Relevant bibliographies by topics / Multi-target drug

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Multi-target drug'

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Published: 10 March 2023

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Journal articles on the topic "Multi-target drug"

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Kondej, Magda, Piotr Stępnicki, and Agnieszka A. Kaczor. "Multi-Target Approach for Drug Discovery against Schizophrenia." International Journal of Molecular Sciences 19, no. 10 (October 10, 2018): 3105. http://dx.doi.org/10.3390/ijms19103105.

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Polypharmacology is nowadays considered an increasingly crucial aspect in discovering new drugs as a number of original single-target drugs have been performing far behind expectations during the last ten years. In this scenario, multi-target drugs are a promising approach against polygenic diseases with complex pathomechanisms such as schizophrenia. Indeed, second generation or atypical antipsychotics target a number of aminergic G protein-coupled receptors (GPCRs) simultaneously. Novel strategies in drug design and discovery against schizophrenia focus on targets beyond the dopaminergic hypothesis of the disease and even beyond the monoamine GPCRs. In particular these approaches concern proteins involved in glutamatergic and cholinergic neurotransmission, challenging the concept of antipsychotic activity without dopamine D2 receptor involvement. Potentially interesting compounds include ligands interacting with glycine modulatory binding pocket on N-methyl-d-aspartate (NMDA) receptors, positive allosteric modulators of α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, positive allosteric modulators of metabotropic glutamatergic receptors, agonists and positive allosteric modulators of α7 nicotinic receptors, as well as muscarinic receptor agonists. In this review we discuss classical and novel drug targets for schizophrenia, cover benefits and limitations of current strategies to design multi-target drugs and show examples of multi-target ligands as antipsychotics, including marketed drugs, substances in clinical trials, and other investigational compounds.
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de Oliveira Viana, Jessika, Hamilton Mitsugu Ishiki, Marcus Tullius Scotti, and Luciana Scotti. "Multi-Target Antitubercular Drugs." Current Topics in Medicinal Chemistry 18, no. 9 (July 31, 2018): 750–58. http://dx.doi.org/10.2174/1568026618666180528124414.

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Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis, which has high levels of mortality worldwide and has already gained resistance to first- and second-line drugs. The study by new chemical entities with promising activities becomes paramount to broaden the therapeutic strategies in the cure of the patients affected with this disease. In this context, in this review we report the discovery of 3 classes of compounds that can simultaneously interact with more than one target of Mycobacterium tuberculosis.
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Jaiswal, Varun. "Multi target drug design for gastrointestinal cancer." Annals of Oncology 28 (June 2017): iii18—iii19. http://dx.doi.org/10.1093/annonc/mdx261.020.

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Lu, Jin-Jian, Wei Pan, Yuan-Jia Hu, and Yi-Tao Wang. "Multi-Target Drugs: The Trend of Drug Research and Development." PLoS ONE 7, no. 6 (June 29, 2012): e40262. http://dx.doi.org/10.1371/journal.pone.0040262.

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Mei, Suyu, and Kun Zhang. "A Multi-Label Learning Framework for Drug Repurposing." Pharmaceutics 11, no. 9 (September 9, 2019): 466. http://dx.doi.org/10.3390/pharmaceutics11090466.

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Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% of the entire training dataset are necessarily required. Such a large number of negative examples that do not come from experimental observations inevitably decrease the credibility of predictions. In this study, we propose a multi-label learning framework to find new uses for old drugs and discover new drugs for known target genes. In the framework, each drug is treated as a class label and its target genes are treated as the class-specific training data to train a supervised learning model of l2-regularized logistic regression. As such, the inter-drug associations are explicitly modelled into the framework and all the class-specific training data come from experimental observations. In addition, the data constraint is less demanding, for instance, the chemical substructures of a drug are no longer needed and the novel target genes are inferred only from the underlying patterns of the known genes targeted by the drug. Stratified multi-label cross-validation shows that 84.9% of known target genes have at least one drug correctly recognized, and the proposed framework correctly recognizes 86.73% of the independent test drug-target interactions (DTIs) from DrugBank. These results show that the proposed framework could generalize well in the large drug/class space without the information of drug chemical structures and target protein structures. Furthermore, we use the trained model to predict new drugs for the known target genes, identify new genes for the old drugs, and infer new associations between old drugs and new disease phenotypes via the OMIM database. Gene ontology (GO) enrichment analyses and the disease associations reported in recent literature provide supporting evidences to the computational results, which potentially shed light on new clinical therapies for new and/or old disease phenotypes.
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Braga, Susana Santos. "Multi-target drugs active against leishmaniasis: A paradigm of drug repurposing." European Journal of Medicinal Chemistry 183 (December 2019): 111660. http://dx.doi.org/10.1016/j.ejmech.2019.111660.

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Zanni, Riccardo, María Galvez-Llompart, Jorge Galvez, and Ramon García-Domenech. "QSAR Multi-Target in Drug Discovery: A Review." Current Computer Aided-Drug Design 10, no. 2 (July 31, 2014): 129–36. http://dx.doi.org/10.2174/157340991002140708105124.

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Peng, Lihong, Bo Liao, Wen Zhu, Zejun Li, and Keqin Li. "Predicting Drug–Target Interactions With Multi-Information Fusion." IEEE Journal of Biomedical and Health Informatics 21, no. 2 (March 2017): 561–72. http://dx.doi.org/10.1109/jbhi.2015.2513200.

