Academic literature on the topic 'In silico drug repurposing pipeline development'
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Journal articles on the topic "In silico drug repurposing pipeline development"
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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.
Full textAbstract:
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.
Full textDissertations / Theses on the topic "In silico drug repurposing pipeline development"
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Chakraborti, Sohini. "Protein-small molecule interactions: Structural insights and applications in computational drug discovery." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5520.
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Deviation from normal healthy conditions, termed as disease, can often be triggered due to the malfunctioning of proteins. Modulating the functions of proteins by administering therapeutic agents (drugs) may alleviate the disease conditions. The majority of the drugs currently available in the market are small organic molecules due to their pharmacological and commercial advantages. These small molecule drugs interact with the protein targets through specific sites on the surface of the protein structure (binding sites). Thus, the structural data of protein-small molecule complexes forms a crucial starting point for most drug discovery programs. The work reported in this thesis deals with understanding various aspects of protein-small molecule interactions. The thesis begins (Chapter 1) with a general introduction on the implication of proteins structural data in drug discovery programs. Chapter 2 provides a fundamental understanding of the general trend in local quality of protein-small molecule crystal complexes deposited in the Protein Data Bank (PDB). Our results suggest ‘seeing is not always believing’ and aims to sensitize the non-crystallographer user community that high-resolution need not always guarantee confident small molecule binding poses. The study indicates 35% of the inspected ~0.28 million protein-small molecule binding site pairs available from ~66000 PDB entries, need serious attention before using those as input in any important applications. Results reported in Chapter 3 suggest that the stereochemical quality of bound small molecules generally agrees well with their crystallographic quality. The findings from this work could be the stepping-stones for developing structure determination technique-independent ligand pose validation tools. The learning from Chapter 3 is extended to Chapter 4 to investigate the stereochemical quality of the small molecules bound to protein structures determined by cryo-EM. Our data shows that the stereochemical quality of small molecules bound to high-resolution protein structures determined by cryo-EM is comparable to high-quality small molecules bound to protein crystal structures. Chapter 5 presents a computational analysis aimed at providing insights into the molecular basis of the specificity of a novel anti-tubercular compound, NU-6027 (identified in a phenotypic screening by experimental collaborators), towards two out of the eleven known Serine-Threonine Protein Kinases in Mycobacterium tuberculosis (Mtb). Chapter 6 reports the development of a freely available web server that facilitates the identification of new uses of old drugs and aid in drug repurposing. In Chapter 7, the principles of ‘neighborhood behavior’ are exploited to identify potential known drugs that could be repurposed against the main protease of SARS-CoV-2. Chapter 8 discusses a virtual screening strategy to identify potential binders of a novel Mtb target, Rv1636 (or the Universal Stress Protein). Collaborators have experimentally validated some of the compounds shortlisted from the computational studies. Chapter 9 summarizes the findings from work reported in the entire thesis and future applications. Overall, this thesis inspects protein-small molecule complexes from a local perspective, aiding the design of rigorous computational experiments that can contribute to solving global unmet medical needs.
Interested readers may contact the author directly for Supplementary data at "sohini@iisc.ac.in"DST-INSPIRE fellowship
Book chapters on the topic "In silico drug repurposing pipeline development"
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Sankhe, R., A. Kumar, E. Rathi, and A. Kishore. "Development of New Neprilysin Inhibitor as a Modulator of Chronic Kidney and Heart Disease Using In Silico Drug Repurposing Approach." In Special Publications, 45–49. Cambridge: Royal Society of Chemistry, 2019. http://dx.doi.org/10.1039/9781839160783-00045.
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Srinivasa Rao, Kareti, and P. Subash. "In Silico Drug Repurposing: An Effective Tool to Accelerate the Drug Discovery Process." In Drug Repurposing - Advances, Scopes and Opportunities in Drug Discovery [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.109312.
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Repurposing “old” drugs to treat both common and rare diseases is increasingly emerging as an attractive proposition due to the use of de-risked compounds, with potential for lower overall development costs and shorter development timelines. This is due to the high attrition rates, significant costs, and slow pace of new drug discovery and development. Drug repurposing is the process of finding new, more efficient uses for already-available medications. Numerous computational drug repurposing techniques exist, there are three main types of computational drug-repositioning methods used on COVID-19 are network-based models, structure-based methods and artificial intelligence (AI) methods used to discover novel drug–target relationships useful for new therapies. In order to assess how a chemical molecule can interact with its biological counterpart and try to find new uses for medicines already on the market, structure-based techniques made it possible to identify small chemical compounds capable of binding macromolecular targets. In this chapter, we explain strategies for drug repurposing, discuss about difficulties encountered by the repurposing community, and suggest reported drugs through the drug repurposing. Moreover, metabolic and drug discovery network resources, tools for network construction, analysis and protein–protein interaction analysis to enable drug repurposing to reach its full potential.
