101
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M Shaju A, Panicker N, Chandni V, Lakshmi Prasanna VM, Nair G, Subeesh V. Drugs-associated with red man syndrome: An integrative approach using disproportionality analysis and Pharmip. J Clin Pharm Ther 2022; 47:1650-1658. [PMID: 35730973 DOI: 10.1111/jcpt.13716] [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: 04/15/2022] [Revised: 05/04/2022] [Accepted: 05/18/2022] [Indexed: 12/01/2022]
Abstract
WHAT IS KNOWN AND OBJECTIVE Red man syndrome (RMS) is a non-IgE-mediated anaphylactoid adverse event frequently witnessed after a rapid infusion of vancomycin. This study aims to unravel drugs and associated off-label targets that induce RMS by exploiting FDA Adverse Event Reporting System (FAERS) and Pharmacovigilance/Pharmacogenomics Insilico Pipeline (PHARMIP). METHODS The case/non-case retrospective observational study was conducted in the FAERS database. Reporting odds ratio (ROR) and proportional reporting ratio (PRR) data mining algorithms were used to evaluate the strength of the signal. The off-label targets of the drugs with potential signals were obtained using online servers by applying a similarity ensemble approach and a reverse pharmacophore database, which was further validated by molecular docking studies. RESULTS AND DISCUSSION Oritavancin exhibited a strong positive signal (PRR:1185.20 and ROR:1256), which suggests a higher risk for causing RMS. The literature search revealed the involvement of the MRGPRX2 gene in the development of RMS. PHARMIP study unearthed Carbonic anhydrase II (CA2) as the common off-label target among the drugs causing RMS. The results obtained from molecular docking studies reinforced the findings as mentioned earlier, wherein the highest docking score was disinterred for oritavancin (-9.4 for MRGPRX2 and - 8.7 for CA2). WHAT IS NEW AND CONCLUSION Many antibiotics and other classes of medications have been discovered in the quest for drugs that may induce RMS, although a causal relationship could not be established. The implication of MRGPX2 and CA2 in the initial stages of pathogenesis necessitates the development of inhibitors that could be used as potential therapeutic agents against RMS.
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Affiliation(s)
- Aina M Shaju
- Department of Pharmacy Practice, Faculty of Pharmacy, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Nishi Panicker
- Department of Pharmacy Practice, Faculty of Pharmacy, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Venkumahanti Chandni
- Department of Pharmacy Practice, Faculty of Pharmacy, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - V Marise Lakshmi Prasanna
- Department of Pharmacy Practice, Faculty of Pharmacy, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Gouri Nair
- Department of Pharmacology, Faculty of Pharmacy, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Viswam Subeesh
- Department of Pharmacy Practice, Faculty of Pharmacy, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India.,Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Udupi, Karnataka, India
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102
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Nguyen M, An S, Nguyen Y, Hyun YE, Choi H, Pham L, Kim JA, Noh M, Kim G, Jeong LS. Design, Synthesis, and Biological Activity of l-1′-Homologated Adenosine Derivatives. ACS Med Chem Lett 2022; 13:1131-1136. [DOI: 10.1021/acsmedchemlett.2c00159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Mai Nguyen
- College of Pharmacy and Research Institute of Drug Development, Chonnam National University, Gwangju 61186, Korea
| | - Seungchan An
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 08826, Korea
- Natural Products Research Institute, Seoul National University, Seoul 08826, Korea
| | - Yen Nguyen
- College of Pharmacy and Research Institute of Drug Development, Chonnam National University, Gwangju 61186, Korea
| | - Young Eum Hyun
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 08826, Korea
| | - Hongseok Choi
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 08826, Korea
| | - Linh Pham
- College of Pharmacy and Research Institute of Drug Development, Chonnam National University, Gwangju 61186, Korea
| | - Jung-Ae Kim
- College of Pharmacy, Yeungnam University, Gyeongsan 38541, Korea
| | - Minsoo Noh
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 08826, Korea
- Natural Products Research Institute, Seoul National University, Seoul 08826, Korea
| | - Gyudong Kim
- College of Pharmacy and Research Institute of Drug Development, Chonnam National University, Gwangju 61186, Korea
| | - Lak Shin Jeong
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 08826, Korea
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103
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Schipper LJ, Zeverijn LJ, Garnett MJ, Voest EE. Can Drug Repurposing Accelerate Precision Oncology? Cancer Discov 2022; 12:1634-1641. [PMID: 35642948 DOI: 10.1158/2159-8290.cd-21-0612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 01/28/2022] [Accepted: 04/11/2022] [Indexed: 11/16/2022]
Abstract
Ongoing new insights in the field of cancer diagnostics, genomic profiling, and cancer behavior have raised the demand for novel, personalized cancer treatments. As the development of new cancer drugs is a challenging, costly, and time-consuming endeavor, drug repurposing is regarded as an attractive alternative to potentially accelerate this. In this review, we describe strategies for drug repurposing of anticancer agents, translation of preclinical findings in novel trial designs, and associated challenges. Furthermore, we provide suggestions to further utilize the potential of drug repurposing within precision oncology, with a focus on combinatorial approaches. SIGNIFICANCE Oncologic drug development is a timely and costly endeavor, with only few compounds progressing to meaningful therapy options. Although repurposing of existing agents for novel, oncologic indications provides an opportunity to accelerate this process, it is not without challenges.
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Affiliation(s)
- Luuk J Schipper
- Division of Molecular Oncology and Immunology, Netherlands Cancer Institute, Amsterdam, the Netherlands.,Oncode Institute, Utrecht, the Netherlands
| | - Laurien J Zeverijn
- Division of Molecular Oncology and Immunology, Netherlands Cancer Institute, Amsterdam, the Netherlands.,Oncode Institute, Utrecht, the Netherlands
| | | | - Emile E Voest
- Division of Molecular Oncology and Immunology, Netherlands Cancer Institute, Amsterdam, the Netherlands.,Oncode Institute, Utrecht, the Netherlands
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104
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Wang H, Huang F, Xiong Z, Zhang W. A heterogeneous network-based method with attentive meta-path extraction for predicting drug-target interactions. Brief Bioinform 2022; 23:6596318. [PMID: 35641162 DOI: 10.1093/bib/bbac184] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/09/2022] [Accepted: 04/23/2022] [Indexed: 11/13/2022] Open
Abstract
Predicting drug-target interactions (DTIs) is crucial at many phases of drug discovery and repositioning. Many computational methods based on heterogeneous networks (HNs) have proved their potential to predict DTIs by capturing extensive biological knowledge and semantic information from meta-paths. However, existing methods manually customize meta-paths, which is overly dependent on some specific expertise. Such strategy heavily limits the scalability and flexibility of these models, and even affects their predictive performance. To alleviate this limitation, we propose a novel HN-based method with attentive meta-path extraction for DTI prediction, named HampDTI, which is capable of automatically extracting useful meta-paths through a learnable attention mechanism instead of pre-definition based on domain knowledge. Specifically, by scoring multi-hop connections across various relations in the HN with each relation assigned an attention weight, HampDTI constructs a new trainable graph structure, called meta-path graph. Such meta-path graph implicitly measures the importance of every possible meta-path between drugs and targets. To enable HampDTI to extract more diverse meta-paths, we adopt a multi-channel mechanism to generate multiple meta-path graphs. Then, a graph neural network is deployed on the generated meta-path graphs to yield the multi-channel embeddings of drugs and targets. Finally, HampDTI fuses all embeddings from different channels for predicting DTIs. The meta-path graphs are optimized along with the model training such that HampDTI can adaptively extract valuable meta-paths for DTI prediction. The experiments on benchmark datasets not only show the superiority of HampDTI in DTI prediction over several baseline methods, but also, more importantly, demonstrate the effectiveness of the model discovering important meta-paths.
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Affiliation(s)
- Hongzhun Wang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Wuhan, China
| | - Feng Huang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Wuhan, China
| | - Zhankun Xiong
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Wuhan, China
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Wuhan, China
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105
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Luo L, Yang J, Wang C, Wu J, Li Y, Zhang X, Li H, Zhang H, Zhou Y, Lu A, Chen S. Natural products for infectious microbes and diseases: an overview of sources, compounds, and chemical diversities. SCIENCE CHINA. LIFE SCIENCES 2022; 65:1123-1145. [PMID: 34705221 PMCID: PMC8548270 DOI: 10.1007/s11427-020-1959-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/27/2021] [Indexed: 12/13/2022]
Abstract
As coronavirus disease 2019 (COVID-19) threatens human health globally, infectious disorders have become one of the most challenging problem for the medical community. Natural products (NP) have been a prolific source of antimicrobial agents with widely divergent structures and a range vast biological activities. A dataset comprising 618 articles, including 646 NP-based compounds from 672 species of natural sources with biological activities against 21 infectious pathogens from five categories, was assembled through manual selection of published articles. These data were used to identify 268 NP-based compounds classified into ten groups, which were used for network pharmacology analysis to capture the most promising lead-compounds such as agelasine D, dicumarol, dihydroartemisinin and pyridomycin. The distribution of maximum Tanimoto scores indicated that compounds which inhibited parasites exhibited low diversity, whereas the chemistries inhibiting bacteria, fungi, and viruses showed more structural diversity. A total of 331 species of medicinal plants with compounds exhibiting antimicrobial activities were selected to classify the family sources. The family Asteraceae possesses various compounds against C. neoformans, the family Anacardiaceae has compounds against Salmonella typhi, the family Cucurbitacea against the human immunodeficiency virus (HIV), and the family Ancistrocladaceae against Plasmodium. This review summarizes currently available data on NP-based antimicrobials against refractory infections to provide information for further discovery of drugs and synthetic strategies for anti-infectious agents.