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Ma, Xiao Hua, Zhe Shi, Chunyan Tan, Yuyang Jiang, Mei Lin Go, Boon Chuan Low, and Yu Zong Chen. "In-Silico Approaches to Multi-target Drug Discovery." Pharmaceutical Research 27, no. 5 (March 11, 2010): 739–49. http://dx.doi.org/10.1007/s11095-010-0065-2.

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Liu, X., F. Zhu, X. H. Ma, Z. Shi, S. Y. Yang, Y. Q. Wei, and Y. Z. Chen. "Predicting Targeted Polypharmacology for Drug Repositioning and Multi- Target Drug Discovery." Current Medicinal Chemistry 20, no. 13 (March 1, 2013): 1646–61. http://dx.doi.org/10.2174/0929867311320130005.

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Dissertations / Theses on the topic "Multi-target drug"

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Pérez, Areales Francisco Javier. "Novel multi-target directed ligands as drug candidates against Alzheimer’s disease." Doctoral thesis, Universitat de Barcelona, 2017. http://hdl.handle.net/10803/404781.

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Alzheimer’s disease (AD) is the main neurodegenerative disorder and one of the most important health-care problems worldwide, because of its high prevalence and personal and economic impact. To aggravate this situation, current treatments are only symptomatic, but do not prevent, halt, or delay the disease progression. In the light of the multiple mechanisms involved in its pathogenesis, such as dysfunction of cholinergic and glutamatergic neurotransmitter systems, amyloid and tau pathologies, or oxidative stress, among others, the traditional medicinal chemistry approach of developing drugs based on the reductionist pattern of “one molecule-one target” is being increasingly perceived as ineffective. Alternatively, the so- called multitarget directed ligands (MTDLs), which consist of molecules designed to hit simultaneously different key targets of the complex pathological network, are emerging as a more realistic option to confront the disease. In this context, the purpose of the present PhD Thesis was the design, synthesis and biological evaluation of four novel families of compounds, endowed with multi-target profile, as drug candidates for the treatment of AD: 1) firstly, a family of shogaol–huprine hybrids, with purported dual antioxidant and anticholinesterase activity, with those activities to be imparted by their shogaol-derived and huprine moieties, respectively, and with β-amyloid and tau anti-aggregating activity likely arising from the planar aromatic moieties of their two constituting units; 2) secondly, a second generation of rhein–huprine hybrids designed by modification of the huprine aromatic ring of the lead compound of a previous generation of compounds, developed in our group, to explore the effect of pyridinic ring basicity on the different biological activities, with the hope of identifying an optimized hybrid with favorable activity profile on cholinesterases, β-secretase 1, β-amyloid and tau aggregation, and free radicals, and with reduced basicity, and, hence, with expectable better bioavailability; 3) thirdly, a family of CR-6–tacrine hybrids, which was designed to achieve a dual site binding within both acetylcholinesterase and β-secretase 1, apart from antioxidant activity, by combining a unit of the potent acetylcholinesterase inhibitor 6-chlorotacrine with a moiety derived from CR-6, a potent antioxidant; and 4) finally, a class of benzoadamantane–tacrine hybrids intented to act as acetylcholinesterase inhibitors and NMDA receptor antagonists, to combat neurodegeneration as well as improve memory and cognition. A crucial property for central nervous system drugs, the blood–brain permeability, was additionally assessed for all the abovementioned compounds.
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Koptelov, Maksim. "Link prediction in bipartite multi-layer networks, with an application to drug-target interaction prediction." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC211.

