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Mouysset B, Le Grand M, Camoin L, Pasquier E. Poly-pharmacology of existing drugs: How to crack the code? Cancer Lett 2024; 588:216800. [PMID: 38492768 DOI: 10.1016/j.canlet.2024.216800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/15/2024] [Accepted: 03/05/2024] [Indexed: 03/18/2024]
Abstract
Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner.
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Affiliation(s)
- Baptiste Mouysset
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Marion Le Grand
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Luc Camoin
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Eddy Pasquier
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
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2
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Mansouri M, Fussenegger M. Small-Molecule Regulators for Gene Switches to Program Mammalian Cell Behaviour. Chembiochem 2024; 25:e202300717. [PMID: 38081780 DOI: 10.1002/cbic.202300717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/11/2023] [Indexed: 01/13/2024]
Abstract
Synthetic or natural small molecules have been extensively employed as trigger signals or inducers to regulate engineered gene circuits introduced into living cells in order to obtain desired outputs in a controlled and predictable manner. Here, we provide an overview of small molecules used to drive synthetic-biology-based gene circuits in mammalian cells, together with examples of applications at different levels of control, including regulation of DNA manipulation, RNA synthesis and editing, and protein synthesis, maturation, and trafficking. We also discuss the therapeutic potential of these small-molecule-responsive gene circuits, focusing on the advantages and disadvantages of using small molecules as triggers, the mechanisms involved, and the requirements for selecting suitable molecules, including efficiency, specificity, orthogonality, and safety. Finally, we explore potential future directions for translation of these devices to clinical medicine.
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Affiliation(s)
- Maysam Mansouri
- ETH Zurich, Department of Biosystems Science and Engineering, Klingelbergstrasse 48, CH-4056, Basel, Switzerland
| | - Martin Fussenegger
- ETH Zurich, Department of Biosystems Science and Engineering, Klingelbergstrasse 48, CH-4056, Basel, Switzerland
- University of Basel, Faculty of Science, Klingelbergstrasse 48, CH-4056, Basel, Switzerland
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3
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Smith MD, Darryl Quarles L, Demerdash O, Smith JC. Drugging the entire human proteome: Are we there yet? Drug Discov Today 2024; 29:103891. [PMID: 38246414 DOI: 10.1016/j.drudis.2024.103891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.
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Affiliation(s)
- Micholas Dean Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - L Darryl Quarles
- Departments of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; ORRxD LLC, 3404 Olney Drive, Durham, NC 27705, USA
| | - Omar Demerdash
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Jeremy C Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
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4
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Myint KZ, Balasubramanian B, Venkatraman S, Phimsen S, Sripramote S, Jantra J, Choeiphuk C, Mingphruedhi S, Muangkaew P, Rungsakulkij N, Tangtawee P, Suragul W, Farquharson WV, Wongprasert K, Chutipongtanate S, Sanvarinda P, Ponpuak M, Poungvarin N, Janvilisri T, Suthiphongchai T, Yacqub-Usman K, Grabowska AM, Bates DO, Tohtong R. Therapeutic Implications of Ceritinib in Cholangiocarcinoma beyond ALK Expression and Mutation. Pharmaceuticals (Basel) 2024; 17:197. [PMID: 38399413 PMCID: PMC10892566 DOI: 10.3390/ph17020197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
Cholangiocarcinoma (CCA) is a difficult-to-treat cancer, with limited therapeutic options and surgery being the only curative treatment. Standard chemotherapy involves gemcitabine-based therapies combined with cisplatin, oxaliplatin, capecitabine, or 5-FU with a dismal prognosis for most patients. Receptor tyrosine kinases (RTKs) are aberrantly expressed in CCAs encompassing potential therapeutic opportunity. Hence, 112 RTK inhibitors were screened in KKU-M213 cells, and ceritinib, an approved targeted therapy for ALK-fusion gene driven cancers, was the most potent candidate. Ceritinib's cytotoxicity in CCA was assessed using MTT and clonogenic assays, along with immunofluorescence, western blot, and qRT-PCR techniques to analyze gene expression and signaling changes. Furthermore, the drug interaction relationship between ceritinib and cisplatin was determined using a ZIP synergy score. Additionally, spheroid and xenograft models were employed to investigate the efficacy of ceritinib in vivo. Our study revealed that ceritinib effectively killed CCA cells at clinically relevant plasma concentrations, irrespective of ALK expression or mutation status. Ceritinib modulated multiple signaling pathways leading to the inhibition of the PI3K/Akt/mTOR pathway and activated both apoptosis and autophagy. Additionally, ceritinib and cisplatin synergistically reduced CCA cell viability. Our data show ceritinib as an effective treatment of CCA, which could be potentially explored in the other cancer types without ALK mutations.
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Affiliation(s)
- Kyaw Zwar Myint
- Graduate Program in Molecular Medicine, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; (K.Z.M.); (B.B.); (S.V.); (T.J.)
| | - Brinda Balasubramanian
- Graduate Program in Molecular Medicine, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; (K.Z.M.); (B.B.); (S.V.); (T.J.)
- Translational Medical Sciences Unit, School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK
| | - Simran Venkatraman
- Graduate Program in Molecular Medicine, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; (K.Z.M.); (B.B.); (S.V.); (T.J.)
| | - Suchada Phimsen
- Department of Biochemistry, Faculty of Medical Science, Naresuan University, Phitsanulok 65000, Thailand; (S.P.); (C.C.)
| | - Supisara Sripramote
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; (S.S.); (J.J.); (T.S.)
| | - Jeranan Jantra
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; (S.S.); (J.J.); (T.S.)
| | - Chaiwat Choeiphuk
- Department of Biochemistry, Faculty of Medical Science, Naresuan University, Phitsanulok 65000, Thailand; (S.P.); (C.C.)
| | - Somkit Mingphruedhi
- Hepato-Pancreatic-Biliary Surgery Unit, Department of Surgery, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; (S.M.); (P.M.); (N.R.); (P.T.); (W.S.); (W.V.F.)
| | - Paramin Muangkaew
- Hepato-Pancreatic-Biliary Surgery Unit, Department of Surgery, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; (S.M.); (P.M.); (N.R.); (P.T.); (W.S.); (W.V.F.)
| | - Narongsak Rungsakulkij
- Hepato-Pancreatic-Biliary Surgery Unit, Department of Surgery, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; (S.M.); (P.M.); (N.R.); (P.T.); (W.S.); (W.V.F.)
| | - Pongsatorn Tangtawee
- Hepato-Pancreatic-Biliary Surgery Unit, Department of Surgery, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; (S.M.); (P.M.); (N.R.); (P.T.); (W.S.); (W.V.F.)
| | - Wikran Suragul
- Hepato-Pancreatic-Biliary Surgery Unit, Department of Surgery, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; (S.M.); (P.M.); (N.R.); (P.T.); (W.S.); (W.V.F.)
| | - Watoo Vassanasiri Farquharson
- Hepato-Pancreatic-Biliary Surgery Unit, Department of Surgery, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; (S.M.); (P.M.); (N.R.); (P.T.); (W.S.); (W.V.F.)
| | - Kanokpan Wongprasert
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok 10400, Thailand;
| | - Somchai Chutipongtanate
- Division of Epidemiology, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Pimtip Sanvarinda
- Department of Pharmacology, Faculty of Science, Mahidol University, Bangkok 10400, Thailand;
| | - Marisa Ponpuak
- Department of Microbiology, Faculty of Science, Mahidol University, Bangkok 10400, Thailand;
| | - Naravat Poungvarin
- Department of Clinical Pathology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand;
| | - Tavan Janvilisri
- Graduate Program in Molecular Medicine, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; (K.Z.M.); (B.B.); (S.V.); (T.J.)
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; (S.S.); (J.J.); (T.S.)
| | - Tuangporn Suthiphongchai
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; (S.S.); (J.J.); (T.S.)
| | - Kiren Yacqub-Usman
- Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK; (K.Y.-U.); (A.M.G.); (D.O.B.)
| | - Anna M. Grabowska
- Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK; (K.Y.-U.); (A.M.G.); (D.O.B.)
| | - David O. Bates
- Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK; (K.Y.-U.); (A.M.G.); (D.O.B.)
| | - Rutaiwan Tohtong
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; (S.S.); (J.J.); (T.S.)
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Catalani E, Brunetti K, Del Quondam S, Bongiorni S, Picchietti S, Fausto AM, Lupidi G, Marcantoni E, Perrotta C, Achille G, Buonanno F, Ortenzi C, Cervia D. Exposure to the Natural Compound Climacostol Induces Cell Damage and Oxidative Stress in the Fruit Fly Drosophila melanogaster. Toxics 2024; 12:102. [PMID: 38393197 PMCID: PMC10891975 DOI: 10.3390/toxics12020102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/25/2024]
Abstract
The ciliate Climacostomum virens produces the metabolite climacostol that displays antimicrobial activity and cytotoxicity on human and rodent tumor cells. Given its potential as a backbone in pharmacological studies, we used the fruit fly Drosophila melanogaster to evaluate how the xenobiotic climacostol affects biological systems in vivo at the organismal level. Food administration with climacostol demonstrated its harmful role during larvae developmental stages but not pupation. The midgut of eclosed larvae showed apoptosis and increased generation of reactive oxygen species (ROS), thus demonstrating gastrointestinal toxicity. Climacostol did not affect enteroendocrine cell proliferation, suggesting moderate damage that does not initiate the repairing program. The fact that climacostol increased brain ROS and inhibited the proliferation of neural cells revealed a systemic (neurotoxic) role of this harmful substance. In this line, we found lower expression of relevant antioxidant enzymes in the larvae and impaired mitochondrial activity. Adult offsprings presented no major alterations in survival and mobility, as well the absence of abnormal phenotypes. However, mitochondrial activity and oviposition behavior was somewhat affected, indicating the chronic toxicity of climacostol, which continues moderately until adult stages. These results revealed for the first time the detrimental role of ingested climacostol in a non-target multicellular organism.
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Affiliation(s)
- Elisabetta Catalani
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
| | - Kashi Brunetti
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
| | - Simona Del Quondam
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
| | - Silvia Bongiorni
- Department of Ecological and Biological Sciences (DEB), Università degli Studi della Tuscia, 01100 Viterbo, Italy;
| | - Simona Picchietti
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
| | - Anna Maria Fausto
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
| | - Gabriele Lupidi
- School of Science and Technology, Section of Chemistry, Università degli Studi di Camerino, 62032 Camerino, Italy; (G.L.); (E.M.)
| | - Enrico Marcantoni
- School of Science and Technology, Section of Chemistry, Università degli Studi di Camerino, 62032 Camerino, Italy; (G.L.); (E.M.)
| | - Cristiana Perrotta
- Department of Biomedical and Clinical Sciences (DIBIC), Università degli Studi di Milano, 20157 Milano, Italy;
| | - Gabriele Achille
- Laboratory of Protistology and Biology Education, Department of Education, Cultural Heritage, and Tourism (ECHT), Università degli Studi di Macerata, 62100 Macerata, Italy; (G.A.); (F.B.); (C.O.)
| | - Federico Buonanno
- Laboratory of Protistology and Biology Education, Department of Education, Cultural Heritage, and Tourism (ECHT), Università degli Studi di Macerata, 62100 Macerata, Italy; (G.A.); (F.B.); (C.O.)
| | - Claudio Ortenzi
- Laboratory of Protistology and Biology Education, Department of Education, Cultural Heritage, and Tourism (ECHT), Università degli Studi di Macerata, 62100 Macerata, Italy; (G.A.); (F.B.); (C.O.)
| | - Davide Cervia
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), Università degli Studi della Tuscia, 01100 Viterbo, Italy; (E.C.); (K.B.); (S.D.Q.); (S.P.); (A.M.F.)