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Affiliation(s)
- Lu Luo
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Jun Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Cheng Wang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100006, China
| | - Jie Wu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Yafang Li
- Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Xu Zhang
- weMED Health, Houston, 77054, USA
| | - Hui Li
- Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Hui Zhang
- Akupunktur Akademiet, Aabyhoej, Aarhus, 8230, Denmark
| | - Yumei Zhou
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, 518033, China
| | - Aiping Lu
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, 999077, China
| | - Shilin Chen
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
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106
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Phenotypic drug discovery: recent successes, lessons learned and new directions. Nat Rev Drug Discov 2022; 21:899-914. [DOI: 10.1038/s41573-022-00472-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/14/2022] [Indexed: 12/29/2022]
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107
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Abstract
Drug repurposing is the use of a given therapeutic agent for indications other than that for which it was originally designed or intended. The concept is appealing because of potentially lower development costs and shorter timelines than are needed to produce a new drug. To date, drug repurposing for cardiovascular indications has been opportunistic and driven by knowledge of disease mechanisms or serendipitous observation rather than by systematic endeavours to match an existing drug to a new indication. Innovations in two areas of personalized medicine — computational approaches to associate drug effects with disease signatures and predictive model systems to screen drugs for disease-modifying activities — support efforts that together create an efficient pipeline to systematically repurpose drugs to treat cardiovascular disease. Furthermore, new experimental strategies that guide the medicinal chemistry re-engineering of drugs could improve repurposing efforts by tailoring a medicine to its new indication. In this Review, we summarize the historical approach to repurposing and discuss the technological advances that have created a new landscape of opportunities. Drugs can be repurposed for new therapeutic indications. In this Review, Mercola and colleagues summarize the latest techniques for systematic drug repurposing and re-engineering, which could increase the pace, efficiency and cost-effectiveness of drug discovery for the treatment of cardiovascular disease. Contemporary technologies are expected to make drug repurposing large-scale, systematic and deliberate rather than opportunistic. New experimental and computational tools harness patient genomics for drug repurposing. Discovery of repurposed drugs on the basis of patient genomics has implications for precision prescribing of medicines to treat individual patients. The treatment of rare, monogenic diseases, which often provide too little return on investment to incentivize conventional drug discovery, might benefit because the molecular aetiologies of these diseases are well suited to the discovery of drug repurposing candidates.
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108
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Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection. Sci Rep 2022; 12:7843. [PMID: 35551258 PMCID: PMC9096754 DOI: 10.1038/s41598-022-11879-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/22/2022] [Indexed: 12/03/2022] Open
Abstract
As there are no clear on-target mechanisms that explain the increased risk for thrombosis and viral infection or reactivation associated with JAK inhibitors, the observed elevated risk may be a result of an off-target effect. Computational approaches combined with in vitro studies can be used to predict and validate the potential for an approved drug to interact with additional (often unwanted) targets and identify potential safety-related concerns. Potential off-targets of the JAK inhibitors baricitinib and tofacitinib were identified using two established machine learning approaches based on ligand similarity. The identified targets related to thrombosis or viral infection/reactivation were subsequently validated using in vitro assays. Inhibitory activity was identified for four drug-target pairs (PDE10A [baricitinib], TRPM6 [tofacitinib], PKN2 [baricitinib, tofacitinib]). Previously unknown off-target interactions of the two JAK inhibitors were identified. As the proposed pharmacological effects of these interactions include attenuation of pulmonary vascular remodeling, modulation of HCV response, and hypomagnesemia, the newly identified off-target interactions cannot explain an increased risk of thrombosis or viral infection/reactivation. While further evidence is required to explain both the elevated thrombosis and viral infection/reactivation risk, our results add to the evidence that these JAK inhibitors are promiscuous binders and highlight the potential for repurposing.
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109
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Xuan P, Zhang X, Zhang Y, Hu K, Nakaguchi T, Zhang T. multi-type neighbors enhanced global topology and pairwise attribute learning for drug-protein interaction prediction. Brief Bioinform 2022; 23:6581435. [PMID: 35514190 DOI: 10.1093/bib/bbac120] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 03/07/2022] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Accurate identification of proteins interacted with drugs helps reduce the time and cost of drug development. Most of previous methods focused on integrating multisource data about drugs and proteins for predicting drug-target interactions (DTIs). There are both similarity connection and interaction connection between two drugs, and these connections reflect their relationships from different perspectives. Similarly, two proteins have various connections from multiple perspectives. However, most of previous methods failed to deeply integrate these connections. In addition, multiple drug-protein heterogeneous networks can be constructed based on multiple kinds of connections. The diverse topological structures of these networks are still not exploited completely. RESULTS We propose a novel model to extract and integrate multi-type neighbor topology information, diverse similarities and interactions related to drugs and proteins. Firstly, multiple drug-protein heterogeneous networks are constructed according to multiple kinds of connections among drugs and those among proteins. The multi-type neighbor node sequences of a drug node (or a protein node) are formed by random walks on each network and they reflect the hidden neighbor topological structure of the node. Secondly, a module based on graph neural network (GNN) is proposed to learn the multi-type neighbor topologies of each node. We propose attention mechanisms at neighbor node level and at neighbor type level to learn more informative neighbor nodes and neighbor types. A network-level attention is also designed to enhance the context dependency among multiple neighbor topologies of a pair of drug and protein nodes. Finally, the attribute embedding of the drug-protein pair is formulated by a proposed embedding strategy, and the embedding covers the similarities and interactions about the pair. A module based on three-dimensional convolutional neural networks (CNN) is constructed to deeply integrate pairwise attributes. Extensive experiments have been performed and the results indicate GCDTI outperforms several state-of-the-art prediction methods. The recall rate estimation over the top-ranked candidates and case studies on 5 drugs further demonstrate GCDTI's ability in discovering potential drug-protein interactions.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.,School of Computer Science, Shaanxi Normal University, Xi'an 710062, China
| | - Xiaowen Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Yu Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Kaimiao Hu
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba 2638522, Japan
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin 150080, China
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110
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Die Erhellung des “Bewusstseinsmoleküls”: Photomodulation des 5‐HT
2A
Rezeptors durch ein licht‐steuerbares N,N‐Dimethyltryptamin‐Derivat. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202203034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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111
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Raut SB, Marathe PA, van Eijk L, Eri R, Ravindran M, Benedek DM, Ursano RJ, Canales JJ, Johnson LR. Diverse therapeutic developments for post-traumatic stress disorder (PTSD) indicate common mechanisms of memory modulation. Pharmacol Ther 2022; 239:108195. [PMID: 35489438 DOI: 10.1016/j.pharmthera.2022.108195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/19/2022] [Accepted: 04/20/2022] [Indexed: 12/20/2022]
Abstract
Post-traumatic stress disorder (PTSD), characterized by abnormally persistent and distressing memories, is a chronic debilitating condition in need of new treatment options. Current treatment guidelines recommend psychotherapy as first line management with only two drugs, sertraline and paroxetine, approved by U.S. Food and Drug Administration (FDA) for treatment of PTSD. These drugs have limited efficacy as they only reduce symptoms related to depression and anxiety without producing permanent remission. PTSD remains a significant public health problem with high morbidity and mortality requiring major advances in therapeutics. Early evidence has emerged for the beneficial effects of psychedelics particularly in combination with psychotherapy for management of PTSD, including psilocybin, MDMA, LSD, cannabinoids, ayahuasca and ketamine. MDMA and psilocybin reduce barrier to therapy by increasing trust between therapist and patient, thus allowing for modification of trauma related memories. Furthermore, research into the memory reconsolidation mechanisms has allowed for identification of various pharmacological targets to disrupt abnormally persistent memories. A number of pre-clinical and clinical studies have investigated novel and re-purposed pharmacological agents to disrupt fear memory in PTSD. Novel therapeutic approaches like neuropeptide Y, oxytocin, cannabinoids and neuroactive steroids have also shown potential for PTSD treatment. Here, we focus on the role of fear memory in the pathophysiology of PTSD and propose that many of these new therapeutic strategies produce benefits through the effect on fear memory. Evaluation of recent research findings suggests that while a number of drugs have shown promising results in preclinical studies and pilot clinical trials, the evidence from large scale clinical trials would be needed for these drugs to be incorporated in clinical practice.
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Affiliation(s)
- Sanket B Raut
- Schools of Psychological Sciences, College of Health and Medicine, University of Tasmania, TAS 7250, Australia
| | - Padmaja A Marathe
- Department of Pharmacology and Therapeutics, Seth GS Medical College & KEM Hospital, Parel, Mumbai 400 012, India
| | - Liza van Eijk
- Department of Psychology, College of Healthcare Sciences, James Cook University, QLD 4811, Australia
| | - Rajaraman Eri
- Health Sciences, College of Health and Medicine, University of Tasmania, TAS 7250, Australia
| | - Manoj Ravindran
- Medicine, College of Health and Medicine, University of Tasmania, TAS 7250, Australia; Department of Psychiatry, North-West Private Hospital, Burnie TAS 7320, Australia
| | - David M Benedek
- Centre for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine, Bethesda, MD 20814, USA
| | - Robert J Ursano
- Centre for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine, Bethesda, MD 20814, USA
| | - Juan J Canales
- Schools of Psychological Sciences, College of Health and Medicine, University of Tasmania, TAS 7250, Australia
| | - Luke R Johnson
- Schools of Psychological Sciences, College of Health and Medicine, University of Tasmania, TAS 7250, Australia; Centre for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine, Bethesda, MD 20814, USA.
