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Tinivella, Annachiara, Luca Pinzi, Guido Gambacorta, Ian Baxendale, and Giulio Rastelli. "Identification of potential biological targets of oxindole scaffolds via in silico repositioning strategies." F1000Research 11 (February 23, 2022): 217. http://dx.doi.org/10.12688/f1000research.109017.1.
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Background: Drug repurposing is an alternative strategy to traditional drug discovery that aims at predicting new uses for already existing drugs or clinical candidates. Drug repurposing has many advantages over traditional drug development, such as reduced attrition rates, time and costs. This is especially the case considering that most drugs investigated for repurposing have already been assessed for their safety in clinical trials. Repurposing campaigns can also be designed for libraries of already synthesized molecules at different levels of biological experimentation, from null to in vitro and in vivo. Such an extension of the “repurposing” concept is expected to provide significant advantages for the identification of novel drugs, as the synthetic accessibility of the desired compounds is often one of the limiting factors in the traditional drug discovery pipeline. Methods: In this work, we performed a computational repurposing campaign on a library of previously synthesized oxindole-based compounds, in order to identify potential new targets for this versatile scaffold. To this aim, ligand-based approaches were firstly applied to evaluate the similarity degree of the investigated compound library, with respect to ligands extracted from the DrugBank, Protein Data Bank (PDB) and ChEMBL databases. In particular, the 2D fingerprint-based and 3D shape-based similarity profiles were evaluated and compared for the oxindole derivates. Results: The analyses predicted a set of potential candidate targets for repurposing, some of them emerging by consensus of different computational analyses. One of the identified targets, i.e., the vascular endothelial growth factor receptor 2 (VEGFR-2) kinase, was further investigated by means of docking calculations, followed by biological testing of one candidate. Conclusions: While the compound did not show potent inhibitory activity towards VEGFR-2, the study highlighted several other possibilities of therapeutically relevant targets that may be worth of consideration for drug repurposing.
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Tinivella, Annachiara, Luca Pinzi, Guido Gambacorta, Ian Baxendale, and Giulio Rastelli. "Identification of potential biological targets of oxindole scaffolds via in silico repositioning strategies." F1000Research 11 (March 23, 2022): 217. http://dx.doi.org/10.12688/f1000research.109017.2.
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Background: Drug repurposing is an alternative strategy to traditional drug discovery that aims at predicting new uses for already existing drugs or clinical candidates. Drug repurposing has many advantages over traditional drug development, such as reduced attrition rates, time and costs. This is especially the case considering that most drugs investigated for repurposing have already been assessed for their safety in clinical trials. Repurposing campaigns can also be designed for libraries of already synthesized molecules at different levels of biological experimentation, from null to in vitro and in vivo. Such an extension of the “repurposing” concept is expected to provide significant advantages for the identification of novel drugs, as the synthetic accessibility of the desired compounds is often one of the limiting factors in the traditional drug discovery pipeline. Methods: In this work, we performed a computational repurposing campaign on a library of previously synthesized oxindole-based compounds, in order to identify potential new targets for this versatile scaffold. To this aim, ligand-based approaches were firstly applied to evaluate the similarity degree of the investigated compound library, with respect to ligands extracted from the DrugBank, Protein Data Bank (PDB) and ChEMBL databases. In particular, the 2D fingerprint-based and 3D shape-based similarity profiles were evaluated and compared for the oxindole derivates. Results: The analyses predicted a set of potential candidate targets for repurposing, some of them emerging by consensus of different computational analyses. One of the identified targets, i.e., the vascular endothelial growth factor receptor 2 (VEGFR-2) kinase, was further investigated by means of docking calculations, followed by biological testing of one candidate. Conclusions: While the compound did not show potent inhibitory activity towards VEGFR-2, the study highlighted several other possibilities of therapeutically relevant targets that may be worth of consideration for drug repurposing.
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Ferraz, Witor Ribeiro, Renan Augusto Gomes, Andre Luis S Novaes, and Gustavo Henrique Goulart Trossini. "Ligand and structure-based virtual screening applied to the SARS-CoV-2 main protease: an in silico repurposing study." Future Medicinal Chemistry 12, no. 20 (October 2020): 1815–28. http://dx.doi.org/10.4155/fmc-2020-0165.
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Aim: The identification of drugs for the coronavirus disease-19 pandemic remains urgent. In this manner, drug repurposing is a suitable strategy, saving resources and time normally spent during regular drug discovery frameworks. Essential for viral replication, the main protease has been explored as a promising target for the drug discovery process. Materials & methods: Our virtual screening pipeline relies on the known 3D properties of noncovalent ligands and features of crystalized complexes, applying consensus analyses in each step. Results: Two oral (bedaquiline and glibenclamide) and one buccal drug (miconazole) presented 3D similarity to known ligands, reasonable predicted binding modes and micromolar predicted binding affinity values. Conclusion: We identified three approved drugs as promising inhibitors of the main viral protease and suggested design insights for future studies for development of novel selective inhibitors.
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Pérez-Moraga, Raul, Jaume Forés-Martos, Beatriz Suay-García, Jean-Louis Duval, Antonio Falcó, and Joan Climent. "A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework." Pharmaceutics 13, no. 4 (April 2, 2021): 488. http://dx.doi.org/10.3390/pharmaceutics13040488.
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Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection.
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Ngwewondo, Adela, Ivan Scandale, and Sabine Specht. "Onchocerciasis drug development: from preclinical models to humans." Parasitology Research 120, no. 12 (October 13, 2021): 3939–64. http://dx.doi.org/10.1007/s00436-021-07307-4.
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Abstract Twenty diseases are recognized as neglected tropical diseases (NTDs) by World Health Assembly resolutions, including human filarial diseases. The end of NTDs is embedded within the Sustainable Development Goals for 2030, under target 3.3. Onchocerciasis afflicts approximately 20.9 million people worldwide with > 90% of those infected residing in Africa. Control programs have made tremendous efforts in the management of onchocerciasis by mass drug administration and aerial larviciding; however, disease elimination is not yet achieved. In the new WHO roadmap, it is recognized that new drugs or drug regimens that kill or permanently sterilize adult filarial worms would significantly improve elimination timelines and accelerate the achievement of the program goal of disease elimination. Drug development is, however, handicapped by high attrition rates, and many promising molecules fail in preclinical development or in subsequent toxicological, safety and efficacy testing; thus, research and development (R&D) costs are, in aggregate, very high. Drug discovery and development for NTDs is largely driven by unmet medical needs put forward by the global health community; the area is underfunded and since no high return on investment is possible, there is no dedicated drug development pipeline for human filariasis. Repurposing existing drugs is one approach to filling the drug development pipeline for human filariasis. The high cost and slow pace of discovery and development of new drugs has led to the repurposing of “old” drugs, as this is more cost-effective and allows development timelines to be shortened. However, even if a drug is marketed for a human or veterinary indication, the safety margin and dosing regimen will need to be re-evaluated to determine the risk in humans. Drug repurposing is a promising approach to enlarging the pool of active molecules in the drug development pipeline. Another consideration when providing new treatment options is the use of combinations, which is not addressed in this review. We here summarize recent advances in the late preclinical or early clinical stage in the search for a potent macrofilaricide, including drugs against the nematode and against its endosymbiont, Wolbachia pipientis.
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Khan, Saba, Jaya Agnihotri, Sunanda Patil, and Nikhat Khan. "Drug repurposing: A futuristic approach in drug discovery." Journal of Pharmaceutical and Biological Sciences 11, no. 1 (July 15, 2023): 66–69. http://dx.doi.org/10.18231/j.jpbs.2023.011.
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Drug repurposing (DR), also known as drug repositioning, is a strategy aimed at identifying new therapeutic uses for existing drugs. It offers an effective approach to discovering or developing drug molecules with novel pharmacological or therapeutic indications. In recent years, pharmaceutical companies have increasingly embraced the drug repurposing strategy in their drug discovery and development programs, leading to the identification of new biological targets. This strategy is highly efficient, time-saving, cost-effective, and carries a lower risk of failure compared to traditional drug discovery methods. By maximizing the therapeutic value of existing drugs, drug repurposing increases the likelihood of success. It serves as a valuable alternative to the lengthy, expensive, and resource-intensive process of finding new molecular entities (NMEs) through traditional or de novo drug discovery approaches. Drug repurposing combines activity-based or experimental methods with in silico-based or computational approaches to rationally develop or identify new uses for drug molecules. It leverages the existing safety data of drugs tested in humans and redirects their application based on valid target molecules. This approach holds great promise, particularly in addressing rare, difficult-to-treat diseases, and neglected diseases. By utilizing the wealth of knowledge and resources available, drug repurposing presents an emerging strategy for optimizing the therapeutic potential of existing medicines. It offers a pathway to rapidly identify effective treatments and repurpose approved drugs for new indications, benefiting patients and healthcare systems alike.