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Bhowmik P, Baezid HM, Arabi II. Assessment of antidiabetic activity of three Phenylspirodrimanes from fungus Stachybotrys chartarum MUT 3308 by ADME, QSAR, molecular docking and molecular dynamics simulation studies against protein tyrosine phosphatase 1B (PTP1B). J Biomol Struct Dyn 2023:1-15. [PMID: 37698508 DOI: 10.1080/07391102.2023.2256410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 08/24/2023] [Indexed: 09/13/2023]
Abstract
Phenylspirodrimanes (PSD) are the sesquiterpene quinone type meroterpenoids found in nature. PSDs are found to exhibit inhibitory activity against immunocomplex diseases, and tyrosine kinase receptors. Three of the different PSDs C1, C2, and C3 that have been reported to be isolated from the sponge-associated fungus Stachybotrys chartarum MUT 3308 are selected for studying their possible inhibitory effect against type 2 diabetes mellitus. Mechanistically, blocking protein tyrosine phosphatase 1B (PTP1B) helps to reduce the insulin resistance induction caused by the high expression of PTP1B. The QSAR, ADME, toxicity (T) study was carried out to predict the pharmacokinetic properties and the biological activities of the PSDs. PASS prediction web tool was used to find and select the target proteins 1NNY, and 2HNP. According to the molecular docking simulations, C1 and C2 showed better binding affinity of -8.5 kcal/mol, and -8.1 kcal/mol respectively against 1NNY compared to the control ligand. RMSD, RMSF, Rg, and SASA analysis revealed that both C1, and C2 showed better stability, minor conformational changes, and minor fluctuation upon binding to PTP1B. Protein contact analysis was carried out to validate the residues that are in contact with the ligands according to molecular docking studies. Overall, C1, and C2 could be proposed as novel natural hits to be developed and small modifications of these PSDs could result in inducing the binding affinity significantly, although experimental validation is required for further evaluation of the work.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Prasenjit Bhowmik
- Department of Chemistry-BMC, Biochemistry, Disciplinary Domain of Science and Technology, Uppsala University, Uppsala, Sweden
- Department of Chemistry, Faculty of Science, University of Chittagong, Chittagong, Bangladesh
- Department of Textile Engineering, Green University of Bangladesh, Narayanganj, Bangladesh
| | - Hossain Mohammad Baezid
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong, Bangladesh
| | - Ishmam Ibnul Arabi
- Department of Chemistry, Faculty of Science, University of Chittagong, Chittagong, Bangladesh
- Department of Textile Engineering, Green University of Bangladesh, Narayanganj, Bangladesh
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Fronza MG, Alves D, Praticò D, Savegnago L. The neurobiology and therapeutic potential of multi-targeting β-secretase, glycogen synthase kinase 3β and acetylcholinesterase in Alzheimer's disease. Ageing Res Rev 2023; 90:102033. [PMID: 37595640 DOI: 10.1016/j.arr.2023.102033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/04/2023] [Accepted: 08/14/2023] [Indexed: 08/20/2023]
Abstract
Alzheimer's Disease (AD) is the most common form of dementia, affecting almost 50 million of people around the world, characterized by a complex and age-related progressive pathology with projections to duplicate its incidence by the end of 2050. AD pathology has two major hallmarks, the amyloid beta (Aβ) peptides accumulation and tau hyperphosphorylation, alongside with several sub pathologies including neuroinflammation, oxidative stress, loss of neurogenesis and synaptic dysfunction. In recent years, extensive research pointed out several therapeutic targets which have shown promising effects on modifying the course of the disease in preclinical models of AD but with substantial failure when transposed to clinic trials, suggesting that modulating just an isolated feature of the pathology might not be sufficient to improve brain function and enhance cognition. In line with this, there is a growing consensus that an ideal disease modifying drug should address more than one feature of the pathology. Considering these evidence, β-secretase (BACE1), Glycogen synthase kinase 3β (GSK-3β) and acetylcholinesterase (AChE) has emerged as interesting therapeutic targets. BACE1 is the rate-limiting step in the Aβ production, GSK-3β is considered the main kinase responsible for Tau hyperphosphorylation, and AChE play an important role in modulating memory formation and learning. However, the effects underlying the modulation of these enzymes are not limited by its primarily functions, showing interesting effects in a wide range of impaired events secondary to AD pathology. In this sense, this review will summarize the involvement of BACE1, GSK-3β and AChE on synaptic function, neuroplasticity, neuroinflammation and oxidative stress. Additionally, we will present and discuss new perspectives on the modulation of these pathways on AD pathology and future directions on the development of drugs that concomitantly target these enzymes.
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Affiliation(s)
- Mariana G Fronza
- Neurobiotechnology Research Group (GPN) - Centre for Technology Development CDTec, Federal University of Pelotas (UFPel), Pelotas, RS, Brazil
| | - Diego Alves
- Laboratory of Clean Organic Synthesis (LASOL), Center for Chemical, Pharmaceutical and Food Sciences (CCQFA), UFPel, RS, Brazil
| | - Domenico Praticò
- Alzheimer's Center at Temple - ACT, Temple University, Lewis Katz School of Medicine, Philadelphia, PA, United States
| | - Lucielli Savegnago
- Neurobiotechnology Research Group (GPN) - Centre for Technology Development CDTec, Federal University of Pelotas (UFPel), Pelotas, RS, Brazil.
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Coqueiro A, Fernandes DC, Danuello A, Regasini LO, Cardoso-Lopes EM, Young MCM, Brandão Torres LM, Campos VP, Silva DHS, da Silva Bolzani V, de Oliveira DF. Nematostatic activity of isoprenylated guanidine alkaloids from Pterogyne nitens and their interaction with acetylcholinesterase. Exp Parasitol 2023; 250:108542. [PMID: 37178971 DOI: 10.1016/j.exppara.2023.108542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/03/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023]
Abstract
Although new nematicides have appeared, the demand for new products less toxic and more efficient for the control of plant-parasitic nematodes are still high. Consequently, studies on natural secondary metabolites from plants, to develop new nematicides, have increased. In this work, nineteen extracts from eleven Brazilian plant species were screened for activity against Meloidogyne incognita. Among them, the extracts of Piterogyne nitens showed a potent nematostatic activity. The alkaloid fraction obtained from the ethanol extract of leaves of P. nitens was more active than the coming extract. Due to the promising activity from the alkaloid fraction, three isoprenylated guanidine alkaloids isolated from this fraction, galegine (1), pterogynidine (2), and pterogynine (3) were tested, showing similar activity to the alkaloid fraction, which was comparable to that of the positive control Temik at 250 μg/mL. At lower concentrations (125-50 μg/mL), compound 2 showed to be the most active one. As several nematicides act through inhibition of acetylcholinesterase (AChE), the guanidine alkaloids were also employed in two in vitro AChE assays. In both cases, compound 2 was more active than compounds 1 and 3. Its activity was considered moderated compared to the control (physostigmine). Compound 2 was selected for an in silico study with the electric eel (Electrophorus electricus) AChE, showing to bind mostly to the same site of physostigmine in the AChEs, pointing out that this could be the mechanism of action for this compound. These results suggested that the guanidine alkaloids 1,2 and 3 from P. nitens are promising for the development of new products to control M. incognita, especially guanidine 2, and encourage new investigations to confirm the mechanism of action, as well as to determine the structure-activity relationship of the guanidine alkaloids.
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Affiliation(s)
- Aline Coqueiro
- Nuclei of Bioassays, Biosynthesis and Ecophysiology of Natural Products (NuBBE), Department of Organic Chemistry, Institute of Chemistry, São Paulo State University (UNESP), Araraquara, SP, 14801-970, Brazil; Department of Chemistry, Federal University of Technology - Paraná (UTFPR), Ponta Grossa, PR, 84017-220, Brazil.
| | - Daniara Cristina Fernandes
- Nuclei of Bioassays, Biosynthesis and Ecophysiology of Natural Products (NuBBE), Department of Organic Chemistry, Institute of Chemistry, São Paulo State University (UNESP), Araraquara, SP, 14801-970, Brazil; Federal Institute of Education, Science and Technology of São Paulo (IFSP), Matão, SP, 15991-502, Brazil
| | - Amanda Danuello
- Nuclei of Bioassays, Biosynthesis and Ecophysiology of Natural Products (NuBBE), Department of Organic Chemistry, Institute of Chemistry, São Paulo State University (UNESP), Araraquara, SP, 14801-970, Brazil; Institute of Chemistry, Federal University of Uberlândia (UFU), Uberlândia, MG, 38408-100, Brazil
| | - Luis Octávio Regasini
- Nuclei of Bioassays, Biosynthesis and Ecophysiology of Natural Products (NuBBE), Department of Organic Chemistry, Institute of Chemistry, São Paulo State University (UNESP), Araraquara, SP, 14801-970, Brazil
| | | | | | | | - Vicente Paulo Campos
- Department of Phytopathology, Federal University of Lavras (UFLA), Lavras, MG, 37200-000, Brazil
| | - Dulce Helena Siqueira Silva
- Nuclei of Bioassays, Biosynthesis and Ecophysiology of Natural Products (NuBBE), Department of Organic Chemistry, Institute of Chemistry, São Paulo State University (UNESP), Araraquara, SP, 14801-970, Brazil
| | - Vanderlan da Silva Bolzani
- Nuclei of Bioassays, Biosynthesis and Ecophysiology of Natural Products (NuBBE), Department of Organic Chemistry, Institute of Chemistry, São Paulo State University (UNESP), Araraquara, SP, 14801-970, Brazil
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9
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Yang SQ, Zhang LX, Ge YJ, Zhang JW, Hu JX, Shen CY, Lu AP, Hou TJ, Cao DS. In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences. J Cheminform 2023; 15:48. [PMID: 37088813 PMCID: PMC10123967 DOI: 10.1186/s13321-023-00720-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 04/08/2023] [Indexed: 04/25/2023] Open
Abstract
Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.
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Affiliation(s)
- Su-Qing Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Liu-Xia Zhang
- The First Hospital of Hunan University of Chinese Medicine, Changsha, 410007, Hunan, People's Republic of China
| | - You-Jin Ge
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Jin-Wei Zhang
- Departments of Biomedical Engineering and Pathology, School of Basic Medical Science, Central South University, Changsha, 410013, Hunan, People's Republic of China
| | - Jian-Xin Hu
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Cheng-Ying Shen
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, People's Republic of China
| | - Ting-Jun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China.