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112
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Prasetyo WE, Purnomo H, Sadrini M, Wibowo FR, Firdaus M, Kusumaningsih T. Identification of potential bioactive natural compounds from Indonesian medicinal plants against 3-chymotrypsin-like protease (3CL pro) of SARS-CoV-2: molecular docking, ADME/T, molecular dynamic simulations, and DFT analysis. J Biomol Struct Dyn 2022:1-18. [DOI: 10.1080/07391102.2022.2068071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Wahyu Eko Prasetyo
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
| | - Heri Purnomo
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
| | - Miracle Sadrini
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
| | - Fajar Rakhman Wibowo
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
| | - Maulidan Firdaus
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
| | - Triana Kusumaningsih
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
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113
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Alqahtani A. Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:6201067. [PMID: 35509623 PMCID: PMC9060979 DOI: 10.1155/2022/6201067] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/17/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022]
Abstract
Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.
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Affiliation(s)
- Amal Alqahtani
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, 31541, Saudi Arabia
- Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 34212, Saudi Arabia
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114
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Gao S, Han L, Luo D, Xiao Z, Liu G, Zhang Y, Zhou W. Deep Learning Applications for the accurate identification of low-transcriptional activity drugs and their mechanism of actions. Pharmacol Res 2022; 180:106225. [PMID: 35452801 DOI: 10.1016/j.phrs.2022.106225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/07/2022] [Accepted: 04/15/2022] [Indexed: 10/18/2022]
Abstract
Analysis of drug-induced expression profiles facilitated comprehensive understanding of drug properties. However, many compounds exhibit weak transcription responses though they mostly possess definite pharmacological effects. Actually, as a representative example, over 66.4% of 312,438 molecular signatures in the Library of Integrated Cellular Signatures (LINCS) database exhibit low-transcriptional activities (i.e. TAS-low signatures). When computing the association between TAS-low signatures with shared mechanism of actions (MOAs), commonly used algorithms showed inadequate performance with an average area under receiver operating characteristic curve (AUROC) of 0.55, but the computation accuracy of the same task can be improved by our developed tool Genetic profile activity relationship (GPAR) with an average AUROC of 0.68. Up to 36 out of 74 TAS-low MOAs were well trained with AUROC≥0.7 by GPAR, higher than those by other approaches. Further studies showed that GPAR benefited from the size of training samples more significantly than other approaches. Lastly, in biological validation of the MOA prediction for a TAS-low drug Tropisetron, we found an unreported mechanism that Tropisetron can bind to the glucocorticoid receptor. This study indicated that GPAR can serve as an effective approach for the accurate identification of low-transcriptional activity drugs and their MOAs, thus providing a good tool for drug repurposing with both TAS-low and TAS-high signatures.
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Affiliation(s)
- Shengqiao Gao
- Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China
| | - Lu Han
- Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China
| | - Dan Luo
- Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China
| | - Zhiyong Xiao
- Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China
| | - Gang Liu
- Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China
| | - Yongxiang Zhang
- Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China.
| | - Wenxia Zhou
- Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China.
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El Hadi C, Ayoub G, Bachir Y, Haykal M, Jalkh N, Kourie HR. Polygenic and Network-Based Studies in Risk Identification and Demystification of cancer. Expert Rev Mol Diagn 2022; 22:427-438. [PMID: 35400274 DOI: 10.1080/14737159.2022.2065195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer. AREAS COVERED The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress. EXPERT OPINION In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity.
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Affiliation(s)
| | - George Ayoub
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Yara Bachir
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Michèle Haykal
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Nadine Jalkh
- Medical Genetics Unit, Technology and Health division, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Hampig Raphael Kourie
- Department of Hematology-Oncology, Hotel Dieu de France University Hospital, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
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Lee S, Jeon S, Kim HS. A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus. Endocrinol Metab (Seoul) 2022; 37:195-207. [PMID: 35413782 PMCID: PMC9081315 DOI: 10.3803/enm.2022.1404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/21/2022] [Indexed: 11/11/2022] Open
Abstract
Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.
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Affiliation(s)
- Suehyun Lee
- Department of Biomedical Informatics, Konyang University College of Medicine, Daejeon, Korea
- Health Care Data Science Center, Konyang University Hospital, Daejeon, Korea
| | - Seongwoo Jeon
- Health Care Data Science Center, Konyang University Hospital, Daejeon, Korea
| | - Hun-Sung Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Corresponding author: Hun-Sung Kim Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea Tel: +82-2-2258-8262, Fax: +82-2-2258-8297, E-mail:
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Wang X, Liu J, Zhang C, Wang S. SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning. Int J Mol Sci 2022; 23:ijms23073780. [PMID: 35409140 PMCID: PMC8998983 DOI: 10.3390/ijms23073780] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 03/24/2022] [Accepted: 03/28/2022] [Indexed: 12/10/2022] Open
Abstract
Identifying compound-protein (drug-target, DTI) interactions (CPI) accurately is a key step in drug discovery. Including virtual screening and drug reuse, it can significantly reduce the time it takes to identify drug candidates and provide patients with timely and effective treatment. Recently, more and more researchers have developed CPI's deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and R2 (degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and R2 = 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet.
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Affiliation(s)
- Xun Wang
- College of Computer Science and Technology, China University of Petroleum, Qingdao 266555, China; (X.W.); (J.L.); (C.Z.)
- State Key Laboratory of Computer Architecture, Institute of Computing Technology, University of Chinese Academy of Sciences, Beijing 100080, China
| | - Jiali Liu
- College of Computer Science and Technology, China University of Petroleum, Qingdao 266555, China; (X.W.); (J.L.); (C.Z.)
| | - Chaogang Zhang
- College of Computer Science and Technology, China University of Petroleum, Qingdao 266555, China; (X.W.); (J.L.); (C.Z.)
| | - Shudong Wang
- College of Computer Science and Technology, China University of Petroleum, Qingdao 266555, China; (X.W.); (J.L.); (C.Z.)
- Correspondence:
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McClure-Begley TD, Roth BL. The promises and perils of psychedelic pharmacology for psychiatry. Nat Rev Drug Discov 2022; 21:463-473. [PMID: 35301459 DOI: 10.1038/s41573-022-00421-7] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/04/2022] [Indexed: 11/09/2022]
Abstract
Psychedelic drugs including psilocybin, N,N'-dimethyltryptamine (DMT) and lysergic acid diethylamide (LSD) are undergoing a renaissance as potentially useful drugs for various neuropsychiatric diseases, with a rapid onset of therapeutic activity. Notably, phase II trials have shown that psilocybin can produce statistically significant clinical effects following one or two administrations in depression and anxiety. These findings have inspired a 'gold rush' of commercial interest, with nearly 60 companies already formed to explore opportunities for psychedelics in treating diverse diseases. Additionally, these remarkable phenomenological and clinical observations are informing hypotheses about potential molecular mechanisms of action that need elucidation to realize the full potential of this investigative space. In particular, despite compelling evidence that the 5-HT2A receptor is a critical mediator of the behavioural effects of psychedelic drugs, uncertainty remains about which aspects of 5-HT2A receptor activity in the central nervous system are responsible for therapeutic effects and to what degree they can be isolated by developing novel chemical probes with differing specificity and selectivity profiles. Here, we discuss this emerging area of therapeutics, covering both controversies and areas of consensus related to the opportunities and perils of psychedelic and psychedelic-inspired therapeutics. We highlight how basic science breakthroughs can guide the discovery and development of psychedelic-inspired medications with the potential for improved efficacy without hallucinogenic or rewarding actions.
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Affiliation(s)
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina Chapel Hill School of Medicine, Chapel Hill, NC, USA.
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Wan X, Wu X, Wang D, Tan X, Liu X, Fu Z, Jiang H, Zheng M, Li X. An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph. Brief Bioinform 2022; 23:6547264. [PMID: 35275993 PMCID: PMC9310259 DOI: 10.1093/bib/bbac073] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 02/09/2022] [Accepted: 02/11/2022] [Indexed: 01/10/2023] Open
Abstract
Identifying the potential compound–protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most of the network-based methods use heterogeneous graphs, which is challenging due to their complex structures and heterogeneous attributes. Therefore, in this work, we transformed the compound–protein heterogeneous graph to a homogeneous graph by integrating the ligand-based protein representations and overall similarity associations. We then proposed an Inductive Graph AggrEgator-based framework, named CPI-IGAE, for CPI prediction. CPI-IGAE learns the low-dimensional representations of compounds and proteins from the homogeneous graph in an end-to-end manner. The results show that CPI-IGAE performs better than some state-of-the-art methods. Further ablation study and visualization of embeddings reveal the advantages of the model architecture and its role in feature extraction, and some of the top ranked CPIs by CPI-IGAE have been validated by a review of recent literature. The data and source codes are available at https://github.com/wanxiaozhe/CPI-IGAE.
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Affiliation(s)
- Xiaozhe Wan
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Xiaolong Wu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Dingyan Wang
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | | | - Xiaohong Liu
- AlphaMa Inc., No. 108, Yuxin Road, Suzhou Industrial Park, Suzhou 215128, China
| | - Zunyun Fu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Hualiang Jiang
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China; School of Life Science and Technology, ShanghaiTech University, 393 Huaxiazhong Road, Shanghai 200031, China
| | - Mingyue Zheng
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Xutong Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
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Chen H, Zhang Z, Zhang J. In silico drug repositioning based on integrated drug targets and canonical correlation analysis. BMC Med Genomics 2022; 15:48. [PMID: 35249529 PMCID: PMC8898485 DOI: 10.1186/s12920-022-01203-1] [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: 09/18/2021] [Accepted: 03/02/2022] [Indexed: 01/21/2023] Open
Abstract
Background Besides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level. Methods Herein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug–target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug’s mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets. Results We receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions. Conclusions To the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-022-01203-1.
