Drug repositioning or repurposing is the process of discovering leading-edge indications for authorized or declined/abandoned molecules for use in different diseases. This approach revitalizes the traditional drug discovery method by revealing new therapeutic applications for existing drugs. There are numerous studies available that highlight the triumph of several drugs as repurposed therapeutics. For example, sildenafil to aspirin, thalidomide to adalimumab, and so on. Millions of people worldwide are affected by neurodegenerative diseases. According to a 2021 report, the Alzheimer’s disease Association estimates that 6.2 million Americans are detected with Alzheimer’s disease. By 2030, approximately 1.2 million people in the United States possibly acquire Parkinson’s disease. Drugs that act on a single molecular target benefit people suffering from neurodegenerative diseases. Current pharmacological approaches, on the other hand, are constrained in their capacity to unquestionably alter the course of the disease and provide patients with inadequate and momentary benefits. Drug repositioning–based approaches appear to be very pertinent, expense- and time-reducing strategies for the enhancement of medicinal opportunities for such diseases in the current era. Kinase inhibitors, for example, which were developed for various oncology indications, demonstrated significant neuroprotective effects in neurodegenerative diseases. This review expounds on the classical and recent examples of drug repositioning at various stages of drug development, with a special focus on neurodegenerative disorders and the aspects of threats and issues viz. the regulatory, scientific, and economic aspects.
Drug repurposing has become an effective approach to drug discovery, as it offers a new way to explore drugs. Based on the Science Citation Index Expanded (SCI-E) and Social Sciences Citation Index (SSCI) databases of the Web of Science core collection, this study presents a bibliometric analysis of drug repurposing publications from 2010 to 2020. Data were cleaned, mined, and visualized using Derwent Data Analyzer (DDA) software. An overview of the history and development trend of the number of publications, major journals, major countries, major institutions, author keywords, major contributors, and major research fields is provided. There were 2,978 publications included in the study. The findings show that the United States leads in this area of research, followed by China, the United Kingdom, and India. The Chinese Academy of Science published the most research studies, and NIH ranked first on the h-index. The Icahn School of Medicine at Mt Sinai leads in the average number of citations per study. Sci Rep, Drug Discov. Today, and Brief. Bioinform. are the three most productive journals evaluated from three separate perspectives, and pharmacology and pharmacy are unquestionably the most commonly used subject categories. Cheng, FX; Mucke, HAM; and Butte, AJ are the top 20 most prolific and influential authors. Keyword analysis shows that in recent years, most research has focused on drug discovery/drug development, COVID-19/SARS-CoV-2/coronavirus, molecular docking, virtual screening, cancer, and other research areas. The hotspots have changed in recent years, with COVID-19/SARS-CoV-2/coronavirus being the most popular topic for current drug repurposing research.
Most drug molecules modulate multiple target proteins, leading either to therapeutic effects or unwanted side effects. Such target promiscuity partly contributes to high attrition rates and leads to wasted costs and time in the current drug discovery process, and makes the assessment of compound selectivity an important factor in drug development and repurposing efforts. Traditionally, selectivity of a compound is characterized in terms of its target activity profile (wide or narrow), which can be quantified using various statistical and information theoretic metrics. Even though the existing selectivity metrics are widely used for characterizing the overall selectivity of a compound, they fall short in quantifying how selective the compound is against a particular target protein (e.g., disease target of interest). We therefore extended the concept of compound selectivity towards target-specific selectivity, defined as the potency of a compound to bind to the particular protein in comparison to the other potential targets. We decompose the target-specific selectivity into two components: 1) the compound’s potency against the target of interest (absolute potency), and 2) the compound’s potency against the other targets (relative potency). The maximally selective compound-target pairs are then identified as a solution of a bi-objective optimization problem that simultaneously optimizes these two potency metrics. In computational experiments carried out using large-scale kinase inhibitor dataset, which represents a wide range of polypharmacological activities, we show how the optimization-based selectivity scoring offers a systematic approach to finding both potent and selective compounds against given kinase targets. Compared to the existing selectivity metrics, we show how the target-specific selectivity provides additional insights into the target selectivity and promiscuity of multi-targeting kinase inhibitors. Even though the selectivity score is shown to be relatively robust against both missing bioactivity values and the dataset size, we further developed a permutation-based procedure to calculate empirical p-values to assess the statistical significance of the observed selectivity of a compound-target pair in the given bioactivity dataset. We present several case studies that show how the target-specific selectivity can distinguish between highly selective and broadly-active kinase inhibitors, hence facilitating the discovery or repurposing of multi-targeting drugs.
Purpose: The rapid worldwide spread of Corona Virus Disease 2019 (COVID-19) has become not only a global challenge, but also a lack of effective clinical treatments. Studies have shown that licorice can significantly improve clinical symptoms such as fever, dry cough and shortness of breath in COVID-19 patients with no significant adverse effects. However, there is still a lack of in-depth analysis of the specific active ingredients of licorice in the treatment of COVID-19 and its mechanism of action. Therefore, we used molecular docking and molecular dynamics to explore the mechanism of action of licorice in the treatment of COVID-19.
Methods: We used bioinformatics to screen active pharmaceutical ingredients and potential targets, the disease-core gene target-drug network was established and molecular docking was used for verification. Molecular dynamics simulations were carried out to verify that active ingredients were stably combined with protein targets. The supercomputer platform was used to measure and analyze stability of protein targets at the residue level, solvent accessible surface area, number of hydrogen bonds, radius of gyration and binding free energy.