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China.
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, People's Republic of China.
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10
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Ji KY, Liu C, Liu ZQ, Deng YF, Hou TJ, Cao DS. Comprehensive assessment of nine target prediction web services: which should we choose for target fishing? Brief Bioinform 2023; 24:6995377. [PMID: 36681902 DOI: 10.1093/bib/bbad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/23/2023] Open
Abstract
Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.
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Affiliation(s)
- Kai-Yue Ji
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Chong Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Zhao-Qian Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Ya-Feng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
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11
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Silva RHN, Machado TQ, da Fonseca ACC, Tejera E, Perez-Castillo Y, Robbs BK, de Sousa DP. Molecular Modeling and In Vitro Evaluation of Piplartine Analogs against Oral Squamous Cell Carcinoma. Molecules 2023; 28:molecules28041675. [PMID: 36838660 PMCID: PMC9964404 DOI: 10.3390/molecules28041675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 02/12/2023] Open
Abstract
Cancer is a principal cause of death in the world, and providing a better quality of life and reducing mortality through effective pharmacological treatment remains a challenge. Among malignant tumor types, squamous cell carcinoma-esophageal cancer (EC) is usually located in the mouth, with approximately 90% located mainly on the tongue and floor of the mouth. Piplartine is an alkamide found in certain species of the genus Piper and presents many pharmacological properties including antitumor activity. In the present study, the cytotoxic potential of a collection of piplartine analogs against human oral SCC9 carcinoma cells was evaluated. The analogs were prepared via Fischer esterification reactions, alkyl and aryl halide esterification, and a coupling reaction with PyBOP using the natural compound 3,4,5-trimethoxybenzoic acid as a starting material. The products were structurally characterized using 1H and 13C nuclear magnetic resonance, infrared spectroscopy, and high-resolution mass spectrometry for the unpublished compounds. The compound 4-methoxy-benzyl 3,4,5-trimethoxybenzoate (9) presented an IC50 of 46.21 µM, high selectively (SI > 16), and caused apoptosis in SCC9 cancer cells. The molecular modeling study suggested a multi-target mechanism of action for the antitumor activity of compound 9 with CRM1 as the main target receptor.
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Affiliation(s)
- Rayanne H. N. Silva
- Laboratory of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Federal University of Paraíba, Cidade Universitária, João Pessoa 58051-900, Brazil
| | - Thaíssa Q. Machado
- Postgraduate Program in Applied Science for Health Products, Faculty of Pharmacy, Fluminense Federal University, Niteroi 24241-000, Brazil
| | - Anna Carolina C. da Fonseca
- Postgraduate Program in Dentistry, Health Institute of Nova Friburgo, Fluminense Federal University, Nova Friburgo 28625-650, Brazil
| | - Eduardo Tejera
- Bio-Cheminformatics Research Group, Universidad de Las Américas, Quito 170516, Ecuador
| | - Yunierkis Perez-Castillo
- Facultad de Ingeniería y Ciencias Aplicadas, Área de Ciencias Aplicadas, Universidad de Las Américas, Quito 170516, Ecuador
| | - Bruno K. Robbs
- Departamento de Ciência Básica, Instituto de Saúde de Nova Friburgo, Universidade Federal Fluminense, Nova Friburgo 28625-650, Brazil
- Correspondence: (B.K.R.); (D.P.d.S.)
| | - Damião P. de Sousa
- Laboratory of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Federal University of Paraíba, Cidade Universitária, João Pessoa 58051-900, Brazil
- Correspondence: (B.K.R.); (D.P.d.S.)
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12
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Gallo K, Kemmler E, Goede A, Becker F, Dunkel M, Preissner R, Banerjee P. SuperNatural 3.0-a database of natural products and natural product-based derivatives. Nucleic Acids Res 2022; 51:D654-D659. [PMID: 36399452 PMCID: PMC9825600 DOI: 10.1093/nar/gkac1008] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/07/2022] [Accepted: 11/02/2022] [Indexed: 11/19/2022] Open
Abstract
Natural products (NPs) are single chemical compounds, substances or mixtures produced by a living organism - found in nature. Evolutionarily, NPs have been used as healing agents since thousands of years and still today continue to be the most important source of new potential therapeutic preparations. Natural products have played a key role in modern drug discovery for several diseases. Furthermore, following consumers' increasing demand for natural food ingredients, many efforts have been made to discover natural low-calorie sweeteners in recent years. SuperNatural 3.0 is a freely available database of natural products and derivatives. The updated version contains 449 058 natural compounds along with their structural and physicochemical information. Additionally, information on pathways, mechanism of action, toxicity, vendor information if available, drug-like chemical space prediction for several diseases as antiviral, antibacterial, antimalarial, anticancer, and target specific cells like the central nervous system (CNS) are also provided for the natural compounds. The updated version of the database also provides a valuable pool of natural compounds in which potential highly sweet compounds are expected to be found. The possible taste profile of the natural compounds was predicted using our published VirtualTaste models. The SuperNatural 3.0 database is freely available via http://bioinf-applied.charite.de/supernatural_3, without any login or registration.
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Affiliation(s)
- Kathleen Gallo
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Emanuel Kemmler
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Andrean Goede
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Finnja Becker
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Mathias Dunkel
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Robert Preissner
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Priyanka Banerjee
- To whom correspondence should be addressed. Tel: +49 30 450 528 505; Fax: +49 30 450 540 955;
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13
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Abstract
MOTIVATION The development of novel compounds targeting proteins of interest is one of the most important tasks in the pharmaceutical industry. Deep generative models have been applied to targeted molecular design and have shown promising results. Recently, target-specific molecule generation has been viewed as a translation between the protein language and the chemical language. However, such a model is limited by the availability of interacting protein-ligand pairs. On the other hand, large amounts of unlabelled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations. In this study, we propose exploiting pretrained biochemical language models to initialize (i.e. warm start) targeted molecule generation models. We investigate two warm start strategies: (i) a one-stage strategy where the initialized model is trained on targeted molecule generation and (ii) a two-stage strategy containing a pre-finetuning on molecular generation followed by target-specific training. We also compare two decoding strategies to generate compounds: beam search and sampling. RESULTS The results show that the warm-started models perform better than a baseline model trained from scratch. The two proposed warm-start strategies achieve similar results to each other with respect to widely used metrics from benchmarks. However, docking evaluation of the generated compounds for a number of novel proteins suggests that the one-stage strategy generalizes better than the two-stage strategy. Additionally, we observe that beam search outperforms sampling in both docking evaluation and benchmark metrics for assessing compound quality. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/boun-tabi/biochemical-lms-for-drug-design and the materials (i.e., data, models, and outputs) are archived in Zenodo at https://doi.org/10.5281/zenodo.6832145. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gökçe Uludoğan
- Department of Computer Engineering, Boğaziçi University, İstanbul 34342, Turkey
| | - Elif Ozkirimli
- Data and Analytics Chapter, Pharma International Informatics, F. Hoffmann-La Roche AG 4303, Switzerland
| | - Kutlu O Ulgen
- Department of Chemical Engineering, Boğaziçi University, İstanbul 34342, Turkey
| | - Nilgün Karalı
- Faculty of Pharmacy, Department of Pharmaceutical Chemistry, İstanbul University, İstanbul 34116, Turkey
| | - Arzucan Özgür
- Department of Computer Engineering, Boğaziçi University, İstanbul 34342, Turkey
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14
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Gallo K, Goede A, Preissner R, Gohlke BO. SuperPred 3.0: drug classification and target prediction-a machine learning approach. Nucleic Acids Res 2022; 50:W726-W731. [PMID: 35524552 PMCID: PMC9252837 DOI: 10.1093/nar/gkac297] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/01/2022] [Accepted: 04/13/2022] [Indexed: 11/21/2022] Open
Abstract
Since the last published update in 2014, the SuperPred webserver has been continuously developed to offer state-of-the-art models for drug classification according to ATC classes and target prediction. For the first time, a thoroughly filtered ATC dataset, that is suitable for accurate predictions, is provided along with detailed information on the achieved predictions. This aims to overcome the challenges in comparing different published prediction methods, since performance can vary greatly depending on the training dataset used. Additionally, both ATC and target prediction have been reworked and are now based on machine learning models instead of overall structural similarity, stressing the importance of functional groups for the mechanism of action of small molecule substances. Additionally, the dataset for the target prediction has been extensively filtered and is no longer only based on confirmed binders but also includes non-binding substances to reduce false positives. Using these methods, accuracy for the ATC prediction could be increased by almost 5% to 80.5% compared to the previous version, and additionally the scoring function now offers values which are easily assessable at first glance. SuperPred 3.0 is publicly available without the need for registration at: https://prediction.charite.de/index.php.
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Affiliation(s)
- Kathleen Gallo
- Charité - Universitätsmedizin Berlin, Institute of Physiology and Science IT, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Andrean Goede
- Charité - Universitätsmedizin Berlin, Institute of Physiology and Science IT, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Robert Preissner
- Charité - Universitätsmedizin Berlin, Institute of Physiology and Science IT, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Bjoern-Oliver Gohlke
- Charité - Universitätsmedizin Berlin, Institute of Physiology and Science IT, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
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15
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Kim JY, Cho TM, Park JM, Park S, Park M, Nam KD, Ko D, Seo J, Kim S, Jung E, Farrand L, Nguyen CT, Hoang VH, Thanh La M, Ann J, Nam G, Park HJ, Lee J, Kim YJ, Seo JH. A novel HSP90 inhibitor SL-145 suppresses metastatic triple-negative breast cancer without triggering the heat shock response. Oncogene 2022. [PMID: 35501463 DOI: 10.1038/s41388-022-02269-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 02/15/2022] [Accepted: 03/03/2022] [Indexed: 12/31/2022]
Abstract
Despite recent advances, there remains a significant unmet need for the development of new targeted therapies for triple-negative breast cancer (TNBC). Although the heat shock protein HSP90 is a promising target, previous inhibitors have had issues during development including undesirable induction of the heat shock response (HSR) and off-target effects leading to toxicity. SL-145 is a novel, rationally-designed C-terminal HSP90 inhibitor that induces apoptosis in TNBC cells via the suppression of oncogenic AKT, MEK/ERK, and JAK2/STAT3 signaling and does not trigger the HSR, in contrast to other inhibitors. In an orthotopic allograft model incorporating breast cancer stem cell-enriched TNBC tumors, SL-145 potently suppressed tumor growth, angiogenesis, and metastases concomitant with dysregulation of the JAK2/STAT3 signaling pathway. Our findings highlight the potential of SL-145 in suppressing metastatic TNBC independent of the HSR.