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Chipofya, Mapopa, Hilal Tayara, and Kil To Chong. "Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks." Pharmaceutics 13, no. 11 (November 10, 2021): 1906. http://dx.doi.org/10.3390/pharmaceutics13111906.
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An important stage in the process of discovering new drugs is when candidate molecules are tested of their efficacy. It is reported that testing drug efficacy empirically costs billions of dollars in the drug discovery pipeline. As a mechanism of expediting this process, researchers have resorted to using computational methods to predict the action of molecules in silico. Here, we present a way of predicting the therapeutic-use class of drugs from chemical structures only using graph convolutional networks. In comparison with existing methods which use fingerprints or images as training samples, our approach has yielded better results in all metrics under consideration. In particular, validation accuracy increased from 83–88% to 86–90% for single label tasks. Similarly, the model achieved an accuracy of over 88% on new test data. Finally, our multi-label classification model made new predictions which indicated that some of the drugs could have other therapeutic uses other than those indicated in the dataset. We performed a literature-based evaluation of these predictions and found evidence that validates them. This renders the model a potential tool to be used in search of drugs that are candidates for repurposing.
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Konreddy, Ananda Kumar, Grandhe Usha Rani, Kyeong Lee, and Yongseok Choi. "Recent Drug-Repurposing-Driven Advances in the Discovery of Novel Antibiotics." Current Medicinal Chemistry 26, no. 28 (October 25, 2019): 5363–88. http://dx.doi.org/10.2174/0929867325666180706101404.
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: Drug repurposing is a safe and successful pathway to speed up the novel drug discovery and development processes compared with de novo drug discovery approaches. Drug repurposing uses FDA-approved drugs and drugs that failed in clinical trials, which have detailed information on potential toxicity, formulation, and pharmacology. Technical advancements in the informatics, genomics, and biological sciences account for the major success of drug repurposing in identifying secondary indications of existing drugs. Drug repurposing is playing a vital role in filling the gap in the discovery of potential antibiotics. Bacterial infections emerged as an ever-increasing global public health threat by dint of multidrug resistance to existing drugs. This raises the urgent need of development of new antibiotics that can effectively fight multidrug-resistant bacterial infections (MDRBIs). The present review describes the key role of drug repurposing in the development of antibiotics during 2016–2017 and of the details of recently FDA-approved antibiotics, pipeline antibiotics, and antibacterial properties of various FDA-approved drugs of anti-cancer, anti-fungal, anti-hyperlipidemia, antiinflammatory, anti-malarial, anti-parasitic, anti-viral, genetic disorder, immune modulator, etc. Further, in view of combination therapies with the existing antibiotics, their potential for new implications for MDRBIs is discussed. The current review may provide essential data for the development of quick, safe, effective, and novel antibiotics for current needs and suggest acuity in its effective implications for inhibiting MDRBIs by repurposing existing drugs.
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Shevtsov, Andrey, Mikhail Raevskiy, Alexey Stupnikov, and Yulia Medvedeva. "In Silico Drug Repurposing in Multiple Sclerosis Using scRNA-Seq Data." International Journal of Molecular Sciences 24, no. 2 (January 4, 2023): 985. http://dx.doi.org/10.3390/ijms24020985.
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Multiple sclerosis (MS) is an autoimmune disease of the central nervous system still lacking a cure. Treatment typically focuses on slowing the progression and managing MS symptoms. Single-cell transcriptomics allows the investigation of the immune system—the key player in MS onset and development—in great detail increasing our understanding of MS mechanisms and stimulating the discovery of the targets for potential therapies. Still, de novo drug development takes decades; however, this can be reduced by drug repositioning. A promising approach is to select potential drugs based on activated or inhibited genes and pathways. In this study, we explored the public single-cell RNA data from an experiment with six patients on single-cell RNA peripheral blood mononuclear cells (PBMC) and cerebrospinal fluid cells (CSF) of patients with MS and idiopathic intracranial hypertension. We demonstrate that AIM2 inflammasome, SMAD2/3 signaling, and complement activation pathways are activated in MS in different CSF and PBMC immune cells. Using genes from top-activated pathways, we detected several promising small molecules to reverse MS immune cells’ transcriptomic signatures, including AG14361, FGIN-1-27, CA-074, ARP 101, Flunisolide, and JAK3 Inhibitor VI. Among these molecules, we also detected an FDA-approved MS drug Mitoxantrone, supporting the reliability of our approach.
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Ramamoorthi, Roopa, Katy M. Graef, and Jennifer Dent. "Repurposing pharma assets: an accelerated mechanism for strengthening the schistosomiasis drug development pipeline." Future Medicinal Chemistry 7, no. 6 (April 2015): 727–35. http://dx.doi.org/10.4155/fmc.15.26.
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Farghali, H., N. Kutinová Canová, and M. Arora. "The potential applications of artificial intelligence in drug discovery and development." Physiological Research, S4 (December 30, 2021): S715—S722. http://dx.doi.org/10.33549//physiolres.934765.
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Development of a new dug is a very lengthy and highly expensive process since only preclinical, pharmacokinetic, pharmacodynamic and toxicological studies include a multiple of in silico, in vitro, in vivo experimentations that traditionally last several years. In the present review, we briefly report some examples that demonstrate the power of the computer-assisted drug discovery process with some examples that are published and revealing the successful applications of artificial intelligence (AI) technology on this vivid area. Besides, we address the situation of drug repositioning (repurposing) in clinical applications. Yet few success stories in this regard that provide us with a clear evidence that AI will reveal its great potential in accelerating effective new drug finding. AI accelerates drug repurposing and AI approaches are altogether necessary and inevitable tools in new medicine development. In spite of the fact that AI in drug development is still in its infancy, the advancements in AI and machine-learning (ML) algorithms have an unprecedented potential. The AI/ML solutions driven by pharmaceutical scientists, computer scientists, statisticians, physicians and others are increasingly working together in the processes of drug development and are adopting AI-based technologies for the rapid discovery of medicines. AI approaches, coupled with big data, are expected to substantially improve the effectiveness of drug repurposing and finding new drugs for various complex human diseases.
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Miller, Mark Stevn, Brian Cholewa, John Clifford, Vignesh Gunasekharan, Altaf Mohammed, Shanker Gupta, Robert Shoemaker, and Shizuko Sei. "Abstract IA007: PREVENT agent development pipeline." Cancer Prevention Research 15, no. 12_Supplement_2 (December 1, 2022): IA007. http://dx.doi.org/10.1158/1940-6215.tacpad22-ia007.
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Abstract The NCI’s PREVENT Cancer Preclinical Drug Development Program is a peer-reviewed program designed to support the preclinical development of promising agents and biomarkers for cancer interception/prevention towards clinical applications. PREVENT is not a grant program but allocates NCI contract resources to advance approved projects in a milestone-driven manner. Results obtained through NCI contract resources are returned to the applicant PIs and used to support further development by the applicants or in partnership with NCI. Resources available to PREVENT Program applicants include preclinical efficacy testing, CGMP manufacturing, GLP pharmacokinetic and IND-enabling toxicology studies, and IND filings. The PREVENT Program is focused on preventive agent development in the areas of Immunoprevention (cancer vaccines and immunomodulatory agents), Chemoprevention (novel mechanisms, anti-inflammatory agents, drug repurposing, toxicity reduction via alternative dosing regimens and agent combinations) and clinically translatable mechanistic biomarkers (pharmacodynamics, immune correlates, and tumor preventive efficacy). Submission deadlines for PREVENT Concept Applications occur twice per year on the second Monday in January and July. Further information can be obtained at the PREVENT Program website: https://prevention.cancer.gov/major-programs/prevent-cancer-preclinical Citation Format: Mark Stevn Miller, Brian Cholewa, John Clifford, Vignesh Gunasekharan, Altaf Mohammed, Shanker Gupta, Robert Shoemaker, Shizuko Sei. PREVENT agent development pipeline [abstract]. In: Proceedings of the Second Biennial NCI Meeting: Translational Advances in Cancer Prevention Agent Development (TACPAD); 2022 Sep 7-9. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_2): Abstract nr IA007.
