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Journal articles on the topic 'Multi-target drug'

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Published: 10 March 2023

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1

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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3

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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4

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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5

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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7

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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8

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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9

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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10

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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11

Yang, Ting, Xin Sui, Bing Yu, Youqing Shen, and Hailin Cong. "Recent Advances in the Rational Drug Design Based on Multi-target Ligands." Current Medicinal Chemistry 27, no. 28 (August 6, 2020): 4720–40. http://dx.doi.org/10.2174/0929867327666200102120652.

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Multi-target drugs have gained considerable attention in the last decade owing to their advantages in the treatment of complex diseases and health conditions linked to drug resistance. Single-target drugs, although highly selective, may not necessarily have better efficacy or fewer side effects. Therefore, more attention is being paid to developing drugs that work on multiple targets at the same time, but developing such drugs is a huge challenge for medicinal chemists. Each target must have sufficient activity and have sufficiently characterized pharmacokinetic parameters. Multi-target drugs, which have long been known and effectively used in clinical practice, are briefly discussed in the present article. In addition, in this review, we will discuss the possible applications of multi-target ligands to guide the repositioning of prospective drugs.

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12

Liu, Ying-tao, Yi Li, Zi-fu Huang, Zhi-jian Xu, Zhuo Yang, Zhu-xi Chen, Kai-xian Chen, Ji-ye Shi, and Wei-liang Zhu. "Multi-algorithm and multi-model based drug target prediction and web server." Acta Pharmacologica Sinica 35, no. 3 (February 3, 2014): 419–31. http://dx.doi.org/10.1038/aps.2013.153.

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13

Scotti, Luciana, and Marcus T. Scotti. "Editorial: Multi-Target in Computer-Aided Drug Design Studies." Current Drug Targets 18, no. 5 (February 24, 2017): 498–99. http://dx.doi.org/10.2174/138945011805170224223532.

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14

Liang, Yun, Chen Lin, Yuyou Weng, Hui Li, and Xinyi Liu. "Drug target interaction prediction via multi-task co-attention." International Journal of Data Mining and Bioinformatics 24, no. 2 (2020): 160. http://dx.doi.org/10.1504/ijdmb.2020.10032430.

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Weng, Yuyou, Xinyi Liu, Hui Li, Chen Lin, and Yun Liang. "Drug target interaction prediction via multi-task co-attention." International Journal of Data Mining and Bioinformatics 24, no. 2 (2020): 160. http://dx.doi.org/10.1504/ijdmb.2020.110158.

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Brainin, Michael. "Cerebrolysin: a multi-target drug for recovery after stroke." Expert Review of Neurotherapeutics 18, no. 8 (July 18, 2018): 681–87. http://dx.doi.org/10.1080/14737175.2018.1500459.

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17

Li, Limin, and Menglan Cai. "Drug Target Prediction by Multi-View Low Rank Embedding." IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 5 (September 1, 2019): 1712–21. http://dx.doi.org/10.1109/tcbb.2017.2706267.

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18

Goff, Aaron, Daire Cantillon, Leticia Muraro Wildner, and Simon J. Waddell. "Multi-Omics Technologies Applied to Tuberculosis Drug Discovery." Applied Sciences 10, no. 13 (July 3, 2020): 4629. http://dx.doi.org/10.3390/app10134629.

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Multi-omics strategies are indispensable tools in the search for new anti-tuberculosis drugs. Omics methodologies, where the ensemble of a class of biological molecules are measured and evaluated together, enable drug discovery programs to answer two fundamental questions. Firstly, in a discovery biology approach, to find new targets in druggable pathways for target-based investigation, advancing from target to lead compound. Secondly, in a discovery chemistry approach, to identify the mode of action of lead compounds derived from high-throughput screens, progressing from compound to target. The advantage of multi-omics methodologies in both of these settings is that omics approaches are unsupervised and unbiased to a priori hypotheses, making omics useful tools to confirm drug action, reveal new insights into compound activity, and discover new avenues for inquiry. This review summarizes the application of Mycobacterium tuberculosis omics technologies to the early stages of tuberculosis antimicrobial drug discovery.