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16
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Beltrán-Noboa A, Proaño-Ojeda J, Guevara M, Gallo B, Berrueta LA, Giampieri F, Perez-Castillo Y, Battino M, Álvarez-Suarez JM, Tejera E. Metabolomic profile and computational analysis for the identification of the potential anti-inflammatory mechanisms of action of the traditional medicinal plants Ocimum basilicum and Ocimum tenuiflorum. Food Chem Toxicol 2022; 164:113039. [PMID: 35461962 DOI: 10.1016/j.fct.2022.113039] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/28/2022]
Abstract
Ocimum basilicum and Ocimum tenuiflorum are two basil species widely used medicinally as an anti-inflammatory, antimicrobial and cardioprotective agent. This study focuses on the chemical characterization of the majoritarian compounds of both species and their anti-inflammatory potential. Up to 22 compounds such as various types of salvianolic acids, derivatives of rosmaniric acid and flavones were identified in both plants. The identified compounds were very similar between both plants and are consistent with previous finding in other studies in Portugal and Italy. Based on the identified molecules a consensus target prediction was carried out. Among the main predicted target proteins, we found a high representation of the carbonic anhydrase family (CA2, CA7 and CA12) and several key proteins from the arachidonic pathway (LOX5, PLA2, COX1 and COX2). Both pathways are well related to inflammation. The interaction between the compounds and these targets were explored through molecular docking and molecular dynamics simulation. Our results suggest that some molecules present in both plants can induce an anti-inflammatory response through a non-steroidal mechanism of action connected to the carbon dioxide metabolism.
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Affiliation(s)
- Andrea Beltrán-Noboa
- Grupo de Bioquimioinformática. Universidad de Las Américas, Quito, Ecuador; Departamento de Química Analítica, Facultad de Ciencia y Tecnología, Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Bilbao, Spain
| | - John Proaño-Ojeda
- Grupo de Bioquimioinformática. Universidad de Las Américas, Quito, Ecuador; Facultad de Ingeniería y Ciencias Aplicadas. Carrera de Biotecnología, Universidad de Las Américas, Quito, Ecuador
| | - Mabel Guevara
- Grupo de Bioquimioinformática. Universidad de Las Américas, Quito, Ecuador; Grupo de Investigación en Polifenoles. Universidad de Salamanca, Campus Miguel de Unamuno, Salamanca, Spain
| | - Blanca Gallo
- Departamento de Química Analítica, Facultad de Ciencia y Tecnología, Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Bilbao, Spain
| | - Luis A Berrueta
- Departamento de Química Analítica, Facultad de Ciencia y Tecnología, Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Bilbao, Spain
| | - Francesca Giampieri
- Department of Clinical Sciences, Università Politecnica delle Marche, Ancona, Italy; Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Yunierkis Perez-Castillo
- Grupo de Bioquimioinformática. Universidad de Las Américas, Quito, Ecuador; Escuela de Ciencias Físicas y Matemáticas. Universidad de Las Américas, Quito, Ecuador
| | - Maurizio Battino
- Department of Clinical Sciences, Università Politecnica delle Marche, Ancona, Italy; International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang, China
| | - José M Álvarez-Suarez
- Ingeniería en Alimentos, Colegio de Ciencias e Ingenierías, Universidad San Francisco de Quito, Quito, Ecuador; King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia; Instituto de Investigaciones en Biomedicina iBioMed, Universidad San Francisco de Quito, Quito, Ecuador.
| | - Eduardo Tejera
- Grupo de Bioquimioinformática. Universidad de Las Américas, Quito, Ecuador; Facultad de Ingeniería y Ciencias Aplicadas. Carrera de Biotecnología, Universidad de Las Américas, Quito, Ecuador.
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17
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Abstract
The Na-K-2Cl cotransporter NKCC1 and the neuron-specific K-Cl cotransporter KCC2 are considered attractive CNS drug targets because altered neuronal chloride regulation and consequent effects on GABAergic signaling have been implicated in numerous CNS disorders. While KCC2 modulators are not yet clinically available, the loop diuretic bumetanide has been used off-label in attempts to treat brain disorders and as a tool for NKCC1 inhibition in preclinical models. Bumetanide is known to have anticonvulsant and neuroprotective effects under some pathophysiological conditions. However, as shown in several species from neonates to adults (mice, rats, dogs, and by extrapolation in humans), at the low clinical doses of bumetanide approved for diuresis, this drug has negligible access into the CNS, reaching levels that are much lower than what is needed to inhibit NKCC1 in cells within the brain parenchyma. Several drug discovery strategies have been initiated over the last ∼15 years to develop brain-permeant compounds that, ideally, should be selective for NKCC1 to eliminate the diuresis mediated by inhibition of renal NKCC2. The strategies employed to improve the pharmacokinetic and pharmacodynamic properties of NKCC1 blockers include evaluation of other clinically approved loop diuretics; development of lipophilic prodrugs of bumetanide; development of side-chain derivatives of bumetanide; and unbiased high-throughput screening approaches of drug discovery based on large chemical compound libraries. The main outcomes are that (1), non-acidic loop diuretics such as azosemide and torasemide may have advantages as NKCC1 inhibitors vs. bumetanide; (2), bumetanide prodrugs lead to significantly higher brain levels than the parent drug and have lower diuretic activity; (3), the novel bumetanide side-chain derivatives do not exhibit any functionally relevant improvement of CNS accessibility or NKCC1 selectivity vs. bumetanide; (4) novel compounds discovered by high-throughput screening may resolve some of the inherent problems of bumetanide, but as yet this has not been achieved. Thus, further research is needed to optimize the design of brain-permeant NKCC1 inhibitors. In parallel, a major challenge is to identify the mechanisms whereby various NKCC1-expressing cellular targets of these drugs within (e.g., neurons, oligodendrocytes or astrocytes) and outside the brain parenchyma (e.g., the blood-brain barrier, the choroid plexus, and the endocrine system), as well as molecular off-target effects, might contribute to their reported therapeutic and adverse effects.
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Affiliation(s)
- Wolfgang Löscher
- Dept. of Pharmacology, Toxicology, and Pharmacy, University of Veterinary Medicine Hannover, Germany; Center for Systems Neuroscience Hannover, Germany.
| | - Kai Kaila
- Molecular and Integrative Biosciences and Neuroscience Center (HiLIFE), University of Helsinki, Finland
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18
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Bhattacharya D, Becker C, Readhead B, Goossens N, Novik J, Fiel MI, Cousens LP, Magnusson B, Backmark A, Hicks R, Dudley JT, Friedman SL. Repositioning of a novel GABA-B receptor agonist, AZD3355 (Lesogaberan), for the treatment of non-alcoholic steatohepatitis. Sci Rep 2021; 11:20827. [PMID: 34675338 PMCID: PMC8531016 DOI: 10.1038/s41598-021-99008-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 09/14/2021] [Indexed: 01/02/2023] Open
Abstract
Non-alcoholic steatohepatitis (NASH) is a rising health challenge, with no approved drugs. We used a computational drug repositioning strategy to uncover a novel therapy for NASH, identifying a GABA-B receptor agonist, AZD3355 (Lesogaberan) previously evaluated as a therapy for esophageal reflux. AZD3355's potential efficacy in NASH was tested in human stellate cells, human precision cut liver slices (hPCLS), and in vivo in a well-validated murine model of NASH. In human stellate cells AZD3355 significantly downregulated profibrotic gene and protein expression. Transcriptomic analysis of these responses identified key regulatory nodes impacted by AZD3355, including Myc, as well as MAP and ERK kinases. In PCLS, AZD3355 down-regulated collagen1α1, αSMA and TNF-α mRNAs as well as secreted collagen1α1. In vivo, the drug significantly improved histology, profibrogenic gene expression, and tumor development, which was comparable to activity of obeticholic acid in a robust mouse model of NASH, but awaits further testing to determine its relative efficacy in patients. These data identify a well-tolerated clinical stage asset as a novel candidate therapy for human NASH through its hepatoprotective, anti-inflammatory and antifibrotic mechanisms of action. The approach validates computational methods to identify novel therapies in NASH in uncovering new pathways of disease development that can be rapidly translated into clinical trials.
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Affiliation(s)
- Dipankar Bhattacharya
- grid.59734.3c0000 0001 0670 2351Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, Box 1123, 1425 Madison Ave. Room 1170, New York, NY 10029 USA
| | - Christine Becker
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Division of Clinical Immunology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Benjamin Readhead
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.215654.10000 0001 2151 2636Present Address: Arizona State University-Banner Neurodegenerative Disease Research Center, Arizona, USA
| | - Nicolas Goossens
- grid.59734.3c0000 0001 0670 2351Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, Box 1123, 1425 Madison Ave. Room 1170, New York, NY 10029 USA ,grid.150338.c0000 0001 0721 9812Present Address: Division of Gastroenterology, Geneva University Hospital, Geneva, Switzerland
| | - Jacqueline Novik
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Maria Isabel Fiel
- grid.59734.3c0000 0001 0670 2351Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Leslie P. Cousens
- grid.418152.b0000 0004 0543 9493Emerging Innovations, Discovery Sciences, R&D, AstraZeneca, Boston, MA USA
| | - Björn Magnusson
- grid.418151.80000 0001 1519 6403Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Anna Backmark
- grid.418151.80000 0001 1519 6403Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Ryan Hicks
- grid.418151.80000 0001 1519 6403BioPharmaceuticals R&D Cell Therapy, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Joel T. Dudley
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Scott L. Friedman
- grid.59734.3c0000 0001 0670 2351Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, Box 1123, 1425 Madison Ave. Room 1170, New York, NY 10029 USA
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19
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Li G, Peng X, Guo Y, Gong S, Cao S, Qiu F. Currently Available Strategies for Target Identification of Bioactive Natural Products. Front Chem 2021; 9:761609. [PMID: 34660543 PMCID: PMC8515416 DOI: 10.3389/fchem.2021.761609] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/20/2021] [Indexed: 01/04/2023] Open
Abstract
In recent years, biologically active natural products have gradually become important agents in the field of drug research and development because of their wide availability and variety. However, the target sites of many natural products are yet to be identified, which is a setback in the pharmaceutical industry and has seriously hindered the translation of research findings of these natural products as viable candidates for new drug exploitation. This review systematically describes the commonly used strategies for target identification via the application of probe and non-probe approaches. The merits and demerits of each method were summarized using recent examples, with the goal of comparing currently available methods and selecting the optimum techniques for identifying the targets of bioactive natural products.
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Affiliation(s)
- Gen Li
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xuling Peng
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yajing Guo
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Shaoxuan Gong
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Shijie Cao
- Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Feng Qiu
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
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20
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Galati S, Di Stefano M, Martinelli E, Poli G, Tuccinardi T. Recent Advances in In Silico Target Fishing. Molecules 2021; 26:5124. [PMID: 34500568 DOI: 10.3390/molecules26175124] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/14/2021] [Accepted: 08/18/2021] [Indexed: 12/24/2022] Open
Abstract
In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.