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Rudrapal, Mithun, Shubham J. Khairnar, and Anil G. Jadhav. "Drug Repurposing (DR): An Emerging Approach in Drug Discovery." In Drug Repurposing - Hypothesis, Molecular Aspects and Therapeutic Applications. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93193.
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Drug repurposing (DR) (also known as drug repositioning) is a process of identifying new therapeutic use(s) for old/existing/available drugs. It is an effective strategy in discovering or developing drug molecules with new pharmacological/therapeutic indications. In recent years, many pharmaceutical companies are developing new drugs with the discovery of novel biological targets by applying the drug repositioning strategy in drug discovery and development program. This strategy is highly efficient, time saving, low-cost and minimum risk of failure. It maximizes the therapeutic value of a drug and consequently increases the success rate. Thus, drug repositioning is an effective alternative approach to traditional drug discovery process. Finding new molecular entities (NME) by traditional or de novo approach of drug discovery is a lengthy, time consuming and expensive venture. Drug repositioning utilizes the combined efforts of activity-based or experimental and in silico-based or computational approaches to develop/identify the new uses of drug molecules on a rational basis. It is, therefore, believed to be an emerging strategy where existing medicines, having already been tested safe in humans, are redirected based on a valid target molecule to combat particularly, rare, difficult-to-treat diseases and neglected diseases.
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IfedibaluChukwu Ejiofor, InnocentMary, Christabel Chikodili Ekeomodi, Sharon Elomeme, and MaryGeraldine Ebele Ejiofor. "Therapeutic Inhibitors: Natural Product Options through Computer-Aided Drug Design." In Drug Repurposing - Molecular Aspects and Therapeutic Applications. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.104412.
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Drug repurposing involves reusing an active pharmaceutical ingredient that is already in the market and drugs that were unsuccessful in their clinical phases of development for a new indication. It has numerous benefits in drug development. Therapeutic inhibitors are agents that could be of synthetic or natural source with the ability to trigger the down-regulation of an enzyme or protein, thereby inducing therapeutic effect(s). Researchers have embraced synthetic methods in searching for therapeutic molecules through structural activity relationships and other means in the past and recent times. Despite these synthetic drugs, the morbidity and mortality rate of ailment and disease affecting humanity remains overwhelming. Research has shown that solutions to these challenges can be attempted through drug repurposing. In the past, natural products in raw forms have been utilized in traditional, complementary medicine to manage and treat diseases and illnesses, as there are molecules in use today as drugs, which originated from plants and other natural sources. Studies on natural products have led to diverse natural product databases that can serve as a source of repurposing agents. There are also databases for protein and enzymes of human origin, which have an enormous role in the in-silico drug repurposing approach.
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Hosseini, Fatemeh, Mehrdad Azin, Hamideh Ofoghi, and Tahereh Alinejad. "Evaluation of Drug Repositioning by Molecular Docking of Pharmaceutical Resources to Identification of Potential SARS-CoV-2 Viral Inhibitors." In Drug Repurposing - Molecular Aspects and Therapeutic Applications [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101395.
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Unfortunately, to date, there is no approved specific antiviral drug treatment against COVID-19. Due to the costly and time-consuming nature of the de novo drug discovery and development process, in recent days, the computational drug repositioning method has been highly regarded for accelerating the drug-discovery process. The selection of drug target molecule(s), preparation of an approved therapeutics agent library, and in silico evaluation of their affinity to the subjected target(s) are the main steps of a molecular docking-based drug repositioning process, which is the most common computational drug re-tasking process. In this chapter, after a review on origin, pathophysiology, molecular biology, and drug development strategies against COVID-19, recent advances, challenges as well as the future perspective of molecular docking-based drug repositioning for COVID-19 are discussed. Furthermore, as a case study, the molecular docking-based drug repurposing process was planned to screen the 3CLpro inhibitor(s) among the nine Food and Drug Administration (FDA)-approved antiviral protease inhibitors. The results demonstrated that Fosamprenavir had the highest binding affinity to 3CLpro and can be considered for more in silico, in vitro, and in vivo evaluations as an effective repurposed anti-COVID-19 drug.
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Srivastava, Ruby. "Transformation of Drug Discovery towards Artificial Intelligence: An in Silico Approach." In Density Functional Theory - Recent Advances, New Perspectives and Applications [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99018.
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Computational methods play a key role in the design of therapeutically important molecules for modern drug development. With these “in silico” approaches, machines are learning and offering solutions to some of the most complex drug related problems and has well positioned them as a next frontier for potential breakthrough in drug discovery. Machine learning (ML) methods are used to predict compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties to evaluate the drugs and their various applications. Modern artificial intelligence (AI) has the capacity to significantly enhance the role of computational methodology in drug discovery. Use of AI in drug discovery and development, drug repurposing, improving pharmaceutical productivity, and clinical trials will certainly reduce the human workload as well as achieving targets in a short period of time. This chapter elaborates the crosstalk between the machine learning techniques, computational tools and the future of AI in the pharmaceutical industry.