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19

Wang, Ning, Panpan Qiu, Wei Cui, Xiaojun Yan, Bin Zhang, and Shan He. "Recent Advances in Multi-target Anti-Alzheimer Disease Compounds (2013 Up to the Present)." Current Medicinal Chemistry 26, no. 30 (October 26, 2019): 5684–710. http://dx.doi.org/10.2174/0929867326666181203124102.

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: Since the last century, when scientists proposed the lock-and-key model, the discovery of drugs has focused on the development of drugs acting on single target. However, single-target drug therapies are not effective to complex diseases with multi-factorial pathogenesis. Moreover, the combination of single-target drugs readily causes drug resistance and side effects. In recent years, multi-target drugs have increasingly been represented among FDA-approved drugs. Alzheimer’s Disease (AD) is a complex and multi-factorial disease for which the precise molecular mechanisms are still not fully understood. In recent years, rational multi-target drug design methods, which combine the pharmacophores of multiple drugs, have been increasingly applied in the development of anti-AD drugs. In this review, we give a brief description of the pathogenesis of AD and provide detailed discussions about the recent development of chemical structures of anti-AD agents (2013 up to present) that have multiple targets, such as amyloid-β peptide, Tau protein, cholinesterases, monoamine oxidase, β-site amyloid-precursor protein-cleaving enzyme 1, free radicals, metal ions (Fe2+, Cu2+, Zn2+) and so on. In this paper, we also added some novel targets or possible pathogenesis which have been reported in recent years for AD therapy. We hope that these findings may provide new perspectives for the pharmacological treatment of AD.

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20

Scotti, Luciana, Alex France Messias Monteiro, Jéssika de Oliveira Viana, Francisco Jaime Bezerra Mendonça Junior, Hamilton M. Ishiki, Ernestine Nkwengoua Tchouboun, Rodrigo Santos, and Marcus Tullius Scotti. "Multi-Target Drugs Against Metabolic Disorders." Endocrine, Metabolic & Immune Disorders - Drug Targets 19, no. 4 (June 12, 2019): 402–18. http://dx.doi.org/10.2174/1871530319666181217123357.

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Background: Metabolic disorders are a major cause of illness and death worldwide. Metabolism is the process by which the body makes energy from proteins, carbohydrates, and fats; chemically breaking these down in the digestive system towards sugars and acids which constitute the human body's fuel for immediate use, or to store in body tissues, such as the liver, muscles, and body fat. Objective: The efficiency of treatments for multifactor diseases has not been proved. It is accepted that to manage multifactor diseases, simultaneous modulation of multiple targets is required leading to the development of new strategies for discovery and development of drugs against metabolic disorders. Methods: In silico studies are increasingly being applied by researchers due to reductions in time and costs for new prototype synthesis; obtaining substances that present better therapeutic profiles. Discussion: In the present work, in addition to discussing multi-target drug discovery and the contributions of in silico studies to rational bioactive planning against metabolic disorders such as diabetes and obesity, we review various in silico study contributions to the fight against human metabolic pathologies. Conclusion: In this review, we have presented various studies involved in the treatment of metabolic disorders; attempting to obtain hybrid molecules with pharmacological activity against various targets and expanding biological activity by using different mechanisms of action to treat a single pathology.

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Xiong, Zhaoping, Minji Jeon, Robert J. Allaway, Jaewoo Kang, Donghyeon Park, Jinhyuk Lee, Hwisang Jeon, et al. "Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge." PLOS Computational Biology 17, no. 9 (September 14, 2021): e1009302. http://dx.doi.org/10.1371/journal.pcbi.1009302.

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A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

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22

Che, Jingang, Lei Chen, Zi-Han Guo, Shuaiqun Wang, and Aorigele. "Drug Target Group Prediction with Multiple Drug Networks." Combinatorial Chemistry & High Throughput Screening 23, no. 4 (May 19, 2020): 274–84. http://dx.doi.org/10.2174/1386207322666190702103927.

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Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

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23

Zhou, Ying, Yintao Zhang, Xichen Lian, Fengcheng Li, Chaoxin Wang, Feng Zhu, Yunqing Qiu, and Yuzong Chen. "Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents." Nucleic Acids Research 50, no. D1 (October 28, 2021): D1398—D1407. http://dx.doi.org/10.1093/nar/gkab953.

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Abstract Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.