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21
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Bisht N, Sah AN, Bisht S, Joshi H. Emerging Need of Today: Significant Utilization of Various Databases and Softwares in Drug Design and Development. Mini Rev Med Chem 2021; 21:1025-1032. [PMID: 33319657 DOI: 10.2174/1389557520666201214101329] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 10/05/2020] [Accepted: 10/09/2020] [Indexed: 11/22/2022]
Abstract
In drug discovery, in silico methods have become a very important part of the process. These approaches impact the entire development process by discovering and identifying new target proteins as well as designing potential ligands with a significant reduction of time and cost. Furthermore, in silico approaches are also preferred because of reduction in the experimental use of animals as; in vivo testing for safer drug design and repositioning of known drugs. Novel software-based discovery and development such as direct/indirect drug design, molecular modelling, docking, screening, drug-receptor interaction, and molecular simulation studies are very important tools for the predictions of ligand-target interaction pattern, pharmacodynamics as well as pharmacokinetic properties of ligands. On the other part, the computational approaches can be numerous, requiring interdisciplinary studies and the application of advanced computer technology to design effective and commercially feasible drugs. This review mainly focuses on the various databases and software used in drug design and development to speed up the process.
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Affiliation(s)
- Neema Bisht
- Assistant Professor, College of Pharmacy, Graphic Era Hill University, Bhimtal Campus, Sattal Road, Bhimtal, Uttarakhand 263136, India
| | - Archana N Sah
- Head and Dean, Department of Pharmaceutical Sciences, Faculty of Technology, Sir J.C. Bose Technical Campus, Bhimtal, Kumaun University Nainital, Uttarakhand 263136, India
| | - Sandeep Bisht
- Assistant Professor, School of Management, Graphic Era Hill University, Bhimtal Campus, Sattal Road, Bhimtal, Uttarakhand 263136, India
| | - Himanshu Joshi
- Professor, College of Pharmacy, Graphic Era Hill University, Bhimtal Campus, Sattal Road, Bhimtal, Uttarakhand 263136, India
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22
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Tejera E, Pérez-Castillo Y, Toscano G, Noboa AL, Ochoa-Herrera V, Giampieri F, Álvarez-Suarez JM. Computational modeling predicts potential effects of the herbal infusion "horchata" against COVID-19. Food Chem 2021; 366:130589. [PMID: 34311241 PMCID: PMC8314115 DOI: 10.1016/j.foodchem.2021.130589] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 07/08/2021] [Accepted: 07/09/2021] [Indexed: 01/28/2023]
Abstract
Bioactive plant-derived molecules have emerged as therapeutic alternatives in the fight against the COVID-19 pandemic. In this investigation, principal bioactive compounds of the herbal infusion “horchata” from Ecuador were studied as potential novel inhibitors of the SARS-CoV-2 virus. The chemical composition of horchata was determined through a HPLC-DAD/ESI-MSn and GC–MS analysis while the inhibitory potential of the compounds on SARS-CoV-2 was determined by a computational prediction using various strategies, such as molecular docking and molecular dynamics simulations. Up to 51 different compounds were identified. The computational analysis of predicted targets reveals the compounds’ possible anti-inflammatory (no steroidal) and antioxidant effects. Three compounds were identified as candidates for Mpro inhibition: benzoic acid, 2-(ethylthio)-ethyl ester, l-Leucine-N-isobutoxycarbonyl-N-methyl-heptyl and isorhamnetin and for PLpro: isorhamnetin-3-O-(6-Orhamnosyl-galactoside), dihydroxy-methoxyflavanone and dihydroxyphenyl)-5-hydroxy-4-oxochromen-7-yl]oxy-3,4,5-trihydroxyoxane-2-carboxylic acid. Our results suggest the potential of Ecuadorian horchata infusion as a starting scaffold for the development of new inhibitors of the SARS-CoV-2 Mpro and PLpro enzymes.
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Affiliation(s)
- Eduardo Tejera
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador; Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito, Ecuador.
| | - Yunierkis Pérez-Castillo
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador; Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito, Ecuador
| | - Gisselle Toscano
- Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito, Ecuador
| | - Ana Lucía Noboa
- Colegio de Ciencias e Ingenierías, Instituto Biósfera, Universidad San Francisco de Quito, Quito, Ecuador
| | - Valeria Ochoa-Herrera
- Colegio de Ciencias e Ingenierías, Instituto Biósfera, Universidad San Francisco de Quito, Quito, Ecuador; Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, United States
| | - Francesca Giampieri
- Department of Clinical Sciences, Università Politecnica delle Marche, Ancona, Italy; Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - José M Álvarez-Suarez
- Departamento de Ingeniería en Alimentos, Colegio de Ciencias e Ingenierías, Universidad San Francisco de Quito, Quito, Ecuador; Instituto de Investigaciones en Biomedicina iBioMed, Universidad San Francisco de Quito, Quito, Ecuador; King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.
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23
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Born J, Manica M, Cadow J, Markert G, Mill NA, Filipavicius M, Janakarajan N, Cardinale A, Laino T, Rodríguez Martínez M. Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2. Mach Learn : Sci Technol 2021. [DOI: 10.1088/2632-2153/abe808] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Abstract
Bridging systems biology and drug design, we propose a deep learning framework for de novo discovery of molecules tailored to bind with given protein targets. Our methodology is exemplified by the task of designing antiviral candidates to target SARS-CoV-2 related proteins. Crucially, our framework does not require fine-tuning for specific proteins but is demonstrated to generalize in proposing ligands with high predicted binding affinities against unseen targets. Coupling our framework with the automatic retrosynthesis prediction of IBM RXN for Chemistry, we demonstrate the feasibility of swift chemical synthesis of molecules with potential antiviral properties that were designed against a specific protein target. In particular, we synthesize an antiviral candidate designed against the host protein angiotensin converting enzyme 2 (ACE2); a surface receptor on human respiratory epithelial cells that facilitates SARS-CoV-2 cell entry through its spike glycoprotein.
This is achieved as follows. First, we train a multimodal ligand–protein binding affinity model on predicting affinities of bioactive compounds to target proteins and couple this model with pharmacological toxicity predictors. Exploiting this multi-objective as a reward function of a conditional molecular generator that consists of two variational autoencoders (VAE), our framework steers the generation toward regions of the chemical space with high-reward molecules. Specifically, we explore a challenging setting of generating ligands against unseen protein targets by performing a leave-one-out-cross-validation on 41 SARS-CoV-2-related target proteins. Using deep reinforcement learning, it is demonstrated that in 35 out of 41 cases, the generation is biased towards sampling binding ligands, with an average increase of 83% comparing to an unbiased VAE. The generated molecules exhibit favorable properties in terms of target binding affinity, selectivity and drug-likeness. We use molecular retrosynthetic models to provide a synthetic accessibility assessment of the best generated hit molecules. Finally, with this end-to-end framework, we synthesize 3-Bromobenzylamine, a potential inhibitor of the host ACE2 protein, solely based on the recommendations of a molecular retrosynthesis model and a synthesis protocol prediction model. We hope that our framework can contribute towards swift discovery of de novo molecules with desired pharmacological properties.
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24
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Wang C, Kurgan L. Survey of Similarity-Based Prediction of Drug-Protein Interactions. Curr Med Chem 2021; 27:5856-5886. [PMID: 31393241 DOI: 10.2174/0929867326666190808154841] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 04/16/2018] [Accepted: 10/23/2018] [Indexed: 12/20/2022]
Abstract
Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.
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Affiliation(s)
- Chen Wang
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
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25
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Ghislat G, Rahman T, Ballester PJ. Identification and Validation of Carbonic Anhydrase II as the First Target of the Anti-Inflammatory Drug Actarit. Biomolecules 2020; 10:biom10111570. [PMID: 33227945 PMCID: PMC7699199 DOI: 10.3390/biom10111570] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 11/13/2020] [Accepted: 11/16/2020] [Indexed: 12/31/2022] Open
Abstract
Background and purpose: Identifying the macromolecular targets of drug molecules is a fundamental aspect of drug discovery and pharmacology. Several drugs remain without known targets (orphan) despite large-scale in silico and in vitro target prediction efforts. Ligand-centric chemical-similarity-based methods for in silico target prediction have been found to be particularly powerful, but the question remains of whether they are able to discover targets for target-orphan drugs. Experimental Approach: We used one of these in silico methods to carry out a target prediction analysis for two orphan drugs: actarit and malotilate. The top target predicted for each drug was carbonic anhydrase II (CAII). Each drug was therefore quantitatively evaluated for CAII inhibition to validate these two prospective predictions. Key Results: Actarit showed in vitro concentration-dependent inhibition of CAII activity with submicromolar potency (IC50 = 422 nM) whilst no consistent inhibition was observed for malotilate. Among the other 25 targets predicted for actarit, RORγ (RAR-related orphan receptor-gamma) is promising in that it is strongly related to actarit’s indication, rheumatoid arthritis (RA). Conclusion and Implications: This study is a proof-of-concept of the utility of MolTarPred for the fast and cost-effective identification of targets of orphan drugs. Furthermore, the mechanism of action of actarit as an anti-RA agent can now be re-examined from a CAII-inhibitor perspective, given existing relationships between this target and RA. Moreover, the confirmed CAII-actarit association supports investigating the repositioning of actarit on other CAII-linked indications (e.g., hypertension, epilepsy, migraine, anemia and bone, eye and cardiac disorders).
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Affiliation(s)
- Ghita Ghislat
- Centre d’Immunologie de Marseille-Luminy, Inserm, U1104, CNRS UMR7280, F-13288 Marseille, France
- Correspondence: (G.G.); (P.J.B.)
| | - Taufiq Rahman
- Department of Pharmacology, University of Cambridge, Cambridge CB2 1PD, UK;
| | - Pedro J. Ballester
- Centre de Recherche en Cancérologie de Marseille (CRCM), Inserm, U1068, F-13009 Marseille, France
- CNRS, UMR7258, F-13009 Marseille, France
- Institut Paoli-Calmettes, F-13009 Marseille, France
- Aix-Marseille University, UM 105, F-13284 Marseille, France
- Correspondence: (G.G.); (P.J.B.)