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De nombreux problèmes réels relèvent d’une structure bi-relationnelle et peuvent être modélisés suivant des réseaux bipartis. Une telle modélisation permet l'utilisation de solutions standards pour la prédiction et/ou la recommandation de nouvelles relations entre objets de ces réseaux. La tâche de prédiction de liens est un problème largement étudié dans les réseaux simples, c’est-à-dire les réseaux avec un seul type d'interaction entre sommets. Cependant, pour les réseaux multicouche (i.e. réseaux avec plusieurs types d'arêtes entre sommets), ce problème n'est pas encore entièrement résolu.Cette thèse est motivée par l'importance d'une tâche réelle, à savoir la prédiction d'interaction entre un médicament et une cible thérapeutique. La recherche de candidats médicaments prometteurs pour une cible thérapeutique biologique donnée est une partie essentielle de la conception d’un médicament moderne. Dans cette thèse, nous modélisons ce problème comme une tâche de prédiction de lien dans un réseau multicouche biparti. Cette modélisation du problème permet de rassembler différentes sources d'information en une seule structure et ainsi d'améliorer la qualité de la prédiction d’un lien.Cette thèse se concentre sur le problème de la prédiction de liens dans les réseaux multicouches bipartis et apporte deux contributions principales à ce sujet. La première contribution est une solution pour résoudre la prédiction de liens sans limiter le nombre et le type de réseaux, ce qui est le principal défaut des méthodes de l'état de l'art. L'algorithme que nous avons développé modélise une marche aléatoire à la manière du PageRank et est capable de prédire de nouvelles interactions dans le réseau que nous construisons à partir de différentes sources d'information. La deuxième contribution, qui porte aussi sur ce problème, s’appuie sur les méthodes de détection de communautés. Cette solution, moins immédiate et plus dépendante du choix des valeurs des paramètres, donne de meilleurs résultats. Pour cela, nous adaptons des mesures utilisées pour la détection de communautés à la problématique de la prédiction de liens dans les réseaux multicouche bipartis et nous développons de nouvelles méthodes associant des communautés pour la prédiction de liens. Nous évaluons aussi nos méthodes sur des données autres que celles des interactions entre médicaments et cibles thérapeutiques montrant ainsi le caractère générique de notre approche.D’autre part, nous proposons un protocole expérimental de validation des interactions prédites reposant sur l’exploitation de ressources externes. Fondé sur une collection de concepts biomédicaux utilisés comme source de connaissances, ce protocole effectue une validation des paires de médicaments-cibles thérapeutiques qui sont prédites à partir de scores de confiance que nous avons définis. Une évaluation des interactions prédites sur des données tests montre l'efficacité de ce protocole.Enfin, nous nous intéressons au problème de l'identification et de la caractérisation de composés promiscues qui existe dans le processus de développement de médicaments. Nous modélisons ce problème comme une tâche de classification et le résolvons par l'apprentissage automatique. Notre contribution repose sur une approche d'exploration de graphes et d'échantillonnage. De plus, nous avons développé une interface graphique pour fournir un retour d'information aux experts sur les résultats
Many aspects from real life with bi-relational structure can be modeled as bipartite networks. This modeling allows the use of some standard solutions for prediction and/or recommendation of new relations between these objects in such networks. Known as the link prediction task, it is a widely studied problem in network science for single graphs, networks assuming one type of interaction between vertices. For multi-layer networks, allowing more than one type of edges between vertices, the problem is not yet fully solved.The motivation of this thesis comes from the importance of an application task, drug-target interaction prediction. Searching valid drug candidates for a given biological target is an essential part of modern drug development. In this thesis, the problem is modeled as link prediction in a bipartite multi-layer network. Modeling the problem in this setting helps to aggregate different sources of information into one single structure and as a result to improve the quality of link prediction.The thesis mostly focuses on the problem of link prediction in bipartite multi-layer networks and makes two main contributions on this topic.The first contribution provides a solution for solving link prediction in the given setting without limiting the number and type of networks, the main constrains of the state of the art methods. Modeling random walk in the fashion of PageRank, the algorithm that we developed is able to predict new interactions in the network constructed from different sources of information. The second contribution, which solves link prediction using community information, is less straight-forward and more dependent on fixing the parameters, but provides better results. Adopting existing community measures for link prediction to the case of bipartite multi-layer networks and proposing alternative ways for exploiting communities, the method offers better performance and efficiency. Additional evaluation on the data of a different origin than drug-target interactions demonstrate the genericness of proposed approach.In addition to the developed approaches, we propose a framework for validation of predicted interactions founded on an external resource. Based on a collection of biomedical concepts used as a knowledge source, the framework is able to perform validation of drug-target pairs using proposed confidence scores. An evaluation of predicted interactions performed on unseen data shows effectiveness of this framework.At the end, a problem of identification and characterization of promiscuous compounds existing in the drug development process is discussed. The problem is solved as a machine learning classification task. The contribution includes graph mining and sampling approaches. In addition, a graphical interface was developed to provide feedback of the result for experts
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Di, Pietro Ornella. "Exploring heterocyclic scaffolds in the development of multi-target anti-Alzheimer and multi-trypanosomatid compounds." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/318585.

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The aim of this PhD thesis consists of the synergistic combination of both highly efficient synthetic approaches and molecular modelling tools for the structure-based drug design and synthesis of novel bioactive heterocyclic compounds. The work carried out has followed two main research lines, namely the development of novel disease-modifying anti-Alzheimer agents and still unexplored chemical entities for the treatment of Neglected Tropical Diseases (NTDs). The results obtained have been presented as a compendium of publications and draft manuscripts. In the framework of the anti-Alzheimer research line, first the hit-to-lead optimization of a practically inactive propidium-related compound easily accessed via a Povarov multicomponent reaction (MCR) approach (Di Pietro O. et al. Eur. J. Med. Chem., 2014, 73, 141), and the subsequent molecular hybridization with a 6-chlorotacrine unit through a molecular dynamics-driven tether length optimization, overall led to one of the most potent non-covalent dual binding site acetylcholinesterase inhibitor (AChEI) ever described in the literature (Di Pietro O. et al. Eur. J. Med. Chem., 2014, 84, 107). Second, the combined recourse to the highly versatile click-chemistry strategy, through the well-known Cu-catalyzed azide-alkyne cycloaddition reaction, and convenient computational chemistry tools, allowed the rational design and synthesis of a novel series of 1,4-disubstituted triazole-based propargylamines as irreversible MAO-B inhibitors (draft manuscript) with the perspective to be further linked to a second pharmacophoric moiety to derive novel MTDLs as potential anti-Alzheimer drug candidates. Furthermore, an extensive computation of the BACE-1 apo conformational ensemble by means of combined molecular dynamics technique and Principal Component Analysis (PCA) method, allowed to carry out an exhaustive study of a secondary transient druggable pocket (draft manuscript) and a virtual screening of 500,000 commercially available fragments for further drug discovery purposes. Finally, in the framework of the NTDs research line, 2−4-step sequences involving as the key step an initial Povarov MCR gave easy access to a small library of quinolones and tricyclic heterofused quinolines, which were subjected to phenotypic whole-cell screenings, leading to the individuation of several low micromolar multi-trypanosomatid hit compounds (Di Pietro et al. Eur. J. Med. Chem. 2015, accepted with minor revision).
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Herman, Jonathan David. "Halofuginone: A Story of How Target Identification of an Ancient Chinese Medicine and Multi-Step Evolution Informs Malaria Drug Discovery." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11540.