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Zhou, Ying, Yintao Zhang, Xichen Lian, Fengcheng Li, Chaoxin Wang, Feng Zhu, Yunqing Qiu, and Yuzong Chen. "Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents." Nucleic Acids Research 50, no. D1 (October 28, 2021): D1398—D1407. http://dx.doi.org/10.1093/nar/gkab953.

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Abstract Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.

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25

Xie, Li, and Lei Xie. "Pathway-Centric Structure-Based Multi-Target Compound Screening for Anti-Virulence Drug Repurposing." International Journal of Molecular Sciences 20, no. 14 (July 17, 2019): 3504. http://dx.doi.org/10.3390/ijms20143504.

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The emergence of superbugs that are resistant to last-resort antibiotics poses a serious threat to human health, and we are in a “race against time to develop new antibiotics.” New approaches are urgently needed to control drug-resistant pathogens, and to reduce the emergence of new drug-resistant microbes. Targeting bacterial virulence has emerged as an important strategy for combating drug-resistant pathogens. It has been shown that pyocyanin, which is produced by the phenazine biosynthesis pathway, plays a key role in the virulence of Pseudomonas aeruginosa infection, making it an attractive target for anti-infective drug discovery. In order to discover efficient therapeutics that inhibit the phenazine biosynthesis in a timely fashion, we screen 2004 clinical and pre-clinical drugs to target multiple enzymes in the phenazine biosynthesis pathway, using a novel procedure of protein–ligand docking. Our detailed analysis suggests that kinase inhibitors, notably Lifirafenib, are promising lead compounds for inhibiting aroQ, phzG, and phzS enzymes that are involved in the phenazine biosynthesis, and merit further experimental validations. In principle, inhibiting multiple targets in a pathway will be more effective and have less chance of the emergence of drug resistance than targeting a single protein. Our multi-target structure-based drug design strategy can be applied to other pathways, as well as provide a systematic approach to polypharmacological drug repositioning.

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CSERMELY, P., V. AGOSTON, and S. PONGOR. "The efficiency of multi-target drugs: the network approach might help drug design." Trends in Pharmacological Sciences 26, no. 4 (April 2005): 178–82. http://dx.doi.org/10.1016/j.tips.2005.02.007.

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27

de Oliveira, Pedro Gonçalves, Lara Termini, Edison Luiz Durigon, Ana Paula Lepique, Andrei C. Sposito, and Enrique Boccardo. "Diacerein: A potential multi-target therapeutic drug for COVID-19." Medical Hypotheses 144 (November 2020): 109920. http://dx.doi.org/10.1016/j.mehy.2020.109920.

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28

Agyemang, Brighter, Wei-Ping Wu, Michael Yelpengne Kpiebaareh, Zhihua Lei, Ebenezer Nanor, and Lei Chen. "Multi-view self-attention for interpretable drug–target interaction prediction." Journal of Biomedical Informatics 110 (October 2020): 103547. http://dx.doi.org/10.1016/j.jbi.2020.103547.

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29

Tibon, Natasha Stella, Chew Hee Ng, and Siew Lee Cheong. "Current progress in antimalarial pharmacotherapy and multi-target drug discovery." European Journal of Medicinal Chemistry 188 (February 2020): 111983. http://dx.doi.org/10.1016/j.ejmech.2019.111983.

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30

Cheong, Siew Lee, Jian Kai Tiew, Yi Hang Fong, How Wan Leong, Yew Mun Chan, Zhi Ling Chan, and Ethan Wei Jie Kong. "Current Pharmacotherapy and Multi-Target Approaches for Alzheimer’s Disease." Pharmaceuticals 15, no. 12 (December 14, 2022): 1560. http://dx.doi.org/10.3390/ph15121560.

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Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by decreased synaptic transmission and cerebral atrophy with appearance of amyloid plaques and neurofibrillary tangles. Cognitive, functional, and behavioral alterations are commonly associated with the disease. Different pathophysiological pathways of AD have been proposed, some of which interact and influence one another. Current treatment for AD mainly involves the use of therapeutic agents to alleviate the symptoms in AD patients. The conventional single-target treatment approaches do not often cause the desired effect in the disease due to its multifactorial origin. Thus, multi-target strategies have since been undertaken, which aim to simultaneously target multiple targets involved in the development of AD. In this review, we provide an overview of the pathogenesis of AD and the current drug therapies for the disease. Additionally, rationales of the multi-target approaches and examples of multi-target drugs with pharmacological actions against AD are also discussed.