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26
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Ariey-Bonnet J, Carrasco K, Le Grand M, Hoffer L, Betzi S, Feracci M, Tsvetkov P, Devred F, Collette Y, Morelli X, Ballester P, Pasquier E. In silico molecular target prediction unveils mebendazole as a potent MAPK14 inhibitor. Mol Oncol 2020; 14:3083-3099. [PMID: 33021050 PMCID: PMC7718943 DOI: 10.1002/1878-0261.12810] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 08/27/2020] [Accepted: 09/29/2020] [Indexed: 12/15/2022] Open
Abstract
The concept of polypharmacology involves the interaction of drug molecules with multiple molecular targets. It provides a unique opportunity for the repurposing of already-approved drugs to target key factors involved in human diseases. Herein, we used an in silico target prediction algorithm to investigate the mechanism of action of mebendazole, an antihelminthic drug, currently repurposed in the treatment of brain tumors. First, we confirmed that mebendazole decreased the viability of glioblastoma cells in vitro (IC50 values ranging from 288 nm to 2.1 µm). Our in silico approach unveiled 21 putative molecular targets for mebendazole, including 12 proteins significantly upregulated at the gene level in glioblastoma as compared to normal brain tissue (fold change > 1.5; P < 0.0001). Validation experiments were performed on three major kinases involved in cancer biology: ABL1, MAPK1/ERK2, and MAPK14/p38α. Mebendazole could inhibit the activity of these kinases in vitro in a dose-dependent manner, with a high potency against MAPK14 (IC50 = 104 ± 46 nm). Its direct binding to MAPK14 was further validated in vitro, and inhibition of MAPK14 kinase activity was confirmed in live glioblastoma cells. Consistent with biophysical data, molecular modeling suggested that mebendazole was able to bind to the catalytic site of MAPK14. Finally, gene silencing demonstrated that MAPK14 is involved in glioblastoma tumor spheroid growth and response to mebendazole treatment. This study thus highlighted the role of MAPK14 in the anticancer mechanism of action of mebendazole and provides further rationale for the pharmacological targeting of MAPK14 in brain tumors. It also opens new avenues for the development of novel MAPK14/p38α inhibitors to treat human diseases.
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Affiliation(s)
- Jeremy Ariey-Bonnet
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Kendall Carrasco
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Marion Le Grand
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Laurent Hoffer
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Stéphane Betzi
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Mikael Feracci
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Philipp Tsvetkov
- CNRS, UMR 7051, INP, Inst Neurophysiopathol, Fac Pharm, Aix Marseille Université, France
| | - Francois Devred
- CNRS, UMR 7051, INP, Inst Neurophysiopathol, Fac Pharm, Aix Marseille Université, France
| | - Yves Collette
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Xavier Morelli
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Pedro Ballester
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Eddy Pasquier
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
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27
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Affiliation(s)
- Su‐Qing Yang
- Xiangya School of Pharmaceutical Sciences Central South University Changsha Hunan China
| | - Qing Ye
- College of Pharmaceutical Sciences Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University Hangzhou, Zhejiang China
| | - Jun‐Jie Ding
- Beijing Institute of Pharmaceutical Chemistry Beijing China
| | - Yin
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital Central South University Changsha Hunan China
| | - Ai‐Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine Hong Kong Baptist University Hong Kong China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital Central South University Changsha Hunan China
| | - Ting‐Jun Hou
- College of Pharmaceutical Sciences Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University Hangzhou, Zhejiang China
| | - Dong‐Sheng Cao
- Xiangya School of Pharmaceutical Sciences Central South University Changsha Hunan China
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine Hong Kong Baptist University Hong Kong China
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28
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Mathai N, Kirchmair J. Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope. Int J Mol Sci 2020; 21:ijms21103585. [PMID: 32438666 PMCID: PMC7279241 DOI: 10.3390/ijms21103585] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 05/13/2020] [Accepted: 05/16/2020] [Indexed: 12/20/2022] Open
Abstract
Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space.
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Affiliation(s)
- Neann Mathai
- Department of Chemistry and Computational Biology Unit (CBU), University of Bergen, N-5020 Bergen, Norway;
| | - Johannes Kirchmair
- Department of Chemistry and Computational Biology Unit (CBU), University of Bergen, N-5020 Bergen, Norway;
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria
- Correspondence:
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29
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Wang L, You ZH, Li LP, Yan X, Zhang W. Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions. Sci Rep 2020; 10:6641. [PMID: 32313024 DOI: 10.1038/s41598-020-62891-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 03/12/2020] [Indexed: 01/29/2023] Open
Abstract
Accumulating evidence has shown that drug-target interactions (DTIs) play a crucial role in the process of genomic drug discovery. Although biological experimental technology has made great progress, the identification of DTIs is still very time-consuming and expensive nowadays. Hence it is urgent to develop in silico model as a supplement to the biological experiments to predict the potential DTIs. In this work, a new model is designed to predict DTIs by incorporating chemical sub-structures and protein evolutionary information. Specifically, we first use Position-Specific Scoring Matrix (PSSM) to convert the protein sequence into the numerical descriptor containing biological evolutionary information, then use Discrete Cosine Transform (DCT) algorithm to extract the hidden features and integrate them with the chemical sub-structures descriptor, and finally utilize Rotation Forest (RF) classifier to accurately predict whether there is interaction between the drug and the target protein. In the 5-fold cross-validation (CV) experiment, the average accuracy of the proposed model on the benchmark datasets of Enzymes, Ion Channels, GPCRs and Nuclear Receptors reached 0.9140, 0.8919, 0.8724 and 0.8111, respectively. In order to fully evaluate the performance of the proposed model, we compare it with different feature extraction model, classifier model, and other state-of-the-art models. Furthermore, we also implemented case studies. As a result, 8 of the top 10 drug-target pairs with the highest prediction score were confirmed by related databases. These excellent results indicate that the proposed model has outstanding ability in predicting DTIs and can provide reliable candidates for biological experiments.
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Bagherian M, Sabeti E, Wang K, Sartor MA, Nikolovska-Coleska Z, Najarian K. Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. Brief Bioinform 2020; 22:247-269. [PMID: 31950972 PMCID: PMC7820849 DOI: 10.1093/bib/bbz157] [Citation(s) in RCA: 139] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/01/2019] [Accepted: 11/07/2019] [Indexed: 12/12/2022] Open
Abstract
The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
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Affiliation(s)
- Maryam Bagherian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Elyas Sabeti
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kai Wang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Maureen A Sartor
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - Kayvan Najarian
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
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Vaz WF, Custodio JMF, D'Oliveira GDC, Neves BJ, Junior PSC, Filho JTM, Andrade CH, Perez CN, Silveira-Lacerda EP, Napolitano HB. Dihydroquinoline derivative as a potential anticancer agent: synthesis, crystal structure, and molecular modeling studies. Mol Divers 2020; 25:55-66. [PMID: 31900682 DOI: 10.1007/s11030-019-10024-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 12/05/2019] [Indexed: 01/16/2023]
Abstract
Cancer is one of the leading causes of death worldwide and requires intense and growing research investments from the public and private sectors. This is expected to lead to the development of new medicines. A determining factor in this process is the structural understanding of molecules with potential anticancer properties. Since the major compounds used in cancer therapies fail to encompass every spectrum of this disease, there is a clear need to research new molecules for this purpose. As it follows, we have studied the class of quinolinones that seem effective for such therapy. This paper describes the structural elucidation of a novel dihydroquinoline by single-crystal X-ray diffraction and spectroscopy characterization. Topology studies were carried through Hirshfeld surfaces analysis and molecular electrostatic potential map; electronic stability was evaluated from the calculated energy of frontier molecular orbitals. Additionally, in silico studies by molecular docking indicated that this dihydroquinoline could act as an anticancer agent due to their higher binding affinity with human aldehyde dehydrogenase 1A1 (ALDH 1A1). Tests in vitro were performed for VERO (normal human skin keratinocytes), B16F10 (mouse melanoma), and MDA-MB-231 (metastatic breast adenocarcinoma), and the results certified that compound as a potential anticancer agent. A Dihydroquinoline derivative was tested against three cancer cell lines and the results attest that compound as potential anticancer agent.
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Affiliation(s)
- W F Vaz
- Universidade Estadual de Goiás, Anápolis, GO, 75132-400, Brazil.
- Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso, Lucas do Rio Verde, MT, 78455-000, Brazil.
| | - J M F Custodio
- Universidade Federal de Goiás, Goiânia, GO, 74690-900, Brazil
| | | | - B J Neves
- LabMol, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, 74605-170, Brazil
| | - P S C Junior
- Universidade Federal de Mato Grosso do Sul, Nova Andradina, MS, 79750-000, Brazil
| | - J T M Filho
- Universidade Federal de Goiás, Goiânia, GO, 74690-900, Brazil
| | - C H Andrade
- LabMol, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, 74605-170, Brazil
| | - C N Perez
- Universidade Federal de Goiás, Goiânia, GO, 74690-900, Brazil
| | - E P Silveira-Lacerda
- Laboratório de Genética Molecular e Citogenética, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia, GO, 74605-170, Brazil
| | - H B Napolitano
- Universidade Estadual de Goiás, Anápolis, GO, 75132-400, Brazil
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Teixeira MG, Alvarenga ES, Lopes DT, Oliveira DF. Herbicidal activity of isobenzofuranones and in silico identification of their enzyme target. Pest Manag Sci 2019; 75:3331-3339. [PMID: 31026360 DOI: 10.1002/ps.5456] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Revised: 03/23/2019] [Accepted: 04/26/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND Given the weed resistance to various herbicides with different mechanisms of action, the search for new compounds that are more effective and exhibit low levels of impact to other species in nature has been imperative in the field of the agriculture. For this purpose, 16 phthalides, and furan-2(5H)-one were synthetized and evaluated for their effectiveness as herbicides in seeds of Sorghum bicolor (sorghum), Cucumis sativus (cucumber), and Allium cepa (onion). Furthermore, a preliminary in silico study was carried out to identify the enzyme target of the most active compounds. RESULTS In the assays with S. bicolor, the mixture rac-(3aR,4R,5S,6S,7S,7aS)-5,6-dibromohexahydro-4,7-methanoisobenzofuran-1(3H)-one + rac-(3aR,4R,5R,6R,7S,7aS)-5,6-dibromohexahydro-4,7-methanoisobenzofuran-1(3H)-one (15a + 15b) showed comparable inhibitory activity to (S)-metolachlor, which was used as control herbicide at concentrations ranging from 50 μm to 1000 μm. The developments of the seeds evaluated were altered by all 17 compounds, either stimulating or inhibiting. The best results were presented by compounds 15a, and 15b, either in their pure form or as a mixture. CONCLUSION The results presented by 15a, and 15b were superior to the activity of the commercial herbicide (S)-metolachlor in the assays with C. sativus, and A. cepa. The in silico study provides strong evidence that the most active compounds bind to strigolactones esterases D14 through the same binding site of (5R)-5-hydroxy-3-methylfuran-2(5H)-one (H3M), which is one of the strigolactones (SLs) cleavage products. © 2019 Society of Chemical Industry.