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Borbély E, Varga V, Szögi T, Schuster I, Bozsó Z, Penke B, Fülöp L. Impact of Two Neuronal Sigma-1 Receptor Modulators, PRE084 and DMT, on Neurogenesis and Neuroinflammation in an Aβ 1-42-Injected, Wild-Type Mouse Model of AD. Int J Mol Sci 2022; 23:2514. [PMID: 35269657 PMCID: PMC8910266 DOI: 10.3390/ijms23052514] [Citation(s) in RCA: 2] [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: 12/21/2021] [Revised: 02/14/2022] [Accepted: 02/23/2022] [Indexed: 02/01/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia characterized by cognitive dysfunctions. Pharmacological interventions to slow the progression of AD are intensively studied. A potential direction targets neuronal sigma-1 receptors (S1Rs). S1R ligands are recognized as promising therapeutic agents that may alleviate symptom severity of AD, possibly via preventing amyloid-β-(Aβ-) induced neurotoxicity on the endoplasmic reticulum stress-associated pathways. Furthermore, S1Rs may also modulate adult neurogenesis, and the impairment of this process is reported to be associated with AD. We aimed to investigate the effects of two S1R agonists, dimethyltryptamine (DMT) and PRE084, in an Aβ-induced in vivo mouse model characterizing neurogenic and anti-neuroinflammatory symptoms of AD, and the modulatory effects of S1R agonists were analyzed by immunohistochemical methods and western blotting. DMT, binding moderately to S1R but with high affinity to 5-HT receptors, negatively influenced neurogenesis, possibly as a result of activating both receptors differently. In contrast, the highly selective S1R agonist PRE084 stimulated hippocampal cell proliferation and differentiation. Regarding neuroinflammation, DMT and PRE084 significantly reduced Aβ1-42-induced astrogliosis, but neither had remarkable effects on microglial activation. In summary, the highly selective S1R agonist PRE084 may be a promising therapeutic agent for AD. Further studies are required to clarify the multifaceted neurogenic and anti-neuroinflammatory roles of these agonists.
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Affiliation(s)
| | | | | | | | | | | | - Lívia Fülöp
- Department of Medical Chemistry, University of Szeged, Dóm Tér 8, H-6720 Szeged, Hungary; (E.B.); (V.V.); (T.S.); (I.S.); (Z.B.); (B.P.)
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Levit Kaplan A, Strachan RT, Braz JM, Craik V, Slocum S, Mangano T, Amabo V, O'Donnell H, Lak P, Basbaum AI, Roth BL, Shoichet BK. Structure-Based Design of a Chemical Probe Set for the 5-HT 5A Serotonin Receptor. J Med Chem 2022; 65:4201-4217. [PMID: 35195401 DOI: 10.1021/acs.jmedchem.1c02031] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The 5-HT5A receptor (5-HT5AR), for which no selective agonists and a few antagonists exist, remains the least understood serotonin receptor. A single commercial antagonist, SB-699551, has been widely used to investigate the 5-HT5AR function in neurological disorders, including pain, but this molecule has substantial liabilities as a chemical probe. Accordingly, we sought to develop an internally controlled probe set. Docking over 6 million molecules against a 5-HT5AR homology model identified 5 mid-μM ligands, one of which was optimized to UCSF678, a 42 nM arrestin-biased partial agonist at the 5-HT5AR with a more restricted off-target profile and decreased assay liabilities versus SB-699551. Site-directed mutagenesis supported the docked pose of UCSF678. Surprisingly, analogs of UCSF678 that lost the 5-HT5AR activity revealed that 5-HT5AR engagement is nonessential for alleviating pain, contrary to studies with less-selective ligands. UCSF678 and analogs constitute a selective probe set with which to study the function of the 5-HT5AR.
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Affiliation(s)
- Anat Levit Kaplan
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94143, United States
| | - Ryan T Strachan
- Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina 27514, United States
| | - Joao M Braz
- Department of Anatomy, University of California, San Francisco, San Francisco, California 94143, United States
| | - Veronica Craik
- Department of Anatomy, University of California, San Francisco, San Francisco, California 94143, United States
| | - Samuel Slocum
- National Institute of Mental Health Psychoactive Drug Screening Program, School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina 27514, United States
| | - Thomas Mangano
- National Institute of Mental Health Psychoactive Drug Screening Program, School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina 27514, United States
| | - Vanessa Amabo
- National Institute of Mental Health Psychoactive Drug Screening Program, School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina 27514, United States
| | - Henry O'Donnell
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94143, United States
| | - Parnian Lak
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94143, United States
| | - Allan I Basbaum
- Department of Anatomy, University of California, San Francisco, San Francisco, California 94143, United States
| | - Bryan L Roth
- Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina 27514, United States.,National Institute of Mental Health Psychoactive Drug Screening Program, School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina 27514, United States.,Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina 27514, United States
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94143, United States
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Bahmad HF, Demus T, Moubarak MM, Daher D, Alvarez Moreno JC, Polit F, Lopez O, Merhe A, Abou-Kheir W, Nieder AM, Poppiti R, Omarzai Y. Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing. Med Sci (Basel) 2022; 10:medsci10010015. [PMID: 35225948 PMCID: PMC8883996 DOI: 10.3390/medsci10010015] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/12/2022] [Accepted: 02/16/2022] [Indexed: 12/12/2022] Open
Abstract
Prostate cancer (PCa) is the second most common cancer in men. Common treatments include active surveillance, surgery, or radiation. Androgen deprivation therapy and chemotherapy are usually reserved for advanced disease or biochemical recurrence, such as castration-resistant prostate cancer (CRPC), but they are not considered curative because PCa cells eventually develop drug resistance. The latter is achieved through various cellular mechanisms that ultimately circumvent the pharmaceutical’s mode of action. The need for novel therapeutic approaches is necessary under these circumstances. An alternative way to treat PCa is by repurposing of existing drugs that were initially intended for other conditions. By extrapolating the effects of previously approved drugs to the intracellular processes of PCa, treatment options will expand. In addition, drug repurposing is cost-effective and efficient because it utilizes drugs that have already demonstrated safety and efficacy. This review catalogues the drugs that can be repurposed for PCa in preclinical studies as well as clinical trials.
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Affiliation(s)
- Hisham F. Bahmad
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
- Correspondence: or ; Tel.: +1-786-961-0216
| | - Timothy Demus
- Division of Urology, Columbia University, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (T.D.); (A.M.N.)
| | - Maya M. Moubarak
- Department of Anatomy, Cell Biology, and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut 1107-2020, Lebanon; (M.M.M.); (W.A.-K.)
- CNRS, IBGC, UMR5095, Universite de Bordeaux, F-33000 Bordeaux, France
| | - Darine Daher
- Faculty of Medicine, American University of Beirut, Beirut 1107-2020, Lebanon;
| | - Juan Carlos Alvarez Moreno
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
| | - Francesca Polit
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
| | - Olga Lopez
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
| | - Ali Merhe
- Department of Urology, Jackson Memorial Hospital, University of Miami, Leonard M. Miller School of Medicine, Miami, FL 33136, USA;
| | - Wassim Abou-Kheir
- Department of Anatomy, Cell Biology, and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut 1107-2020, Lebanon; (M.M.M.); (W.A.-K.)
| | - Alan M. Nieder
- Division of Urology, Columbia University, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (T.D.); (A.M.N.)
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
| | - Robert Poppiti
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
| | - Yumna Omarzai
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
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Discovery of Kinase and Carbonic Anhydrase Dual Inhibitors by Machine Learning Classification and Experiments. Pharmaceuticals (Basel) 2022; 15:ph15020236. [PMID: 35215348 PMCID: PMC8875555 DOI: 10.3390/ph15020236] [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: 12/17/2021] [Revised: 02/11/2022] [Accepted: 02/12/2022] [Indexed: 02/04/2023] Open
Abstract
A multi-target small molecule modulator is advantageous for treating complicated diseases such as cancers. However, the strategy and application for discovering a multi-target modulator have been less reported. This study presents the dual inhibitors for kinase and carbonic anhydrase (CA) predicted by machine learning (ML) classifiers, and validated by biochemical and biophysical experiments. ML trained by CA I and CA II inhibitor molecular fingerprints predicted candidates from the protein-specific bioactive molecules approved or under clinical trials. For experimental tests, three sulfonamide-containing kinase inhibitors, 5932, 5946, and 6046, were chosen. The enzyme assays with CA I, CA II, CA IX, and CA XII have allowed the quantitative comparison in the molecules’ inhibitory activities. While 6046 inhibited weakly, 5932 and 5946 exhibited potent inhibitions with 100 nM to 1 μM inhibitory constants. The ML screening was extended for finding CAs inhibitors of all known kinase inhibitors. It found XMU-MP-1 as another potent CA inhibitor with an approximate 30 nM inhibitory constant for CA I, CA II, and CA IX. Differential scanning fluorimetry confirmed the direct interaction between CAs and small molecules. Cheminformatics studies, including docking simulation, suggest that each molecule possesses two separate functional moieties: one for interaction with kinases and the other with CAs.