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Verma, Vikrant. "In Silico Development of Anticancer Drugs." Journal of Clinical Research and Analytical Reviews 1, no. 1 (March 5, 2022): 1–5. http://dx.doi.org/10.56391/jcrar.2022.1019.
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The development of new drugs has been recognised as a complex, costly, time-consuming, and difficult enterprise. A new medication discovery via typical drugdevelopment pipeline is predicted to take around 12 years and 2.7 billion USD on average. Methods to cut research costs and accelerate the development process of new drugs has become a difficult and pressing topic for the pharmaceutical sector. Computer-aided drug discovery (CADD) has developed as a potent and promising technique for designing drugs that are quicker, cheaper, and more effective. Recently, the fast development of computational techniques for drug discovery, particularly anticancertherapies, has had a substantial and exceptional influence on anticancer medication design, as well as beneficial insights into the field of cancer therapy.This article describes docking of Crizotinib, Sunitinibmalate, and their analogues with the Anaplastic lymphoma kinase receptor.On docking with the Anaplastic lymphoma kinase receptor, Crizotinib and Sunitinibmalate yielded energy values of -9.85 and -8.25, respectively.
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Entonu, Moses Edache, Mbateudi Danjuma IKA, Ekpa Emmanuel, Clifford Liki Barnabas, Daniel Danladi Gaiya, and Stella Kuyet UDU. "Drug repurposing: Recent advancements, challenges, and future therapeutics for cancer treatment." Journal of Bacteriology & Mycology: Open Access 10, no. 2 (2022): 26–30. http://dx.doi.org/10.15406/jbmoa.2022.10.00322.
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Cancer is a prime public health burden that accounts for approximately 9.9 million deaths worldwide. Despite recent advances in treatment regimen and huge capital investment in the pharmaceutical sector, there has been little success in improving the chances of survival of cancer patients. Drug repurposing sometimes termed drug repositioning is a strategy of discovery and redeveloping existing drugs for new therapeutic purposes. This novel approach is highly efficient, considerably cuts research and development costs, reduces the drug development timeline, maximizes therapeutic value and consequently increases success rate with minimum risk of failure. In this review, prioritizing drug repurposing to activate immune and inflammatory responses to target tumor cells through immune surveillance mechanism is a promising strategy for cancer immunotherapy. Cancer immunotherapy cover myriad of therapeutic approaches as cytokine therapy, immune checkpoint blockade therapy, cancer vaccines, natural killer cells, adoptive T cell therapies, monoclonal antibodies, oncolytic viruses, computational approach and host of others. In the current pipeline, drug repurposing is devoid of adequate funding and the necessary legal support for research and development by stakeholders. At the moment, immunotherapy strategies combine with computational biology could be considered the new milestone in drug re-profiling for cancer treatment.
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Lloyd, Katie, Stamatia Papoutsopoulou, Emily Smith, Philip Stegmaier, Francois Bergey, Lorna Morris, Madeleine Kittner, et al. "Using systems medicine to identify a therapeutic agent with potential for repurposing in inflammatory bowel disease." Disease Models & Mechanisms 13, no. 11 (September 21, 2020): dmm044040. http://dx.doi.org/10.1242/dmm.044040.
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ABSTRACTInflammatory bowel diseases (IBDs) cause significant morbidity and mortality. Aberrant NF-κB signalling is strongly associated with these conditions, and several established drugs influence the NF-κB signalling network to exert their effect. This study aimed to identify drugs that alter NF-κB signalling and could be repositioned for use in IBD. The SysmedIBD Consortium established a novel drug-repurposing pipeline based on a combination of in silico drug discovery and biological assays targeted at demonstrating an impact on NF-κB signalling, and a murine model of IBD. The drug discovery algorithm identified several drugs already established in IBD, including corticosteroids. The highest-ranked drug was the macrolide antibiotic clarithromycin, which has previously been reported to have anti-inflammatory effects in aseptic conditions. The effects of clarithromycin effects were validated in several experiments: it influenced NF-κB-mediated transcription in murine peritoneal macrophages and intestinal enteroids; it suppressed NF-κB protein shuttling in murine reporter enteroids; it suppressed NF-κB (p65) DNA binding in the small intestine of mice exposed to lipopolysaccharide; and it reduced the severity of dextran sulphate sodium-induced colitis in C57BL/6 mice. Clarithromycin also suppressed NF-κB (p65) nuclear translocation in human intestinal enteroids. These findings demonstrate that in silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of IBD, and that further clinical assessment of clarithromycin in the management of IBD is required.This article has an associated First Person interview with the joint first authors of the paper.
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Miroshnichenko, I. I., E. A. Valdman, and I. I. Kuz'min. "Old Drugs, New Indications (Review)." Drug development & registration 12, no. 1 (February 28, 2023): 182–90. http://dx.doi.org/10.33380/2305-2066-2023-12-1-182-190.
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Introduction. The drug can be used in the treatment of one disease and for the prevention and treatment of another pathological process. This is possible due to the repurposing of medicines. Creating drugs from scratch takes a long time to develop and implement, which leads to large financial costs, and also has a high dropout rate of candidate substances and requires significant financial costs. The main advantage of repurposing instead of creating new drug is relatively low financial costs and a significant reduction in the first two phases of clinical trials.Text. Drug repurposing is based on pharmacology, pharmacokinetics, pharmacodynamics, pharmaceuticals and clinical trials, where the first two phases are significantly reduced compared to the creation of a completely new. There are examples of successful repurposing and negative side effects with off-label drug use, which is unsafe but the best solution for orphan diseases. A targeted search for the possibility of repurposing drugs using an automatic procedure is being carried out, where a large number of chemical compounds are tested for activity or affinity for receptors and enzymes – high-throughput screening. Computer design has become widespread, which or repurposing "in silico", where information about the drug is used: targets, chemical structures, metabolic pathways, side effects, followed by the construction of appropriate models. Machine learning (ML) algorithms: Bayes classifier, logistic regression, support vector machine, decision tree, random forest and others are successfully used in biochemical pharmaceutical, toxicological research. But the most promising development of reprofiling is associated with the use of deep neural networks (DNN). Using deep learning, DNN were found to outperform other algorithms for drug development and toxicity prediction.Conclusion. Currently, interest in drug repurposing has grown markedly. A search for the keywords «drug repurposing» showed 2,422 articles on the problem of new uses for drugs that already exist in medicine.
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Beck, Samuel, Jun-Yeong Lee, and Jarod Rollins. "In silico identification of anti-aging pharmaceutics from community knowledge." Innovation in Aging 5, Supplement_1 (December 1, 2021): 672. http://dx.doi.org/10.1093/geroni/igab046.2533.
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Abstract In this era of Big Data, the volume of biological data is growing exponentially. Systematic profiling and analysis of these data will provide a new insight into biology and human health. Among diverse types of biological data, gene expression data closely mirror both the static phenotypes and the dynamic changes in biological systems. Drug-to-drug or drug-to-disease comparison of gene expression signature allows repurposing/repositioning of existing pharmaceutics to treat additional diseases that, in turn, provides a rapid and cost-effective approach for drug discovery. Thanks to technological advances, gene expression profiling by mRNA-seq became a routine tool to address all aspects of the problem in modern biological research. Here, we present how drug repositioning using published mRNA-seq data can provide unbiased and applicable pharmaco-chemical intervention strategies to human diseases and aging. In specifics, we profiled over a half-million gene expression profiling data generated from various contexts, and using this, we screened conditions that can suppress age-associated gene expression changes. As a result, our analysis identified various previously validated aging intervention strategies as positive hits. Furthermore, our analysis also predicted a novel group of chemicals that has not been studied from an aging context, and this indeed significantly extended the life span in model animals. Taken together, our data demonstrate that our community knowledge-guided in silico drug-discovery pipeline provides a useful and effective tool to identify the novel aging intervention strategy.
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Francisco, Amanda F., Shiromani Jayawardhana, Francisco Olmo, Michael D. Lewis, Shane R. Wilkinson, Martin C. Taylor, and John M. Kelly. "Challenges in Chagas Disease Drug Development." Molecules 25, no. 12 (June 17, 2020): 2799. http://dx.doi.org/10.3390/molecules25122799.
