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- Volume 21, Issue 9, 2021
Current Topics in Medicinal Chemistry - Volume 21, Issue 9, 2021
Volume 21, Issue 9, 2021
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Brevifoliol and its Analogs: A New Class of Anti-tubercular Agents
Brevifoliol is an abeo-taxane isolated from the Taxus wallichiana needles; eighteen semisynthetic esters derivatives of brevifoliol were prepared by Steglich esterification and screened for their anti-tubercular potential against Mycobacterium tuberculosis H37Ra avirulent strain. The 3- [chloro (7)] and 3, 5-[dinitro (8)] benzoic acid ester derivatives were most active (MIC 25 ug/ml) against the pathogen. Further, in silico docking studies of the active derivative 7 with mycobacterium enzyme inhA (enoyl-ACP reductase) gave the LibDock score of 152.68 and binding energy of -208.62 and formed three hydrogen bonds with SER94, MET98, and SER94. Similarly, when derivative 8 docked with inhA, it gave the LibDock score of 113.55 and binding energy of -175.46 and formed a single hydrogen bond with GLN100 and Pi-interaction with PHE97. On the other hand, the known standard drug isoniazid (INH) gave the LibDock score of 61.63, binding energy of -81.25 and formed one hydrogen bond with ASP148. These molecular docking results and the way of binding pattern indicated that compounds 7 and 8 bound well within the binding pocket of inhA and showed a higher binding affinity than the known drug isoniazid. Additionally, both the derivatives (7 and 8) showed no cytotoxicity, with CC50 195.10 and 111.36, respectively towards the mouse bone marrow-derived macrophages.
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Hybridization Approach to Drug Discovery Inhibiting Mycobacterium tuberculosis-An Overview
Authors: Daniele Zampieri and Maria G. MamoloTuberculosis is one of the top 10 causes of death worldwide and the leading cause of death from a single infectious agent, mainly due to Mycobacterium tuberculosis (MTB). Recently, clinical prognoses have worsened due to the emergence of multi-drug resistant (MDR) and extensive-drug resistant (XDR) tuberculosis, which lead to the need for new, efficient and safe drugs. Among the several strategies, polypharmacology could be considered one of the best solutions, in particular, the multitarget directed ligands strategy (MTDLs), based on the synthesis of hybrid ligands acting against two targets of the pathogen. The framework strategy comprises linking, fusing and merging approaches to develop new chemical entities. With these premises, this review aims to provide an overview of the recent hybridization approach, in medicinal chemistry, of the most recent and promising multitargeting antimycobacterial candidates.
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A Multi-target Drug Designing for BTK, MMP9, Proteasome and TAK1 for the Clinical Treatment of Mantle Cell Lymphoma
Background: Mantle cell lymphoma (MCL) is a type of non-Hodgkin lymphoma characterized by the mutation and overexpression of the cyclin D1 protein by the reciprocal chromosomal translocation t(11;14)(q13:q32). Aim: The present study aims to identify potential inhibition of MMP9, Proteasome, BTK, and TAK1 and determine the most suitable and effective protein target for the MCL. Methodology: Nine known inhibitors for MMP9, 24 for proteasome, 15 for BTK and 14 for TAK1 were screened. SB-3CT (PubChem ID: 9883002), oprozomib (PubChem ID: 25067547), zanubrutinib (PubChem ID: 135565884) and TAK1 inhibitor (PubChem ID: 66760355) were recognized as drugs with high binding capacity with their respective protein receptors. 41, 72, 102 and 3 virtual screened compounds were obtained after the similarity search with compound (PubChem ID:102173753), PubChem compound SCHEMBL15569297 (PubChem ID:72374403), PubChem compound SCHEMBL17075298 (PubChem ID:136970120) and compound CID: 71814473 with best virtual screened compounds. Result: MMP9 inhibitors show commendable affinity and good interaction profile of compound holding PubChem ID:102173753 over the most effective established inhibitor SB-3CT. The pharmacophore study of the best virtual screened compound reveals its high efficacy based on various interactions. The virtual screened compound's better affinity with the target MMP9 protein was deduced using toxicity and integration profile studies. Conclusion: Based on the ADMET profile, the compound (PubChem ID: 102173753) could be a potent drug for MCL treatment. Similar to the established SB-3CT, the compound was non-toxic with LD50 values for both the compounds lying in the same range.