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Malaria is a treatable communicable disease yet remains a common cause of death and disease especially among pregnant women and children. Most of malaria's worldwide burden disproportionately lies in Southeast Asia and Sub-Saharan Africa. Western medicine's 100+ year history of combating Plasmodium falciparum has taught us that the global population of malaria parasites has a unique and dangerous ability to rapidly evolve and spread drug resistance. Recently it was documented that resistance to the first-line antimalarial artemisinin may be developing in Southeast Asia.
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Loguercio, Salvatore. "Reductionist and Integrative approaches to explore the H.pylori genome." Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3425099.

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The reductionist approach of decomposing biological systems into their constituent parts has dominated molecular biology for half a century. Since organisms are composed solely of atoms and molecules without the participation of extraneous forces, it has been assumed that it should be possible to explain biological systems on the basis of the physico-chemical properties of their individual components, down to the atomic level. However, despite the remarkable success of methodological reductionism in analyzing individual cellular components, it is now generally accepted that the behavior of complex biological systems cannot be understood by studying their individual parts in isolation. To tackle the complexity inherent in understanding large networks of interacting biomolecules, the integrative viewpoint emphasizes cybernetic and systems theoretical methods, using a combination of mathematics, computation and empirical observation. Such an approach is beginning to become feasible in prokaryotes, combining an almost complete view of the genome and transcriptome with a reasonably extensive picture of the proteome. Pathogenic bacteria are undoubtedly the most investigated subjects among prokaryotes. A paradigmatic example is the the human pathogen H.pylori, a causative agent of severe gastroduodenal disorders that infects almost half of the world population. In this thesis, we investigated various aspects of Helicobacter pylori molecular physiology using both reductionist and integrative approaches. In Section I, we have employed a reductionist, bottom-up perspective in studying the Cysteine oxidised/reduced state and the disulphide bridge pattern of an unusual GroES homolog expressed by H.pylori, Heat Shock protein A (HspA). This protein possesses a high Cys content, is involved in nickel binding and exhibits an extended subcellular localization, ranging from cytoplasm to cell surface. We have produced and characterized a recombinant HspA and mutants Cys94Ala and C94A/C111A. The disulphide bridge pattern has been assigned by integrating biochemical methodologies with mass spectrometry. All Cys are engaged in disulphide bonds that force the C-term domain to assume a peculiar closed loop structure, prone to host nickel ions. This novel Ni binding structural arrangement can be related to the Ni uptake/delivery to the extracellular urease, essential for the bacterium survival. In Section II, we combined different computational methods with two main goals: 1) Analyze the H.pylori biomolecular interaction network in an attempt to select new molecular targets against H.pylori infection (Chapters 4 & 5); 2) Model and simulate the signaling perturbations induced by invading H.pylori proteins in the host ephitelial cells (Chapter 6). Chapter 4 explores the 'robust yet fragile' feature of the H.pylori cell, viewed as a complex system in which robustness in response to certain perturbation is inevitably associated with fragility in response to other perturbations. With this in mind, we developed a general strategy aimed at identify control points in bacterial metabolic networks, which could be targets for novel drugs. The methodology is implemented on Helicobacter pylori 26695. The entire metabolic network of the pathogen is analyzed to find biochemically critical points, e.g. enzymes which uniquely consume and/or produce a certain metabolite. Once identified, the list of critical enzymes is filtered in order to find candidate targets wich are non-homologous with the human enzymes. Finally, the essentiality of the identified targets is cross-validated by in silico deletion studies using flux-balance analysis (FBA) on a recent genome-scale metabolic model of H. pylori. Following this approach, we identified some enzymes which could be interesting targets for inhibition studies of H.pylori infection. The study reported in Chapter 5 extends the previously described approach in light of recent theoretical studies on biological networks. These studies suggested that multiple weak attacks on selected targets are inevitably more efficient than the knockout of a single target, thus providing a conceptual framework for the recent success of multi-target drugs. We used this concept to exploit H.pylori metabolic robustness through multiple weak attacks on selected enzymes, therefore directing us toward target-sets discovery for combinatorial therapies. We used the known metabolic and protein interaction data to build an integrated biomolecular network of the pathogen. The network was subsequently screened to find central elements of network communication, e.g. hubs, bridges with high betweenness centrality and overlaps of network communities. The selected enzymes were then classified on the basis of available data about cellular function and essentiality in an attempt to predict successful target-combinations. In order to evaluate the network effect triggered by the partial inactivation of candidate targets, robustness analysis was performed on small groups of selected enzymes using flux balance analysis (FBA) on a recent genome-scale metabolic model of H.pylori. In particular, the FBA simulation framework allowed to predict the growth phenotype associated to every partial inactivation set. The preliminary results obtained so far may help to restrict the initial target-pool in search of target-sets for novel combinatorial drugs against H.pylori persistence. However, our long-term goal is to better understand the indirect network effects that lie at the heart of multi-target drug action and, ultimately, how multiple weak hits can perturb complex biological systems. H.pylori produces various a cytotoxic protein, CagA, that interfere with a very important host signaling pathway, i.e. the epidermal growth factor receptor (EGFR) signaling network. EGFR signaling is one of the most extensively studied areas of signal transduction, since it regulates growth, survival, proliferation and differentiation in mammalian cells. In Chapter 6, we attempted to build an executable model of the EGFR-signaling core process using a process algebra approach. In the EGFR network, the core process is the heart of its underlying hour-glass architecture, as it plays a central role in downstream signaling cascades to gene expression through activation of multiple transcription factors. It consists in a dense array of molecules and interactions wich are tightly coupled to each other. In order to build the executable model, a small set of EGFR core molecules and their interactions is tentatively translated in a BetaWB model. BetaWB is a framework for modelling and simulating biological processes based on Beta-binders language and its stochastic extension. Once obtained, the computational model of the EGFR core process can be used to test and compare hypotheses regarding the principles of operation of the signaling network, i.e. how the EGFR network generates different responses for each set of combinatorial stimuli. In particular, probabilistic model checking can be used to explore the states and possible state changes of the computational model, whereas stochastic simulation (corresponding to the execution of the BetaWB model) may give quantitative insights into the dynamic behaviour of the system in response to different stimuli. Information from the above tecniques allows model validation through comparison within the experimental data available in the literature. The inherent compositionality of the process algebra modeling approach enables further expansion of the EGFR core model, as well as the study of its behavior under specific perturbations, such as invading H.pylori proteins. This latter aspect might be of great value for H.pylori pathogenesis research, as signaling through the EGF receptors is intricately involved in gastric cancer and in many other gastroduodenal diseases.
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Dalla, Via Martina. "Development of multifunctional anticancer agents: design, synthesis and evaluation of hybrid compounds containing kinase inhibitor moieties." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421807.

