimental biology, computer science, informatics, mathematics and statistics, systems biologists integrate data across diverse platforms and scales of time and space to create computational and mathematical models of the integrative, holistic functions of living systems. Endocrine-related cancers are well suited to study from a systems perspective because of the signaling complexities arising from the roles of growth factors, hormones and their receptors as critical regulators of cancer cell biology and from the interactions among cancer cells, normal cells and signaling molecules in the tumor microenvironment. Moreover, growth factors, hormones and their receptors are often effective targets for therapeutic intervention, such as estrogen biosynthesis, estrogen receptors or HER2 in breast cancer and androgen receptors in prostate cancer. Given the complexity underlying the molecular control networks in these cancers, a simple, intuitive understanding of how endocrine-related cancers respond to therapeutic protocols has proved incomplete and unsatisfactory. Systems biology offers an alternative paradigm for understanding these cancers and their treatment. To correctly interpret the results of systems-based studies requires some knowledge of how in silico models are built, and how they are used to describe a system and to predict the effects of perturbations on system function. In this review, we provide a general perspective on the field of cancer systems biology, and we explore some of the advantages, limitations and pitfalls associated with using predictive multiscale modeling to study endocrine-related cancers.
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Affiliation(s)
- Robert Clarke
- Department of Oncology, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - John J Tyson
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Ming Tan
- Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - William T Baumann
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Lu Jin
- Department of Oncology, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Jianhua Xuan
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, Virginia, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, Virginia, USA
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Huang CT, Hsieh CH, Chung YH, Oyang YJ, Huang HC, Juan HF. Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery. iScience 2019; 15:291-306. [PMID: 31102995 PMCID: PMC6525321 DOI: 10.1016/j.isci.2019.04.039] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 02/02/2019] [Accepted: 04/29/2019] [Indexed: 02/07/2023] Open
Abstract
Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases. Compound signatures targeting non-oncogene addiction for combinatorial drug discovery These signatures are reproducible and linked to cancer hallmarks and drug sensitivity Two synergistic drug combinations are experimentally confirmed in vitro
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Affiliation(s)
- Chen-Tsung Huang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
| | - Chiao-Hui Hsieh
- Institute of Molecular and Cellular Biology, National Taiwan University, Taipei 10617, Taiwan
| | - Yun-Hsien Chung
- Department of Life Science, National Taiwan University, Taipei 10617, Taiwan
| | - Yen-Jen Oyang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
| | - Hsuan-Cheng Huang
- Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei 11221, Taiwan.
| | - Hsueh-Fen Juan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan; Institute of Molecular and Cellular Biology, National Taiwan University, Taipei 10617, Taiwan; Department of Life Science, National Taiwan University, Taipei 10617, Taiwan.
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