查看更多>>摘要：Drug repositioning (DR) is an effective way for developing drugs for rare and intractable diseases (RIDs). Preparation of the ontology is essential for drug development in RIDs, in which disease names have been inconsistently used worldwide. Ontology-based analysis of clinical trial data revealed that DR occurs actively in RIDs. Drugs and their target genes are keys to explore repositionable drugs, because shared target genes between diseases indicate a common mechanism of drug action. This approach visualizes a DR landscape that facilitates drug development. Here, we review the current situation of ontology in RIDs, the trends in drug development, and an efficient strategy for DR based on drug target gene information.
查看更多>>摘要：Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.
查看更多>>摘要：Autophagy is a multistep degradation pathway involving the lysosome, which supports nutrient reuse and metabolic balance, and has been implicated as a process that regulates cancer genesis and development. Targeting tumors by regulating autophagy has become a therapeutic strategy of interest. Drugs with other indications can have antitumor activity by modulating autophagy, providing a shortcut to developing novel antitumor drugs (i.e., drug repurposing/repositioning), as successfully performed for chloroquine (CQ); an increasing number of repurposed drugs have since advanced into clinical trials. In this review, we describe the application of different drugrepurposing approaches in autophagy for the treatment of cancer and focus on repurposing drugs that target autophagy to treat malignant neoplasms.
Hoang, AnthonyDilmore, Christopher R.DeStefano, Jeffrey J.Arnold, Eddy...
15页
查看更多>>摘要：Infection with HIV can cripple the immune system and lead to AIDS. Hepatitis B virus (HBV) is a hepadnavirus that causes human liver diseases. Both pathogens are major public health problems affecting millions of people worldwide. The polymerases from both viruses are the most common drug target for viral inhibition, sharing common architecture at their active sites. The L-nucleoside drugs emtricitabine and lamivudine are widely used HIV reverse transcriptase (RT) and HBV polymerase (Pol) inhibitors. Nevertheless, structural details of their binding to RT(Pol)/nucleic acid remained unknown until recently. Here, we discuss the implications of these structures, alongside related complexes with L-dNTPs, for the development of novel L-nucleos(t)ide drugs, and prospects for repurposing them.
Choudhury, ChinmayeeMurugan, N. ArulPriyakumar, U. Deva
15页
查看更多>>摘要：The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time. Structure-based drug repurposing is a popular in silico repurposing approach. In this review, we discuss traditional and modern AI-based computational methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. Additionally, we highlight the role of generative models in generating molecules with scaffolds from repurposable chemical space.
查看更多>>摘要：Clostridioides difficile bacteria can cause life-threatening diarrhea and colitis owing to limited treatment options and unacceptably high recurrence rates among infected patients. This necessitates the development of alternative routes for C. difficile treatment. Drug repurposing with new indications represents a proven shortcut. Here, we present a refined focus on 16 FDA-approved drugs that would be suitable for further development as potential anti-C. difficile drugs. Of these drugs, clinical trials have been conducted on five currently used drugs; however, ursodeoxycholic acid is the only drug to enter Phase IV clinical trials to date. Thus, drug repurposing promotes the study of mechanistic and therapeutic strategies, providing new options for the development of next-generation anti-C. difficile agents.
查看更多>>摘要：For emerging and re-emerging epidemic infections, researchers face challenges to develop broad-spectrum antivirals as well as reducing development time and costs, and drug resistance. Drug repurposing is a reliable strategy for rapidly discovering potent new antiviral agents, reducing the need for clinical trials. In this review, we outline antiviral drug candidates identified using the drug repurposing approach, with their potential modes of action and biological responses against various epidemic viral infectious diseases.
查看更多>>摘要：Cyclophilin A (CypA) is linked to diverse human diseases including viral infections. With the worldwide emergence of posing has been highlighted as a strategy with the potential to speed up antiviral development. Because CypA acts as a proviral component in hepatitis C virus, coronavirus and HIV, its inhibitors have been suggested as potential treatments for these infections. Here, we review the structure of cyclosporin A and sanglifehrin A analogs as well as synthetic micromolecules inhibiting CypA; and we discuss their broad-spectrum antiviral efficacy in the context of the virus lifecycle.
Murugan, N. ArulPriya, Gnana RubaSastry, G. NarahariMarkidis, Stefano...
11页
查看更多>>摘要：A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.
NSTL
Elsevier