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Cancer is a complex and a multiple-genes involved disease; for this reason it can not be treated or cured with a single drug modulating the biological function of a single target. The innovation related to multi-targeted drugs, combining the activity of different cancer progression relevant targets, became a burgeoning research topic. Drugs that act on multiple targets can enhance efficacy and reduce chemo-resistance that causes disease relapse and metastasis and remains the main obstacle to cancer therapy. One of the main target nowadays are tyrosine kinases (TKs); since most protein kinases stimulate cell growth and proliferation, cell survival and migration, they can, if overexpressed, amplified or constitutively active, assume oncogenic properties. Other ideal biological targets are enzymes as histone deacetylases (HDAC) and mitochondrial functions. Herein we present the development and the preliminary evaluation of new Abl/HDAC inhibitors bearing the pyrido-pyrimidine main scaffold; the functionalization of the most active compounds with metal ions (i.e. Zn2+, Cu2+ and Fe3+); the development of novel multi-kinase inhibitors bearing the 4-anilinopyrimidine scaffold; the development of novel cKIT/wtRET/V804MRET inhibitors bearing the 4-anilinopyridine scaffold. Besides, the development of multi-kinase inhibitors endowed with antifibrotic properties as well as novel topoisomerase inhibitors are reported.
Il cancro è una patologia complessa che coinvolge più geni; per questo motivo non può essere trattato o curato con un singolo farmaco che regola l'attività biologica di un unico bersaglio. L'innovazione dei farmaci multi-target, che combinano l'attività contro diversi bersagli coinvolti nella progressione del tumore, è diventato un promettente argomento di ricerca. Farmaci che agiscono su più bersagli possono aumentare l'efficacia della terapia riducendo il fenomeno di resistenza che causa ricadute e metastasi restando uno dei maggiori ostacoli della terapia antitumorale. Le tirosin-chinasi sono considerate ad oggi tra i principali bersagli in quanto molte protein chinasi stimolando la crescita, la proliferazione e la migrazione cellulare e se sovra espresse, amplificate o costitutivamente attivate assumono proprietà oncogeniche. Altri bersagli biologici ideali sono enzimi quali gli istone deacetilasi e le funzioni mitocondriali. Nella tesi sono presentati lo sviluppo e la valutazione biologica preliminare di nuovi inibitori duali di Abl e HDAC caratterizzati da una porzione pirido-pirimidinica; la funzionalizzazione dei composti più attivi con ioni metallici (i.e. Zn2+, Cu2+ and Fe3+); lo sviluppo di nuovi inibitori multi-chinasi caratterizzati da una porzione 4-anilinopirimidinica; lo sviluppo di nuovi inibitori di cKIT/wtRET/V804MRET a struttura 4-anilinopiridinica. Sono inoltre riportati lo sviluppo di nuovi inibitori multichinasici ad attività antifibrotica ed inibitori di topoisomerasi.
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Teponnou, Gerard A. Kenfack. "Tacrine, trolox and tryptoline as lead compounds for the design and synthesis of multi-target drugs for Alzheimer's disease therapy." Thesis, University of the Western Cape, 2016. http://hdl.handle.net/11394/5344.