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Affiliation(s)
- Milena G Teixeira
- Departament of Chemistry, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Elson S Alvarenga
- Departament of Chemistry, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Dayane T Lopes
- Departament of Chemistry, Universidade Federal de Viçosa, Viçosa, Brazil
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Lopes SP, Castillo YP, Monteiro ML, Menezes RRPPB, Almeida RN, Martins AMC, Sousa DP. Trypanocidal Mechanism of Action and in silico Studies of p-Coumaric Acid Derivatives. Int J Mol Sci 2019; 20:E5916. [PMID: 31775321 DOI: 10.3390/ijms20235916] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 11/16/2019] [Accepted: 11/17/2019] [Indexed: 12/16/2022] Open
Abstract
Trypanosoma species are responsible for chronic and systemic infections in millions of people around the world, compromising life quality, and family and government budgets. This group of diseases is classified as neglected and causes thousands of deaths each year. In the present study, the trypanocidal effect of a set of 12 ester derivatives of the p-coumaric acid was tested. Of the test derivatives, pentyl p-coumarate (7) (5.16 ± 1.28 μM; 61.63 ± 28.59 μM) presented the best respective trypanocidal activities against both epimastigote and trypomastigote forms. Flow cytometry analysis revealed an increase in the percentage of 7-AAD labeled cells, an increase in reactive oxygen species, and a loss of mitochondrial membrane potential; indicating cell death by necrosis. This mechanism was confirmed by scanning electron microscopy, noting the loss of cellular integrity. Molecular docking data indicated that of the chemical compounds tested, compound 7 potentially acts through two mechanisms of action, whether by links with aldo-keto reductases (AKR) or by comprising cruzain (CZ) which is one of the key Trypanosoma cruzi development enzymes. The results indicate that for both enzymes, van der Waals interactions between ligand and receptors favor binding and hydrophobic interactions with the phenolic and aliphatic parts of the ligand. The study demonstrates that p-coumarate derivatives are promising molecules for developing new prototypes with antiprotozoal activity.
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Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev 2019; 151-152:169-190. [PMID: 31071378 DOI: 10.1016/j.addr.2019.05.001] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 04/14/2019] [Accepted: 05/02/2019] [Indexed: 02/07/2023]
Abstract
Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.
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Affiliation(s)
- Parichehr Hassanzadeh
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
| | - Fatemeh Atyabi
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
| | - Rassoul Dinarvand
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
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Abstract
Target fishing is the process of identifying the protein target of a bioactive small molecule. To do so experimentally requires a significant investment of time and resources, which can be expedited with a reliable computational target fishing model. The development of computational target fishing models using machine learning has become very popular over the last several years because of the increased availability of large amounts of public bioactivity data. Unfortunately, the applicability and performance of such models for natural products has not yet been comprehensively assessed. This is, in part, due to the relative lack of bioactivity data available for natural products compared to synthetic compounds. Moreover, the databases commonly used to train such models do not annotate which compounds are natural products, which makes the collection of a benchmarking set difficult. To address this knowledge gap, a data set composed of natural product structures and their associated protein targets was generated by cross-referencing 20 publicly available natural product databases with the bioactivity database ChEMBL. This data set contains 5589 compound-target pairs for 1943 unique compounds and 1023 unique targets. A synthetic data set comprising 107 190 compound-target pairs for 88 728 unique compounds and 1907 unique targets was used to train k-nearest neighbors, random forest, and multilayer perceptron models. The predictive performance of each model was assessed by stratified 10-fold cross-validation and benchmarking on the newly collected natural product data set. Strong performance was observed for each model during cross-validation with area under the receiver operating characteristic (AUROC) scores ranging from 0.94 to 0.99 and Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC) scores from 0.89 to 0.94. When tested on the natural product data set, performance dramatically decreased with AUROC scores ranging from 0.70 to 0.85 and BEDROC scores from 0.43 to 0.59. However, the implementation of a model stacking approach, which uses logistic regression as a meta-classifier to combine model predictions, dramatically improved the ability to correctly predict the protein targets of natural products and increased the AUROC score to 0.94 and BEDROC score to 0.73. This stacked model was deployed as a web application, called STarFish, and has been made available for use to aid in target identification for natural products.
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Affiliation(s)
- Nicholas T Cockroft
- Division of Medicinal Chemistry & Pharmacognosy, College of Pharmacy , The Ohio State University , Columbus , Ohio 43210 , United States
| | - Xiaolin Cheng
- Division of Medicinal Chemistry & Pharmacognosy, College of Pharmacy , The Ohio State University , Columbus , Ohio 43210 , United States
| | - James R Fuchs
- Division of Medicinal Chemistry & Pharmacognosy, College of Pharmacy , The Ohio State University , Columbus , Ohio 43210 , United States
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Tejera E, Carrera I, Jimenes-Vargas K, Armijos-Jaramillo V, Sánchez-Rodríguez A, Cruz-Monteagudo M, Perez-Castillo Y. Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction. PLoS One 2019; 14:e0223276. [PMID: 31589649 PMCID: PMC6779297 DOI: 10.1371/journal.pone.0223276] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 09/17/2019] [Indexed: 12/21/2022] Open
Abstract
The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. To date, the most common prediction strategies are based upon machine learning or other quantitative structure-activity relationships (QSAR) based approaches. In the present research, we propose and discuss a straightforward strategy not based on any learning modelling but exclusively relying upon the chemical similarity of a query compound to reference compounds with annotated activity against cell lines. We also compare the performance of the proposed method to machine learning predictions on the same problem. A curated database of compounds-cell lines associations derived from ChemBL version 22 was created for algorithm construction and cross-validation. Validation was done using 10-fold cross-validation and testing the models on new data obtained from ChemBL version 25. In terms of accuracy, both methods perform similarly with values around 0.65 across 750 cell lines in 10-fold cross-validation experiments. By combining both methods it is possible to achieve 66% of correct classification rate in more than 26000 newly reported interactions comprising 11000 new compounds. A Web Service implementing the described approaches (both similarity and machine learning based models) is freely available at: http://bioquimio.udla.edu.ec/cellfishing.
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Affiliation(s)
- E. Tejera
- Ingeniería en Biotecnología, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito, Ecuador
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
| | - I. Carrera
- Departamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito, Ecuador
- Departamento de Ciências de Computadores, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
| | - Karina Jimenes-Vargas
- Ingeniería en Biotecnología, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito, Ecuador
| | - V. Armijos-Jaramillo
- Ingeniería en Biotecnología, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito, Ecuador
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
| | - A. Sánchez-Rodríguez
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
- Universidad Técnica Particular de Loja, Loja, Ecuador
| | - M. Cruz-Monteagudo
- Center for Computational Science (CCS), University of Miami (UM), Miami, FL, United States of America
- West Coast University, Miami, Florida, United States of America
| | - Y. Perez-Castillo
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
- Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito, Ecuador
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Abstract
The ability to collect, store and analyze massive amounts of molecular and clinical data is fundamentally transforming the scientific method and its application in translational medicine. Collecting observations has always been a prerequisite for discovery, and great leaps in scientific understanding are accompanied by an expansion of this ability. Particle physics, astronomy and climate science, for example, have all greatly benefited from the development of new technologies enabling the collection of larger and more diverse data. Unlike medicine, however, each of these fields also has a mature theoretical framework on which new data can be evaluated and incorporated-to say it another way, there are no 'first principals' from which a healthy human could be analytically derived. The worry, and it is a valid concern, is that, without a strong theoretical underpinning, the inundation of data will cause medical research to devolve into a haphazard enterprise without discipline or rigor. The Age of Big Data harbors tremendous opportunity for biomedical advances, but will also be treacherous and demanding on future scientists.
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Affiliation(s)
- Nicholas P Tatonetti
- Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, NY, USA
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Rao MS, Gupta R, Liguori MJ, Hu M, Huang X, Mantena SR, Mittelstadt SW, Blomme EAG, Van Vleet TR. Novel Computational Approach to Predict Off-Target Interactions for Small Molecules. Front Big Data 2019; 2:25. [PMID: 33693348 PMCID: PMC7931946 DOI: 10.3389/fdata.2019.00025] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 06/26/2019] [Indexed: 12/01/2022] Open
Abstract
Most small molecule drugs interact with unintended, often unknown, biological targets and these off-target interactions may lead to both preclinical and clinical toxic events. Undesired off-target interactions are often not detected using current drug discovery assays, such as experimental polypharmacological screens. Thus, improvement in the early identification of off-target interactions represents an opportunity to reduce safety-related attrition rates during preclinical and clinical development. In order to better identify potential off-target interactions that could be linked to predictable safety issues, a novel computational approach to predict safety-relevant interactions currently not covered was designed and evaluated. These analyses, termed Off-Target Safety Assessment (OTSA), cover more than 7,000 targets (~35% of the proteome) and > 2,46,704 preclinical and clinical alerts (as of January 20, 2019). The approach described herein exploits a highly curated training set of >1 million compounds (tracking >20 million compound-structure activity relationship/SAR data points) with known in vitro activities derived from patents, journals, and publicly available databases. This computational process was used to predict both the primary and secondary pharmacological activities for a selection of 857 diverse small molecule drugs for which extensive secondary pharmacology data are readily available (456 discontinued and 401 FDA approved). The OTSA process predicted a total of 7,990 interactions for these 857 molecules. Of these, 3,923 and 4,067 possible high-scoring interactions were predicted for the discontinued and approved drugs, respectively, translating to an average of 9.3 interactions per drug. The OTSA process correctly identified the known pharmacological targets for >70% of these drugs, but also predicted a significant number of off-targets that may provide additional insight into observed in vivo effects. About 51.5% (2,025) and 22% (900) of these predicted high-scoring interactions have not previously been reported for the discontinued and approved drugs, respectively, and these may have a potential for repurposing efforts. Moreover, for both drug categories, higher promiscuity was observed for compounds with a MW range of 300 to 500, TPSA of ~200, and clogP ≥7. This computation also revealed significantly lower promiscuity (i.e., number of confirmed off-targets) for compounds with MW > 700 and MW<200 for both categories. In addition, 15 internal small molecules with known off-target interactions were evaluated. For these compounds, the OTSA framework not only captured about 56.8% of in vitro confirmed off-target interactions, but also identified the right pharmacological targets for 14 compounds as one of the top scoring targets. In conclusion, the OTSA process demonstrates good predictive performance characteristics and represents an additional tool with utility during the lead optimization stage of the drug discovery process. Additionally, the computed physiochemical properties such as clogP (i.e., lipophilicity), molecular weight, pKa and logS (i.e., solubility) were found to be statistically different between the approved and discontinued drugs, but the internal compounds were close to the approved drugs space in most part.