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Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models
Background: Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology. Objective: In principle, we can perform a hand-on checking (Manual Curation). However, this is a challenging task due to the high number of combinations of pairs of nodes (possible metabolic reactions). Results: The CPTML linear model obtained using the LDA algorithm is able to discriminate nodes (metabolites) with the correct assignation of reactions from incorrect nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series. Methods: In this work, we used Combinatorial Perturbation Theory and Machine Learning techniques to seek a CPTML model for MRNs >40 organisms compiled by Barabasis’ group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms. Conclusion: Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens the door to the study of MRNs of multiple organisms using PTML models.
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The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union
Authors: Ricardo Santana, Enrique Onieva, Robin Zuluaga, Aliuska Duardo-Sánchez and Piedad GañánBackground: Machine Learning (ML) has experienced an increasing use, given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models capable of predicting biological activity and physicochemical characteristics of new components with high success rates in training and test partitions. Given the current gaps of scientific knowledge and the need for efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in the European Union and the role of ML in the authorization process. Methods: In terms of methodology, a dogmatic study of the European regulation and the guidance of the European Medicine Agency on the use of predictive models for nanomaterials was carried out. The study has, as the framework of reference, the European Regulation 726/2004 and has focused on the analysis of how ML processes are contemplated in the regulations. Results: As a result, we present a discussion of the information that must be provided for every case for simulation methods. The results show a favorable and flexible position for the development of the use of predictive models to complement the applicant's information. Conclusion: It is concluded that Machine Learning has the capacity to help improve the application of nanotechnology medicine products regulation. Future regulations should promote this kind of information given the advanced state of the art in terms of algorithms that are able to build accurate predictive models. This especially applies to methods, such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development (OECD), European Union regulations, and European Authority Medicine. To our best knowledge, this is the first study focused on nanotechnology medicine products and machine learning used to support technical European public assessment reports (EPAR) for complementary information.
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Corrigendum to: Computational Modeling of Environmental Co-exposure on Oil-Derived Hydrocarbon Overload by Using Substrate-Specific Transport Protein (TodX) with Graphene Nanostructures
Due to an oversight of the publisher, Page no 2310 was missing in the published paper and page no 2311 repeated twice in the article entitled “Computational Modeling of Environmental Co-exposure on Oil-Derived Hydrocarbon Overload by Using Substrate-Specific Transport Protein (TodX) with Graphene Nanostructures, 2020, 20(25), 2308-2325 [1]. The page no 2310 is added in the article and the repetition of page no 2311 is corrected. The original article can be found online at https://doi.org/10.2174/1568026620666200820145412
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Volumes & issues
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Volume 25 (2025)
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Volume (2025)
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Volume 24 (2024)
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Volume 23 (2023)
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Volume 22 (2022)
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Volume 21 (2021)
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Volume 20 (2020)
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Volume 19 (2019)
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Volume 18 (2018)
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Volume 17 (2017)
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Volume 16 (2016)
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Volume 15 (2015)
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Volume 14 (2014)
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Volume 13 (2013)
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Volume 12 (2012)
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Volume 11 (2011)
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Volume 10 (2010)
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Volume 9 (2009)
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Volume 8 (2008)
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Volume 7 (2007)
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Volume 6 (2006)
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Volume 5 (2005)
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Volume 4 (2004)
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Volume 3 (2003)
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Volume 2 (2002)
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Volume 1 (2001)
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