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Magister Pharmaceuticae - MPharm
The cascade of neurotoxic events involved in the pathogenesis of Alzheimer's disease may explain the inefficacy of currently available treatment based on acetylcholinesterase inhibitors (AChEI - donepezil, galantamine, rivastigmine) and N-methyl-D-aspartate (NMDA) antagonists (memantine). These drugs were designed based on the "one-moleculeone- target" paradigm and only address a single target. Conversely, the multi-target drug design strategy increasingly gains recognition. Based on the versatile biological activities of tacrine, trolox and β-carboline derivatives, the attention they have received as lead structures for the design of multifunctional drugs for the treatment of Alzheimer's disease, and the topology of the active site of AChE, we have designed tacrine-trolox and tacrine-tryptoline hybrids with various linker chain lengths. The aim with these hybrids was to provide additive or synergistic therapeutic effects that might help overcome the limitation of current anti Alzheimer's disease drugs. All synthesized compounds were designed from lead structures (tacrine, tryptoline and trolox) to obtain cholinesterase (ChE) multisite binders and multifunctional AD agents. The study was rationalized by docking all structures in the active site of TcAChE using Molecular Operating Environment (MOE) software before proceeding with the synthesis. ChE inhibition was assessed in a UV enzyme inhibition assay using Ellman's method. Antioxidant activities were assessed using the 2, 2-diphenyl-1-picrylhydrazyl (DPPH.) absorbance assay. The hybrids containing the trolox moiety (compounds 8a-e) showed moderate to high AChE inhibitory activity in the nano to micro molar range (IC₅₀: 17.37 - 2200 nM), BuChE inhibition was observed in the same range (IC₅₀: 3.16 – 128.82 nM), and free radical scavenging activities in micro molar range (IC50: 11.48 – 49.23 µM). These are comparable or slightly higher than their reference compounds donepezil (AChE IC₅₀ = 220 nM), tacrine (BuChE IC₅₀: 14.12 nM), and trolox (DPPH IC₅₀: 17.57 µM). The hybrids with longer linker chain lengths, 6 and 8 carbons (8d and 8e), showed better ChE inhibitory activity than the shorter ones, 2, 3, and 4 carbons (8a-c respectively). This correlates well with literature. Free radical scavenging activities, however, seems not to be significantly affected by varying linker chain lengths. The hybrid compound (14) containing the tryptoline moiety linked with a 7 carbon spacer displayed the best AChE and BuChE inhibitory activity (IC₅₀ = 17.37 and 3.16 nM) but poor free radical scavenging activity. Novel anti-Alzheimer's disease drugs with multi-target neuroprotective activities were thus obtained and hybrid molecules that exhibit good ChE inhibition (8d, 8e and 14) and anti-oxidant (8d and 8e) activity were identified as suitable candidates for further investigation.
National Research Foundation (NRF)
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Gencarelli, Manuela. "Revisiting targets for HCN blockers in the heart and urinary bladder: evidence for antimuscarinic activity in human atrial preparations, rat urinary bladder and recombinant muscarinic receptors." Doctoral thesis, 2022. http://hdl.handle.net/2158/1280560.

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Books on the topic "Multi-target drug"

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Roy, Kunal, ed. Multi-Target Drug Design Using Chem-Bioinformatic Approaches. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-8733-7.

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Morphy, J. Richard, and C. John Harris, eds. Designing Multi-Target Drugs. Cambridge: Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912.

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Designing Multi-Target Drugs. Royal Society of Chemistry, The, 2012.

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Multi-Target Drug Design Using Chem-Bioinformatic Approaches. Humana, 2018.

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Herman, Jonathan David. HALOFUGINONE: A STORY OF HOW TARGET IDENTIFICATION OF AN ANCIENT CHINESE MEDICINE AND MULTI-STEP EVOLUTION INFORMS MALARIA DRUG DISCOVERY. 2014.

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Krueger, Darcy A., and Jamie Capal. Familial CNS Tumor Syndromes. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0136.

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Tuberous sclerosis complex is an autosomal dominant multi-system disease that involves the skin, brain, heart, lungs, and kidneys and is associated with seizures including infantile spasms, intellectual disability, autism and pulmonary and heart disease. Skin lesions can be particularly disfiguring and infantile spasms can be associated with marked cognitive decline. The outlook for patients has improved markedly with the recognition that TSC is caused by upregulation of the mammalian target of rapamycin (mTOR) enzyme, which connects energy needs and supply with cellular and neuronal growth. mTOR is upregulated in TSC because of mutations in hamartin or tuberin, which normally serve as a brake on mTOR. The drug rapamycin is commonly used as an immunosuppressive for patients undergoing kidney transplants; it has also found a new use in patients with TSC. Although the drug is immunosuppressive for non-TSC patients, careful titration of the drug in TSC patients corrects its upregulation but is not particulary immunosuppressive. Additional mTOR inhibitors such as everolimus have been developed and have been shown to be effective for pulmonary disease associated with TSC. Rapamycin in ointment form is dramatically effective in suppressing skin lesions of TSC and studies are underway to test the effect of mTOR inhibitors on seizures, brain tubers, intellect, and features of autism. Infantile spasms associated with TSC are very responsive to vigabatrin.
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Book chapters on the topic "Multi-target drug"

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Abdolmaleki, Azizeh, Fereshteh Shiri, and Jahan B. Ghasemi. "Computational Multi-Target Drug Design." In Methods in Pharmacology and Toxicology, 51–90. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/7653_2018_23.

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Jayaraman, Prem kumar, Mohammad Imran Siddiqi, Meena K. Sakharkar, Ramesh Chandra, and Kishore R. Sakharkar. "Hypothesis Driven Multi-target Drug Design." In Post-genomic Approaches in Drug and Vaccine Development, 133–77. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003339090-7.

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Richard Morphy, J. "Chapter 10. The Challenges of Multi-Target Lead Optimization." In Drug Discovery, 141–54. Cambridge: Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00141.