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Hu K, Cui H, Zhang T, Sun C, Xuan P. ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction. Brief Bioinform 2022; 23:6519792. [PMID: 35108362 DOI: 10.1093/bib/bbab606] [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: 10/25/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Effective computational methods to predict drug-protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of drug development. Recent DPI prediction methods mainly exploit graph data composed of multiple kinds of connections among drugs and proteins. Each node in the graph usually has topological structures with multiple scales formed by its first-order neighbors and multi-order neighbors. However, most of the previous methods do not consider the topological structures of multi-order neighbors. In addition, deep integration of the multi-modality similarities of drugs and proteins is also a challenging task. RESULTS We propose a model called ALDPI to adaptively learn the multi-scale topologies and multi-modality similarities with various significance levels. We first construct a drug-protein heterogeneous graph, which is composed of the interactions and the similarities with multiple modalities among drugs and proteins. An adaptive graph learning module is then designed to learn important kinds of connections in heterogeneous graph and generate new topology graphs. A module based on graph convolutional autoencoders is established to learn multiple representations, which imply the node attributes and multiple-scale topologies composed of one-order and multi-order neighbors, respectively. We also design an attention mechanism at neighbor topology level to distinguish the importance of these representations. Finally, since each similarity modality has its specific features, we construct a multi-layer convolutional neural network-based module to learn and fuse multi-modality features to obtain the attribute representation of each drug-protein node pair. Comprehensive experimental results show ALDPI's superior performance over six state-of-the-art methods. The results of recall rates of top-ranked candidates and case studies on five drugs further demonstrate the ability of ALDPI to discover potential drug-related protein candidates. CONTACT zhang@hlju.edu.cn.
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Affiliation(s)
- Kaimiao Hu
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne 3083, Australia
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin 150080, China
| | - Chang Sun
- College of Computer Science, Nankai University, Tianjin 300071, China
| | - Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
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Zou Z, Iwata M, Yamanishi Y, Oki S. Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses. BMC Bioinformatics 2022; 23:51. [PMID: 35073843 PMCID: PMC8785570 DOI: 10.1186/s12859-022-04571-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 01/10/2022] [Indexed: 12/14/2022] Open
Abstract
Abstract
Background
Elucidating the modes of action (MoAs) of drugs and drug candidate compounds is critical for guiding translation from drug discovery to clinical application. Despite the development of several data-driven approaches for predicting chemical–disease associations, the molecular cues that organize the epigenetic landscape of drug responses remain poorly understood.
Results
With the use of a computational method, we attempted to elucidate the epigenetic landscape of drug responses, in terms of transcription factors (TFs), through large-scale ChIP-seq data analyses. In the algorithm, we systematically identified TFs that regulate the expression of chemically induced genes by integrating transcriptome data from chemical induction experiments and almost all publicly available ChIP-seq data (consisting of 13,558 experiments). By relating the resultant chemical–TF associations to a repository of associated proteins for a wide range of diseases, we made a comprehensive prediction of chemical–TF–disease associations, which could then be used to account for drug MoAs. Using this approach, we predicted that: (1) cisplatin promotes the anti-tumor activity of TP53 family members but suppresses the cancer-inducing function of MYCs; (2) inhibition of RELA and E2F1 is pivotal for leflunomide to exhibit antiproliferative activity; and (3) CHD8 mediates valproic acid-induced autism.
Conclusions
Our proposed approach has the potential to elucidate the MoAs for both approved drugs and candidate compounds from an epigenetic perspective, thereby revealing new therapeutic targets, and to guide the discovery of unexpected therapeutic effects, side effects, and novel targets and actions.
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Tanaka T, Asano T, Okui T, Kuraoka S, Singh SA, Aikawa M, Aikawa E. Computational Screening Strategy for Drug Repurposing Identified Niclosamide as Inhibitor of Vascular Calcification. Front Cardiovasc Med 2022; 8:826529. [PMID: 35127876 PMCID: PMC8811128 DOI: 10.3389/fcvm.2021.826529] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 12/27/2021] [Indexed: 12/12/2022] Open
Abstract
Vascular calcification is a cardiovascular disorder with no therapeutic options. We recently reported that o-octanoyltransferase (CROT) suppression can inhibit vascular calcification in vivo and in vitro through amelioration of mitochondrial function and fatty acid metabolism. Inhibiting calcification with a small molecule compound targeting CROT-associated mechanisms will be a promising non-invasive treatment of vascular calcification. Here we used a computational approach to search for existing drugs that can inhibit vascular calcification through the CROT pathway. For screening of the compounds that reduce CROT expression, we utilized the Connectivity Map encompassing the L1000 computational platform that contains transcription profiles of various cell lines and perturbagens including small molecules. Small molecules (n = 13) were identified and tested in human primary smooth muscle cells cultured in osteogenic media to induce calcification. Niclosamide, an FDA-improved anthelmintic drug, markedly inhibited calcification along with reduced alkaline phosphatase activity and CROT mRNA expression. To validate this compound in vivo, LDL receptor (Ldlr)-deficient mice fed a high fat diet were given oral doses of niclosamide (0 or 750 ppm admixed with diet) for 10 weeks. Niclosamide treatment decreased aortic and carotid artery calcification as determined by optical near infrared molecular imaging (OsteoSense680) and histological analysis. In addition, niclosamide improved features of fatty liver, including decreased cholesterol levels along with decreased Crot expression, while plasma total cholesterol levels did not change. Proteomic analysis of aortic samples demonstrated that niclosamide affected wingless/integrated (Wnt) signaling pathway and decreased runt-related transcription factor 2 (Runx2) expression, an essential factor for calcification. Our target discovery strategy using a genetic perturbation database with existing drugs identified niclosamide, that in turn inhibited calcification in vivo and in vitro, indicating its potential for the treatment of vascular calcification.
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Affiliation(s)
- Takeshi Tanaka
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Takaharu Asano
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Takehito Okui
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Shiori Kuraoka
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Sasha A. Singh
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Cardiovascular Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
- Department of Human Pathology, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Cardiovascular Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
- Department of Human Pathology, Sechenov First Moscow State Medical University, Moscow, Russia
- *Correspondence: Elena Aikawa
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Douglass EF, Allaway RJ, Szalai B, Wang W, Tian T, Fernández-Torras A, Realubit R, Karan C, Zheng S, Pessia A, Tanoli Z, Jafari M, Wan F, Li S, Xiong Y, Duran-Frigola M, Bertoni M, Badia-i-Mompel P, Mateo L, Guitart-Pla O, Chung V, Tang J, Zeng J, Aloy P, Saez-Rodriguez J, Guinney J, Gerhard DS, Califano A. A community challenge for a pancancer drug mechanism of action inference from perturbational profile data. Cell Rep Med 2022; 3:100492. [PMID: 35106508 PMCID: PMC8784774 DOI: 10.1016/j.xcrm.2021.100492] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 08/08/2021] [Accepted: 12/15/2021] [Indexed: 12/14/2022]
Abstract
The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action. Drug-perturbed RNA sequencing data can be used to identify drug targets Technology-based drug-target definitions often subsume literature definitions Literature and screening datasets provide complementary information on drug mechanisms
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Affiliation(s)
- Eugene F. Douglass
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave., New York, NY 10032, USA
- Pharmaceutical and Biomedical Sciences, University of Georgia, 250 W. Green Street, Athens, GA 30602, USA
| | - Robert J. Allaway
- Computational Oncology Group, Sage Bionetworks, 2901 Third Ave., Ste 330, Seattle, WA 98121, USA
| | - Bence Szalai
- Semmelweis University, Faculty of Medicine, Department of Physiology, Budapest, Hungary
| | - Wenyu Wang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Tingzhong Tian
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Adrià Fernández-Torras
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Ron Realubit
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave., New York, NY 10032, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave., New York, NY 10032, USA
| | - Shuyu Zheng
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Alberto Pessia
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ziaurrehman Tanoli
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Mohieddin Jafari
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Fangping Wan
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Shuya Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Yuanpeng Xiong
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Martino Bertoni
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Pau Badia-i-Mompel
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Lídia Mateo
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Oriol Guitart-Pla
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Verena Chung
- Computational Oncology Group, Sage Bionetworks, 2901 Third Ave., Ste 330, Seattle, WA 98121, USA
| | | | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Justin Guinney
- Computational Oncology Group, Sage Bionetworks, 2901 Third Ave., Ste 330, Seattle, WA 98121, USA
| | - Daniela S. Gerhard
- Office of Cancer Genomics, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave., New York, NY 10032, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave., New York, NY 10032, USA
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY 10032, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, 701 W 168th Street, New York, NY 10032, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY 10032, USA
- Corresponding author
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Sahoo BM, Bhattamisra SK, Das S, Tiwari A, Tiwari V, Kumar M, Singh S. Computational Approach to Combat COVID-19 Infection: Emerging Tool for Accelerating Drug Research. Curr Drug Discov Technol 2022; 19:e170122200314. [PMID: 35040405 DOI: 10.2174/1570163819666220117161308] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 10/05/2021] [Accepted: 10/11/2021] [Indexed: 01/08/2023]
Abstract
BACKGROUND Drug discovery and development process is an expensive, complex, time-consuming and risky. There are different techniques involved in the drug development process which include random screening, computational approach, molecular manipulation and serendipitous research. Among these methods, the computational approach is considered as an efficient strategy to accelerate and economize the drug discovery process. OBJECTIVE This approach is mainly applied in various phases of drug discovery process including target identification, target validation, lead identification and lead optimization. Due to increase in the availability of information regarding various biological targets of different disease states, computational approaches such as molecular docking, de novo design, molecular similarity calculation, virtual screening, pharmacophore-based modeling and pharmacophore mapping have been applied extensively. METHODS Various drug molecules can be designed by applying computational tools to explore the drug candidates for treatment of Coronavirus infection. The world health organization has announced the novel corona virus disease as COVID-19 and declared it as pandemic globally on 11 February 2020. So, it is thought of interest to scientific community to apply computational methods to design and optimize the pharmacological properties of various clinically available and FDA approved drugs such as remdesivir, ribavirin, favipiravir, oseltamivir, ritonavir, arbidol, chloroquine, hydroxychloroquine, carfilzomib, baraticinib, prulifloxacin, etc for effective treatment of COVID-19 infection. RESULTS Further, various survey reports suggest that the extensive studies are carried out by various research communities to find out the safety and efficacy profile of these drug candidates. CONCLUSION This review is focused on the study of various aspects of these drugs related to their target sites on virus, binding interactions, physicochemical properties etc.