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The protozoan parasite Trypanosoma cruzi causes Chagas disease, an important public health problem throughout Latin America. Current therapeutic options are characterised by limited efficacy, long treatment regimens and frequent toxic side-effects. Advances in this area have been compromised by gaps in our knowledge of disease pathogenesis, parasite biology and drug activity. Nevertheless, several factors have come together to create a more optimistic scenario. Drug-based research has become more systematic, with increased collaborations between the academic and commercial sectors, often within the framework of not-for-profit consortia. High-throughput screening of compound libraries is being widely applied, and new technical advances are helping to streamline the drug development pipeline. In addition, drug repurposing and optimisation of current treatment regimens, informed by laboratory research, are providing a basis for new clinical trials. Here, we will provide an overview of the current status of Chagas disease drug development, highlight those areas where progress can be expected, and describe how fundamental research is helping to underpin the process.
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Chen, Xin, Giuseppe Gumina, and Kristopher G. Virga. "Recent Advances in Drug Repurposing for Parkinson’s Disease." Current Medicinal Chemistry 26, no. 28 (October 25, 2019): 5340–62. http://dx.doi.org/10.2174/0929867325666180719144850.
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:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.
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Zeng, Xiangxiang, Siyi Zhu, Xiangrong Liu, Yadi Zhou, Ruth Nussinov, and Feixiong Cheng. "deepDR: a network-based deep learning approach to in silico drug repositioning." Bioinformatics 35, no. 24 (May 22, 2019): 5191–98. http://dx.doi.org/10.1093/bioinformatics/btz418.
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Abstract Motivation Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging. Results In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug–disease, one drug-side-effect, one drug–target and seven drug–drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multi-modal deep autoencoder. Then the learned low-dimensional representation of drugs together with clinically reported drug–disease pairs are encoded and decoded collectively via a variational autoencoder to infer candidates for approved drugs for which they were not originally approved. We found that deepDR revealed high performance [the area under receiver operating characteristic curve (AUROC) = 0.908], outperforming conventional network-based or machine learning-based approaches. Importantly, deepDR-predicted drug–disease associations were validated by the ClinicalTrials.gov database (AUROC = 0.826) and we showcased several novel deepDR-predicted approved drugs for Alzheimer’s disease (e.g. risperidone and aripiprazole) and Parkinson’s disease (e.g. methylphenidate and pergolide). Availability and implementation Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR Supplementary information Supplementary data are available online at Bioinformatics.
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Gaikwad, Nikita Maruti, Pravin Digambar Chaudhari, Karimunnisa Sameer Shaikh, Somdatta Yashwant Chaudhari, Rasha Mohammed Saleem, Mohammad Algahtani, Ahmed E. Altyar, Ghadeer M. Albadrani, Mohamed Kamel, and Mohamed M. Abdel-Daim. "Albendazole repurposing on VEGFR-2 for possible anticancer application: In-silico analysis." PLOS ONE 18, no. 8 (August 16, 2023): e0287198. http://dx.doi.org/10.1371/journal.pone.0287198.
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Drug repurposing is the finding new activity of the existing drug. Recently, Albendazole’s well-known antihelmintic has got the attention of an anticancer drug. Plausible evidence of the interaction of Albendazole with one of the types of tyrosine kinase protein receptor, vascular endothelial growth factor receptor-2 (VEGFR-2) is still not well understood. Inhibition of the VEGFR-2 receptor can prevent tumor growth. The current study investigated the interaction of Albendazole with VEGFR-2.It was found that the said interaction exhibited potent binding energy ΔG = -7.12 kcal/mol, inhibitory concentration (Ki) = 6.04 μM, and as positive control comparison with standard drug (42Q1170A) showed ΔG = -12.35 kcal/mol and Ki = 881 μM. The key residue Asp1046 was formed involved hydrogen bonding with Albendazole. The molecular dynamics simulation study revealed the stable trajectory of the VEGFR-2 receptor with Albendazole bound complex having significant high free energy of binding as calculated from Molecular Mechanics Generalized Born and Surface Area study ΔG = -42.07±2.4 kcal/mol. The binding energy is significantly high for greater stability of the complex. Principal component analysis of molecular docking trajectories exhibited ordered motion at higher modes, implying a high degree of VEGFR-2 and Albendazole complex stability as seen with the standard drug 42Q. Therefore, the current work suggests the role of Albendazole as a potent angiogenesis inhibitor as ascertained by its potential interaction with VEGFR-2. The findings of research will aid in the future development of Albendazole in anticancer therapy.
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Lochab, Amit, Rakhi Thareja, Sangeeta D. Gadre, and Reena Saxena. "Potential Protein and Enzyme Targets for In‐silico Development and Repurposing of Drug Against Coronaviruses." ChemistrySelect 6, no. 46 (December 8, 2021): 13363–81. http://dx.doi.org/10.1002/slct.202103350.
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Bruno, Agostino, Gabriele Costantino, Luca Sartori, and Marco Radi. "The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization." Current Medicinal Chemistry 26, no. 21 (September 19, 2019): 3838–73. http://dx.doi.org/10.2174/0929867324666171107101035.
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Background: Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug. Methods: In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. Results: A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given. Conclusion: The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.
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Choudhary, Gajendra, Niharika Dadoo Dadoo, Manisha Prajapat, and Bikash Medhi. "In-silico Pharmacology for Evidence-Based and Precision Medicine." International Journal of Pharmaceutical Sciences and Nanotechnology(IJPSN) 16, no. 3 (August 4, 2023): 6489–90. http://dx.doi.org/10.37285/ijpsn.2023.16.3.1.
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Precision medicine, driven by genetic and physical characteristics, has emerged as a transformative approach in healthcare, aiming to provide personalised treatments with optimised efficacy and minimised side effects. This approach contrasts evidence-based medicine, which emphasises population-level data and trends. Technological advancements in pharmacometrics and quantitative systems pharmacology have revolutionised pharmaceutical research, enabling the identification of new drug targets and the development of innovative drug delivery systems. Computational methods, such as quantitative structure-activity relationship (QSAR) analysis and in silico pharmacology tools, have played a pivotal role in identifying potential drugs and repurposing existing ones. These computational approaches leverage diverse data sets and predictive models, leading to significant advancements in optimising drug safety and effectiveness. This transformative era, driven by precision medicine and computational pharmacology, holds immense potential for improving patient outcomes and advancing the field of medicine towards personalised and targeted therapeutic interventions.
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Bintener, Tamara, Maria Pires Pacheco, and Thomas Sauter. "Towards the routine use of in silico screenings for drug discovery using metabolic modelling." Biochemical Society Transactions 48, no. 3 (May 5, 2020): 955–69. http://dx.doi.org/10.1042/bst20190867.
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Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.
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De, Baishakhi, Koushik Bhandari, Francisco J. B. Mendonça, Marcus T. Scotti, and Luciana Scotti. "Computational Studies in Drug Design Against Cancer." Anti-Cancer Agents in Medicinal Chemistry 19, no. 5 (June 27, 2019): 587–91. http://dx.doi.org/10.2174/1871520618666180911125700.
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Background: The application of in silico tools in the development of anti cancer drugs. Objective: The summing of different computer aided drug design approaches that have been applied in the development of anti cancer drugs. Methods: Structure based, ligand based, hybrid protein-ligand pharmacophore methods, Homology modeling, molecular docking aids in different steps of drug discovery pipeline with considerable saving in time and expenditure. In silico tools also find applications in the domain of cancer drug development. Results: Structure-based pharmacophore modeling aided in the identification of PUMA inhibitors, structure based approach with high throughput screening for the development of Bcl-2 inhibitors, to derive the most relevant protein-protein interactions, anti mitotic agents; I-Kappa-B Kinase β (IKK- β) inhibitor, screening of new class of aromatase inhibitors that can be important targets in cancer therapy. Conclusion: Application of computational methods in the design of anti cancer drugs was found to be effective.
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Donadu, Matthew Gavino, Stefania Zanetti, Basem Battah, Helal F. Hetta, Danica Matusovits, Krisztina Kárpáti, Virág Finta, et al. "Drug repurposing in the context of common bacterial pathogens." Acta Biologica Szegediensis 66, no. 2 (May 27, 2023): 140–49. http://dx.doi.org/10.14232/abs.2022.2.140-149.