Results: Licorice had 255 gene targets, COVID-19 had 4,628 gene targets, the intersection gene targets were 101. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) analysis showed that licorice played an important role mainly through the signaling pathways of inflammatory factors and oxidative stress. Molecular docking showed that Glycyrol, Phaseol and Glyasperin F in licorice may playe a role in treating COVID-19 by acting on STAT3, IL2RA, MMP1, and CXCL8. Molecular dynamics were used to demonstrate and analyze the binding stability of active ingredients to protein targets.
Conclusion: This study found that Phaseol in licorice may reduce inflammatory cell activation and inflammatory response by inhibiting the activation of CXCL8 and IL2RA; Glycyrol may regulate cell proliferation and survival by acting on STAT3. Glyasperin F may regulate cell growth by inhibiting the activation of MMP1, thus reducing tissue damage and cell death caused by excessive inflammatory response and promoting the growth of new tissues. Therefore, licorice is proposed as an effective candidate for the treatment of COVID-19 through STAT3, IL2RA, MMP1, and CXCL8.
The mechanisms analysis of licorice in the treatment of COVID-19.
COVID-19 caused by SARS-CoV-2 has raised a health crisis worldwide. The high morbidity and mortality associated with COVID-19 and the lack of effective drugs or vaccines for SARS-CoV-2 emphasize the urgent need for standard treatment and prophylaxis of COVID-19. The receptor-binding domain (RBD) of the glycosylated spike protein (S protein) is capable of binding to human angiotensin-converting enzyme 2 (hACE2) and initiating membrane fusion and virus entry. Hence, it is rational to inhibit the RBD activity of the S protein by blocking the RBD interaction with hACE2, which makes the glycosylated S protein a potential target for designing and developing antiviral agents. In this study, the molecular features of the S protein of SARS-CoV-2 are highlighted, such as the structures, functions, and interactions of the S protein and ACE2. Additionally, computational tools developed for the treatment of COVID-19 are provided, for example, algorithms, databases, and relevant programs. Finally, recent advances in the novel development of antivirals against the S protein are summarized, including screening of natural products, drug repurposing and rational design. This study is expected to provide novel insights for the efficient discovery of promising drug candidates against the S protein and contribute to the development of broad-spectrum anti-coronavirus drugs to fight against SARS-CoV-2.
Nitazoxanide has been investigated for colorectal cancer and breast cancer. However, its molecular targets and pathways have not yet been explored for hepatocellular carcinoma (HCC) treatment. Utilizing a network pharmacology approach, nitazoxanide’s potential targets and molecular pathways for HCC treatment were investigated. HCC targets were extracted from the GeneCards database. Potential targets of nitazoxanide were predicted using Swiss Target Prediction and Super Pred. Intersecting targets were analyzed with VENNY online tool. Using Cytoscape, a protein-protein interaction (PPI), cluster, and core targets-pathways networks were constructed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. The nitazoxanide was molecularly docked with anti-HCC core targets by employing Auto Dock Vina. A total of 168 potential targets of nitazoxanide, 13,415 HCC-related targets, and 153 intersecting targets were identified. The top eight anti-HCC core targets were identified: SRC, EGFR, CASP3, MMP9, mTOR, HIF1A, ERBB2, and PPARG. GO enrichment analysis showed that nitazoxanide might have anti-HCC effects by affecting gene targets involved in multiple biological processes (BP) (protein phosphorylation, transmembrane receptor protein tyrosine kinase (RTKs) signaling pathway, positive regulation of MAP kinase activity, etc.). KEGG pathways and core targets-pathways network analysis indicated that pathways in cancer and proteoglycans in cancer are two key pathways that significantly contribute to the anti-HCC effects of nitazoxanide. Results of molecular docking demonstrated the potential for active interaction between the top eight anti-HCC core targets and nitazoxanide. Our research offers a theoretical basis for the notion that nitazoxanide may have distinct therapeutic effects in HCC, and the identified pharmacological targets and pathways might function as biomarkers for HCC therapy.
Background: Lenvatinib combined with a PD-1 inhibitor has obtained a satisfactory antitumor effect in several solid tumors. However, the efficacy and tumor response of lenvatinib with a PD-1 inhibitor in advanced intrahepatic cholangiocarcinoma still need further exploration.
Methods: This is a single-arm study for the assessment of the efficacy and tolerability of lenvatinib with a PD-1 inhibitor in intrahepatic cholangiocarcinoma patients who had chemotherapy failure. Efficacy was evaluated based on the Response Evaluation Criteria in Solid Tumors RECIST Version 1.1 (RECIST 1.1).
Results: A total of 40 patients with advanced intrahepatic cholangiocarcinoma were enrolled after the chemorefractory effect. The median progression-free survival was 5.83 ± 0.76 months. The 3-month and 6-month progression-free survival rates were 80.0% and 32.5%, respectively. The median overall survival was 14.30 ± 1.30 months. The 12-month and 18-month overall survival rates were 61.4% and 34.7%. The 3-month RECIST 1.1 evaluation was that seven patients (17.5%) showed partial response, 23 patients (57.5%) had stable disease, and 10 patients (25.0%) had progressive disease. The objective response rate was 17.5%, and the disease control rate was 75.0%. All the recorded any-grade adverse events inducing treatment termination were controllable, and there were no AE-related deaths.
Conclusion: Our study showed that a combination of lenvatinib with the PD-1 inhibitor could be an effective treatment for advanced intrahepatic cholangiocarcinoma after the chemorefractory effect.