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Bianco, Maria da Conceição Avelino Dias, Debora Inacio Leite, Frederico Silva Castelo Branco, Nubia Boechat, Elisa Uliassi, Maria Laura Bolognesi, and Monica Macedo Bastos. "The Use of Zidovudine Pharmacophore in Multi-Target-Directed Ligands for AIDS Therapy." Molecules 27, no. 23 (December 3, 2022): 8502. http://dx.doi.org/10.3390/molecules27238502.

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The concept of polypharmacology embraces multiple drugs combined in a therapeutic regimen (drug combination or cocktail), fixed dose combinations (FDCs), and a single drug that binds to different targets (multi-target drug). A polypharmacology approach is widely applied in the treatment of acquired immunodeficiency syndrome (AIDS), providing life-saving therapies for millions of people living with HIV. Despite the success in viral load suppression and patient survival of combined antiretroviral therapy (cART), the development of new drugs has become imperative, owing to the emergence of resistant strains and poor adherence to cART. 3′-azido-2′,3′-dideoxythymidine, also known as azidothymidine or zidovudine (AZT), is a widely applied starting scaffold in the search for new compounds, due to its good antiretroviral activity. Through the medicinal chemistry tool of molecular hybridization, AZT has been included in the structure of several compounds allowing for the development of multi-target-directed ligands (MTDLs) as antiretrovirals. This review aims to systematically explore and critically discuss AZT-based compounds as potential MTDLs for the treatment of AIDS. The review findings allowed us to conclude that: (i) AZT hybrids are still worth exploring, as they may provide highly active compounds targeting different steps of the HIV-1 replication cycle; (ii) AZT is a good starting point for the preparation of co-drugs with enhanced cell permeability.

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Talevi, Alan. "Tailored Multi-Target Agents. Applications and Design Considerations." Current Pharmaceutical Design 22, no. 21 (May 30, 2016): 3164–70. http://dx.doi.org/10.2174/1381612822666160308141203.

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33

Gejjalagere Honnappa, Chethan, and Unnikrishnan Mazhuvancherry Kesavan. "A concise review on advances in development of small molecule anti-inflammatory therapeutics emphasising AMPK: An emerging target." International Journal of Immunopathology and Pharmacology 29, no. 4 (October 6, 2016): 562–71. http://dx.doi.org/10.1177/0394632016673369.

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Inflammatory diseases are complex, multi-factorial outcomes of evolutionarily conserved tissue repair processes. For decades, non-steroidal anti-inflammatory drugs and cyclooxygenase inhibitors, the primary drugs of choice for the management of inflammatory diseases, addressed individual targets in the arachidonic acid pathway. Unsatisfactory safety and efficacy profiles of the above have necessitated the development of multi-target agents to treat complex inflammatory diseases. Current anti-inflammatory therapies still fall short of clinical needs and the clinical trial results of multi-target therapeutics are anticipated. Additionally, new drug targets are emerging with improved understanding of molecular mechanisms controlling the pathophysiology of inflammation. This review presents an outline of small molecules and drug targets in anti-inflammatory therapeutics with a summary of a newly identified target AMP-activated protein kinase, which constitutes a novel therapeutic pathway in inflammatory pathology.

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Coban, Mathew A., Juliet Morrison, Sushila Maharjan, David Hyram Hernandez Medina, Wanlu Li, Yu Shrike Zhang, William D. Freeman, et al. "Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement." Biomolecules 11, no. 6 (May 23, 2021): 787. http://dx.doi.org/10.3390/biom11060787.

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COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors—S/Ace2, Tmprss2, Cathepsins L and K, and Mpro—to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.

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Raevsky, Oleg A., Azat Mukhametov, Veniamin Y. Grigorev, Alexey Ustyugov, Shwu-Chen Tsay, Reuben Jih-Ru Hwu, Nagendra Sastry Yarla, Vadim V. Tarasov, Gjumrakch Aliev, and Sergey O. Bachurin. "Applications of Multi-Target Computer-Aided Methodologies in Molecular Design of CNS Drugs." Current Medicinal Chemistry 25, no. 39 (January 17, 2019): 5293–314. http://dx.doi.org/10.2174/0929867324666170920154111.