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The clinical problem of multidrug resistance (MDR) in bacteria is due to the lack of novel antibiotics in development and the dwindling pipeline of drugs receiving market authorization. Repurposing of non-antibiotic pharmacological agents may be an attractive pathway to provide new antimicrobial drugs. The aim of the present study was to ascertain the antibacterial and adjuvant properties of a wide range of pharmaceuticals against antibiotic-susceptible and drug-resistant bacteria. Sixty-five (n = 65) pharmacological agents were included in our experiments. For Gram-positive bacteria, Staphylococcus aureus ATCC 43300 (methicillin-resistant), S. epidermidis ATCC 12228, Streptococcus pyogenes ATCC 12384 and Enterococcus faecalis ATCC 29212 were used, while for Gram-negative bacteria, Enterobacter cloacae ATCC 13047 (extended-spectrum β-lactamase-positive), Klebsiella pneumoniae ATCC 49619, Serratia marcescens ATCC 29632 and Pseudomonas aeruginosa ATCC 27853 were included as representative strains. The minimum inhibitory concentrations (MICs) of the tested compounds were determined using the standard broth microdilution method, while a MIC reduction assay was included to ascertain the effect of the tested compounds on the MICs of standard antibiotics (ceftriaxone, ciprofloxacin and gentamicin). Seventeen and twelve drug molecules tested showed measurable antibacterial activities (MIC: 32-512 µg/mL) against Gram-positive and Gram-negative bacteria, respectively. Several compounds decreased the MICs of ciprofloxacin and gentamicin. Although there are increasing number of studies in this field, there are still significant gaps in the evidence to the potential use of non-antibiotic drugs in antimicrobial drug repurposing.
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Hudson, Matthew L., and Ram Samudrala. "Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform." Molecules 26, no. 9 (April 28, 2021): 2581. http://dx.doi.org/10.3390/molecules26092581.
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Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.
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Lyne, Seán B., and Bakhtiar Yamini. "An Alternative Pipeline for Glioblastoma Therapeutics: A Systematic Review of Drug Repurposing in Glioblastoma." Cancers 13, no. 8 (April 18, 2021): 1953. http://dx.doi.org/10.3390/cancers13081953.
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The treatment of glioblastoma (GBM) remains a significant challenge, with outcome for most pa-tients remaining poor. Although novel therapies have been developed, several obstacles restrict the incentive of drug developers to continue these efforts including the exorbitant cost, high failure rate and relatively small patient population. Repositioning drugs that have well-characterized mechanistic and safety profiles is an attractive alternative for drug development in GBM. In ad-dition, the relative ease with which repurposed agents can be transitioned to the clinic further supports their potential for examination in patients. Here, a systematic analysis of the literature and clinical trials provides a comprehensive review of primary articles and unpublished trials that use repurposed drugs for the treatment of GBM. The findings demonstrate that numerous drug classes that have a range of initial indications have efficacy against preclinical GBM models and that certain agents have shown significant potential for clinical benefit. With examination in randomized, placebo-controlled trials and the targeting of particular GBM subgroups, it is pos-sible that repurposing can be a cost-effective approach to identify agents for use in multimodal anti-GBM strategies.
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Lin, Ko-Hong, Yejin Kim, Dung-Fang Lee, and Xiaoqian Jiang. "Abstract LB169: Machine learning-based approach for glioblastoma drug repurposing on real-world patient data." Cancer Research 83, no. 8_Supplement (April 14, 2023): LB169. http://dx.doi.org/10.1158/1538-7445.am2023-lb169.
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Abstract Glioblastoma (GBM) is the most aggressive and deadly type of brain cancer. The poor survival rate of GBM is often attributed to the low therapeutic efficiency of current front-line therapy, treatment resistance, and high recurrence rate. Sizeable clinical data of cancer patients preserve a great potential for drug discovery and drug repurposing research, enabling comprehensive screening of treatment effects among frequently-prescribed medications. To efficiently characterize potent drug candidates and develop novel treatment strategies for GBM, we designed a machine-learning-based pipeline that predicts repurposable drug combinations from large-scale patient claims data. Our pipeline first identified pseudo-randomized drug-user and non-user cohorts from heterogeneous clinical records and balanced the confounding effects using the Inverse Probability of Treatment Weighting (IPTW) method. Next, our model computed the averaged treatment effect on the treated (ATT) values to determine the treatment effects of our candidates. Our methodology identified several promising drug combinations that have been found to be effective in inhibiting GBM in cell line experiments. This research was conducted by integrating computational and experimental approaches to identify drug combinations, providing a promising strategy for the development of new treatments for GBM. Citation Format: Ko-Hong Lin, Yejin Kim, Dung-Fang Lee, Xiaoqian Jiang. Machine learning-based approach for glioblastoma drug repurposing on real-world patient data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB169.
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Aherfi, Sarah, Bruno Pradines, Christian Devaux, Stéphane Honore, Philippe Colson, Bernard La Scola, and Didier Raoult. "Drug repurposing against SARS-CoV-1, SARS-CoV-2 and MERS-CoV." Future Microbiology 16, no. 17 (November 2021): 1341–70. http://dx.doi.org/10.2217/fmb-2021-0019.
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Since the beginning of the COVID-19 pandemic, large in silico screening studies and numerous in vitro studies have assessed the antiviral activity of various drugs on SARS-CoV-2. In the context of health emergency, drug repurposing represents the most relevant strategy because of the reduced time for approval by international medicines agencies, the low cost of development and the well-known toxicity profile of such drugs. Herein, we aim to review drugs with in vitro antiviral activity against SARS-CoV-2, combined with molecular docking data and results from preliminary clinical studies. Finally, when considering all these previous findings, as well as the possibility of oral administration, 11 molecules consisting of nelfinavir, favipiravir, azithromycin, clofoctol, clofazimine, ivermectin, nitazoxanide, amodiaquine, heparin, chloroquine and hydroxychloroquine, show an interesting antiviral activity that could be exploited as possible drug candidates for COVID-19 treatment.
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Verma, Yogesh Kumar, and Gurudutta Gangenahalli. "Data Mining for Drug Repurposing and New Targets Identification for Radioprotection." Defence Life Science Journal 2, no. 3 (August 3, 2017): 343. http://dx.doi.org/10.14429/dlsj.2.11671.
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<p>Ionising radiation (IR) is responsible for various types of tissue injury leading to morbidity at low doses and mortality at high radiation exposure. Although many radioprotective and pharmacological agents are being tested for decreasing radiation injury, however, the availability of Amifostine as the only clinically used radioprotector with limited indication has prompted us to find out new potential molecules through drugs repurposing for protecting or decreasing radiation damage by data mining. In this work we have used text-mining based network generation approach to find out the gene targets of radioprotectors under evaluation by Agilent Literature Search app in Cytoscape. Extracted genes were evaluated for their association with radiation in Radiation Genes database. These genes were searched against therapeutic drugs and molecules under clinical trial in the Drug Gene Interaction database. We found that most of the radiation target genes were involved in cell death, proliferation, homeostasis, cell cycle and cancer pathways. Many of these genes were druggable and could be targeted by the drugs under clinical research, whereas there were few genes (new targets), which were never considered for radioprotective drug development. This study would likely help in repurposing of identified drugs for use in the event of radiation fallout, keeping in mind that no radiation medical countermeasure for acute radiation syndrome has been approved by the US FDA for use in humans. Results also revealed new target genes for drug targeting and indicates use of similar pipeline in other pathologies for drug repurposing and development.<br /><br /></p>
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Baby, Krishnaprasad, Swastika Maity, Chetan H. Mehta, Akhil Suresh, Usha Y. Nayak, and Yogendra Nayak. "Targeting SARS-CoV-2 RNA-dependent RNA polymerase: An in silico drug repurposing for COVID-19." F1000Research 9 (September 23, 2020): 1166. http://dx.doi.org/10.12688/f1000research.26359.1.