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Affiliation(s)
- Biswa Mohan Sahoo
- Roland Institute of Pharmaceutical Sciences, Berhampur-760010, Odisha, India
| | - Subrat Kumar Bhattamisra
- Department of Pharmaceutical Technology, School of Medical Sciences, Adamas University, Jagannathpur, Kolkata-700126, West Bengal, India
| | - Sarita Das
- Microbiology Laboratory, Department of Botany, Berhampur University, Bhanja Bihar, Berhampur- 760007, Odisha, India
| | - Abhishek Tiwari
- Devasthali Vidyapeeth College of Pharmacy, Lalpur, Rudrapur-263148, Uttarakhand, India
| | - Varsha Tiwari
- Devasthali Vidyapeeth College of Pharmacy, Lalpur, Rudrapur-263148, Uttarakhand, India
| | - Manish Kumar
- M.M. College of Pharmacy, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala-133207, Haryana, India
| | - Sunil Singh
- Shri Sai College of Pharmacy, Handia, Prayagraj, Uttar Pradesh, 221503, India
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130
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Madhavan M, Mustafa S. Systems biology–the transformative approach to integrate sciences across disciplines. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2021-0102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Life science is the study of living organisms, including bacteria, plants, and animals. Given the importance of biology, chemistry, and bioinformatics, we anticipate that this chapter may contribute to a better understanding of the interdisciplinary connections in life science. Research in applied biological sciences has changed the paradigm of basic and applied research. Biology is the study of life and living organisms, whereas science is a dynamic subject that as a result of constant research, new fields are constantly emerging. Some fields come and go, whereas others develop into new, well-recognized entities. Chemistry is the study of composition of matter and its properties, how the substances merge or separate and also how substances interact with energy. Advances in biology and chemistry provide another means to understand the biological system using many interdisciplinary approaches. Bioinformatics is a multidisciplinary or rather transdisciplinary field that encourages the use of computer tools and methodologies for qualitative and quantitative analysis. There are many instances where two fields, biology and chemistry have intersection. In this chapter, we explain how current knowledge in biology, chemistry, and bioinformatics, as well as its various interdisciplinary domains are merged into life sciences and its applications in biological research.
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Affiliation(s)
- Maya Madhavan
- Department of Biochemistry , Government College for Women , Thiruvananthapuram , Kerala , India
| | - Sabeena Mustafa
- Department of Biostatistics and Bioinformatics , King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard Health Affairs (MNGHA) , Riyadh , Kingdom of Saudi Arabia
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Abstract
Drug repurposing refers to finding new indications for existing drugs. The paradigm shift from traditional drug discovery to drug repurposing is driven by the fact that new drug pipelines are getting dried up because of mounting Research & Development (R&D) costs, long timeline for new drug development, low success rate for new molecular entities, regulatory hurdles coupled with revenue loss from patent expiry and competition from generics. Anaemic drug pipelines along with increasing demand for newer effective, cheaper, safer drugs and unmet medical needs call for new strategies of drug discovery and, drug repurposing seems to be a promising avenue for such endeavours. Drug repurposing strategies have progressed over years from simple serendipitous observations to more complex computational methods in parallel with our ever-growing knowledge on drugs, diseases, protein targets and signalling pathways but still the knowledge is far from complete. Repurposed drugs too have to face many obstacles, although lesser than new drugs, before being successful.
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132
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Polypharmacology: The science of multi-targeting molecules. Pharmacol Res 2022; 176:106055. [PMID: 34990865 DOI: 10.1016/j.phrs.2021.106055] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/23/2021] [Accepted: 12/31/2021] [Indexed: 12/28/2022]
Abstract
Polypharmacology is a concept where a molecule can interact with two or more targets simultaneously. It offers many advantages as compared to the conventional single-targeting molecules. A multi-targeting drug is much more efficacious due to its cumulative efficacy at all of its individual targets making it much more effective in complex and multifactorial diseases like cancer, where multiple proteins and pathways are involved in the onset and development of the disease. For a molecule to be polypharmacologic in nature, it needs to possess promiscuity which is the ability to interact with multiple targets; and at the same time avoid binding to antitargets which would otherwise result in off-target adverse effects. There are certain structural features and physicochemical properties which when present would help researchers to predict if the designed molecule would possess promiscuity or not. Promiscuity can also be identified via advanced state-of-the-art computational methods. In this review, we also elaborate on the methods by which one can intentionally incorporate promiscuity in their molecules and make them polypharmacologic. The polypharmacology paradigm of "one drug-multiple targets" has numerous applications especially in drug repurposing where an already established drug is redeveloped for a new indication. Though designing a polypharmacological drug is much more difficult than designing a single-targeting drug, with the current technologies and information regarding different diseases and chemical functional groups, it is plausible for researchers to intentionally design a polypharmacological drug and unlock its advantages.
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133
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Bustamante C, Muskus C, Ochoa R. Rational computational approaches to predict novel drug candidates against leishmaniasis. ANNUAL REPORTS IN MEDICINAL CHEMISTRY 2022. [DOI: 10.1016/bs.armc.2022.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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134
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AI-powered drug repurposing for developing COVID-19 treatments. REFERENCE MODULE IN BIOMEDICAL SCIENCES 2022. [PMCID: PMC8865759 DOI: 10.1016/b978-0-12-824010-6.00005-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Emerging infectious diseases are an ever-present threat to public health, and COVID-19 is the most recent example. There is an urgent need to develop a robust framework to combat the disease with safe and effective therapeutic options. Compared to de novo drug discovery, drug repurposing may offer a lower-cost and faster drug discovery paradigm to explore potential treatment options of existing drugs. This chapter elucidates the advantages of artificial intelligence (AI) in enhancing the drug repurposing process from a data science perspective, using COVID-19 as an example. First, we elaborate on how AI-powered drug repurposing benefits from the accumulated data and knowledge of COVID-19 natural history and pathogenesis. Second, we summarize the pros and cons of AI-powered drug repurposing strategies to facilitate fit-for-purpose selection. Finally, we outline challenges of AI-powered drug repurposing from a regulatory perspective and suggest some potential solutions. AI-powered drug purposing is promising for emerging treatments for COVID-19 infection. Accumulated biological data profiles facilitate AI-based drug repurposing efforts for development of COVID-19 therapies. The ‘fit-for-purpose selection of AI-powered drug repurposing strategies is key to uncovering hidden information among drugs, targets, and diseases. Efforts from different stakeholders boost the adoption of AI-powered drug repurposing in the regulatory setting.
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Yang B, Wang X, Dong D, Pan Y, Wu J, Liu J. Existing Drug Repurposing for Glioblastoma to Discover Candidate Drugs as a New a Approach. LETT DRUG DES DISCOV 2022. [DOI: 10.2174/1570180818666210509141735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Aims:
Repurposing of drugs has been hypothesized as a means of identifying novel
treatment methods for certain diseases.
Background:
Glioblastoma (GB) is an aggressive type of human cancer; the most effective treatment
for glioblastoma is chemotherapy, whereas, when repurposing drugs, a lot of time and money can be
saved.
Objective:
Repurposing of the existing drug may be used to discover candidate drugs for individualized
treatments of GB.
Method:
We used the bioinformatics method to obtain the candidate drugs. In addition, the drugs
were verified by MTT assay, Transwell® assays, TUNEL staining, and in vivo tumor formation experiments,
as well as statistical analysis.
Result:
We obtained 4 candidate drugs suitable for the treatment of glioma, camptothecin, doxorubicin,
daunorubicin and mitoxantrone, by the expression spectrum data IPAS algorithm analysis and
drug-pathway connectivity analysis. These validation experiments showed that camptothecin was
more effective in treating the GB, such as MTT assay, Transwell® assays, TUNEL staining, and in
vivo tumor formation.
Conclusion:
With regard to personalized treatment, this present study may be used to guide the research
of new drugs via verification experiments and tumor formation. The present study also provides
a guide to systematic, individualized drug discovery for complex diseases and may contribute
to the future application of individualized treatments.