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The discovery of drugs for diseases of the central nervous system (CNS) faces high attrition rates in clinical trials. Neural diseases are extremely complex in nature and typically associated with multiple drug targets. A conception of multi-target directed ligands (MTDL), widely applied to the discovery of cancer pharmaceuticals, may be a perspective solution for CNS diseases. Special bioinformatics approaches have been developed which can assist the medicinal chemists in identification and structural optimization of MTDL. In this review, we analyze the current status of the development of multitarget approaches in quantitative structure-activity relationships (mt-QSAR) for CNS drug discovery; and describes applications of multi-target approaches in molecular modelling (which can be called mt-MM), as well as perspectives for multi-target approaches in bioinformatics in relation to Alzheimer’s disease.

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Tao, Lin, Feng Zhu, Feng Xu, Zhe Chen, Yu Yang Jiang, and Yu Zong Chen. "Co-targeting cancer drug escape pathways confers clinical advantage for multi-target anticancer drugs." Pharmacological Research 102 (December 2015): 123–31. http://dx.doi.org/10.1016/j.phrs.2015.09.019.

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Cavalli, Andrea, Maria Laura Bolognesi, Anna Minarini, Michela Rosini, Vincenzo Tumiatti, Maurizio Recanatini, and Carlo Melchiorre. "Multi-Target-Directed Ligands To Combat Neurodegenerative Diseases." Journal of Medicinal Chemistry 51, no. 7 (April 2008): 2326. http://dx.doi.org/10.1021/jm800210c.

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Koutsoukas, Alexios, Benjamin Simms, Johannes Kirchmair, Peter J. Bond, Alan V. Whitmore, Steven Zimmer, Malcolm P. Young, et al. "From in silico target prediction to multi-target drug design: Current databases, methods and applications." Journal of Proteomics 74, no. 12 (November 2011): 2554–74. http://dx.doi.org/10.1016/j.jprot.2011.05.011.

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Cortes-Ciriano, Isidro, Alexios Koutsoukas, Olga Abian, Robert C. Glen, Adrian Velazquez-Campoy, and Andreas Bender. "Experimental validation of in silico target predictions on synergistic protein targets." MedChemComm 4, no. 1 (2013): 278–88. http://dx.doi.org/10.1039/c2md20286g.

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Two relatively recent trends have become apparent in current early stage drug discovery settings: firstly, a revival of phenotypic screening strategies and secondly, the increasing acceptance that some drugs work by modulating multiple targets in parallel (‘multi-target drugs’).

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Calcatierra, Verónica, Óscar López, José G. Fernández-Bolaños, Gabriela B. Plata, and José M. Padrón. "Phenolic thio- and selenosemicarbazones as multi-target drugs." European Journal of Medicinal Chemistry 94 (April 2015): 63–72. http://dx.doi.org/10.1016/j.ejmech.2015.02.037.

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Wu, Tzu-Chien, Pei-Yuan Lee, Chiao-Ling Lai, and Chian-Hui Lai. "Synthesis of Multi-Functional Nano-Vectors for Target-Specific Drug Delivery." Polymers 13, no. 3 (January 30, 2021): 451. http://dx.doi.org/10.3390/polym13030451.

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Magnetic nanoparticles have gained attention in cancer therapy due to their non-toxic properties and high bio-compatibility. In this report, we synthesize a dual-responsive magnetic nanoparticle (MNP) that is sensitive to subtle pH and temperature change as in the tumor microenvironment. Thus, the functional doxorubicin (DOX)-loaded MNP (DOX-PNIPAM-PMAA@Fe3O4) can perform specific DOX releases in the cancer cell. The particle was characterized by scanning electron microscopy (SEM), dynamic light scattering (DLS), zeta-potential, Fourier-transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA). The microscopy data revealed the particle as having a spherical shape. The zeta-potential and size distribution analysis data demonstrated the difference for the stepwise modified MNPs. The FTIR spectrum showed characteristic absorption bands of NH2-SiO2@Fe3O4, CPDB@Fe3O4, PMAA@Fe3O4, and PNIPAM-PMAA@Fe3O4. Drug-loading capacity and releasing efficiency were evaluated under different conditions. Through an in vitro analysis, we confirmed that PNIPAM-PMAA@Fe3O4 has enhanced drug releasing efficiency under acidic and warmer conditions. Finally, cellular uptake and cell viability were estimated via different treatments in an MDA-MB-231 cell line. Through the above analysis, we concluded that the DOX-loaded particles can be internalized by cancer cells, and such a result is positive and prospective.