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Background: The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), took more lives than combined epidemics of SARS, MERS, H1N1, and Ebola. Currently, the prevention and control of spread are the goals in COVID-19 management as there are no specific drugs to cure or vaccines available for prevention. Hence, the drug repurposing was explored by many research groups, and many target proteins have been examined. The major protease (Mpro), and RNA-dependent RNA polymerase (RdRp) are two target proteins in SARS-CoV-2 that have been validated and extensively studied for drug development in COVID-19. The RdRp shares a high degree of homology between those of two previously known coronaviruses, SARS-CoV and MERS-CoV. Methods: In this study, the FDA approved library of drugs were docked against the active site of RdRp using Schrodinger's computer-aided drug discovery tools for in silico drug-repurposing. Results: We have shortlisted 14 drugs from the Standard Precision docking and interaction-wise study of drug-binding with the active site on the enzyme. These drugs are antibiotics, NSAIDs, hypolipidemic, coagulant, thrombolytic, and anti-allergics. In molecular dynamics simulations, pitavastatin, ridogrel and rosoxacin displayed superior binding with the active site through ARG555 and divalent magnesium. Conclusion: Pitavastatin, ridogrel and rosoxacin can be further optimized in preclinical and clinical studies to determine their possible role in COVID-19 treatment.
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Bernatchez, Jean A., Emily Chen, Mitchell V. Hull, Case W. McNamara, James H. McKerrow, and Jair L. Siqueira-Neto. "High-Throughput Screening of the ReFRAME Library Identifies Potential Drug Repurposing Candidates for Trypanosoma cruzi." Microorganisms 8, no. 4 (March 26, 2020): 472. http://dx.doi.org/10.3390/microorganisms8040472.
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Chagas disease, caused by the kinetoplastid parasite Trypanosoma cruzi, affects between 6 and 7 million people worldwide, with an estimated 300,000 to 1 million of these cases in the United States. In the chronic phase of infection, T. cruzi can cause severe gastrointestinal and cardiac disease, which can be fatal. Currently, only benznidazole is clinically approved by the FDA for pediatric use to treat this infection in the USA. Toxicity associated with this compound has driven the search for new anti-Chagas agents. Drug repurposing is a particularly attractive strategy for neglected diseases, as pharmacological parameters and toxicity are already known for these compounds, reducing costs and saving time in the drug development pipeline. Here, we screened 7680 compounds from the Repurposing, Focused Rescue, and Accelerated Medchem (ReFRAME) library, a collection of drugs or compounds with confirmed clinical safety, against T. cruzi. We identified seven compounds of interest with potent in vitro activity against the parasite with a therapeutic index of 10 or greater, including the previously unreported activity of the antiherpetic compound 348U87. These results provide the framework for further development of new T. cruzi leads that can potentially move quickly to the clinic.
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An, Gary, John Bartels, and Yoram Vodovotz. "In silico augmentation of the drug development pipeline: examples from the study of acute inflammation." Drug Development Research 72, no. 2 (December 16, 2010): 187–200. http://dx.doi.org/10.1002/ddr.20415.
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Galati, Salvatore, Miriana Di Stefano, Elisa Martinelli, Giulio Poli, and Tiziano Tuccinardi. "Recent Advances in In Silico Target Fishing." Molecules 26, no. 17 (August 24, 2021): 5124. http://dx.doi.org/10.3390/molecules26175124.
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In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.
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Galeana-Ascencio, Roberto A., Liliana Mendieta, Daniel I. Limon, Dino Gnecco, Joel L. Terán, María L. Orea, and Alan Carrasco-Carballo. "β-Secretase-1: In Silico Drug Reposition for Alzheimer’s Disease." International Journal of Molecular Sciences 24, no. 9 (May 3, 2023): 8164. http://dx.doi.org/10.3390/ijms24098164.
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The β-secretase-1 enzyme (BACE-1) performs a key role in the production of beta-Amyloid protein (Aβ), which is associated with the development of Alzheimer’s disease (AD). The inhibition of BACE-1 has been an important pharmacological strategy in the treatment of this neurodegenerative disease. This study aims to identify new potential candidates for the treatment of Alzheimer’s with the help of in silico studies, such as molecular docking and ADME prediction, from a broad list of candidates provided by the DrugBank database. From this analysis, 1145 drugs capable of interacting with the enzyme with a higher coupling energy than Verubecestat were obtained, subsequently only 83 presented higher coupling energy than EJ7. Applying the oral route of administration as inclusion criteria, only 41 candidates met this requirement; however, 6 of them are associated with diagnostic tests and not treatment, so 33 candidates were obtained. Finally, five candidates were identified as possible BACE-1 inhibitors drugs: Fluphenazine, Naratriptan, Bazedoxifene, Frovatriptan, and Raloxifene. These candidates exhibit pharmacophore-specific features, including the indole or thioindole group, and interactions with key amino acids in BACE-1. Overall, this study provides insights into the potential use of in silico methods for drug repurposing and identification of new candidates for the treatment of Alzheimer’s disease, especially those targeting BACE-1.
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Roundhill, Elizabeth Ann, Pan Pantziarka, Danielle E. Liddle, Lucy A. Shaw, Ghadeer Albadrani, and Susan Ann Burchill. "Exploiting the Stemness and Chemoresistance Transcriptome of Ewing Sarcoma to Identify Candidate Therapeutic Targets and Drug-Repurposing Candidates." Cancers 15, no. 3 (January 26, 2023): 769. http://dx.doi.org/10.3390/cancers15030769.
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Outcomes for most patients with Ewing sarcoma (ES) have remained unchanged for the last 30 years, emphasising the need for more effective and tolerable treatments. We have hypothesised that using small-molecule inhibitors to kill the self-renewing chemotherapy-resistant cells (Ewing sarcoma cancer stem-like cells; ES-CSCs) responsible for progression and relapse could improve outcomes and minimise treatment-induced morbidities. For the first time, we demonstrate that ABCG1, a potential oncogene in some cancers, is highly expressed in ES-CSCs independently of CD133. Using functional models, transcriptomics and a bespoke in silico drug-repurposing pipeline, we have prioritised a group of tractable small-molecule inhibitors for further preclinical studies. Consistent with the cellular origin of ES, 21 candidate molecular targets of pluripotency, stemness and chemoresistance were identified. Small-molecule inhibitors to 13 of the 21 molecular targets (62%) were identified. POU5F1/OCT4 was the most promising new therapeutic target in Ewing sarcoma, interacting with 10 of the 21 prioritised molecular targets and meriting further study. The majority of small-molecule inhibitors (72%) target one of two drug efflux proteins, p-glycoprotein (n = 168) or MRP1 (n = 13). In summary, we have identified a novel cell surface marker of ES-CSCs and cancer/non-cancer drugs to targets expressed by these cells that are worthy of further preclinical evaluation. If effective in preclinical models, these drugs and drug combinations might be repurposed for clinical evaluation in patients with ES.
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Hajjo, Rima, Dima A. Sabbah, Osama H. Abusara, Reham Kharmah, and Sanaa Bardaweel. "Targeting Human Proteins for Antiviral Drug Discovery and Repurposing Efforts: A Focus on Protein Kinases." Viruses 15, no. 2 (February 19, 2023): 568. http://dx.doi.org/10.3390/v15020568.
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Despite the great technological and medical advances in fighting viral diseases, new therapies for most of them are still lacking, and existing antivirals suffer from major limitations regarding drug resistance and a limited spectrum of activity. In fact, most approved antivirals are directly acting antiviral (DAA) drugs, which interfere with viral proteins and confer great selectivity towards their viral targets but suffer from resistance and limited spectrum. Nowadays, host-targeted antivirals (HTAs) are on the rise, in the drug discovery and development pipelines, in academia and in the pharmaceutical industry. These drugs target host proteins involved in the virus life cycle and are considered promising alternatives to DAAs due to their broader spectrum and lower potential for resistance. Herein, we discuss an important class of HTAs that modulate signal transduction pathways by targeting host kinases. Kinases are considered key enzymes that control virus-host interactions. We also provide a synopsis of the antiviral drug discovery and development pipeline detailing antiviral kinase targets, drug types, therapeutic classes for repurposed drugs, and top developing organizations. Furthermore, we detail the drug design and repurposing considerations, as well as the limitations and challenges, for kinase-targeted antivirals, including the choice of the binding sites, physicochemical properties, and drug combinations.
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Tugcu, Gulcin, Hande Sipahi, and Ahmet Aydin. "Application of a Validated QSTR Model for Repurposing COX-2 Inhibitor Coumarin Derivatives as Potential Antitumor Agents." Current Topics in Medicinal Chemistry 19, no. 13 (August 27, 2019): 1121–28. http://dx.doi.org/10.2174/1568026619666190618143552.