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Cai, Xiong, and Changgeng Qian. "Chapter 14. Discovery of HDAC-Inhibiting Multi-Target Inhibitors." In Drug Discovery, 221–42. Cambridge: Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00221.

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Rankovic, Zoran, and Richard Morphy. "CHAPTER 19. Multi-target Drug Discovery for Psychiatric Disorders." In Drug Discovery, 510–33. Cambridge: Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734943-00510.

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Gupta, Neelima, Prateek Pandya, and Seema Verma. "Computational Predictions for Multi-Target Drug Design." In Methods in Pharmacology and Toxicology, 27–50. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/7653_2018_26.

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Shahid, Mohammed. "Chapter 2. Clinical Need and Rationale for Multi-Target Drugs in Psychiatry." In Drug Discovery, 14–31. Cambridge: Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00014.

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Mason, Jonathan S. "Chapter 5. Designing Multi-Target Drugs: In Vitro Panel Screening – Biological Fingerprinting." In Drug Discovery, 66–85. Cambridge: Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00066.

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Ma, Xiaohou, and Yuzong Chen. "Chapter 9. In Silico Lead Generation Approaches in Multi-Target Drug Discovery." In Drug Discovery, 130–40. Cambridge: Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00130.

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Bolognesi, Maria Laura, Carlo Melchiorre, Cornelis J. Van der Schyf, and Moussa Youdim. "Chapter 18. Discovery of Multi-Target Agents for Neurological Diseases via Ligand Design." In Drug Discovery, 290–315. Cambridge: Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00290.

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Conference papers on the topic "Multi-target drug"

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Cai, Ruichu, Zhenjie Zhang, Srinivasan Parthasarathy, Anthony K. H. Tung, Zhifeng Hao, and Wen Zhang. "Multi-Domain Manifold Learning for Drug-Target Interaction Prediction." In Proceedings of the 2016 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2016. http://dx.doi.org/10.1137/1.9781611974348.3.

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Chen, Jiatao, Liang Zhang, Ke Cheng, Bo Jin, Xinjiang Lu, Chao Che, and Yiwei Liu. "Exploring Multi-level Mutual Information for Drug-target Interaction Prediction." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313395.

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Nishamol, P. H., and G. Gopakumar. "Multi-target drug discovery using system polypharmacology-state of the art." In 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2015. http://dx.doi.org/10.1109/spices.2015.7091430.

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Weng, Yuyou, Chen Lin, Xiangxiang Zeng, and Yun Liang. "Drug Target Interaction Prediction using Multi-task Learning and Co-attention." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983254.

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Adilova, Fatima, and Alisher Ikramov. "Using Support Vector Regression in multi-target prediction of drug toxicity." In 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2020. http://dx.doi.org/10.1109/aict50176.2020.9368837.

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Ma, Xin, and Yujing Cheng. "Prediction of Drug-Target Interaction Based on Multi-Head Self-Attention." In 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2022. http://dx.doi.org/10.1109/tocs56154.2022.10016085.

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Pillai, Suchitha Chandran, P. K. Krishnan Namboori, N. C. Anil Kumar, K. Varun Gopal, A. Suresh Kumar, and P. Bharath. "Investigating multi target drug action of Aloe vera using computational analysis." In 2011 2nd National Conference on Emerging Trends and Applications in Computer Science (NCETACS). IEEE, 2011. http://dx.doi.org/10.1109/ncetacs.2011.5751406.

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Fitriawan, Aries, Ito Wasito, Arida Ferti Syafiandini, Mukhlis Amien, and Arry Yanuar. "Multi-label classification using deep belief networks for virtual screening of multi-target drug." In 2016 International Conference on Computer, Control, Informatics and its Applications (IC3INA). IEEE, 2016. http://dx.doi.org/10.1109/ic3ina.2016.7863032.

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Spolaor, Simone, Daniele M. Papetti, Paolo Cazzaniga, Daniela Besozzi, and Marco S. Nobile. "A comparison of multi-objective optimization algorithms to identify drug target combinations." In 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2021. http://dx.doi.org/10.1109/cibcb49929.2021.9562773.

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Jin, Xu, MingMing Liu, Lin Wang, WenQian He, YaLou Huang, and MaoQiang Xie. "Multi-Resolutional Collaborative Heterogeneous Graph Convolutional Auto-Encoder for Drug-Target Interaction Prediction." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313489.

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Reports on the topic "Multi-target drug"

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Ayoul-Guilmard, Q., F. Nobile, S. Ganesh, M. Nuñez, R. Tosi, C. Soriano, and R. Rosi. D5.5 Report on the application of multi-level Monte Carlo to wind engineering. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.03.

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We study the use of multi-level Monte Carlo methods for wind engineering. This report brings together methodological research on uncertainty quantification and work on target applications of the ExaQUte project in wind and civil engineering. First, a multi-level Monte Carlo for the estimation of the conditional value at risk and an adaptive algorithm are presented. Their reliability and performance are shown on the time-average of a non-linear oscillator and on the lift coefficient of an airfoil, with both preset and adaptively refined meshes. Then, we propose an adaptive multi-fidelity Monte Carlo algorithm for turbulent fluid flows where multilevel Monte Carlo methods were found to be inefficient. Its efficiency is studied and demonstrated on the benchmark problem of quantifying the uncertainty on the drag force of a tall building under random turbulent wind conditions. All numerical experiments showcase the open-source software stack of the ExaQUte project for large-scale computing in a distributed environment.
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Jorgensen, Frieda, Andre Charlett, Craig Swift, Anais Painset, and Nicolae Corcionivoschi. A survey of the levels of Campylobacter spp. contamination and prevalence of selected antimicrobial resistance determinants in fresh whole UK-produced chilled chickens at retail sale (non-major retailers). Food Standards Agency, June 2021. http://dx.doi.org/10.46756/sci.fsa.xls618.