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Rossi, Michele, Michela Freschi, Luciana de Camargo Nascente, Alessandra Salerno, Sarah de Melo Viana Teixeira, Florian Nachon, Fabien Chantegreil, et al. "Sustainable Drug Discovery of Multi-Target-Directed Ligands for Alzheimer’s Disease." Journal of Medicinal Chemistry 64, no. 8 (April 8, 2021): 4972–90. http://dx.doi.org/10.1021/acs.jmedchem.1c00048.

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Wang, Shuyu, Peng Shan, Yuliang Zhao, and Lei Zuo. "GanDTI: A multi-task neural network for drug-target interaction prediction." Computational Biology and Chemistry 92 (June 2021): 107476. http://dx.doi.org/10.1016/j.compbiolchem.2021.107476.

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Mahmud, S. M. Hasan, Wenyu Chen, Hosney Jahan, Yougsheng Liu, and S. M. Mamun Hasan. "Dimensionality reduction based multi-kernel framework for drug-target interaction prediction." Chemometrics and Intelligent Laboratory Systems 212 (May 2021): 104270. http://dx.doi.org/10.1016/j.chemolab.2021.104270.

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Mongia, Aanchal, and Angshul Majumdar. "Drug-target interaction prediction using Multi Graph Regularized Nuclear Norm Minimization." PLOS ONE 15, no. 1 (January 16, 2020): e0226484. http://dx.doi.org/10.1371/journal.pone.0226484.

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Li, Limin. "MPGraph: multi‐view penalised graph clustering for predicting drug–target interactions." IET Systems Biology 8, no. 2 (April 2014): 67–73. http://dx.doi.org/10.1049/iet-syb.2013.0040.

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Vuylsteke, Valerie, Lisa M. Chastain, Geeta A. Maggu, and Crystal Brown. "Imeglimin: A Potential New Multi-Target Drug for Type 2 Diabetes." Drugs in R&D 15, no. 3 (August 8, 2015): 227–32. http://dx.doi.org/10.1007/s40268-015-0099-3.

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Tian, Xiao-ying, and Liang Liu. "Drug discovery enters a new era with multi-target intervention strategy." Chinese Journal of Integrative Medicine 18, no. 7 (April 11, 2012): 539–42. http://dx.doi.org/10.1007/s11655-011-0900-2.

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Jin, Hao, Hu-Guang Dan, and Guo-Wu Rao. "Research progress in quinazoline derivatives as multi-target tyrosine kinase inhibitors." Heterocyclic Communications 24, no. 1 (February 23, 2018): 1–10. http://dx.doi.org/10.1515/hc-2017-0066.

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Abstract Receptor tyrosine kinases (RTKs), such as epidermal growth factor receptor (EGFR), are involved in multiple human tumors. Therefore, RTKs are attractive targets for various antitumor strategies. Two classes of tyrosine kinase antagonists were applied in the clinic for monoclonal antibodies and small-molecule tyrosine kinase inhibitors. A well-studied class of small-molecule inhibitors is represented by 4-anilinoquinazolines, exemplified by gefitinib and erlotinib as mono-targeted EGFR inhibitors, which were approved for the treatment of non-small-cell lung cancer. Mono-target drugs may result in drug resistance and the innovation of multi-target drugs has grown up to be an active field. Recent advances in research on antitumor bioactivity of 4-anilino(or phenoxy)quinazoline derivatives with multiple targets are reviewed in this paper. At the same time, synthetic methods of quinazolines were introduced from the point of building the ring skeleton and based on the types of reaction.

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Concu, Riccardo, M. Natália D. S. Cordeiro, Martín Pérez-Pérez, and Florentino Fdez-Riverola. "MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug–Enzyme Interactions." Molecules 28, no. 3 (January 25, 2023): 1182. http://dx.doi.org/10.3390/molecules28031182.

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Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug–enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

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