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Affiliation(s)
- Bo Yang
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
| | - Xiande Wang
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
| | - Dong Dong
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
| | - Yunqing Pan
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
| | - Junhua Wu
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
| | - Jianjian Liu
- Department of Neurosurgery, Hangzhou Medical College Affiliated Lin’an People’s Hospital, The First People’s
Hospital of Hangzhou Lin’an District, Hangzhou, Zhejiang, 311300, China
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Alakus TB, Turkoglu I. A Comparative Study of Amino Acid Encoding Methods for Predicting Drug-Target Interactions in COVID-19 Disease. MODELING, CONTROL AND DRUG DEVELOPMENT FOR COVID-19 OUTBREAK PREVENTION 2022:619-643. [DOI: 10.1007/978-3-030-72834-2_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Antifungal Activity of N-(4-Halobenzyl)amides against Candida spp. and Molecular Modeling Studies. Int J Mol Sci 2021; 23:ijms23010419. [PMID: 35008845 PMCID: PMC8745543 DOI: 10.3390/ijms23010419] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/08/2021] [Accepted: 12/10/2021] [Indexed: 12/28/2022] Open
Abstract
Fungal infections remain a high-incidence worldwide health problem that is aggravated by limited therapeutic options and the emergence of drug-resistant strains. Cinnamic and benzoic acid amides have previously shown bioactivity against different species belonging to the Candida genus. Here, 20 cinnamic and benzoic acid amides were synthesized and tested for inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019. Five compounds inhibited the Candida strains tested, with compound 16 (MIC = 7.8 µg/mL) producing stronger antifungal activity than fluconazole (MIC = 16 µg/mL) against C. krusei ATCC 14243. It was also tested against eight Candida strains, including five clinical strains resistant to fluconazole, and showed an inhibitory effect against all strains tested (MIC = 85.3–341.3 µg/mL). The MIC value against C. krusei ATCC 6258 was 85.3 mcg/mL, while against C. krusei ATCC 14243, it was 10.9 times smaller. This strain had greater sensitivity to the antifungal action of compound 16. The inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019 was also achieved by compounds 2, 9, 12, 14 and 15. Computational experiments combining target fishing, molecular docking and molecular dynamics simulations were performed to study the potential mechanism of action of compound 16 against C. krusei. From these, a multi-target mechanism of action is proposed for this compound that involves proteins related to critical cellular processes such as the redox balance, kinases-mediated signaling, protein folding and cell wall synthesis. The modeling results might guide future experiments focusing on the wet-lab investigation of the mechanism of action of this series of compounds, as well as on the optimization of their inhibitory potency.
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Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme. Int J Mol Sci 2021; 22:ijms222413213. [PMID: 34948010 PMCID: PMC8706582 DOI: 10.3390/ijms222413213] [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] [Received: 11/04/2021] [Revised: 11/30/2021] [Accepted: 12/04/2021] [Indexed: 11/29/2022] Open
Abstract
Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.
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Bai X, Yin Y. Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development. J Cheminform 2021; 13:95. [PMID: 34872613 PMCID: PMC8650415 DOI: 10.1186/s13321-021-00574-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 11/20/2021] [Indexed: 11/10/2022] Open
Abstract
Predicting compound-protein interactions (CPIs) is of great importance for drug discovery and repositioning, yet still challenging mainly due to the sparse nature of CPI matrixes, resulting in poor generalization performance. Hence, unlike typical CPI prediction models focused on representation learning or model selection, we propose a deep neural network-based strategy, PCM-AAE, that re-explores and augments the pharmacological space of kinase inhibitors by introducing the adversarial auto-encoder model (AAE) to improve the generalization of the prediction model. To complete the data space, we constructed Ensemble of PCM-AAE (EPA), an ensemble model that quickly and accurately yields quantitative predictions of binding affinity between any human kinase and inhibitor. In rigorous internal validation, EPA showed excellent performance, consistently outperforming the model trained with the imbalanced set, especially for targets with relatively fewer training data points. Improved prediction accuracy of EPA for external datasets enhances its generalization ability, making it possible to gracefully handle previously unseen kinases and inhibitors. EPA showed promising potential when directly applied to virtual screening and off-target prediction, exhibiting its practicality in hit prediction. Our strategy is expected to facilitate kinase-centric drug development, as well as to solve more challenging prediction problems with insufficient data points.
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Affiliation(s)
- Xinyu Bai
- Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China.,Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, People's Republic of China
| | - Yuxin Yin
- Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China. .,Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, People's Republic of China. .,Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China.
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140
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Ng YL, Salim CK, Chu JJH. Drug repurposing for COVID-19: Approaches, challenges and promising candidates. Pharmacol Ther 2021; 228:107930. [PMID: 34174275 PMCID: PMC8220862 DOI: 10.1016/j.pharmthera.2021.107930] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 05/10/2021] [Accepted: 05/20/2021] [Indexed: 02/07/2023]
Abstract
Traditional drug development and discovery has not kept pace with threats from emerging and re-emerging diseases such as Ebola virus, MERS-CoV and more recently, SARS-CoV-2. Among other reasons, the exorbitant costs, high attrition rate and extensive periods of time from research to market approval are the primary contributing factors to the lag in recent traditional drug developmental activities. Due to these reasons, drug developers are starting to consider drug repurposing (or repositioning) as a viable alternative to the more traditional drug development process. Drug repurposing aims to find alternative uses of an approved or investigational drug outside of its original indication. The key advantages of this approach are that there is less developmental risk, and it is less time-consuming since the safety and pharmacological profile of the repurposed drug is already established. To that end, various approaches to drug repurposing are employed. Computational approaches make use of machine learning and algorithms to model disease and drug interaction, while experimental approaches involve a more traditional wet-lab experiments. This review would discuss in detail various ongoing drug repurposing strategies and approaches to combat the current COVID-19 pandemic, along with the advantages and the potential challenges.
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Affiliation(s)
- Yan Ling Ng
- Laboratory of Molecular RNA Virology and Antiviral Strategies, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 5 Science Drive 2, 117545, Singapore,Infectious Diseases Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, 117597, Singapore
| | - Cyrill Kafi Salim
- Laboratory of Molecular RNA Virology and Antiviral Strategies, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 5 Science Drive 2, 117545, Singapore,Infectious Diseases Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, 117597, Singapore
| | - Justin Jang Hann Chu
- Laboratory of Molecular RNA Virology and Antiviral Strategies, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 5 Science Drive 2, 117545, Singapore,Infectious Diseases Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, 117597, Singapore,Collaborative and Translation Unit for HFMD, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore,Corresponding author at: Laboratory of Molecular RNA Virology and Antiviral Strategies, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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141
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Dafniet B, Cerisier N, Boezio B, Clary A, Ducrot P, Dorval T, Gohier A, Brown D, Audouze K, Taboureau O. Development of a chemogenomics library for phenotypic screening. J Cheminform 2021; 13:91. [PMID: 34819133 PMCID: PMC8611952 DOI: 10.1186/s13321-021-00569-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 11/06/2021] [Indexed: 12/03/2022] Open
Abstract
With the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of novel and safe drugs. However, phenotypic screening does not rely on knowledge of specific drug targets and needs to be combined with chemical biology approaches to identify therapeutic targets and mechanisms of actions induced by drugs and associated with an observable phenotype. In this study, we developed a system pharmacology network integrating drug-target-pathway-disease relationships as well as morphological profile from an existing high content imaging-based high-throughput phenotypic profiling assay known as “Cell Painting”. Furthermore, from this network, a chemogenomic library of 5000 small molecules that represent a large and diverse panel of drug targets involved in diverse biological effects and diseases has been developed. Such a platform and a chemogenomic library could assist in the target identification and mechanism deconvolution of some phenotypic assays. The usefulness of the platform is illustrated through examples.
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Affiliation(s)
- Bryan Dafniet
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Natacha Cerisier
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Batiste Boezio
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Anaelle Clary
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Pierre Ducrot
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Thierry Dorval
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Arnaud Gohier
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - David Brown
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Karine Audouze
- Université de Paris, INSERM UMR S-1124, 75006, Paris, France
| | - Olivier Taboureau
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France.
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142
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Slocum ST, DiBerto JF, Roth BL. Molecular insights into psychedelic drug action. J Neurochem 2021; 162:24-38. [PMID: 34797943 DOI: 10.1111/jnc.15540] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/11/2021] [Accepted: 11/11/2021] [Indexed: 12/14/2022]
Abstract
A confluence of factors has renewed interest in the scientific understanding and translational potential of psychedelic drugs such as lysergic acid diethylamide (LSD), mescaline, and psilocybin: the desire for additional approaches to mental health care, incremental progress in basic and clinical research, and the reconsideration and relaxation of existing drug policies. With the United States Food and Drug Administration's designation of psilocybin as a "Breakthrough Therapy" for treatment-resistant depression, a new path has been forged for the conveyance of psychedelics to the clinic. Essential to the further development of such applications, however, is a clearer understanding of how these drugs exert their effects at the molecular level. Here we review the current knowledge regarding the molecular details of psychedelic drug actions and suggest that these discoveries can facilitate new insights into their hallucinogenic and therapeutic mechanisms.
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Affiliation(s)
- Samuel T Slocum
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | - Jeffrey F DiBerto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
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143
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Venkateswaran MR, Vadivel TE, Jayabal S, Murugesan S, Rajasekaran S, Periyasamy S. A review on network pharmacology based phytotherapy in treating diabetes- An environmental perspective. ENVIRONMENTAL RESEARCH 2021; 202:111656. [PMID: 34265348 DOI: 10.1016/j.envres.2021.111656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/19/2021] [Accepted: 07/04/2021] [Indexed: 06/13/2023]
Abstract
Diabetes has become common lifestyle disorder associated with obesity and cardiovascular diseases. Environmental factors like physical inactivity, polluted surroundings and unhealthy dieting also plays a vital role in diabetes pathogenesis. As the current anti-diabetic drugs possess unprecedented side effects, traditional herbal medicine can be used an alternative therapy. The paramount challenge with the herbal formulation usage is the lack of standardized procedure, entangled with little knowledge on drug safety and mechanism of drug action. Heavy metal contamination is a major environmental hazard where plants tend to accumulate toxic metals like nickel, chromium and lead through industrial and agricultural activities. It becomes inappropriate to use these plants for phytotherapy as it may affect the human health on long term consumption. This review discuss about the environmental risk factors related to diabetes and better implication of medicinal plants in anti-diabetic therapy using network pharmacology. It is an in silico analytical tool that helps to unravel the multi-targeted action of herbal formulations rich in secondary metabolites. Also, a special focus is attempted to pool the databases regarding the medicinal plants for diabetes and associated diseases, their bioactive compounds, possible diabetic targets, drug-target interaction and toxicology reports that may open an aisle in safer, effective and toxicity-free drug discovery.