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Tarín-Pelló, Antonio, Beatriz Suay-García, Antonio Falcó, and María Teresa Pérez-Gracia. "Big Data to Expand the Antimicrobial Therapeutic Arsenal." In Encyclopedia of Information Science and Technology, Sixth Edition, 1–29. IGI Global, 2024. http://dx.doi.org/10.4018/978-1-6684-7366-5.ch027.
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The current big data era has vast amounts of biomedical data that present great interest in processes related to the search of new drugs. Due to the information available in open access databases, many efforts have been directed towards the application of in silico discovery strategies aimed at drug discovery and drug repurposing. These approaches could be useful in finding solutions for antimicrobial resistance, which already causes 700,000 deaths every year. Mathematical prediction models provide a time- and cost-effective solution to this global health threat, as they save all the resources necessary for the development of the molecule. The literature provides computational techniques that have been successfully used in the discovery and repurposing of drugs. For this reason, the aim of this article is to present the different types of prediction models that have been used for the discovery and repurposing of new antimicrobial drugs and to prove the efficiency of these strategies to provide new molecules and therapeutic opportunities to drugs found in biomedical datasets.
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Tewelde, Eyael, and Solomon Tadesse. "Drug Discovery and Development for Soil-Transmitted Helminthiasis: Current Anthelmentics and Compounds in the Pipeline." In Roundworms - A Survey From Past to Present [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.106830.
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Soil-transmitted helminthiasis (STH), one of 20 neglected tropical diseases, afflicts about a quarter of the world’s population. A handful of medications, albendazole, mebendazole, pyrantel pamoate, levamisole, and ivermectin, have long constituted the cornerstone of therapy for these infections in both humans and animals. The continuous and long-term reliance on these small range of compounds has led to the emergence of drug resistance in many helminthic strains in animals. The threat of resistance also seems inevitable in humans thereby hampering the World Health Organization’s efforts to control or eradicate these neglected tropical illnesses. Hence, there is an urgent need for the discovery and development of new treatment options with broad spectrum activity against various helmintic infections that act via novel mechanisms of action. Different strategies are employed in this endeavor which include the identification of promising compounds from natural and synthetic origin, drug repurposing and modification of existing drugs, and vaccine development. The prospect of a “pan-anthelmintic vaccine” also seems encouraging, despite the various obstacles facing the development of vaccines. Here we discuss drug discovery and development efforts for STH.
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Modanwal, Shristi, Viswajit Mulpuru, and Nidhi Mishra. "Computational Drug Discovery Against COVID-19." In COVID-19: Origin, Impact and Management (Part 2), 96–110. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815165944123010010.
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The global spread of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), which causes the disease COVID-19, has increased drastically since the first cases in Wuhan, People's Republic of China, at the end of 2019. There is no single drug that can be used specifically to treat COVID. The crucial stage in the drug development process is screening huge libraries of bioactive molecules against a biological target, usually a receptor or a protein. Virtual Screening (VS) has become a valuable tool in the drug development process as it allows for efficient in silico searches of millions of compounds, resulting in higher yields of possible therapeutic leads, and is cost-effective. The spread of the SARS-CoV-2 virus presents a major threat to world health and has resulted in a global crisis because of the high mortality rate and absence of clinically authorised treatments and vaccines for COVID-19. Finding effective drugs or repurposing available antiviral drugs is a critical need in the fight against COVID-19. VS can be classified as either Structural-Based Virtual Screening or Ligand-Based Virtual Screening. VS techniques have been widely applied in the field of antiviral drug design and have aided in the identification of new compounds as possible anti-viral drugs. Both LBVS and SBVS approaches have proved extremely helpful in identifying several prospective anti-viral drugs with nanomolar range. VS, in contrast to experimental approaches, is quick and cost-effective on the one side but has low prediction accuracy on the other.
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Bourdon, Allen K., Greg Villareal, George Perry, and Clyde F. Phelix. "Alzheimer's and Parkinson's Disease Novel Therapeutic Target." In Research Anthology on Diagnosing and Treating Neurocognitive Disorders, 411–26. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3441-0.ch021.
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Thiazolidinedione (TZD) drugs (Takeda Pharmaceuticals and Metabolic Solutions Development Company) targeting inhibition of the mitochondrial pyruvate carrier (MPC) are currently being tested in clinical trials to prevent progression into mild cognitive impairment of Alzheimer's disease (AD) or in the pipeline to prevent neurodegeneration in Parkinson's disease (PD). These have Ki values in the µM range. This study was focused on identifying candidate drug precursors of the natural cinnamic acid products that might have good bioavailability in the nM ranges forming covalent thiol bonds with targets. In silico protein homology modeling and ligand docking has demonstrated that binding cysteine residues within the transport channel is a key part of the inhibitory mechanism. These are covalent thiohemiacetal bonds with the alpha-carbon, carboxylate group, off a phenol ring. Like the classic MPC inhibitors, these natural derivatives of hydroxycinnamic acid have a conjugated pi-system used to form thiol bonds with the cysteine residue via Michael addition.