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Background: The discovery of novel potent molecules for both cancer prevention and treatment has been continuing over the past decade. In recent years, identification of new, potent, and safe anticancer agents through drug repurposing has been regarded as an expeditious alternative to traditional drug development. The cyclooxygenase-2 is known to be over-expressed in several types of human cancer. For this reason cyclooxygenase-2 inhibition may be useful tool for cancer chemotherapy. Objective: The first aim of the study was to develop a validated linear model to predict antitumor activity. Subsequently, applicability of the model for repurposing these cyclooxygenase-2 inhibitors as antitumor compounds to abridge drug development process. Method: We performed a quantitative structure-toxicity relationship (QSTR) study on a set of coumarin derivatives using a large set of molecular descriptors. A linear model predicting growth inhibition on leukemia CCRF cell lines was developed and consequently validated internally and externally. Accordingly, the model was applied on a set of 143 cyclooxygenase-2 inhibitor coumarin derivatives to explore their antitumor activity. Results: The results indicated that the developed QSAR model would be useful for estimating inhibitory activity of coumarin derivatives on leukemia cell lines. Electronegativity was found to be a prominent property of the molecules in describing antitumor activity. The applicability domain of the developed model highlighted the potential antitumor compounds. Conclusion: The promising results revealed that applied integrated in silico approach for repurposing by combining both the biological activity similarity and the molecular similarity via the computational method could be efficiently used to screen potential antitumor compounds among cyclooxygenase-2 inhibitors.
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Taibe, Noha Samir, Maimona A. Kord, Mohamed Ahmed Badawy, Iart Luca Shytaj, and Mahmoud M. Elhefnawi. "Progress, pitfalls, and path forward of drug repurposing for COVID-19 treatment." Therapeutic Advances in Respiratory Disease 16 (January 2022): 175346662211327. http://dx.doi.org/10.1177/17534666221132736.
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On 30 January 2020, the World Health Organization (WHO) declared the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic a public health emergency of international concern. The viral outbreak led in turn to an exponential growth of coronavirus disease 2019 (COVID-19) cases, that is, a multiorgan disease that has led to more than 6.3 million deaths worldwide, as of June 2022. There are currently few effective drugs approved for treatment of SARS-CoV-2/COVID-19 patients. Many of the compounds tested so far have been selected through a drug repurposing approach, that is, by identifying novel indications for drugs already approved for other conditions. We here present an up-to-date review of the main Food and Drug Administration (FDA)–approved drugs repurposed against SARS-CoV-2 infection, discussing their mechanism of action and their most important preclinical and clinical results. Reviewed compounds were chosen to privilege those that have been approved for use in SARS-CoV-2 patients or that have completed phase III clinical trials. Moreover, we also summarize the evidence on some novel and promising repurposed drugs in the pipeline. Finally, we discuss the current stage and possible steps toward the development of broadly effective drug combinations to suppress the onset or progression of COVID-19.
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Sahoo, Alaka, Shivkanya Fuloria, Shasank S. Swain, Sujogya K. Panda, Mahendran Sekar, Vetriselvan Subramaniyan, Maitreyee Panda, Ajaya K. Jena, Kathiresan V. Sathasivam, and Neeraj Kumar Fuloria. "Potential of Marine Terpenoids against SARS-CoV-2: An In Silico Drug Development Approach." Biomedicines 9, no. 11 (October 20, 2021): 1505. http://dx.doi.org/10.3390/biomedicines9111505.
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In an emergency, drug repurposing is the best alternative option against newly emerged severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. However, several bioactive natural products have shown potential against SARS-CoV-2 in recent studies. The present study selected sixty-eight broad-spectrum antiviral marine terpenoids and performed molecular docking against two novel SARS-CoV-2 enzymes (main protease or Mpro or 3CLpro) and RNA-dependent RNA polymerase (RdRp). In addition, the present study analysed the physiochemical-toxicity-pharmacokinetic profile, structural activity relationship, and phylogenetic tree with various computational tools to select the ‘lead’ candidate. The genomic diversity study with multiple sequence analyses and phylogenetic tree confirmed that the newly emerged SARS-CoV-2 strain was up to 96% structurally similar to existing CoV-strains. Furthermore, the anti-SARS-CoV-2 potency based on a protein−ligand docking score (kcal/mol) exposed that the marine terpenoid brevione F (−8.4) and stachyflin (−8.4) exhibited similar activity with the reference antiviral drugs lopinavir (−8.4) and darunavir (−7.5) against the target SARS−CoV−Mpro. Similarly, marine terpenoids such as xiamycin (−9.3), thyrsiferol (−9.2), liouvilloside B (−8.9), liouvilloside A (−8.8), and stachyflin (−8.7) exhibited comparatively higher docking scores than the referral drug remdesivir (−7.4), and favipiravir (−5.7) against the target SARS-CoV-2−RdRp. The above in silico investigations concluded that stachyflin is the most ‘lead’ candidate with the most potential against SARS-CoV-2. Previously, stachyflin also exhibited potential activity against HSV-1 and CoV-A59 within IC50, 0.16–0.82 µM. Therefore, some additional pharmacological studies are needed to develop ‘stachyflin’ as a drug against SARS-CoV-2.
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Ibrahim, Kareem A., Omneya M. Helmy, Mona T. Kashef, Tharwat R. Elkhamissy, and Mohammed A. Ramadan. "Identification of Potential Drug Targets in Helicobacter pylori Using In Silico Subtractive Proteomics Approaches and Their Possible Inhibition through Drug Repurposing." Pathogens 9, no. 9 (September 12, 2020): 747. http://dx.doi.org/10.3390/pathogens9090747.
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The class 1 carcinogen, Helicobacter pylori, is one of the World Health Organization’s high priority pathogens for antimicrobial development. We used three subtractive proteomics approaches using protein pools retrieved from: chokepoint reactions in the BIOCYC database, the Kyoto Encyclopedia of Genes and Genomes, and the database of essential genes (DEG), to find putative drug targets and their inhibition by drug repurposing. The subtractive channels included non-homology to human proteome, essentiality analysis, sub-cellular localization prediction, conservation, lack of similarity to gut flora, druggability, and broad-spectrum activity. The minimum inhibitory concentration (MIC) of three selected ligands was determined to confirm anti-helicobacter activity. Seventeen protein targets were retrieved. They are involved in motility, cell wall biosynthesis, processing of environmental and genetic information, and synthesis and metabolism of secondary metabolites, amino acids, vitamins, and cofactors. The DEG protein pool approach was superior, as it retrieved all drug targets identified by the other two approaches. Binding ligands (n = 42) were mostly small non-antibiotic compounds. Citric, dipicolinic, and pyrophosphoric acid inhibited H. pylori at an MIC of 1.5–2.5 mg/mL. In conclusion, we identified potential drug targets in H. pylori, and repurposed their binding ligands as possible anti-helicobacter agents, saving time and effort required for the development of new antimicrobial compounds.
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Makhouri, Farahnaz R., and Jahan B. Ghasemi. "Combating Diseases with Computational Strategies Used for Drug Design and Discovery." Current Topics in Medicinal Chemistry 18, no. 32 (March 5, 2019): 2743–73. http://dx.doi.org/10.2174/1568026619666190121125106.
Full textAbstract:
Computer-aided drug discovery (CADD) tools have provided an effective way in the drug discovery pipeline for expediting of this long process and economizing the cost of research and development. Due to the dramatic increase in the availability of human proteins as drug targets and small molecule information due to the advances in bioinformatics, cheminformatics, genomics, proteomics, and structural information, the applicability of in silico drug discovery has been extended. Computational approaches have been used at almost all stages in the drug discovery pipeline including target identification and validation, lead discovery and optimization, and pharmacokinetic and toxicity profiles prediction. As each area covers a variety of computational methods, it is unmanageable to assess comprehensively all areas of CADD applications or every aspect of an area in one review article. However, in this article, we tried to present an overview of computational methods used in almost all the areas concerned with drug design and highlight some of the recent successes.
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Vlainić, Josipa, Ozren Jović, Ivan Kosalec, Oliver Vugrek, Rozelindra Čož-Rakovac, and Tomislav Šmuc. "In Vitro Confirmation of Siramesine as a Novel Antifungal Agent with In Silico Lead Proposals of Structurally Related Antifungals." Molecules 26, no. 12 (June 8, 2021): 3504. http://dx.doi.org/10.3390/molecules26123504.
