Viral diseases are the most devastating health concern worldwide. Outbreaks of coronavirus (CoVs)-related acute respiratory diseases are responsible for the massive health/socio-economic breakdown in the last two decades including the Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), the third reported spillover SARS-CoV-2 from an animal coronavirus to humans. After the H1N1 pandemic influenza (2009), SARS-CoV-2 (novel-beta coronavirus) causing COVID-19 has stretched across 215 countries in 5 major continents with 200,523,190 confirmed cases (4 August 2021; https://www.worldometers.info/coronavirus/). COVID-19 patients had cough, fever, dyspnea, headache, and respiratory failure, as well as shock, acute respiratory distress syndrome, and sepsis in severe instances. Independent of two preceding epidemics, SARS (2002) and MERS (2012), a knowledge gap about the emerging medical manifestations as well as complications of SARS-CoV-2 (2019-2020) infections in humans must be filled, with a focus on immunological complications and computational genomics for forecasting/ preparedness for a similar outbreak in the future. This paper aims to address aspects of this gap.
Drug Repositioning

Although sciences and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In view of these circumstances, ‘drug repurposing’ (or ‘drug repositioning’) has appeared as an alternative tool to accelerate drug development process by seeking new indications for already approved drugs rather than discovering de novo drug compounds, nowadays accounting for 30% of newly marked drugs in the U.S. In the meantime, the explosive and large-scale growth of molecular, genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called computational drug repurposing. This review provides an overview of recent progress in the area of computational drug repurposing. First, it summarizes available repositioning strategies, followed by computational methods commonly used. Then, it describes validation techniques for repurposing studies. Finally, it concludes by discussing the remaining challenges in computational repurposing.
Data Mining ; Drug Repositioning ; Machine Learning

Drug Repositioning ; Drug Resistance, Microbial ; Humans ; Organizational Objectives ; Tuberculosis ; drug therapy ; prevention & control ; World Health Organization

Malaria is an ancient infectious disease that threatens millions of lives globally even today. The discovery of artemisinin, inspired by traditional Chinese medicine (TCM), has brought in a paradigm shift and been recognized as the "best hope for the treatment of malaria" by World Health Organization. With its high potency and low toxicity, the wide use of artemisinin effectively treats the otherwise drug-resistant parasites and helps many countries, including China, to eventually eradicate malaria. Here, we will first review the initial discovery of artemisinin, an extraordinary journey that was in stark contrast with many drugs in western medicine. We will then discuss how artemisinin and its derivatives could be repurposed to treat cancer, inflammation, immunoregulation-related diseases, and COVID-19. Finally, we will discuss the implications of the "artemisinin story" and how that can better guide the development of TCM today. We believe that artemisinin is just a starting point and TCM will play an even bigger role in healthcare in the 21st century.
Artemisinins/therapeutic use* ; COVID-19/drug therapy* ; Drug Repositioning ; Humans ; Medicine, Chinese Traditional ; Neoplasms/drug therapy*

OBJECTIVES: Recently, comparison of drug responses on gene expression has been a major approach to identifying the functional similarity of drugs. Previous studies have mostly focused on a single feature, the expression differences of individual genes. We provide a more robust and accurate method to compare the functional similarity of drugs by diversifying the features of comparison in gene expression and considering the sample dependent variations. METHODS: For differentially expressed gene measurement, we modified the conventional t-test to normalize variations in diverse experimental conditions of individual samples. To extract significant differentially co-expressed gene modules, we searched maximal cliques among the co-expressed gene network. Finally, we calculated a combined similarity score by averaging the two scaled scores from the above two measurements. RESULTS: This method shows significant performance improvement in comparison to other approaches in the test with Connectivity Map data. In the test to find the drugs based on their own expression profiles with leave-one-out cross validation, the proposed method showed an area under the curve (AUC) score of 0.99, which is much higher than scores obtained with previous methods, ranging from 0.71 to 0.93. In the drug networks, we could find well clustered drugs having the same target proteins and novel relations among drugs implying the possibility of drug repurposing. CONCLUSIONS: Inclusion of the features of a co-expressed module provides more implications to infer drug action. We propose that this method be used to find collaborative cellular mechanisms associated with drug action and to simply identify drugs having similar responses.
Biomarkers, Pharmacological ; Drug Repositioning ; Gene Expression Regulation ; Gene Expression* ; Gene Regulatory Networks ; Methods ; Transcriptome*

Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL).
Drug Repositioning ; Learning ; Machine Learning ; Natural Language Processing ; Neural Networks (Computer) ; Neurons ; Running

Artemisia annua and its phytocompounds have a rich history in the research and treatment of malaria, rheumatoid arthritis, systemic lupus erythematosus, and other diseases. Currently, the World Health Organization recommends artemisinin-based combination therapy as the first-line treatment for multi-drug-resistant malaria. Due to the various research articles on the use of antimalarial drugs to treat coronaviruses, a question is raised: would A. annua and its compounds provide anti-severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) properties? PubMed/MEDLINE, Scopus, and Google Scholar were searched for peer-reviewed articles that investigated the antiviral effects and mechanisms of A. annua and its phytochemicals against SARS-CoVs. Particularly, articles that evidenced the herb's role in inhibiting the coronavirus-host proteins were favored. Nineteen studies were retrieved. From these, fourteen in silico molecular docking studies demonstrated potential inhibitory properties of artemisinins against coronavirus-host proteins including 3CL
Antiviral Agents/pharmacology* ; Artemisia annua ; COVID-19/drug therapy* ; Drug Repositioning ; Humans ; Molecular Docking Simulation ; SARS-CoV-2

OBJECTIVETo identify hub genes and key pathways associated with anaplastic thyroid carcinoma (ATC), and to explore possible intervention strategy.

METHODSThe differentially expressed genes (DEGs) in ATC were identified by Gene Expression Omnibus (GEO) combined with using R language; the pathway enrichment of DEGs were performed by using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The protein-protein interaction (PPI) network of DEGs was constructed by STRING database and visualized by Cytoscape. Furthermore, the hub genes and key nodes were calculated by MCODE. Finally, the drug repurposing was performed by L1000CDS.

RESULTSA total of 2087 DEGs were identified. The DEGs were clustered based on functions and pathways with significant enrichment analysis, among which PI3K-Akt signaling pathway, p53 signaling pathway, inflammatory response, extracellular matrix organization were significantly upregulated. The PPI network was constructed and the most significant three modules and nine genes were filtered. Twenty-two potential compounds were repurposed for ATC treatment.

CONCLUSIONSUsing integrated bioinformatics analysis, we have identified hub genes and key pathways in ATC, and provide novel strategy for the treatment of ATC.

Angiotensin-Converting Enzyme 2 ; COVID-19 ; Drug Repositioning ; Enzyme-Linked Immunosorbent Assay ; Humans ; Pharmaceutical Preparations ; SARS-CoV-2

Zika virus (ZIKV) is an emerging pathogen associated with neurological complications, such as Guillain-Barré syndrome in adults and microcephaly in fetuses and newborns. This mosquito-borne flavivirus causes important social and sanitary problems owing to its rapid dissemination. However, the development of antivirals against ZIKV is lagging. Although various strategies have been used to study anti-ZIKV agents, approved drugs or vaccines for the treatment (or prevention) of ZIKV infections are currently unavailable. Repurposing clinically approved drugs could be an effective approach to quickly respond to an emergency outbreak of ZIKV infections. The well-established safety profiles and optimal dosage of these clinically approved drugs could provide an economical, safe, and efficacious approach to address ZIKV infections. This review focuses on the recent research and development of agents against ZIKV infection by repurposing clinical drugs. Their characteristics, targets, and potential use in anti-ZIKV therapy are presented. This review provides an update and some successful strategies in the search for anti-ZIKV agents are given.
Adult ; Animals ; Drug Repositioning ; Humans ; Infant, Newborn ; Microcephaly ; Pharmaceutical Preparations ; Zika Virus ; Zika Virus Infection/prevention & control*