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Campylobacter spp. are the most common bacterial cause of foodborne illness in the UK, with chicken considered to be the most important vehicle for this organism. The UK Food Standards Agency (FSA) agreed with industry to reduce Campylobacter spp. contamination in raw chicken and issued a target to reduce the prevalence of the most contaminated chickens (those with more than 1000 cfu per g chicken neck skin) to below 10 % at the end of the slaughter process, initially by 2016. To help monitor progress, a series of UK-wide surveys were undertaken to determine the levels of Campylobacter spp. on whole UK-produced, fresh chicken at retail sale in the UK. The data obtained for the first four years was reported in FSA projects FS241044 (2014/15) and FS102121 (2015 to 2018). The FSA has indicated that the retail proxy target for the percentage of highly contaminated raw whole retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target. This report presents results from testing chickens from non-major retailer stores (only) in a fifth survey year from 2018 to 2019. In line with previous practise, samples were collected from stores distributed throughout the UK (in proportion to the population size of each country). Testing was performed by two laboratories - a Public Health England (PHE) laboratory or the Agri-Food & Biosciences Institute (AFBI), Belfast. Enumeration of Campylobacter spp. was performed using the ISO 10272-2 standard enumeration method applied with a detection limit of 10 colony forming units (cfu) per gram (g) of neck skin. Antimicrobial resistance (AMR) to selected antimicrobials in accordance with those advised in the EU harmonised monitoring protocol was predicted from genome sequence data in Campylobacter jejuni and Campylobacter coli isolates The percentage (10.8%) of fresh, whole chicken at retail sale in stores of smaller chains (for example, Iceland, McColl’s, Budgens, Nisa, Costcutter, One Stop), independents and butchers (collectively referred to as non-major retailer stores in this report) in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. has decreased since the previous survey year but is still higher than that found in samples from major retailers. 8 whole fresh raw chickens from non-major retailer stores were collected from August 2018 to July 2019 (n = 1009). Campylobacter spp. were detected in 55.8% of the chicken skin samples obtained from non-major retailer shops, and 10.8% of the samples had counts above 1000 cfu per g chicken skin. Comparison among production plant approval codes showed significant differences of the percentages of chicken samples with more than 1000 cfu per g, ranging from 0% to 28.1%. The percentage of samples with more than 1000 cfu of Campylobacter spp. per g was significantly higher in the period May, June and July than in the period November to April. The percentage of highly contaminated samples was significantly higher for samples taken from larger compared to smaller chickens. There was no statistical difference in the percentage of highly contaminated samples between those obtained from chicken reared with access to range (for example, free-range and organic birds) and those reared under standard regime (for example, no access to range) but the small sample size for organic and to a lesser extent free-range chickens, may have limited the ability to detect important differences should they exist. Campylobacter species was determined for isolates from 93.4% of the positive samples. C. jejuni was isolated from the majority (72.6%) of samples while C. coli was identified in 22.1% of samples. A combination of both species was found in 5.3% of samples. C. coli was more frequently isolated from samples obtained from chicken reared with access to range in comparison to those reared as standard birds. C. jejuni was less prevalent during the summer months of June, July and August compared to the remaining months of the year. Resistance to ciprofloxacin (fluoroquinolone), erythromycin (macrolide), tetracycline, (tetracyclines), gentamicin and streptomycin (aminoglycosides) was predicted from WGS data by the detection of known antimicrobial resistance determinants. Resistance to ciprofloxacin was detected in 185 (51.7%) isolates of C. jejuni and 49 (42.1%) isolates of C. coli; while 220 (61.1%) isolates of C. jejuni and 73 (62.9%) isolates of C. coli isolates were resistant to tetracycline. Three C. coli (2.6%) but none of the C. jejuni isolates harboured 23S mutations predicting reduced susceptibility to erythromycin. Multidrug resistance (MDR), defined as harbouring genetic determinants for resistance to at least three unrelated antimicrobial classes, was found in 10 (8.6%) C. coli isolates but not in any C. jejuni isolates. Co-resistance to ciprofloxacin and erythromycin was predicted in 1.7% of C. coli isolates. 9 Overall, the percentages of isolates with genetic AMR determinants found in this study were similar to those reported in the previous survey year (August 2016 to July 2017) where testing was based on phenotypic break-point testing. Multi-drug resistance was similar to that found in the previous survey years. It is recommended that trends in AMR in Campylobacter spp. isolates from retail chickens continue to be monitored to realise any increasing resistance of concern, particulary to erythromycin (macrolide). Considering that the percentage of fresh, whole chicken from non-major retailer stores in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. continues to be above that in samples from major retailers more action including consideration of interventions such as improved biosecurity and slaughterhouse measures is needed to achieve better control of Campylobacter spp. for this section of the industry. The FSA has indicated that the retail proxy target for the percentage of highly contaminated retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target.
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