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Affiliation(s)
- Meenakshi R Venkateswaran
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Tamil Elakkiya Vadivel
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Sasidharan Jayabal
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Selvakumar Murugesan
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Subbiah Rajasekaran
- Department of Biochemistry, ICMR-National Institute for Research in Environmental Health, Bhopal, India.
| | - Sureshkumar Periyasamy
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India.
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144
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Yang J, He S, Zhang Z, Bo X. NegStacking: Drug-Target Interaction Prediction Based on Ensemble Learning and Logistic Regression. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2624-2634. [PMID: 31985434 DOI: 10.1109/tcbb.2020.2968025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Drug-target interactions (DTIs) identification is an important issue of drug research, and many methods proposed to predict potential DTIs based on machine learning treat it as a binary classification problem. However, the number of known interacting drug-target pairs (positive samples) is far less than that of non-interacting pairs (negative samples). Most methods do not utilize these large numbers of negative samples sufficiently, which limits their prediction performance. To address this problem, we proposed a stacking framework named NegStacking. First, it uses sampling to obtain multiple completely different negative sample sets. Then, each weak learner is trained with a different negative sample set and the same positive sample set, and the logistic regression (LR) is used as a meta-learner to adaptively combine these weak learners. Moreover, in the training process, feature subspacing and hyperparameter perturbation are applied to increase ensemble diversity. Finally, the trained model could be used to predict new samples. We compared NegStacking with other methods, and the experimental results show that our model is superior. NegStacking can improve the performance of predictive DTIs, and it has broad application prospects for improving the drug discovery process. The source code and datasets are available at https://github.com/Open-ss/NegStacking.
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145
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Deep learning in target prediction and drug repositioning: Recent advances and challenges. Drug Discov Today 2021; 27:1796-1814. [PMID: 34718208 DOI: 10.1016/j.drudis.2021.10.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/02/2021] [Accepted: 10/21/2021] [Indexed: 12/12/2022]
Abstract
Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.
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146
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Ciriaco F, Gambacorta N, Alberga D, Nicolotti O. Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach. J Chem Inf Model 2021; 61:4868-4876. [PMID: 34570498 DOI: 10.1021/acs.jcim.1c00498] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a new quantitative ligand-based bioactivity prediction approach employing a multifingerprint similarity search algorithm, enabling the polypharmacological profiling of small molecules. Quantitative bioactivity predictions are made on the basis of the statistical distributions of multiple Tanimoto similarity θ values, calculated through 13 different molecular fingerprints, and of the variation of the measured biological activity, reported as ΔpIC50, for all of the ligands sharing a given protein drug target. The application data set comprises as much as 4241 protein drug targets as well as 418 485 ligands selected from ChEMBL (release 25) by employing a set of well-defined filtering rules. Several large internal and external validation studies were carried out to demonstrate the robustness and the predictive potential of the herein proposed method. Additional comparative studies, carried out on two freely available and well-known ligand-target prediction platforms, demonstrated the reliability of our proposed approach for accurate ligand-target matching. Moreover, two applicative cases were also discussed to practically describe how to use our predictive algorithm, which is freely available as a user-friendly web platform. The user can screen single or multiple queries at a time and retrieve the output as a terse html table or as a json file including all of the information concerning the explored similarities to obtain a deeper understanding of the results. High-throughput virtual reverse screening campaigns, allowing for a given query compound the quick detection of the potential drug target from a large collection of them, can be carried out in batch on demand.
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Affiliation(s)
- Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
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147
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Antolin AA, Clarke PA, Collins I, Workman P, Al-Lazikani B. Evolution of kinase polypharmacology across HSP90 drug discovery. Cell Chem Biol 2021; 28:1433-1445.e3. [PMID: 34077750 PMCID: PMC8550792 DOI: 10.1016/j.chembiol.2021.05.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/12/2021] [Accepted: 05/05/2021] [Indexed: 12/14/2022]
Abstract
Most small molecules interact with several target proteins but this polypharmacology is seldom comprehensively investigated or explicitly exploited during drug discovery. Here, we use computational and experimental methods to identify and systematically characterize the kinase cross-pharmacology of representative HSP90 inhibitors. We demonstrate that the resorcinol clinical candidates ganetespib and, to a lesser extent, luminespib, display unique off-target kinase pharmacology as compared with other HSP90 inhibitors. We also demonstrate that polypharmacology evolved during the optimization to discover luminespib and that the hit, leads, and clinical candidate all have different polypharmacological profiles. We therefore recommend the computational and experimental characterization of polypharmacology earlier in drug discovery projects to unlock new multi-target drug design opportunities.
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Affiliation(s)
- Albert A Antolin
- Department of Data Science, The Institute of Cancer Research, London SM2 5NG, UK; Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London SM2 5NG, UK.
| | - Paul A Clarke
- Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London SM2 5NG, UK
| | - Ian Collins
- Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London SM2 5NG, UK
| | - Paul Workman
- Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London SM2 5NG, UK.
| | - Bissan Al-Lazikani
- Department of Data Science, The Institute of Cancer Research, London SM2 5NG, UK; Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London SM2 5NG, UK.
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148
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Xuan P, Hu K, Cui H, Zhang T, Nakaguchi T. Learning multi-scale heterogeneous representations and global topology for drug-target interaction prediction. IEEE J Biomed Health Inform 2021; 26:1891-1902. [PMID: 34673498 DOI: 10.1109/jbhi.2021.3121798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Identification of drug-target interactions (DTIs) plays a critical role in drug discovery and repositioning. Deep integration of inter-connections and intra-similarities between heterogeneous multi-source data related to drugs and targets, however, is a challenging issue. We propose a DTI prediction model by learning from drug and protein related multi-scale attributes and global topology formed by heterogeneous connections. A drug-protein-disease heterogeneous network (RPD-Net) is firstly constructed to associate diverse similarities, interactions and associations across nodes. Secondly, we propose a multi-scale pairwise deep representation learning module consisting of a new embedding strategy to integrate diverse inter-relations and intra-relations, and dilation convolutions for multi-scale deep representation extraction. A global topology learning module is proposed which is composed of strategy based on non-negative matrix factorization (NMF) to extract topology from RPD-Net, and a new relational-level attention mechanism for discriminative topology embedding. Experimental results using public dataset demonstrate improved performance over state-of-the-art methods and contributions of our major innovations. Evaluation results by top k recall rates and case studies on five drugs further show the effectiveness in retrieving potential target candidates for drugs.
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149
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Fernández-Torras A, Comajuncosa-Creus A, Duran-Frigola M, Aloy P. Connecting chemistry and biology through molecular descriptors. Curr Opin Chem Biol 2021; 66:102090. [PMID: 34626922 DOI: 10.1016/j.cbpa.2021.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 08/23/2021] [Accepted: 09/03/2021] [Indexed: 01/14/2023]
Abstract
Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties. Here, we give an overview of biologically relevant descriptors, covering chemical compounds, proteins and other biological entities, such as diseases and cell lines, while aligning them to the major contributions in the field from disciplines, such as natural language processing or computer vision. We now envision a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations, such as distance measures, additions, and subtractions.
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Affiliation(s)
- Adrià Fernández-Torras
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Arnau Comajuncosa-Creus
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Ersilia Open Source Initiative, Cambridge, United Kingdom
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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150
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Wen Q, Liu R, Zhang P. Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases. BMC Med Inform Decis Mak 2021; 21:263. [PMID: 34560862 PMCID: PMC8461864 DOI: 10.1186/s12911-021-01617-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 08/23/2021] [Indexed: 11/10/2022] Open
Abstract
Background Drug repurposing, the process of identifying additional therapeutic uses for existing drugs, has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials. Results In this study, we propose a novel framework based on clinical connectivity mapping for drug repurposing to analyze therapeutic effects of drugs on diseases. We firstly establish clinical drug effect vectors (i.e., drug-laboratory results associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data, then establish clinical disease sign vectors (i.e., disease-laboratory results associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Eventually, a repurposing possibility score for each drug-disease pair is computed by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. During the experiment, we comprehensively evaluate 392 drugs for 6 important chronic diseases (include asthma, coronary heart disease, congestive heart failure, heart attack, type 2 diabetes, and stroke). The experiment results not only reflect known associations between diseases and drugs, but also include some hidden drug-disease associations. The code for this paper is available at: https://github.com/HoytWen/CCMDR Conclusions The proposed clinical connectivity map framework uses laboratory results found from electronic clinical information to bridge drugs and diseases, which make their relations explainable and has better translational power than existing computational methods. Experimental results demonstrate the effectiveness of our proposed framework, further case analysis also proves our method can be used to repurposing existing drugs opportunities.
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Affiliation(s)
- Qianlong Wen
- Department of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Ave, Columbus, Ohio, 43210, USA
| | - Ruoqi Liu
- Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, Ohio, 43210, USA
| | - Ping Zhang
- Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, Ohio, 43210, USA. .,Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, Ohio, 43210, USA.
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