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Affiliation(s)
- Mohan S Rao
- Global Preclinical Safety, Abbvie, North Chicago, IL, United States
| | - Rishi Gupta
- Information Research, Abbvie, North Chicago, IL, United States
| | | | - Mufeng Hu
- Discovery and Early Pipeline Statistics, Abbvie, North Chicago, IL, United States
| | - Xin Huang
- Discovery and Early Pipeline Statistics, Abbvie, North Chicago, IL, United States
| | | | | | - Eric A G Blomme
- Global Preclinical Safety, Abbvie, North Chicago, IL, United States
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Turkez H, Nóbrega FRD, Ozdemir O, Bezerra Filho CDSM, Almeida RND, Tejera E, Perez-Castillo Y, Sousa DPD. NFBTA: A Potent Cytotoxic Agent against Glioblastoma. Molecules 2019; 24:E2411. [PMID: 31261921 PMCID: PMC6651752 DOI: 10.3390/molecules24132411] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 05/28/2019] [Accepted: 05/29/2019] [Indexed: 12/15/2022] Open
Abstract
Piplartine (PPL), also known as piperlongumine, is a biologically active alkaloid extracted from the Piper genus which has been found to have highly effective anticancer activity against several tumor cell lines. This study investigates in detail the antitumoral potential of a PPL analogue; (E)-N-(4-fluorobenzyl)-3-(3,4,5-trimethoxyphenyl) acrylamide (NFBTA). The anticancer potential of NFBTA on the glioblastoma multiforme (GBM) cell line (U87MG) was determined by 3-(4,5-dimethyl-2-thia-zolyl)-2, 5-diphenyl-2H-tetrazolium bromide (MTT), and lactate dehydrogenase (LDH) release analysis, and the selectivity index (SI) was calculated. To detect cell apoptosis, fluorescent staining via flow cytometry and Hoechst 33258 staining were performed. Oxidative alterations were assessed via colorimetric measurement methods. Alterations in expressions of key genes related to carcinogenesis were determined. Additionally, in terms of NFBTA cytotoxic, oxidative, and genotoxic damage potential, the biosafety of this novel agent was evaluated in cultured human whole blood cells. Cell viability analyses revealed that NFBTA exhibited strong cytotoxic activity in cultured U87MG cells, with high selectivity and inhibitory activity in apoptotic processes, as well as potential for altering the principal molecular genetic responses in U87MG cell growth. Molecular docking studies strongly suggested a plausible anti-proliferative mechanism for NBFTA. The results of the experimental in vitro human glioblastoma model and computational approach revealed promising cytotoxic activity for NFBTA, helping to orient further studies evaluating its antitumor profile for safe and effective therapeutic applications.
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Affiliation(s)
- Hasan Turkez
- Department of Molecular Biology and Genetics, Erzurum Technical University, Erzurum 25240, Turkey
- Department of Pharmacy, "G. d'Annunzio" University of Chieti-Pescara, Via dei Vestini 31, 66013 Chieti Scalo, Italy
| | - Flávio Rogério da Nóbrega
- Department of Pharmaceutical Sciences, Federal University of Paraíba, João Pessoa, PB 58051-085, Brazil
| | - Ozlem Ozdemir
- Department of Molecular Biology and Genetics, Erzurum Technical University, Erzurum 25240, Turkey
| | | | | | - Eduardo Tejera
- Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito 170125, Ecuador
| | | | - Damião Pergentino de Sousa
- Department of Pharmaceutical Sciences, Federal University of Paraíba, João Pessoa, PB 58051-085, Brazil.
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Peón A, Li H, Ghislat G, Leung KS, Wong MH, Lu G, Ballester PJ. MolTarPred: A web tool for comprehensive target prediction with reliability estimation. Chem Biol Drug Des 2019; 94:1390-1401. [PMID: 30916462 DOI: 10.1111/cbdd.13516] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 02/07/2019] [Accepted: 03/03/2019] [Indexed: 12/17/2022]
Abstract
Molecular target prediction can provide a starting point to understand the efficacy and side effects of phenotypic screening hits. Unfortunately, the vast majority of in silico target prediction methods are not available as web tools. Furthermore, these are limited in the number of targets that can be predicted, do not estimate which target predictions are more reliable and/or lack comprehensive retrospective validations. We present MolTarPred ( http://moltarpred.marseille.inserm.fr/), a user-friendly web tool for predicting protein targets of small organic compounds. It is powered by a large knowledge base comprising 607,659 compounds and 4,553 macromolecular targets collected from the ChEMBL database. In about 1 min, the predicted targets for the supplied molecule will be listed in a table. The chemical structures of the query molecule and the most similar compounds annotated with the predicted target will also be shown to permit visual inspection and comparison. Practical examples of the use of MolTarPred are showcased. MolTarPred is a new resource for scientists that require a more complete knowledge of the polypharmacology of a molecule. The introduction of a reliability score constitutes an attractive functionality of MolTarPred, as it permits focusing experimental confirmatory tests on the most reliable predictions, which leads to higher prospective hit rates.
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Affiliation(s)
- Antonio Peón
- Centre de Recherche en Cancérologie de Marseille (CRCM), U1068, Inserm, Marseille, France.,UMR7258, CNRS, Marseille, France.,Institut Paoli-Calmettes, Marseille, France.,UM 105, Aix-Marseille University, Marseille, France
| | - Hongjian Li
- SDIVF R&D Centre, Hong Kong Science Park, Sha Tin, New Territories, Hong Kong.,CUHK-SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong
| | - Ghita Ghislat
- U1104, CNRS UMR7280, Centre d'Immunologie de Marseille-Luminy, Inserm, Marseille, France
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong
| | - Man-Hon Wong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong
| | - Gang Lu
- CUHK-SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong
| | - Pedro J Ballester
- Centre de Recherche en Cancérologie de Marseille (CRCM), U1068, Inserm, Marseille, France.,UMR7258, CNRS, Marseille, France.,Institut Paoli-Calmettes, Marseille, France.,UM 105, Aix-Marseille University, Marseille, France
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Nogueira MS, Koch O. The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction. J Chem Inf Model 2019; 59:1238-1252. [DOI: 10.1021/acs.jcim.8b00773] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Mauro S. Nogueira
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
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Van Vleet TR, Liguori MJ, Lynch JJ, Rao M, Warder S. Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals. SLAS Discov 2018; 24:1-24. [PMID: 30196745 DOI: 10.1177/2472555218799713] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.
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Affiliation(s)
- Terry R Van Vleet
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - Michael J Liguori
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - James J Lynch
- 2 Department of Integrated Science and Technology, AbbVie, N Chicago, IL, USA
| | - Mohan Rao
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - Scott Warder
- 3 Department of Target Enabling Science and Technology, AbbVie, N Chicago, IL, USA
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43
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Wang C, Kurgan L. Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome. Brief Bioinform 2018; 20:2066-2087. [DOI: 10.1093/bib/bby069] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 06/26/2018] [Accepted: 07/10/2018] [Indexed: 12/18/2022] Open
Abstract
AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.
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Affiliation(s)
- Chen Wang
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Lukasz Kurgan
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
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Sartori SK, Alvarenga ES, Franco CA, Ramos DS, Oliveira DF. One-pot synthesis of anilides, herbicidal activity and molecular docking study. Pest Manag Sci 2018; 74:1637-1645. [PMID: 29318774 DOI: 10.1002/ps.4855] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 12/24/2017] [Accepted: 01/04/2018] [Indexed: 06/07/2023]
Abstract
BACKGROUND In the context of the demand for more efficient herbicides, the aim of the present work was to synthesize anilides via simple methods, and evaluate their herbicidal activities through seed germination assays. In silico studies were carried out to identify the enzyme target sites in plants for the most active anilides. RESULTS A total of 18 anilides were prepared via one-pot reaction in yields that varied from 36 to 98% through reactions of anilines with sorbic chloride and hexanoic anhydride. According to seed germination assays in three dicotyledonous and one monocotyledonous plant species, the most active anilides showed root and shoot growth inhibition superior to that of Dual (S-metolachlor). In silico studies indicated that histone deacetylase was the probable enzyme target site in plants for these substances. The affinities of the most active anilides for the binding sites of this enzyme were equal to or higher than those calculated for its inhibitors. CONCLUSION Anilides 4d, 4e, 4 g, and 4 h are promising candidates for the development of novel herbicides. According to in silico studies, they inhibit histone deacetylase in plants, which can be exploited for the development of new weed control methods. © 2018 Society of Chemical Industry.
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Affiliation(s)
- Suélen K Sartori
- Chemistry Department, Universidade Federal de Viçosa, Viçosa, MG, Brazil
| | - Elson S Alvarenga
- Chemistry Department, Universidade Federal de Viçosa, Viçosa, MG, Brazil
| | - Cristiane A Franco
- Chemistry Department, Universidade Federal de Viçosa, Viçosa, MG, Brazil
| | - Danielle S Ramos
- Chemistry Department, Universidade Federal de Viçosa, Viçosa, MG, Brazil
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45
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Colwell LJ. Statistical and machine learning approaches to predicting protein-ligand interactions. Curr Opin Struct Biol 2018; 49:123-128. [PMID: 29452923 DOI: 10.1016/j.sbi.2018.01.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 01/02/2018] [Indexed: 12/29/2022]
Abstract
Data driven computational approaches to predicting protein-ligand binding are currently achieving unprecedented levels of accuracy on held-out test datasets. Up until now, however, this has not led to corresponding breakthroughs in our ability to design novel ligands for protein targets of interest. This review summarizes the current state of the art in this field, emphasizing the recent development of deep neural networks for predicting protein-ligand binding. We explain the major technical challenges that have caused difficulty with predicting novel ligands, including the problems of sampling noise and the challenge of using benchmark datasets that are sufficiently unbiased that they allow the model to extrapolate to new regimes.
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Affiliation(s)
- Lucy J Colwell
- Department of Chemistry, Cambridge University, Cambridge, UK.
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Naulaerts S, Dang CC, Ballester PJ. Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours. Oncotarget 2017; 8:97025-97040. [PMID: 29228590 PMCID: PMC5722542 DOI: 10.18632/oncotarget.20923] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 08/14/2017] [Indexed: 02/07/2023] Open
Abstract
Cancer drug therapies are only effective in a small proportion of patients. To make things worse, our ability to identify these responsive patients before administering a treatment is generally very limited. The recent arrival of large-scale pharmacogenomic data sets, which measure the sensitivity of molecularly profiled cancer cell lines to a panel of drugs, has boosted research on the discovery of drug sensitivity markers. However, no systematic comparison of widely-used single-gene markers with multi-gene machine-learning markers exploiting genomic data has been so far conducted. We therefore assessed the performance offered by these two types of models in discriminating between sensitive and resistant cell lines to a given drug. This was carried out for each of 127 considered drugs using genomic data characterising the cell lines. We found that the proportion of cell lines predicted to be sensitive that are actually sensitive (precision) varies strongly with the drug and type of model used. Furthermore, the proportion of sensitive cell lines that are correctly predicted as sensitive (recall) of the best single-gene marker was lower than that of the multi-gene marker in 118 of the 127 tested drugs. We conclude that single-gene markers are only able to identify those drug-sensitive cell lines with the considered actionable mutation, unlike multi-gene markers that can in principle combine multiple gene mutations to identify additional sensitive cell lines. We also found that cell line sensitivities to some drugs (e.g. Temsirolimus, 17-AAG or Methotrexate) are better predicted by these machine-learning models.
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Affiliation(s)
- Stefan Naulaerts
- Computational Biology and Drug Design, Cancer Research Center of Marseille, INSERM U1068, Marseille, France.,Institut Paoli-Calmettes, Marseille, France.,Aix-Marseille Université, Marseille, France.,CNRS UMR7258, Marseille, France
| | - Cuong C Dang
- Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
| | - Pedro J Ballester
- Computational Biology and Drug Design, Cancer Research Center of Marseille, INSERM U1068, Marseille, France.,Institut Paoli-Calmettes, Marseille, France.,Aix-Marseille Université, Marseille, France.,CNRS UMR7258, Marseille, France
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