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The limited number of medicinal products available to treat of fungal infections makes control of fungal pathogens problematic, especially since the number of fungal resistance incidents increases. Given the high costs and slow development of new antifungal treatment options, repurposing of already known compounds is one of the proposed strategies. The objective of this study was to perform in vitro experimental tests of already identified lead compounds in our previous in silico drug repurposing study, which had been conducted on the known Drugbank database using a seven-step procedure which includes machine learning and molecular docking. This study identifies siramesine as a novel antifungal agent. This novel indication was confirmed through in vitro testing using several yeast species and one mold. The results showed susceptibility of Candida species to siramesine with MIC at concentration 12.5 µg/mL, whereas other candidates had no antifungal activity. Siramesine was also effective against in vitro biofilm formation and already formed biofilm was reduced following 24 h treatment with a MBEC range of 50–62.5 µg/mL. Siramesine is involved in modulation of ergosterol biosynthesis in vitro, which indicates it is a potential target for its antifungal activity. This implicates the possibility of siramesine repurposing, especially since there are already published data about nontoxicity. Following our in vitro results, we provide additional in depth in silico analysis of siramesine and compounds structurally similar to siramesine, providing an extended lead set for further preclinical and clinical investigation, which is needed to clearly define molecular targets and to elucidate its in vivo effectiveness as well.
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Leelananda, Sumudu P., and Steffen Lindert. "Computational methods in drug discovery." Beilstein Journal of Organic Chemistry 12 (December 12, 2016): 2694–718. http://dx.doi.org/10.3762/bjoc.12.267.
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The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.
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Andrei, Corina, Dragos Paul Mihai, Anca Zanfirescu, George Mihai Nitulescu, and Simona Negres. "In Silico Drug Repurposing Framework Predicts Repaglinide, Agomelatine and Protokylol as TRPV1 Modulators with Analgesic Activity." Pharmaceutics 14, no. 12 (November 22, 2022): 2563. http://dx.doi.org/10.3390/pharmaceutics14122563.
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Pain is one of the most common symptoms experienced by patients. The use of current analgesics is limited by low efficacy and important side effects. Transient receptor potential vanilloid-1 (TRPV1) is a non-selective cation channel, activated by capsaicin, heat, low pH or pro-inflammatory agents. Since TRPV1 is a potential target for the development of novel analgesics due to its distribution and function, we aimed to develop an in silico drug repositioning framework to predict potential TRPV1 ligands among approved drugs as candidates for treating various types of pain. Structures of known TRPV1 agonists and antagonists were retrieved from ChEMBL databases and three datasets were established: agonists, antagonists and inactive molecules (pIC50 or pEC50 < 5 M). Structures of candidates for repurposing were retrieved from the DrugBank database. The curated active/inactive datasets were used to build and validate ligand-based predictive models using Bemis–Murcko structural scaffolds, plain ring systems, flexophore similarities and molecular descriptors. Further, molecular docking studies were performed on both active and inactive conformations of the TRPV1 channel to predict the binding affinities of repurposing candidates. Variables obtained from calculated scaffold-based activity scores, molecular descriptors criteria and molecular docking were used to build a multi-class neural network as an integrated machine learning algorithm to predict TRPV1 antagonists and agonists. The proposed predictive model had a higher accuracy for classifying TRPV1 agonists than antagonists, the ROC AUC values being 0.980 for predicting agonists, 0.972 for antagonists and 0.952 for inactive molecules. After screening the approved drugs with the validated algorithm, repaglinide (antidiabetic) and agomelatine (antidepressant) emerged as potential TRPV1 antagonists, and protokylol (bronchodilator) as an agonist. Further studies are required to confirm the predicted activity on TRPV1 and to assess the candidates’ efficacy in alleviating pain.
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Ozdemir, E. Sila, Hillary H. Le, Adem Yildirim, and Srivathsan V. Ranganathan. "In Silico Screening and Testing of FDA-Approved Small Molecules to Block SARS-CoV-2 Entry to the Host Cell by Inhibiting Spike Protein Cleavage." Viruses 14, no. 6 (May 24, 2022): 1129. http://dx.doi.org/10.3390/v14061129.
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The COVID-19 pandemic began in 2019, but it is still active. The development of an effective vaccine reduced the number of deaths; however, a treatment is still needed. Here, we aimed to inhibit viral entry to the host cell by inhibiting spike (S) protein cleavage by several proteases. We developed a computational pipeline to repurpose FDA-approved drugs to inhibit protease activity and thus prevent S protein cleavage. We tested some of our drug candidates and demonstrated a decrease in protease activity. We believe our pipeline will be beneficial in identifying a drug regimen for COVID-19 patients.
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Francis, Arul Prakash, Aftab Ahmad, Sri Durga Devi Nagarajan, Harish Sundar Yogeeswarakannan, Krishnaraj Sekar, Shah Alam Khan, Dhanalekshmi Unnikrishnan Meenakshi, Asif Husain, Mohammed A. Bazuhair, and Nandakumar Selvasudha. "Development of a Novel Red Clay-Based Drug Delivery Carrier to Improve the Therapeutic Efficacy of Acyclovir in the Treatment of Skin Cancer." Pharmaceutics 15, no. 7 (July 10, 2023): 1919. http://dx.doi.org/10.3390/pharmaceutics15071919.
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Acyclovir (ACV) is a promising candidate for drug repurposing because of its potential to provide an effective treatment for viral infections and non-viral diseases, such as cancer, for which limited treatment options exist. However, its poor physicochemical properties limit its application. This study aimed to formulate and evaluate an ACV-loaded red clay nanodrug delivery system exhibiting an effective cytotoxicity. The study focused on the preparation of a complex between ACV and red clay (RC) using sucrose stearate (SS) (nanocomplex F1) as an immediate-release drug-delivery system for melanoma treatment. The synthesized nanocomplex, which had nanosized dimensions, a negative zeta potential and the drug release of approximately 85% after 3 h, was found to be promising. Characterization techniques, including FT-IR, XRD and DSC-TGA, confirmed the effective encapsulation of ACV within the nanocomplex and its stability due to intercalation. Cytotoxicity experiments conducted on melanoma cancer cell lines SK-MEL-3 revealed that the ACV release from the nanocomplex formulation F1 effectively inhibited the growth of melanoma cancer cells, with an IC50 of 25 ± 0.09 µg/mL. Additionally, ACV demonstrated a significant cytotoxicity at approximately 20 µg/mL in the melanoma cancer cell line, indicating its potential repurposing for skin cancer treatment. Based on these findings, it can be suggested that the RC-SS complex could be an effective drug delivery carrier for localized cancer therapy. Furthermore, the results of an in silico study suggested the addition of chitosan to the formulation for a more effective drug delivery. Energy and interaction analyses using various modules in a material studio demonstrated the high stability of the composite comprising red clay, sucrose stearate, chitosan and ACV. Thus, it could be concluded that the utilization of the red clay-based drug delivery system is a promising strategy to improve the effectiveness of targeted cancer therapy.
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Chakravarty, Kaushik, Victor G. Antontsev, Maksim Khotimchenko, Nilesh Gupta, Aditya Jagarapu, Yogesh Bundey, Hypatia Hou, Neha Maharao, and Jyotika Varshney. "Accelerated Repurposing and Drug Development of Pulmonary Hypertension Therapies for COVID-19 Treatment Using an AI-Integrated Biosimulation Platform." Molecules 26, no. 7 (March 29, 2021): 1912. http://dx.doi.org/10.3390/molecules26071912.
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The COVID-19 pandemic has reached over 100 million worldwide. Due to the multi-targeted nature of the virus, it is clear that drugs providing anti-COVID-19 effects need to be developed at an accelerated rate, and a combinatorial approach may stand to be more successful than a single drug therapy. Among several targets and pathways that are under investigation, the renin-angiotensin system (RAS) and specifically angiotensin-converting enzyme (ACE), and Ca2+-mediated SARS-CoV-2 cellular entry and replication are noteworthy. A combination of ACE inhibitors and calcium channel blockers (CCBs), a critical line of therapy for pulmonary hypertension, has shown therapeutic relevance in COVID-19 when investigated independently. To that end, we conducted in silico modeling using BIOiSIM, an AI-integrated mechanistic modeling platform by utilizing known preclinical in vitro and in vivo datasets to accurately simulate systemic therapy disposition and site-of-action penetration of the CCBs and ACEi compounds to tissues implicated in COVID-19 pathogenesis.